UNIVERSITAT POLITÈCNICA DE VALÈNCIA

DEPARTMENT OF HYDRAULIC ENGINEERING AND ENVIRONMENTPhD PROGRAM ENGINEERING OF WATER AND ENVIRONMENT

PHD THESISSTUDY OF UTILITY OF THE ESTIMATED

RAINFALL SATELLITE BROADCAST HYDROLOGIC MODELING

A UTHOR

L IA RAM O S F ER NÁ ND EZ

DI R ECTOR

D R . F ÉL IX F R ANC ÉS G AR C Í A

V AL ENC I A , J A N U A R Y 201 3

Chapter 1

INTRODUCTION

Over the past decade, several research groups have focused on the development of satellite sensor technology and their exploitation in order to obtain a real-time estimation of the rain on a global scale. Recent advances especially in terms of quantitative evaluation of rainfall patterns, sensor resolution and sample rate, open up new horizons in global hydrological applications (AghaKouchak et al, 2010;. Nikolopoulos et al, 2010;. Kidd and Levizzani, 2011 ; Tapiador et al, 2012).. And it's clear the usefulness of these measurements, both for meteorological global circulation models to hydrological modeling at smaller scales, as in the case of little or no instrumented basins and thus strengthen the capacity of management of water resources, improve weather and natural disaster prediction and provide scientific rigor to help make informed decisions.

Sensors operating at wavelengths of infrared (IR) of geostationary satellites, providing useful information about identifying them storm clouds by low temperature at the top of the cloud. Instead, microwave sensors (MW), commonly installed in low-orbit satellites reflect the vertical distribution of hydrometeors in cloud but infrequently temporary space. The main challenge is how to benefit from the strengths of the different types of satellite sensors and how to minimize the impacts of their limitations. Therefore, at present mixed media that combine the best identified by MW as often temporary space infrared images (Sorooshian et al used rain, 2002. Dinku et al, 2009;. Kidd and Levizzani, 2011; WMO , 2011; Tapiador et al, 2012)..

There are various products of satellite estimated rainfall, being the best spatial resolution of the PERSIANN-CCS product 0.04º, algorithm using a neural network to combine high sampling frequency infrared satellite cloud images geo synchronous with high quality of passive microwave data provided by the TRMM, NOAA and DMSP satellite sensor. Also, adjust the bias rain GPCP (Sorooshian et al., 2000; Sorooshian et al., 2002; Sorooshian et al., 2005; Hsu and Sorooshian, 2008; Kuo-lin and Sorooshian, 2008) and introduced the categorization of clouds based on height at the top of the cloud, geometry and

texture, estimated from satellite images for different intensities of rain clouds, which helps in the detection of cirrus clouds and distinguish different convective systems (Hong et at., 2004; Hong et al. 2007; Kuo-lin et al., 2010).

On the other hand, the Bayesian rain gauge data and satellite products, combination makes the most of all sources of information available as well, readings of gauges can remove systematic errors of less accurate regional data satellite products and these can then be be used to estimate rainfall in areas where no gauges available (Gorenburg et al., 2001; Todini, 2001a; Todini et al., 2001; Collier, 2002; Mazzetti, 2004; Poluzzi, 2006; Bliznak et al., 2012).

1.1 Background1.1.1 Record

Compared to conventional data (rain gauges) representing a point measurements, measurements with satellite sensors are estimates globally and, as such, fit the need for distributed hydrological models, providing information in inaccessible regions to other systems observation. In this regard, the rain is a complex meteorological variable because of its irregular variation temporary space and various physical processes; these weather conditions pose a challenge to estimate rainfall from satellite measurements (Levizzani, 2008; WMO, 2011;. Tapiador et al, 2012).

1.1.2 Motivation

Rain being a vital component of the hydrological cycle is essential a better understanding of space-time variability; and it is therefore required global data with sufficient precision to enable research. However, rain gauges and weather radars are restricted to populated areas, whereas the estimated satellite provides access to rainfall data globally in real time rain; and it is clear the usefulness of these measurements, both for global circulation models to hydrological modeling at smaller scales as in the case of little or no instrumented basins. Thus, the sensor technology of satellites to provide global rainfall is in continuous progress and future development prospects (Sorooshian et al., 2000; Tapiador, 2002; Turk et al, 2002;.. Kidd et al, 2003 ; Aonashi et al, 2009;. Huffman et al, 2010;. AghaKouchak et al, 2011;. Conti et al, 2011;. Kidd and Levizzani, 2011; Sorooshian et al, 2011;. Behrangi et al, 2012;. Koulali et al, 2012;. Moreno et al. 2012; Tapiador et al, 2012.; Zahraei et al, 2012.; Haile et al., 2013)

Despite all the advances, the estimated satellite rainfall is subject to

various errors due to instrumental problems, nature of the measuring system, theoretical simplifications, nonlinear relationship between the observed variable and rain, among other reasons (Nikolopoulos et al. 2010; Semire et al, 2012). Several authors developed models to characterize the complex stochastic nature of the error. (Bellerby and Sun, 2005; Hossain and Anagnostou, 2006; Hossain and Huffman, 2008). In this regard, Sorooshian et al. (2011) indicate the need to investigate the properties of error in different climatic regions, rainfall patterns, surface conditions, seasons and altitude. These errors in turn introduced uncertainty to be assessed and quantified on hydrological applications.

1.2 Research question and scope

This thesis aims to evaluate the error in the estimated satellite relative to a reference rain land-based rain, through statistical tools; and get a shower of rain gauge and satellite Estimated combination, hereinafter gauge + satellite as rain gauge readings can remove systematic errors of less accurate regional data from satellites and these can then be used to estimate rainfall in areas where no gauges available; to finally assess their performance through a distributed hydrological model in an extratropical Mediterranean. For this analysis was used two products Rain estimated satellite and rain gauge data from ground-based reference. In addition, a distributed hydrological model TETIS (French et al., 2007) and Bayesian model for combining product rain gauges with estimated satellite (Mazzetti and Todini, 2004, Mazzetti and Todini, 2007).

This research aims to answer the following questions:

What are the best statistical tools to characterize and evaluate the error in the estimated satellite rain ?, What is the error propagation of rain estimated satellite through hydrologic modeling ?, The combination improves satellite gauge + representation of the variability of rainfall in the basin? Does the rain gauge + TV combination improves the hydrologic response in mountain basins ?, The structure of spatial heterogeneity of the parametersthe hydrological model to assess the performance with satellite products ?, the density of the network of rain gauges in combination with satellite improves performance in hydrological applications?

It is intended to aid in the use of rain estimated satellite in areas with similar characteristics, particularly in developing countries in North Africa, with climate similar to our study area terrain, as an alternative to conventional gauges scarce countries or nonexistent in these places. Thus, this thesis is a contribution to the process of continuous assessment of the estimated satellite

rain.

1.3 Goals

Analyze the error rain at different levels of aggregation basin, comparing the performance of two products satellites with a barrage of ground-based reference; with statistical tools to synthesize the analysis.

Evaluate the utility of two satellite products through its performance in distributed hydrological modeling with statistical tools to synthesize the analysis.

Analyze the error combined with rain gauge rain Estimated satellite and evaluate their performance on a distributed hydrological model of a mountain basin with statistical tools to synthesize the analysis.

1.4 Structure of the Thesis

The thesis is divided into two chapters more abstract.

In Chapter 2 the tools and techniques used to estimate rainfall from satellite are described, in particular about the spectral bands, sensors installed in geostationary / geosynchronous, polar and non-polar orbit satellites, also based rainfall data estimated global TV. Chapter 3 is devoted to the measurement errors of the estimated satellite rain and performance in hydrologic modeling. The first section of this chapter invites reflection about the failure of the satellite estimated rainfall evaluated through its spatiotemporal characterization; The second section discusses performance and analyzes their potential by combining satellite gauges and estimated in the context of hydrological applications rain. The chapter

It 4 presents a description of the study area that includes map data (digital elevation model, accumulated cells, flow directions, slope, speed and slope maps of hydrological parameters), information on land-based hydrometeorological (rain gauges, flow hydrometric stations and outflow volumes of reservoirs, temperature and evapotranspiration) and information Satellite estimated rainfall.

Because the satellite estimated rainfall is subject to various errors, it is necessary to characterize them as it is the input of rainfall-runoff models; that is why in Chapter 5 Error analysis of satellite estimated rainfall occurs. Thus, in the

first section, statistics tools to synthesize the analysis comparing with rain ground-based reference are detailed; in the second section analyzes the results of the annual, monthly and daily time scale are presented; and in the last section the results of the analysis at different levels of aggregation basin that reflect different fields of application in Hydrology in the Mediterranean basin of the river Júcar are presented.

Because of the multidimensional nature of the error of the estimated satellite rain, it is difficult to establish a priori a product of rain estimated satellite that allows optimum hydrological application and why it is necessary to assess their performance through hydrologic modeling, so that in the first section of Chapter 6 statistical tools to assess their performance are detailed; In the second section the implementation of the hydrological model is presented; and in the following sections analyze the results in terms of calibration, validation, water balance and error propagation in the study area are reported.

Bandas del Espectro Símbolo Longitudes de onda Sensor SatéliteUltra-Violeta UV 0.01 - 0.38 µm

Chapter 2

ESTIMATION TECHNIQUES RAIN FROM SATELLITE

This chapter describes the tools and techniques used to estimate rainfall from satellite are described, in particular about the spectral bands used for estimation, sensors installed in geostationary / geosynchronous, polar and non-polar orbit satellites, in addition to the databases of rain estimated global TV.

2.1 Sensors and satellites used to estimate rain

The sensors coupled meteorological satellites perform readings in five bands of the electromagnetic spectrum (Table 2-1) to estimate the rain with various techniques that are constantly moving towards more direct physically based techniques, which have evolved from radiance measurements the visible (VIS) and infrared (IR), based on active and passive microwave (MW) to techniques that merge information from sensors in the infrared and microwave techniques.

Visible VIS 0.38 - 0.78 µm Infrarojo cercano NIR 0.78 - 1.30 µm Infrarojo de Onda Corta SWIR 1.30 - 3.00 µm

SEVIRI METEOSAT AVHRR NOAA + MetOp VIRS TRMM

Infrarojo Térmico TIR 6.00 - 15.0 µmInfrarojo Lejano FIR 15 µm - 1 mmOnda submilimétrica Sub-mm 100 µm - 1mm

SSM/I DMSPAMSR-E NASA´s Aqua

Microondas Pasivas MWP 1 mm - 30 cm AMSU-A NASA´s AquaASCAT MetOpTMI TRMM

Microondas Activas MWA aprox 2.17 cm PR TRMMTabla 2-1. Bandas del espectro electromagnético para la estimación de la lluvia de satélite: sensor y

satélite en el espectro visible e infrarojo (color plomo), sensor y satélite en el espectro de las microondas pasivas (color verde claro) y microondas activas (color azul claro)

2.1.1 Sensor in the visible (VIS) and infrared (IR) and geostationary satellite / geosynchronous

They appeared in the 70 satellite sensors that detect radiation at wavelengths of infrared (IR) and visible (VIS) range, based on the fact that the formation processes involve the existence of rain cloud droplets Large and / or ice particles in the cloud, which often spread to the top of the cloud so the brightness temperature, are positively correlated with rain therefore are indirect estimates that provide quick information with multiple satellite sensors needed to capture the growth and decay of precipitating clouds. And the best known methods to estimate rainfall include global precipitation rate "GPI" technical convection / stratiform, the autoestimador, the Hydro-and approaches that include using extraction functions. The GPI is based on the assumption that all the clouds with cooler tops a threshold temperature precipitate a fixed intensity, for example to 235 ° K the rainfall of 3 mm / h is a typical value of the eastern equatorial Atlantic (WMO, 2011; Tapiador et al, 2012.).Several authors (.. Dinku et al, 2009; WMO, 2011; Tapiador et al, 2012) indicate that their main constraints are referred to the type of clouds and local atmospheric conditions such as:

Local Variation: multi-layer cloud systems may block the view of the underlying rain. Furthermore, the relationship between the temperature at the top of the cloud and rain is highly dependent on the season and location.

Effect of warm rain: regions near the coast or in mountain areas may experience rain clouds do not reach high enough in the atmosphere to register as cold clouds.

Effect of cirrus clouds: clouds are high enough in the atmosphere, composed of ice crystals and the satellite detected as very cold and therefore associated with the presence of rain, although in reality they are rain clouds that do not develop.

These sensors are installed in geoestacionarios1 satellites revolving synchronously with the rotation of the earth orbits at 36,000 km altitude and orbital period of 24 hours that are always on the same point on Ecuador.

1 GEO: inclination is 0 ° to the plane of Ecuad

These satellites provide coverage of a limited area but with high temporal resolution (images are updated every 30 or 15 minutes). The best known satellites are: GOES "Geostationary Operational Environmental Satellite", launched by the United States and managed by NOAA "National Oceanic and Atmospheric Administration"; Meteosat, launched by the European Union and managed by EUMETSAT "European Organisation for the Exploitation of Meteorological Satellites"; GMS "Geosynchronous Meteorological Satellite", launched by Japan.

2.1.2 Sensor in passive microwave spectrum(MWP) and polar orbiting satellite

It 1978 is available from passive sensors operate with microwave (radiometers) in which the radiation clouds and rain emit, absorb and scatter (directly interact with the rain), reflecting the total water content, vertically integrated, so It is estimated more physical basis. These sensors can measure the thermal net emission emanating from the top of the atmosphere (passive microwave MWP). Its main limitation is that they have low temporal resolution and difficulty in differentiating the signal from rain or other types of surfaces and surface coverages that have similar spectra (Tapiador et al., 2012)

Most passive microwave radiometers operating at frequencies of 6-. 190 GHz and measurement methods are based on two physical principles: emission and dispersion. Below 20 GHz, the water droplets have a coefficient proportional to the values of cloud and rain water integrated "broadcast" vertically making it more applicable in oceans as they are more homogeneous and low emissivity surfaces. But in the face of the earth because of its high and variable emissivity is common frequencies above 60 GHz in the rain under the mechanism of "dispersion" ice is detected but no rain is detected below the freezing point, also often go undetected rain from clouds that contain considerable amounts of ice its upper region (WMO, 2011).

These sensors are installed on polar orbiting satellites 700-800 km high passing almost directly over the poles, it takes approximately 100 minutes to complete one orbit. The images are of higher accuracy and spatial resolution as the satellite is much lower than the geosynchronous orbit, but only one or two observations of the same region on the same day (low temporal resolution). The best known satellites are DMSP "Defense Meteorological Satellite Program," launched by the United States and managed by NOAA; MetOp, launched by the European Union and managed by EUMETSAT.

A variant of the polar orbit is the sun-synchronous orbit in which the motion parameters of the satellite are calculated so that the land area swept by the same is always kept under the same solar illumination, that is, the relative positions of the satellite and Sol remain constant, which ensures that the images of the spectrum are always under the same conditions. The best known satellite is called "Aqua" which is part of the group of EOS satellites "Earth Observing System", launched by the United States and managed by NOAA.

At present, to benefit from the strengths of the different types of satellite sensors and minimize the impacts of their limitations, mixed media that combine the best rain identified by microwave sensors as often temporary space infrared sensors used images (Sorooshian et al., 2002; Dinku et al, 2009;. Kidd and Levizzani, 2011). Improved detection mean higher studies to bring new ideas to the estimation algorithms of rain. In this regard, Levizzani (2008) indicates that you must explore the characteristics of the cloud (microphysics, radiation, dynamic), being more important issues, the content of ice in the clouds and their vertical structure. Furthermore, Sorooshian et al. (2011) indicate that reliably detect extreme events should be considered additional observations such as: lightning and cloud cover.

2.2 Database of rain estimated global satelliteThe World Meteorological Organization (WMO, 2011) indicates that

validation of satellite algorithms for estimating rain is a complex process. Today, in space measurements of rainfall in an area, the higher degree of accuracy is obtained over the tropical oceans, where the method of "Global Precipitation Index GPI" is as effective as the techniques of passive microwave rain periods long (on the order of several months). However, in the phenomena separately mistakes can be large, since the "warm rain" is a common phenomenon in some areas of the tropics. Passive microwave techniques gain in efficiency as it moves toward higher latitudes, where the convective rain is less frequent. In these cases, greater accuracy is achieved by combining passive microwave techniques with infrared observations from geostationary satellites. It is possible to achieve a degree of slightly less accuracy by infrared techniques for convective rain on the surface of the earth, because of the great diversity dynamics and microphysics of rain-cloud systems. This leads to greater rainfall variability and properties in the upper regions of the clouds. The skill of passive microwave techniques is also lower on the surface of the earth, as their emissivity considerably reduces the usefulness of frequencies below 35 GHz.

In 2001 IPWG "International Precipitation Working Group," which provides a forum for exchange of information on satellite measurements of rainfall and its impact on numerical weather prediction and climate studies generally creates. And the 2005 PEHRPP "Program to Evaluate High Resolution Precipitation Products" is created, with the aim of characterizing errors in satellite products in various spatial and temporal scales through various climatic regimes. Both IPWG (2012) and PEHRPP (2012) maintain websites with database rain on a global scale, although publicly available record length formats and varying widely.

In 1998 the ECA "European Climate Assessment", which provides information on changes in climate and extreme weather conditions in Europe creates, and maintains a website with base needed to monitor and analyze these ends (ECA, 2012) Regional data .

To South America and Allured Liebman (2005) developed a technique for combining daily rain gauge data from various sources to spatial resolution1 and 2.5 ° but due to the lack of observations, the coverage of the Andes is not representative; however maintain a website (CIRES,2012) on the basis of data that can be used to evaluate satellite products in South America.

In the following paragraphs techniques that combine information from various sensors and orbiting platforms to estimate global rainfall and available within days of scale satellite observations will be presented.

2.2.1 GPCP

Since 1980, the "Global Precipitation Climatology Project" organized as an international project led by the National Aeronautics and Space Administration (NASA, USA) and the Japanese Space Agency (JAXA, Japan), combines data from rain gauges more 6,000 stations with rainfall estimates infrared and microwave images of geostationary satellites as GOES, GMS and METEOSAT and NOAA polar satellites. Microwave estimates are obtained from satellites DMSP. It has GPCP rainfall data to daily temporal resolution and spatial resolution of 1 since 1993 (Huffman et al., 2001) and version 2 at a monthly time resolution and spatial resolution of 2.5 ° since 1972 (Adler et al., 2003 ; Xie et al., 2003; Huffman et al, 2009).. Information is available on the project website (GPCP, 2012). In Figure 2-1 the five components that is organized GPCP detailed.

Figura 2-1 Componentes en que está organizado el GPCP “Global Precipitation ClimatologyProject”.

Several studies have been conducted to validate data GPCC rain, highlighted in last year's work: Cavalcanti (2012), Dash et al. (2012), Fenoglio-Marc et al. (2012), Fensholt et al. (2012), Jiang et al. (2012), Koulali et al. (2012), Li et al. (2012a), Tapiador et al. (2012), Zhou et al. (2012).

2.2.2 TRMM

In 1997 it enters the TRMM orbit "Tropical Rainfall Measuring Mission" satellite (see Figure 2-2) is a space mission between NASA (USA) andAgency Japan Aerospace Exploration Agency (JAXA) to monitor and study tropical rainfall. It is a satellite of low non-polar orbit, concentrated in the tropics, it carries a radar that transmits over a length of microwave activas2 and microwave radiometers with scanner visible radiation and infrared, whose vertical resolution is about 1 km to the radiometers in visible and infrared spectrum, to about 10 km to the microwave radiometers and 250 m in the case of radar.

2 Measuring the backscattered power of a transmitted series of pulses assets.The use of radar has improved the accuracy of estimates of rain with

respect to previously from space (WMO, 2011).

One of the instruments aboard TRMM satellite is the sensor for images of lightning. In this regard, evaluate lightning data enables better identify convective phenomena and therefore greater likelihood of strong convective rain (Price et al, 2011;. Sorooshian et al, 2011;.. Bliznak et al, 2012).

The World Meteorological Organization (2011) indicates that the low

inclination orbit used by the TRMM allows obtaining samples over a whole series of hours of passage by Ecuador in periods of 24 hours over a month.

Not so with satellites in polar orbit, whose hours of passage by Ecuador are the same. Therefore, the characteristic in the Tropics diurnal cycle could

increase resulting from sampling errors.

Figura 2-2. Instruments aboard the TRMM Satellite: Weather Radar (PR), scanner sensor for visible and infrared radiation (VIRS), passive microwave sensor (TMI), Lightning Imaging Sensor (LIS) and a system of radiant energy from the surface land that uses a database of images of clouds high resolution.

The TRMM offers four products: sensor fusion MW, IR calibrated MW combined sensor fusion MW and an adjusted product gauge data. These algorithms have evolved and now used where possible microwave sensor data including data on the Aqua satellite (launched in 1999) and MetOp-A satellite (launched in 2006), and estimates only uses infrared images to complete missing data (Adler et al., 2000; Huffman et al., 2007; Huffman et al, 2010).. It has information 3 h time resolution spatial resolution of 0.25 ° since 1998 in a coverage of 50 ° N to 50 ° S, available on the website of the Mission (TRMM,

2012).

2.2.3 CMORPH

The Center for Climate Prediction (CPC) NOAA developed the technique "Morphing" to combine information from different satellite sensors, so, using motion vectors in half-hour intervals from infrared images of geostationary satellites in the shape and intensity of the rain change with better information microwave sensors (Joyce et al., 2004). The "morphing" technique incorporates rainfall estimates derived from passive microwave sensors aboard satellites DMSP-13, 14 and 15 (sensor SSM / I), NOAA-15, 16, 17 and 18 (AMSU-B sensor) Aqua (AMSR-E sensor) and TRMM (TMI sensor). These estimates are generated by algorithms Ferraro (1997) for the SSM / I sensor, Ferraro et al. (2000) for the AMSU-B and sensor Kummerow et al. (2001) for TMI sensor. This technique is not an estimation algorithm rain but a means whereby existing data rains combined microwave sensors therefore can incorporate new data rain microwave sensors.

There is information 0.07277º spatial resolution (8 km) and temporal resolution of 30 minutes, in a hedge of 60 ° N to 60 ° S, with information available on the website of the Climate Prediction Center (CPC, 2012). Several studies have been conducted with rain CMORPH data, highlighted in last year's work: Fry et al. (2012), and Narisma Jamandre (2012), Jiang et al. (2012), Mohr et al. (2012), Reid et al. (2012), Turk and Xian (2012).

2.2.4 PERSIANN

The Center for Hydrometeorology and Remote Sensing at the University of California at Irvine (UCI-CHRS) developed the algorithm PERSIANN "Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks", which estimates rainfall information from cloud texture longwave infrared images obtained from multiple satellite images geosincrónicos3 (GOES-8, GOES-10, GMS-5, Meteosat-6 and Meteosat-7) provided by the CPC-NOAA estimates are updated using high quality rain passive microwave sensors TRMM satellite, NOAA-15, NOAA-16, NOAA-17, DMSP-F13, F14-DMSP, DMSP-F15. These data cover of 50 ° S to 50 ° N, with spatial resolution of 0.25 ° and temporal resolution of 6 hours (Hsu et al., 1997; Sorooshian et al., 2000). Subsequently, adjust the bias depending on the product PERSIANN rain GPCP v.2 while preserving the spatial and temporal patterns of PERSIANN. The flowchart PERSIANN generates the algorithm is displayed in Figure 2-3.

3 geosynchronous orbit: they are circular orbits in a plane of Ecuador. If the inclination is 0 ° to the plane of Ecuador will be a geostationary orbit.

Figura 2-3 Flow generated by the algorithm PERSIANN "Precipitation Estimation from RemotelySensed Information using Artificial Neural Networks "(Hsu et al., 1997;. Sorooshian et al,2000).

They also developed a server named Hydis "Hydrologic Data and Information System" website which provides direct access to global precipitation estimates in real time and includes a graphical interface for PERSIANN historical data and interactive map. The Hydis friendly interface (Figure 2-4) allows collecting data in a selected region for a cumulative period interval. It has PERSIANN rainfall data since March 2000. (Sorooshian et al., 2000; Sorooshian et al., 2002; Sorooshian et al., 2005; Hsu and Sorooshian, 2008; Kuo-lin and Sorooshian, 2008).

Figure 2-4 Server Hydis "Hydrologic Data and Information System" that allows rain to collect historical PERSIANN estimated by the product (Hydis, 2012)

2.2.5 PERSIANN-CCS

Recently, the CRS-ICU developed a new version of PERSIANN, the PERSIANN-CCS "PERSIANN-Cloud Classification System", which introduces the categorization of clouds based on height at the top of the cloud, geometry and texture, estimated from satellite images for different intensities of rain clouds and spatial resolution of 0.04 ° (Hong et al., 2004; Hong et al., 2007; Kuo-lin et al., 2010).

Kuo-lin et al. (2010) evaluated PERSIANN-CCS from two hurricane events (Ernesto in 2006 and Katrina in 2005) to the Southeast USA. The algorithm extracts information at three temperatures (220 °, 235 ° and 253 ° K) for different intensities of rain clouds, which helps in the detection of cirrus clouds and distinguish different convective systems, see Figure 2-5.

Figure 2-5. Cloud categorization system with the product PERSIANN-CCS "PERSIANN-CloudClassification System "(Kuo-lin et al., 2010)

The results show that the correlation coefficients better with cold clouds (less than 253 ° K), warm clouds but more research is required and recommended further research on the use of multispectral images as an alternative to identify warm rain clouds.

The CHRS-ICU-GWADI in collaboration with UNESCO, developed the Hydis-GWADI "Water and Development Information for Arid Lands - A Global Network" server (Figure 2-6) available on its website, which allows real-time collect rain PERSIANN-CCS globally. For historical information should be obtained information directly to CHRS-ICU.

Figure 2-6. Hydis server-GWADI "Water and Development Information for Arid Lands - A Global Network" to collect real-time product rain PERSIANN-CCS (Hydis-GWADI, 2012)

Zahraei et al. (2012) propose the PERCAST "PERsiann-Forecast" and PERCAST-GD models "Growth and Decay" PERSIANN-CCS coupled to predict the location and intensity of rain in the next 4 hours using recent satellite images to extract features such as field advection, changes in intensity, growth and decay of the storm; and evaluate these models in USA for the summer season, typical of convective rainfall SCM, for more storms to 256 km2, comparing first rain PERSIANN-CCS with NEXRAD radar rainfall and obtained a correlation coefficient of 0.4, probability of detection 0.4 and a ratio of false alarms of 0.5 to rain threshold of 1 mm / h. Then compare the predicted rains PERCAST-GD PERCAST and rain PERSIANN-CCS and report that GD PERCAST-forecast improvement in terms of probability of detection and false alarm ratio up to 15-20% compared to the model PERCAST. Initial results are encouraging, but requires evaluating for example, life cycles of clouds, conditions in winter season, etc.

Chapter 3

STATE OF THE ART IN THE IMPLEMENTATION OF THE ESTIMATED RAINFALL SATELLITE

This chapter is devoted to the measurement errors of the estimated satellite rain and performance in hydrologic modeling. The first section of this chapter invites reflection about the failure of the satellite estimated rainfall evaluated through its spatiotemporal characterization; The second section discusses performance and analyzes their potential by combining rain gauges and satellite estimated, hereinafter gauge + TV, in the context of hydrological applications.

3.1 Characterization of rain Estimated error Satellite

The estimated satellite rain is subject to various errors due to instrumental problems, nature of the measurement system, theoretical simplifications, nonlinear relationship between the observed variable and rain, among other reasons (Nikolopoulos et al, 2010;. Semire et al. , 2012). Several authors developed models to characterize the complex stochastic nature of the error (Bellerby and Sun, 2005; Hossain and Anagnostou, 2006; Hossain and Huffman, 2008). In this regard, Sorooshian et al. (2011) indicate the need to investigate the properties of error in different climatic regions, rainfall patterns, surface conditions, seasons and altitude. These errors in turn move in uncertainty in the hydrological applications to be assessed and quantified.

It 2003, IPWG "International Precipitation Working Group" started the validation of different estimation algorithms satellite rain in Australia, USA and Europe Northeast (http://cawcr.gov.au/bmrc/SatRainVal/validation- intercomparison. html), and report the results of the validation, the most important conclusions (Ebert et al, 2007):

The performance of products is highly dependent on rainfall. Thus, the more the regime is convective rain, more (less) accurate satellite product (weather prediction model) in the estimates.

Validation of the USA show that satellite products by combining IR-sensors PMW perform almost as well as the radar in terms of bias and frequency of daily rainfall.

Satellite products tend to underestimate light rain but heavy rains overestimate. Also, they do better in summer and worse in winter rains possibly in the presence of clouds lower than in summer. Instead, climate prediction models perform better than all satellite products in winter in all regions analyzed.

In USA, precipitation in snow covered regions and semiarid regions in summer is overestimated.

In Australia, estimates with infrared sensors, microwave and combination of both, they showed similar performance to beat the climate prediction models to heavy rains.

Several authors evaluated the product PERSIANN: Sorooshian et al. (2002) near Rondonia in Brazil TOGA radar observations (in 3 radar cells with spatial resolution of 1), obtained a correlation of maximum rainfall from 0.68 to 0.77 and possibly overestimate the presence of cirrus clouds with clouds cúmulonimbus4. Goncalves et al. (2006) report that in South America areas without rain underestimates, overestimates areas with light rain and bias in the location of areas with higher rainfall intensity. Hughes (2006) in four basins in South Africa reports that PERSIANN is not sensitive to topographical influences so requires local correction. Vernimmen et al. (2011) in the Indonesian archipelago annual report overestimation and underestimation in summer.

Dinku et al. (2010) evaluated the CMORPH and TRMM products in two mountainous regions of Ethiopia and Colombia, reported very low correlation to

gauge data underestimate the presence and amount of rainfall due to terrain and warm rain, and better performance with CMORPH.

Bitew and Gebremichael (2010) evaluated the rain PERSIANN-CCS (resolution 0.04º and 1 hour) and CMORPH (0.08º resolution and 30 minutes) with 22 rain gauges in a grid of 5 km x 5 km in Ethiopia complex topography, semi-humid climate, obtaining heavy rains underestimate by 50% and 32% for PERSIANN-CCS and CMORPH respectively. In addition, PERSIANN-CCS has difficulty in detecting lower rainfall at 1.6 mm / day.

In the past year 2012 numerous studies have been reported as follows:

Kizza et al. (2012) evaluated rain PERSIANN and TRMM-3B43 on Lake Victoria, a tributary of the Nile River Basin and less biased report with PERSIANN and increased rain over the lake by 33% and 85% with TRMM-4B43 and PERSIANN respectively . In this regard, Haile et al. (2013) evaluated two products CMORPH and TRMM, the 3B42RT and 3B42PRT in the Nile River Basin, and reported overestimation in lakes, islands and coastline, mountains and underestimation best occurrence of rain in areas close to Ecuador. In addition CMORPH best in the Lake Tana behaves.

Labó (2012) evaluated three satellite products generated by his research group, called H01, H02 and H03: two products generated by microwave sensor information (SSMI and SSMI / S) (with a resolution of 30 to 40 km), and product mix-MW IR sensor (resolution 5 km) at different rainfall intensities (low, moderate and high) in Hungary and reports Error -5 to 10 mm / h with MW and -15 to 20 mm / h IR-MW heavy rains. And with better detection in summer.

The TRMM-3B42 product is evaluated in tropical climates: Semire et al. (2012) based in Malaysia and GPCC data reporting bias ± 15%; Duncan and Biggs (2012) with gauges APHRODITE in Nepal and report bias in the detection of extreme events, "rainy days" and intensity of rainfall in monsoon season; Jamandre and Narisma (2012) in the Philippines and reported accurately recognizes no daily rain showers and light but performs better than the product CMORPH in extreme intensities greater than 100 mm / d events.

4 Cirrus clouds: clouds above approximately 5000 m, composed of ice crystals, so they generally do not develop filamentous rain. Cumulonimbus clouds: clouds of great vertical development, internally formed by a column of warm, moist air rising in the form of rotating spiral, and typically produce heavy rain and thunderstorms, especially when they are fully developed.

Behrangi et al. (2012) evaluated the rainfall estimates products PERSIANN and PERSIANN-CCS in the US in the summer season 2009 to 2011.

These authors report that PERSIANN has a better correlation, better detection of rain but higher number of false alarms.

The satellite estimated rainfall is subject to various errors. However, measurements of rain gauges generally used as reference for assessing satellite products have highly systematic errors associated with maximum intensity, density of the network of rain gauges, site topography, atmospheric and instrumental factors (WMO, 2011; Semire et al., 2012).

WMO (2011) indicates that the adjusted precipitation of systematic errors(Pk) is given by the equation:

Pk k (Pg P1 P2 P3 P4 P5 )Where k is the

correction factor for the deformation of the wind field over the metering orifice; Pg is the precipitation in the measurement device; andP1 to P5 are corrections according to the magnitude which are detailed in Table 3-1:

3.1

3.2 Hydrologic modeling estimated Satellite rain

Over the past decade, several research groups have focused on the development of satellite sensor technology and their exploitation in order to obtain a real-time estimation of the rain on a global scale. Recent advances especially in terms of quantitative evaluation of rainfall patterns, sensor resolution and sample rate, open up new horizons in global hydrological applications (AghaKouchak et al, 2010;. Nikolopoulos et al, 2010;. Kidd and Levizzani, 2011 ).

Thus, studies have intensified rain products obtained from satellite global hydrologic modeling applied, some of which are detailed below:

Hsu et al. (2002) evaluated the flow of Leaf River Basin (1949 km2), a tributary of the Mississippi River, rain PERSIANN and hydrological model SAC-SMA, obtaining high uncertainty with peak flows. In the same basin, Moradkhani et al. (2006) evaluated the PERSIANN-CCS HyMOD rain and hydrological model, obtaining large uncertainties in simulated flows. Similar results reported work and Meskele Moradkhani (2010).

Stisen et al. (2008) evaluated the product TAMSAT-CCD (spatial resolution of 11 km) in the Senegal River Basin (350,000 km2) in Africa MIKE SHE model calibration, error getting the water balance values of -9.3 to-13.1 2.7% and 22.4% respectively in calibration and validation, then Stisen and Sandholt (2010) evaluated the CMORPH products3B42V6, PERSIANN and specific products for Africa (CPC-FEWS and TAMSAT-CCD), with spatial resolution from 8 to 27 km, getting less biased with CPC-FEWS and TAMSAT-CCD 3B42V6 followed; then they corrected bias and recalibrated the model by obtaining an efficiency of Nash-Sutcliffe (E) of from 0.83 to 0.87 with the specific products to Africa, from 0.63 to 0.70 with 3B42V6, from 0.74 to 0.81 with CMORPH and from 0.76 to 0.80 with PERSIANN.

Nikolopoulos et al. (2010) evaluated the flow of the river basin Veneto (1,200 km2) in Italy, irregular topography with slopes greater than 30 degrees at the top and increased rainfall to 1000 mm / year, with three satellite products TRMM- 3B42 (resolution 0.25 ° and 3 hours) and Kidd (resolution 25 and 4 km, 0.5 hours) and automatic calibration of the hydrological model tribs. These authors report the error propagation of simulated rainfall at different levels of basin flow (between 100 and 1,200 km2) and conclude that:

Satellite products underestimate the errors areal average rainfall ranging from 10 to 80% depending on the TV and the basin scale. This error resulted in a flow rain simulated with an error of the same order of magnitude.

The product with better resolution, KIDD-4 km, has a higher yield (36% error) products of worse resolution KIDD-25 km, TRMM 3B42-biggest mistake they get 55%.

The model performance depends on the algorithm used by the product of TV, satellite resolution basin scale and so can give very different results in terms of the simulated flow.

The error propagation depends on the size of basin; for example, the study reports that basins with smaller areas 400 km2, has a greater capacity buffer error simulated rain to flow the larger scales.

Most recently, several studies have focused on the application of hydrological satellite estimated rainfall. Thus, El-Sadek et al. (2011) simulated the flow of the basin of the Mimbres River (477 km2) in USA, rain PERSIANN and hydrological model SWAT, reporting efficiency of Nash-Sutcliffe (E) of 0.19 in calibration and concluding that the PERSIANN rain may not be appropriate to this mountain basin. Also Bitew and Gebremichael (2011b, 2011a) assessed the PERSIANN rain through calibration of SWAT models and MIKE SHE Gilgel Abay basin (1,656 km2) and Koga (299 km2) in Ethiopia complex terrain, semi-humid climate and rainfall 1300 mm annually with 70% in summer. These authors reported a poor performance simulated worst daily performance and in the basin of 1,656 km2 flow. Then Bitew et al. (2011) evaluated rain CMORPH, 3B42RT, 3B42 and PERSIANN in Koga River Basin (299 km2) in SWAT model calibrated for each satellite, obtaining a significant improvement in simulated flow.

Demaria et al. (2011) investigated the estimation of rain in the case of mesoscale convective systems (MCS) using the product PERSIANN in La Plata River Basin (3.2 × 106 km2) and reported an average underestimation and overestimation of rain rainfall areas. Also, with the hydrological model VIC evaluated the spatial location of the rain in the basin of the Iguazu River (70,000 km2) and concluded that the hydrologic modeling of the basin diminishes the

effect of the error in locating product PERSIANN for convective rainfall exceeding 30 mm / d and basin scale.

Getirana et al. (2011) evaluated products and VSAT GPCP rain in Black River Basin (712,000 km2) in the Amazon with the hydrological model MGB-IPH and report that the VSAT product has higher correlation and probability of detection gauge data regarding derivatives Hyban Observatory. The modeling values obtained Nash Sutcliffe efficiency (E) of -0.24 to 0.79 and -0.38 to 0.61 for GPCP and TMPA respectively.

Li et al. (2012b) evaluated the TRMM product in the Yangtze River Basin (1,550 km2) in China with humid subtropical climate, average rainfall of 1878 mm / year, altitude ranging from 50 to 2138 m, data from 1998 to 2003; and report underestimation of rain and simulated flow with model calibration WATLAC with Nash-Sutcliffe efficiency (E) of 0.71 and 0.86 respectively to daily and monthly scale.

Moreno et al. (2012) evaluated PERSIANN in four river basins Colorado (35-350 km2) in USA, complex topography, warm climate with convective rains, tribs hydrological model calibration; and report underestimation rain with low correlation (0.05 to 0.23) and significant bias (0.31 to 0.63). Also, poor flow simulated with Nash-Sutcliffe efficiency (E) ranging from -0.48 to 0.79 depending on the basin scale. In addition, to meet the understatement of the volume of rain, the model reduces the flow of water balance evapotranspiration.

3.3 Combined with rain gauges estimated Satellite

Direct and indirect measurements of the rain cover a wide range of scales, from specific observations to space radar and satellite aggregations. Each measurement technique has different advantages and limitations, so it is reasonable to combine different types of measures to make the most of all sources of information available. So, readings of gauges can be used to remove systematic errors of less accurate regional radar and satellite data; and these can then be used to estimate rainfall in areas where no gauges available (Gorenburg et al., 2001; Collier, 2002; Mazzetti, 2004; Poluzzi, 2006; Ebert et al, 2007;.. Bliznak et al, 2012) .

It is so in 2004, under the European project MUSIC "Multi-Sensor Integration precipitation measurements, Calibration and floodforecasting" with Contract No. EVK1-CT-2000-00058 (MUSIC, 2004), the University of Bologna developed Bayesian techniques to combine various sources of information (gauges, radars and satellites); where interpolation using gauges by "block kriging" and Kalman filter is made. The Kalman filter allows a posteriori estimate by combining the a priori estimate provided by the "block kriging" in a Bayesian context, obtaining a reduction of bias and variance of the estimated errors; also it allows uniform scales through sequences of "upscaling" and "downscaling" (Todini, 2001a; Mazzetti and Todini, 2004; Poluzzi, 2006). The "block kriging" is an extension of geostatistical technique kriging, and is used in order to regionalize rainfall data. Variogram parameters are updated at each time step using the maximum likelihood estimator (Todini, 2001b; Todini et al., 2001).

Mazzetti (2004) evaluated the Bayesian combination of rainfall data, radar and satellite images, by "block kriging" and Kalman filter, a time scale with 57 rainfall stations, radar SMR (resolution of 1 km) and Meteosat-IR5 ( resolution of 5 km) through the hydrological model Topkapi in Reno river basin (4,930 km2) in Italy, complex terrain, Mediterranean climate; and reports for four events from 1998 to 2000, better quality and efficiency of the simulated flow in combinations with poor performance radar and satellite combinations due to the poor quality of the satellite data. In this regard, Poluzzi (2006) uses the "block kriging" in gauges and radar (WSR-88D, resolution of 4 km) to evaluate obtained with rain

Technical RU "Rapid Update" estimates from satellite (GOES, SSMI and TMI) in Washita River Basin (1,200 km2) and Oklahoma (135,000 km2) in USA; and reported similar results with both land-based references, with a low probability of success rain, high false alarms and found that the error is not related to the meteorological characteristics of the events, or RU algorithm parameters; and it concludes that the estimated satellite rain could be useful for adding information combined with ground-based estimates through Bayesian techniques but requires investigation.

Chiang et al. (2007) combined rain gauges rain PERSIANN-CCS through a neural network to improve the simulated hourly flow in the basin of the Wu River Tu (204 km2) in Taiwan, and reported that due to the high density of the network rain gauge, the contribution of satellite product is insignificant.

5 Satélite geoestacionario con sensor infrarrojo

Li and Shao (2010) used a nonparametric method called Kernel smoothing to merge rain gauge with satellite estimates TMPA3B42 in Australia resulting in improved accuracy of regional rainfall.

Gebregiogis and Hossain (2011) evaluated a focus location (cell by grid cell) to combine three satellite products 3B42RT, CMORPH and PERSIANN-CCS in the basin of the Mississippi River with the VIC model and report improvements comparing the simulated flow that if they used satellite products individually.

Ebert et al. (2007) mention that for a better combination of satellite, radar and rain gauges, you must choose the best product according to the regime of rain.

Furthermore, Jiang et al. (2012) indicate that these combinations can not be effective especially when satellite products have too much errors and / or not available rainfall data; in that case, the hydrological models can correct errors of satellite products through calibration of its parameters (Stisen and Sandholt, 2010; Bitew and Gebremichael, 2011b;. Bitew et al, 2011; Moreno et al., 2012) and propose a Bayesian combination but simulated with three satellite products 3B42V6, 3B42RT and CMORPH with model calibration Xinanjiang Mishui in the river basin (9,972 km2) in China with complex terrain, flow humid subtropical climate and average annual rainfall Of 1560 mm Despite attempts to improve the detection of rain with satellite sensors, this has not been resolved due to the multidimensional nature of the problem. However, technology is constantly progress and sophisticated instruments on board future satellites continue the trend toward better predictions and thus a better understanding of Earth's climate, so that the 2013, NASA and JAXA will launch the first GPM "Global Precipitation Medition" consisting of a satellite like TRMM satellite core and a constellation of satellites with microwave sensors provided, which is an ambitious but necessary idea to generate a significant advance in this field of knowledge because they provide rainfall data of higher resolution, frequency and accuracy to current data (Tapiador et al., 2012).

Chapter 4

STUDY CASE:RIVER BASIN JÚCAR

This chapter presents an overview of the study area that includes map data (digital elevation model, accumulated cells, flow directions, slope, speed and slope maps of hydrological parameters), hydrometeorological land-based information (rain gauges, flow gauging stations, outlet flow and volume of reservoirs, temperature and evapotranspiration) and information satellite estimated rainfall of PERSIANN and PERSIANN-CCS products and 0.25 ° resolution 0.04º respectively, for a period comprised between 01 analysis January 2003 and October 31, 2009.

4.1 Description basinThe study was conducted in the Júcar River Basin (Figure 4-1) that flows into

the Mediterranean sea with a drained area of 21,500 km2, average flow of 43 m3 / s, maximum altitude of 1770 meters and average temperature of 14 ° C.

Figure 4-1. Geographical location of the Júcar River basin east of the Iberian Peninsula (Valencia, Spain). The shaded areas represent sub used in the study. Blue and red squares triangles spatially represent SAIH rainfall stations and AEMET respectively.

Júcar river basin has a Mediterranean climate 6 stark contrast between the wet season (spring and fall) and the dry and hot (summer) (Robles et al., 2002). The rains are mainly Mediterranean origin, in the most extreme cases usually caused by mesoscale convective systems (MCS) in autumn (51% of annual rainfall) but with a very high variability yoy. Atlantic origin rains have greater contribution

6 According to the Koppen climate classification it corresponds to a temperate climate with dry, hot summer

in the mountainous areas of the basin (38% of annual rainfall) tends to decrease toward the coast (Ninyerola et al, 2005;. Rigo and Llasat, 2007, Miró et al., 2009).

It has a very intense use: irrigation channels, drinking water supply, reservoirs, river and sport fishing, tourism etc., intensifying competition for water resources. Paredes et al. (2010) estimate the water dedicated to urban use in 118.64 hm3 / year to 1,030,979 people, while an irrigated area of 187.855 has consumed 1,394 million m3 / year.

According to the Hydrographic Confederation of Júcar (CHJ, 2007), coverage (land use) prevailing in the basin are forests and semi-natural areas with more than 50% of rainfed agricultural areas and agricultural areas 36% cultured with 10%, with citrus, grapes and grain cereals. Robles et al. (2002) indicate that the basin is 53.6% with exclusive presence of carbonate rocks, 44.8% of carbonates rocks or sedimentary evaporite materials basis, and 1.6% prevalence of rocks and with acidic materials. Regarding the lithological classes, CHJ (2007) indicates that calcarenite and marl are the predominant groups but also have proportions of limestone and alluvial material. Carbonate rocks, especially in karst systems for their ability to store water, help regulate the flow of rivers especially in times when the rains do not add water to the rivers.

From a digital elevation model with cell size of 500 x 500 m Studio of the Department of Hydraulic and Environmental Engineering (DIHMA, 2002), the slope map was obtained, with average value of 3% to values ranging from 0 to 36%. Moreover, half the area of the basin has outstanding less than or equal to 2%, 75% of the area has slopes less than or equal to 4% and 90% of its area has lower slopes or equal to 7%. This indicates that the basin is dominated by a gently sloping topography in the presence of steep areas (Figure 4-2).

Figure 4-2. Maps of the distribution of elevation and slope in the basin of the river Jucar MED 500m x 500m.

The relief is formed by mountain ranges of the Iberian system, a continental plateau and the coastal plain; with an average elevation of 808 meters above sea level ranging from 0-1769 meters, with a very mature landscape predominance of tectonic erosion over with and that is reflected in the form of the hypsometric curve (Figure 4-3). The coastal plain is an alluvial platform that provides a nutrient-rich soil that sustains most of the irrigated agricultural production; Thus, the lower reaches of the Júcar is a naranjera area par excellence. At an average height of 650 m, is the aquifer of the Eastern Channel, to drain and refill interactions with the Júcar River. Near the Mediterranean, it is the Albufera lagoon of 2,443 hectares with 0.88 m deep, surrounded by large expanses of paddy fields, and a row of dunes that protect the coast of the Mediterranean Sea.

Intervalos de pendiente Porcentajeen porcentaje de área

0 – 1 421 – 2 162 – 4 204 – 6 106 – 8 5

8 – 10 3Mayor a 10 3

Figure 4-3. Left: hypsometric curve over 500 m x500m. Right: interval pending in relation to the area they cover.

The average annual rainfall is 500 mm, but varies from values lower than 300 in the most southern areas and in other areas reaches values greater than 800 (CHJ, 2007). About Miró (2009) separates the rain in the Jucar River as the meteorological process that originates: of Mediterranean origin with increasing contribution to the coast, representing 43% of the annual rainfall between 1958 to 1978 and 51% from 1988 to 2008; Atlantic origin with the greatest contribution in the mountainous area but with a tendency to decrease, representing 42% (1958-1978) and 38% (1988 to 2008) of annual rainfall; convective origin tends to decrease, representing a 26 or 19% of the annual rainfall, according to the analyzed period; and finally the rains of continental origin lower values representing 10% of the annual rainfall, as shown in Figure 4-4. These rains are influenced by climate and terrain, highlights the effect of the Iberian system (Dunkeloh and Jacobeit, 2003; Sotillo et al, 2003;.. González-Hidalgo et al, 2010), and high temperatures are leading to increased evapotranspiration. (Quereda et al, 2011;.. Lorenzo-Lacruz et al, 2012).

Figura 4-4. Clasificación de la lluvia anual en la cuenca del río Júcar según el procesometeorológico que las origina en la zona litoral y zonainformación de Miró et al. (2009)

de montaña. Elaborado con

The average annual potential evapotranspiration reaches values of 777 mm / year, with values greater than 1000 mm on the coastal area and something less than 800 mm in the interior highlands of the watershed values. And, for the period 1940-2006, lobales values are obtained in natural regime of 21,500 million m3 / year of precipitation and 18,270 million m3 / year of potential evapotranspiration (Quereda et al., 2011).

4.2 Hydrological characteristicsThe TETIS distributed hydrological model to be discussed in Section

6.2.1, requires a structure of parameters describing the spatial variability of soil characteristics, substrate and vegetation cover at the basin scale, represented by estimating a priori parameter maps from the environmental information available, which should be consistent with the topography, land use, vegetation cover, lithology and other characteristics of physical media susceptible spatially represented. In this regard, the more information you have, you can use a smaller cell size but in return will be used more computational time in modeling.

4.2.1 Júcar River Basin

4.2.1.1 Map information

The cartographic information Júcar River Basin (DIHMA, 2002) has cell size 500 mx 500 m which required an upgrade and pre-ArcGIS to generate maps in format to fit the hydrological model. Thus from digital elevation model maps accumulated cells, flow directions, sloping terrain and speed in slope were obtained.

Maps of hydrological parameters with resolution of 500 x 500 m were estimated by the DIHMA (2002). In its work, the soil capillary storage Hu was estimated based on the water content available for different floor levels with the presence of roots and then sum of the products of water content available through the thickness of each layer of soil. Ks infiltration capacity was estimated from soil map and pedo- transfer functions that link the conductivity with some soil physical characteristics such as texture and organic matter. To estimate the percolation capacity Kp, DIHMA (2002) took into account the textural characteristics of the geological formations present in the basin.

It obtained the modal values for the parameters in each mapping unit, DIHMA (2002) estimated the spatial variability of parameters within each mapping unit defining a value for each cell of 500 mx 500 m. The procedure was to relate on a multiple regression model, the modal values of the parameters with various features such as topographic index, curvature of field, threshold runoff, among others. In Figure4-5 maps the parameters observed with a spatial resolution of 500 x 500 m, obtaining average values of 110 mm, 26 mm / h and 173 mm / h for Hu, Ks and Kp, respectively, and their coefficient of variation espacial7.

7 Spatial variation coefficient is the ratio between the mean value and standard deviation of the values in the cells of 500m in the Jucar river.

Figura 4-5. Hydrological parameters of the Jucar river, soil capillary storage (Hu), infiltration capacity (Ks) and percolation capacity (Kp) with cell size of 500m x 500m (DIHMA, 2002).

The maps will be completed hydrological parameters to calibrate the hydrological model using a correction factor, so that the mean values but change its spatial structure and its coefficient of variation is maintained

Tipo de cobertura vegetal ENE FEB MAR ABR MAY JUN JUL AGO SEP OCT NOV DIC

Area

(km2)

Bos que hoja perenne 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 3634.8Cultivo arbóreo perenne 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 835.7Cultivo arbóreo caduco 0.2 0.2 0.4 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.4 0.2 1047.3Matorral 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 5867.3Pradera natural 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 445.1Cultivo es tacional 0.2 0.2 0.6 0.8 0.8 0.8 0.6 0.4 0.2 0.2 0.2 0.2 9259.0Pas tos cultivados 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 1.2Sin vegetación o vegetación pobre 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 48.2Zona urbana 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 167.5Cuerpos de agua, vegetación acuática 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 95.1Arrozales 1.0 0.6 0.2 0.6 1.0 0.9 0.9 0.9 0.9 0.6 1.0 1.0 98.6

Figura 4-6. Factor de vegetación (λ) mensual según tipo de cobertura vegetal en la cuenca del ríoJúcar (DIHMA, 2002).

The vegetation factor (λ) is a parameter representing the behavior of the vegetative cycle of vegetation cover and allows to evaluate the variability in the annual cycle of potential evapotranspiration. It depends on the type of crop (height, degree of ground cover), development of the (harvest date) and climate(Allen et al., 2006). Your monthly values are detailed in the Figure 4-6 and distribution in the basin shown in Figure 4-7, in which a higher percentage of area covered with seasonal crops appreciated.

Figure 4-7. Map of the distribution of vegetation cover in the basin of the river Júcar (DIHMA, 2002).

4.2.1.2 Features of the drainage network

Geomorphological parameters characterize the geometry and the flow resistance in the network of channels, and are obtained by algebraic relations between some types of potential geometrical and / or hydraulic characteristics of each cell representative channel, and a variable associated with a flow rate "a section filled "; in turn, this flow is related to the cumulative area to the cell. The coefficients and the exponents of the equations obtained are estimated using linear regression from a small number of cross sections measured for each homogeneous field geomorphological region (French et al, 2007). For the Jucar river, geomorphological parameters were obtained from the study of the Tagus River Basin (DIHMA, 2001) study in which five geomorphological zones (high mountain area, average basin downstream, detailed header area and riverbed), being chosen by the similarity of morphological zones and exponent coefficient values detailed in Table 4-1.

Table 4-1. Geomorphological parameters of the network channels of the river Júcar (DIHMA, 2001)

4.2.2 Pajaroncillo subbasin

Pajaroncillo subbasin is an area of specific study in Chapter 7 of the thesis. On the maps of Figure 4-8 with cell size 500 mx 500 m spatial distribution of the altitude, slope, river network, hydrological parameters (Hu, Ks, Kp) and vegetation cover shown . Obtaining a drained area of 861 km2, a mountainous relief with average altitude of 1348 meters above sea level ranging from 1011-1699 meters, an average gradient of 5% ranging from 0-17%. With regard to hydrological parameters, mean values of 142 mm, 9 mm / h and 85 mm / h Hu, Ks and Kp respectively and corresponding spatial variation coefficient obtained in the corresponding map (Table 4-2) are obtained . These parameters will be completed to gauge the hydrological model but the coefficient of variation is maintained.

Regarding land cover map in Figure 4-8, the highest percentage of area covered with natural grassland and evergreen forest is appreciated.

Figure 4-8. Distribution maps of elevation, slope, river network, hydrological parameters (Hu, Ks, Kp) and vegetation cover with Cleda size of 500 mx 500 m, in the sub-basin Pajaroncillo Júcar River Basin, sub referenced in Figure 4.1.

Variable Media Mínimo Máximo Coeficiente de variación

Altitud (ms nm) 1348 1013 1689 0.12Pendiente (%) 5 0 17 0.61Hu (mm) 142 32 386 0.37Ks (mm/h) 9 0 878 5.47Kp (mm/h) 85 0 311 0.66

Table 4-2. Variables sub Pajaroncillo: altitude, slope and hydrological parameters(Hu, Ks and Kp).

4.3 Hydrometeorological land-based information

Hydrometeorological land-based information (rainfall, flow, temperature and information from reservoirs) has been provided by the Spanish Meteorological Agency (AEMET) and the Automated Hydrological Information System of the Hydrographic Confederation of Júcar (SAIH-CHJ). The Main characteristics are summarized in Table 4-3.

Variable Fuente Archivo Res olución

TemporalSis tema de

CoordenadasHora

Lluvia, caudal y volumen en embals es

SAIH ASCII Cinco minutal UTM Zona 30N Local

Lluvia AEMET CSV Diario WGS 1984 GMT 07-07 del día s iguiente

Temperatura AEMET CSV Diario WGS 1984 GMT 08-08 del día anterior

has ta la fecha marcada

Table 4-3. Features hydrometeorological land-based information in the basin of the river Júcar

A brief description of the basic information collected that required an analysis and processing, because the data have different temporal resolution, quality and presence of faults is presented.

4.3.1 Estimated Rain gauges

Measurements in the SAIH rainfall stations are transmitted via a telemetry system in real time and record minimum 2.4 mm / h in 5 minutes. In the case of rainfall stations AEMET Hellmann type are collected daily by operators serving the AEMET. In Figure 4-9 the type of gauge SAIH and AEMET is displayed.

Figure 4-9. Left: telemetric rain gauge type SAIH "tipping bucket" that drains into swinging buckets coupled with reading collected in real time. Right: Rain Gauge AEMET type "Hellmann" 200 mm capacity with daily reading for operators.

It 186 rainfall stations, of which 115 are stations AEMET and 71 are the SAIH were used. And, considering that the extent of the basin is 21,500 km2, the density of the network of rain gauges used in the study was about 1 gauge per 116 km2. This significantly increases density gauge to about 1 per 46 km2 in the lower part of the basin, since there are a greater number of gauges installed near the coastline. In this regard, the density of rainfall networks depends on several factors, WMO (2011) recommended minimum densities (Table 4-4) are a guide as it must be determined for each area based on its physiographic and climatic characteristics. For example: convective cells (characteristic of the coastal area of the Júcar basin) are difficult to grasp even with dense rain gauge networks.

Unidad fisiográfica Pluviómetros Pluviógrafos

Costa 900 9,000Montaña 250 2,500Planicie interior 575 5,750Montes/ondulaciones 575 5,750Islas pequeñas 25 250Áreas urbanas - 10 a 20 P o los /t ier r as á r id a s 10 , 0 0 0 1 0 0, 0 0 0

Table 4-4. Recommended minimum rainfall stations (km2 / station) as physiographic unit (WMO, 2011) density.

4.3.2 Hydrometric and reservoirsCHJ (2007) indicates that the river Júcar river network brings 3,100

million m3 / year, of which 26% (820 million m3 / year) comes from direct runoff, and the remaining 74% (2,270 hm3 / year) of the groundwater runoff. Although the average value of 3,100 has been reduced in the past decade to 2,500 million m3 / year. Recent studies (. Lorenzo-Lacruz et al, 2012) indicate that the development of agriculture has been accompanied by the systematic construction of reservoirs that have altered the natural regime and interannual variability; also reported an annual decrease flows in winter and spring trend, while this trend is reversed in summer and autumn. This is probably related to the trend of rain reported by Miró (2009) as well as reforestation processes, increased water demand as a result of population growth and irrigation demands. Also, the mass in the aquifer of the Eastern Channel from 1989 pumps have a negative impact on natural regularity of Júcar, its base and annual report (Gil, 2006) flow.

Pajaroncillo, Albaida, Swedish and input reservoirs Alarcon and Contreras, whose location coordinates are detailed in Appendix A1: To study five hydrometric stations (Figure 4-1) were selected. Flow rates were collected using the SAIHWin server and in the case of reservoirs, flow was attained by balance.

In Figure 4-10 the curves Cota-volume reservoirs is displayed Alarcón Contreras and reconstructed from the daily information from 2000 to 2009 SAIHWin collected using the server. These curves are necessary for the above-mentioned balance.

Figure 4-10.- Cota-volume curves in reservoirs Alarcon and Contreras with daily information reconstructed from 2000 to 2009 SAIH.

The Contreras reservoir with a capacity of 852 hm3, 2,700 ha surface area of 3,427 km2 drainage with water intended, among others, the Júcar-Turia canal which mainly supplies water to the city of Valencia. And the Alarcón reservoir with a capacity of 1105 Hm3 surface of 6,840 ha and drainage area of 2,883 km2, with regulated for hydroelectric and irrigation production of 45,000 ha of crops waters, receives water from the Tajo-Segura so it was necessary to re-establish the natural regime for modeling.

4.3.3 Temperature and evapotranspiration

Long periods of sunlight, along with the continuous circulation of warm air masses originate high temperatures in the basin of the river Júcar (CHJ, 2007). For this study, we offer daily information minimum and maximum temperature in 47 thermometric stations AEMET of March 1, 2000 to October 31, 2009, information is interpolated by the inverse distance weighted method of squared IDW onwards, to generate temperature fields. And an average of 14 ° C is obtained, ranging from 10º C in the mountainous areas of the northwest at 21 ° C in the southeast coast; with a minimum value of -12 ° C and a maximum of 44º C (Figure 4-11). In the case of sub Pajaroncillo a spatial average of 12 ° C with little variation (coefficient of variation of 0.04) it is obtained.

The location and name of the 47 thermometric stations are detailed in theAppendix A1.

The reference evapotranspiration (ET0) was obtained with the equation Hargreaves, see for example, Allen et al. (2006):

ETo C(t med 17.78) Ro * (tmax tmin

)

0.5 4.1

where R0 is the extraterrestrial solar radiation tabulated as a function of latitude month (mm / d); TMED, tmax, tmin are average temperature, maximum and minimum respectively (C); C is the constant calibrated estimates of the Penman-Monteith weather stations where data are available solar radiation, air temperature, humidity and wind speed.

Figure 4-11. Distribution of the average, maximum and minimum temperature in the Jucar river. Testing Period: March 1, 2000 to October 31, 2009.

It has historical information of potential evapotranspiration calculated by the Penman-Monteith in "the stiff" station of the Province of Albacete, available on the website of the Provincial Agricultural (ITAP, 2012) Technical Institute; and seasons "Cerrito Requena", "Bolbaite" and "Villanueva-Castellón" of the Province of Valencia, available on the website of the Valencian Institute of Agrarian Research (IVIA, 2012). This information is used for calibrating the constant "C" of the equation 4.1. A summary of the adjustment made is detailed in Figure 4-12 and Table 4-5.

ProvinciaLongitud Latitud Altitud

ErrorOeste Norte msnmMedio R2

Constante(C)

Albacete 2°05’10" 39°14’30" 695 0.782 0.86 0.0023679

Valencia 1°06’00" 39°29’00" 692 0.407 0.90 0.0020617

Valencia 0°41’20" 39°04’13" 269 0.383 0.89 0.0020366

Valencia 0°31’22" 39°04’00" 58 0.465 0.88 0.0020617

ETo

Har

grea

ves

(mm

/d)

10Estación Las Tiesas9 (Marzo 2000

8 a Noviembre 2009)

7

6

5

4

3

2

1

0y = 0.8815x + 0.4115

R² = 0.8617

8Estación Requena Cerrito

7 (Marzo 2000 a Mayo 2008

6

5

4

3

2

1 y = 0.9264x + 0.1876R² = 0.8964

00 1 2 3 4 5 6 7 8 9 10 11

8Estación Bolbaite

7 (Junio 2006 a Enero 2009)

6

5

4

3

2

1 y = 0.8735x + 0.3962R² = 0.89

00 1 2 3 4 5 6 7 8

0 1 2 3 4 5 6 7 89 Estación Villanueva-Castellón8 (Enero 2000 a Mayo 2009)

7

6

5

4

3

2y = 0.8933x + 0.57091 R² = 0.882

00 1 2 3 4 5 6 7 8 9

ETo Penman-Monteith (mm/d)

Figure 4-12. Scatterplot of daily reference evapotranspiration calculated with Hargreaves and Penman-Monteith method, and correlation coefficient (R2) at the stations indicated.

EstaciónCalibración

Las Tiesas Requena Cerrito Bolbaite Villanueva

-Castellón

Table 4-5. Summary of the correlation coefficients (R2) and tuning constants (C) Hargreaves equation. The tuning constants in bold, were used in the calculation of daily reference evapotranspiration.

It 0.0020617 constant adjustment was used in the calculation of daily ET0 thermometric stations in Valencia to be the best fit, and the constant 0.0023679 be used for thermometric stations Albacete (unique in the province) and Cuenca.

Thus daily ET0 values are obtained in the thermometric stations AEMET 47 of March 1, 2000 to October 31, 2009, information is interpolated by IDW method for generating fields of ET0, obtaining maximum values within the basin 1308 mm / year in the southwest to attenuated by the sea of 955 mm / year in the coastal area (see Figure 4-13) values. In the case of sub Pajaroncillo an average value of 1137 mm / year spatial variation coefficient of 0.01 is obtained.

Figure 4-13. Distribution of reference evapotranspiration (ET0) daily in the river basin and sub-basin Júcar Pajaroncillo. Period analyzed: March 1, 2000 to October 31, 200

4.4 Rain information estimated satellite

In Chapter 2 satellite products are listed with their respective global scale spatial resolution. So: GPCP (1 and 2.5 °), TRMM (0.25 °), CMORPH (0.07277º) PERSIANN (0.25 °) and PERSIANN-CCS (0.04º); which is chosen for the study product better spatial resolution such as CCS and PERSIANN- PERSIANN previous version.

The analysis period is the period from January 1, 2003 and October 31, 2009, and the format of the satellite products are detailed in the Table 4-6.

PERSIANN PERSIANN-CCSEscala espacial Escala temporal Unidades Disponible desde

0.25º (aprox. 28 km)6 h, diario mm/d01/03/2000

0.04º (aprox. 4 km)diario mm/d01/01/2003

Fuente Web HyDISCHRS-Universidad de California en Irvine

Cobertura espacial 50° S - 50° N, 0-360º longitud 60º S – 60º N, 0-360º longitud Geometría 400 filas x 1440 columnas 3000 filas x 9000 columnas Formato original GRID ASCII en coordenadas Binario big endian, row centric, geográficas. G MT 4 byte float, GMT

Table 4-6. Original format of the two products of rain estimated satellite: PERSIANN And PERSIANN-CCS

Using the Hydis server, the product information PERSIANN trimmed to a rectangular area near the basin geometry of 11 columns by 10 rows. For information PERSIANN-CCS was necessary to develop a code in MatLab to process the original format GRID Binary to ASCII format and then cut to a rectangular area near the basin geometry of 74 columns by 62 rows. Finally, 2496 GRID ASCII files, one per day, with features that are detailed in Table 4-7 as the product of satellite estimated rainfall were obtained

PERSIANN PERSIANN-CCS

Header File: n cols 11 n rows

10 xllcorner -2.6250º yllcorner 36.875º cell size

0.25º

Header File: n cols74 n

rows 62 xllcorner -3.24º yllcorner 38.38º cell size 0.04º

PERSIANN information and PERSIANN-CCS, coding required to conform to the format compatible with the hydrological model. Thus enters the model information respecting the spatial variability in ASCII format and interpreted in the model as virtual stations located in the centroid of each grid box Satellite Product: with PERSIANN (mesh 11 columns x 10 rows) were 110 virtual and PERSIANN- CCS stations (mesh 74 columns x 62 rows) were 4588 virtual stations. In Figure 4-14 mesh PERSIANN product displays throughout the Jucar River and part of the mesh product PERSIANN-CCS product in a cell PERSIANN.

Figure 4-14. Left: Spatial distribution of the mesh centroid PERSIANN (blue dashed lines) in the Jucar river. Right: Spatial distribution of the centroids in a part of the mesh PERSIANN-CCS (solid black lines) in a grid box PERSIANN.

Chapter 5

Hydrologic modeling ESTIMATED RAINFALL

WITH SATELLITE

Hydrologic modeling is complex by significant variability temporary space of the physical processes involved, due to variations in physiographic factors such as climate, geology, soil, vegetation, topography and human interventions (Wood, 1995); However, advances in scientific knowledge have helped to understand and relate these processes. Thus, the hydrologic modeling plays an important role in most aspects of water management and the environment.

Current distributed hydrological models to simulate flows not only to the output of a basin, but in any part of the basin but the effectiveness of these models depends on the availability of input data. Thus, the estimated global TV, rain suits these models because they have distributed rainfall data for the entire basin. But due to the multidimensionality of error estimated Satellite rain, it is difficult to establish a priori a product that allows optimum hydrological application in different climatic conditions and that is why it is necessary to assess their performance through hydrologic modeling. Thus, in the first section of this chapter statistical tools to assess their performance are detailed; In the second section the implementation of the hydrological model is presented; and in the following sections analyze the results in terms of calibration, validation, water balance and error propagation in the Mediterranean basin of the river Júcar analysis for the period January 01 reported 2003 to October 31, 2009.

5.1 Efficiency ratios used

The performance of the hydrological model was evaluated, ie how close is the operating model of the functioning of real system through efficiency rates

as Nash-Sutcliffe (E), RSR, error in volume (V) and graphical techniques (comparison of hydrographs and scatter plots). The equations of E, RSR and Ev indices are detailed in section 5.1.1.

For performance rating it compared with levels reported by Moriasi et al. (2007) and Donigian and Imhoff (2002) detailed in Table 6-1.

Rendimiento RSRa Ea [Ev]b

Muy bueno Bueno Satisfactorio Insatisfactorio

0.00 ≤ RSR ≤ 0.500.50 < RSR ≤ 0.600.60 < RSR ≤ 0.70

RSR > 0.70

0.75 < E ≤ 1.000.65 < E ≤ 0.750.50 < E ≤ 0.65

E ≤ 0.50

< 10%10-15%

D esf av ora b le 15 - 25%

Table 6-1. Performance levels of a hydrological model based on the index E, RSR and Ev monthly time interval, according Moriasi et al. (2007) to Donigian- and Imhoff (2002)b

We should note that the levels indicated in Table 6-1 refer to a monthly time interval; and since the hydrological modeling of the studyit is daily, then timescale, we are more demanding in the qualification of our results.

It used the same notation used in Chapter 5 for So, S1 and S2 for rain gauges, rain and rain PERSIANN PERSIANN-CCS respectively. And comparisons, To notation was used to compare the simulated flow gauges and flow observed T1 to compare simulated flow and flow PERSIANN observed, and T2 to compare simulated flow and flow-CCS PERSIANN observed.

5.2 Implementation of a distributed hydrological model

5.2.1 The TETIS Model

The TETIS model is a distributed conceptual hydrological model parameters based physically simulating main processes of the hydrological cycle. The production of runoff is modeled using seven tanks connected in each cell modeling, describing the soil-vegetation-atmosphere-aquifer interactions (Figure 6-1). In the study nor the tank snow cover (To) and interception tank vegetation (T6) they were not used. The vertical flow of water between each tank represent hydrological processes: precipitation (rain or snow, X6), direct evaporation (Y6), effective precipitation (X1), potential evapotranspiration (Y1), gravitational infiltration (X3), percolation (X4 ) and underground losses (X5), while the horizontal flows represent hydrological processes: direct runoff (Y2), interflow (Y3) and flow based (Y4).

The horizontal movement of the flow occurs in two phases separated. In the first phase, direct runoff, interflow and base stream are defined by a mesh tank three layers connected together, wherein the water movement is towards the corresponding tank downstream along the flow directions proposed by the model Digital Elevation (DEM) to reach the main drainage network. The second phase is the movement of the flow channels on the network. The propagation channels is governed by the kinematic wave taking into account the geomorphological features of the network of rivers, in what is called Wave Kinematics Geomorphology (Vélez, 2001;. French et al, 2007; Velez et al., 2009).

Figure vertically 6-1.Esquema conceptual runoff production in TETIS model in each cell (the variables are described in the text).

Hydrologic modeling is affected by various sources of error: in the input variables, the variables observed state (usually the outflow of the basin), in the estimation of parameters, and the conceptualization of the model (Butts et al. , 2004). In addition, we must add the effects of spatial and temporal scale, when

nonlinear processes in which there spatio-temporal aggregate (French et al., 2007). Therefore, the effective use of hydrological model parameters to correct any errors that create uncertainty in modeling; in the case of Tethys, the actual parameters are adjusted through corrective factors that can automatically calibrate the optimization algorithm SCE-UA "Shuffled Complex Evolution" (Duan et al., 1992). Thus, the main virtue TETIS model is the explicit representation of the spatial variability of the physical characteristics and the use of a separate calibration settings for the actual parameter structure. The nine correction factors (French et al., 2007) are detailed in Table 6-2.

Factorcorrector

Parámetro del modelo Símbolo

FC1 Almacenamiento capilar del suelo Hu FC2 Factor de vegetación λ FC3 Capacidad de infiltración Ks FC4 Velocidad en ladera µ FC5 Capacidad de percolación Kp FC6 Conductividad hidráulica del interflujo Kss FC7 Capacidad de pérdidas del acuífero Kps FC8 Conductividad hidráulica del acuífero Ksa

F C 9 V el o c i d a d en l o s c a u c es ν

Table 6-2. Correction factors and respective parameters set (French et al., 2007).

Therefore, the maps of hydrological parameters of the basin were completed to gauge the hydrological model using a correction factor, with which the average values change but maintain their spatial structure and its coefficient of variation.

More details on the conceptual framework and modeling procedure the reader is referred to: French et al. (2002), French et al. (2007), Velez et al. (2007), Morales-de la Cruz and French (2008), Velez and French (2008), Velez et al. (2009).

´ 5.2.2 Information Processing

In the case study of this thesis, the timescale on the hydrologic modeling is one day (t = 1 day), and the spatial scale corresponds to a cell size of 500 mx 500 m.

Hydrometeorological land-based information (time series of rainfall, flow and reference evapotranspiration) and information Satellite estimated rainfall, required an encoding format to fit the CEDEX (data row), compatible with the hydrological model in the period TETIS Analysis of January 1, 2003 to October 31, 2009. In the case of satellite products, entered the model respecting all its spatial variability, which were interpreted in the model as virtual stations located in the centroid of each grid box satellite: 110 virtual stations virtual stations PERSIANN and 4588 in the case of PERSIANN-CCS.

Implementation of the model, required spatial information of the basin in GRID ASCII format. Obtained DIHMA Studio (2002), which has already been presented in Chapter 4 was used: digital elevation model map of accumulated cells (necessary to estimate the speed and flow area), map of directions of flow ( necessary to establish connectivity between different cells), slope map (used in the estimation of surface runoff velocity), velocity map in slope, land cover map and maps of hydrological parameters. Geomorphological channel parameters (k, α, Cd, Cn, φ, α1, α2, θ and ξ), entered as coefficients or exponents geomorphological potential type equations.

In Table 6-3 the model parameters are summarized in the Jucar river. And, since no information is available on Kss, Ksa and KPS, the approach has been adopted: Kss = Ks, and KPS Ksa = Kp = 0.1Kp. Therefore, the calibration correction factors take into account possible errors in estimating magnitude of the initial maps.

The initial conditions of static storage tanks (H1) and underground (H4), were calculated by heating (Velez and French, 2008) for the PERSIANN product.

And in the case of product PERSIANN-CCS, because no information was available, it was estimated by recirculation, in which the initial conditions of moisture are obtained from the final terms of moisture from a previous simulation.

5.2.3 Calibration and validation procedure

In automatic calibration algorithm used TETIS SE-USA for the nine correction factors model. However, you can get multiple sets of parameters that produce acceptable simulations as objective function, in what is called equifinality (Beven, 1989; Quintero et al, 2012.). To reduce the possibility of falling into the problem of equifinality is needed before a manual calibration automatic calibration, further result in fewer iterations to converge to the solution (which has a direct impact on the computation time optimization).

The success of the manual calibration dependent modeling experience and his knowledge and interaction with the model. In this regard, Eckhardt and Arnold (2001) indicate that it is subjective and can consume more time. Therefore, we recommend setting the base stream and then the error rate in the volume are more sensitive to the initial conditions of moisture in the static tank and manually first aquifer level; and leave the setting of maximum flow for automatic calibration. In automatic calibration, it was taken as objective function optimization Nash-Sutcliffe index (E), which is more sensitive to peak flows.

In validation, correction factors obtained from the calibration were used; and for temporal validation, the system response was simulated using a part of the time series data that are not used in the calibration. For validation spatiotemporal system response was simulated in a place different from the calibration station.

5.3 Calibration Results

Cassiraga et al. (2002) note that the maximum intensity of the rain is strongly influenced by its spatio-temporal; and therefore in the best flood forecasting in real time. So, any progress in the temporary space characterization of the rain will allow a more reliable and realistic models of distributed operation. Thus, the calibration is performed from a physical point of view in order to correctly interpret the parameters regarding their spatial variability.

The point calibration was performed at the output of the sub-basin Pajaroncillo (drained area of 861 km2) for the analysis period January 1 to July

31, 2003 with rain gauge (So), rain PERSIANN (S1) and PERSIANN- rain CCS (S2). Table 6-4 shows the correction factors to which was reached after calibration. And we see that the correction factor is reduced evapotranspiration with PERSIANN 71% and 32% increases with PERSIANN-CCS, because the hydrological model tries to compensate for the underestimation of the PERSIANN rain and rain PERSIANN- overestimation of CCS . This is also causing PERSIANN-CCS PERSIANN get higher than in the correction factors static storage values, infiltration, direct, percolation and interflow runoff.

Factores correctores So S1 S2FC-1 Almacenamiento estático 0.897 0.704 0.870FC-2 Evapotranspiración 0.648 0.186 0.853FC-3 Infiltración 0.925 0.558 0.726FC-4 Escorrentía directa 0.004 0.001 0.003FC-5 Percolación 0.114 0.016 0.024FC-6 Interflujo 494.897 113.524 118.891FC-7 Pérdidas subterráneas 0.000 0.000 0.000FC-8 Flujo base 2.002 9.594 2.985FC-9 V eloc i d a d en l o s c auc e s 0.8 3 4 0.6 2 1 0.5 3 7

Table 6-4. Calibration correction factors in the watershed of Pajaroncillo with So, S1 and S2.Calibration period: January 1 to July 31, 2003.

Calibration of hydrological model parameters TETIS has raised the performance modeling. Also, various authors performed a calibration of the hydrological model to improve performance with products of satellite rainfall estimate (Stisen and Sandholt, 2010; Bitew and Gebremichael, 2011b; Bitew et al, 2011;.. Jiang et al, 2012; Moreno et al., 2012).

E values, RSR and Ev reflected in Table 6-5 were obtained, considering that the model performance is "very good" with gauge (So), "unsatisfactory" with PERSIANN (S1) and "satisfactory" with PERSIAN -CCS (S2) according to the levels reported in Table 6-1.

Índices de eficiencia So S1 S2Nash-Stucliffe (E) 0.80 0.27 0.51RMSE estandarizado (RSR) 0.45 0.85 0.70E rror en volu m en e n % (Ev) 0.06 - 1 0 . 4 8 -7.55

Table 6-5. Efficiency ratings in the sub calibration Pajaroncillo with So, S1 and S2.Calibration period: January 1 to July 31, 2003

The results are encouraging with rain PERSIANN-CCS (S2) and it seems that better data resolution raster rain S2, lower FBIAS and an error of

overestimation in the volume of rain (section 5.3.1), cause Satellite this product is better suited hydrologic modeling. Similar results regarding satellite products with better spatial resolution, are reported by Nikolopoulos et al. (2010) with product KIDD (resolution 4 km) from the product-TRMM 3B42 (resolution 0.25 °) and Kidd (resolution 25 km).

Regarding modeling rain PERSIANN (S1), a coarse spatial resolution S1 of the rain and the error of underestimating the volume of rain S1 (section 5.3.1) are negatively affecting the modeling, since there is insufficient rain to feed the hydrological cycle, but this is possibly dampening with the highest probability of detection of S1 rain. All this is resulting in a Nash-Sutcliffe index (E) low. In this regard, Moreno et al. (2012) report on the automatic model calibration tribs rain PERSIANN, E values ranging from -0.48 to 0.79 in four sub-basins of the Colorado River and areas of complex topography ranging from 35-350 km2.

There is significant positive correlation (Figure 6-2) the flow observed and simulated flow in all cases. On stage with values obtained To0.71 and 0.89; Compared with T1 values of 0.53 and 0.58; T2 and comparing values of 0.62 and 0.73 (first Pearson correlation value and second Kendall). It has used a significance level of 5% and the statistical Student t test for the coefficient of Pearson, and statistical test "sum of order" for the coefficient of Kendall (Hirsh et al., 1992).

Figure 6-2. Scatterplot of observed and simulated daily flow calibration subbasin To Pajaroncillo with comparisons (left), T1 (middle) and T2 (right) flow. Calibration period: January 1 to July 31, 2003.

In the hydrograph generated calibration with rain gauges (Figure 6-3 left), the base flow and shape of the recession curve reproduces well, peak flows and days that occur are detected, but underestimates its value maximum 26%.

In hydrographs generated from the proceeds of satellite PERSIANN (Figure 6-3 center), you may notice that it recognizes the basic flow, fails to

detect peak flows and underestimates its maximum value at 59%. Instead, rain PERSIANN-CCS (Figure 6-3 right) play nicely hydrograph base flow and shape of the curve of recession, it detects most peak flows and days when they occur, but underestimates its maximum value 48%. That is, both satellite products are underestimating its maximum value. In this regard, Nikolopoulos et al. (2010) report that the estimated satellite rain underestimate their value in the range of 10 to 80% and this causes a simulated error rate in the same order of magnitude in different satellite products: TRMM-3B42 (0.25 °) Kidd-KIDD-4 km and 25 km.

Figure 6-3. Calibration hydrographs generated in the sub Pajaroncillo with rain gauges (left), rain PERSIANN (center) and rain PERSIANN-CCS (right). Calibration period: January 1 to July 31, 2003.

5.4 Validation Results5.4.1 Time validation

The temporary validation was performed on the same calibration station (at the exit of the sub Pajaroncillo, drained area of 861 km2) for the analysis period August 1, 2003 to October 31, 2009 with rain gauge (So) , rain PERSIANN (S1) and rain PERSIANN-CCS (S2).

With rain gauges, values of E, RSR and Ev (Table 6-6) of 0.79, 0.46 and 13.87% respectively were obtained. Therefore, we can say that the performance of the model D is "very good" according to the levels reported in Table 6-1. Unlike the unsatisfactory performance with both satellite products with values of E, RSR and Ev to -2.02, 1.74 and 54.08% with S1 and values of -0.90, 1.38 and 53.93% with S2, respectively.

Índices de eficiencia So S1 S2Nash-Stucliffe (E) 0.79 -2.02 -0.90RMSE estandarizado (RSR) 0.46 1.74 1.38Error en volu m en en % (Ev) 13. 8 7 54. 0 8 5 3 . 9 3

Table 6-6.Índices efficiency of temporary validation subbasin with Pajaroncillo So, S1 and S2 rains. Validation time period: August 1, 2003 to October 31, 2009.

There is significant positive correlation (Figure 6-4) the flow observed and simulated flow in all cases. With values of 0.43 to 0.90 coefficient Pearson and Kendall in the case of To. However, in the case of T1 values of 0.31 and 0.15 are obtained; and T2 values of 0.18 and 0.52.

Figure 6-4. Scatterplot of observed and simulated daily flow of temporary validation subbasin To Pajaroncillo with comparisons (left), T1 (middle) and T2 (right) flow. Validation time period: August 1, 2003 to October 31, 2009.

In Figure 6-5 (left), the hydrograph simulated rain gauges, flow basis and recognizes the shape of the curve recession well, and detects peak flows occurring days but underestimates its maximum value in most cases. However, hydrographs generated satellite products, not while acknowledging the flow base and fails to detect the maximum flow (Figure 6-5, center and right).

Figure 6-5. Hydrographs generated from the temporary validation subbasin Pajaroncillo with rain gauges (left), rain PERSIANN (center) and rain PERSIANN-CCS (right). Validation time period: August 1, 2003 to October 31 2009.

5.4.2 Validation temporary space

The point of space-time validation was performed at the entrance to the reservoir Contreras (drained area of 3,427 km2) for the analysis period January 1, 2003 to October 31, 2009 with rain gauge (So), rain PERSIANN (S1) and rain PERSIANN-CCS (S2).

Due to evaporation losses and leaks of Contreras reservoir, it is considered using the correction factor for underground losses (resulting in a value FC7 = 0.043) corresponding to a loss of 11% from the rain. E efficiency ratios, RSR and Ev (Table 6-7) to D were 0.58, 1.54 and 0.09% respectively, which is considered a "very good" performance, according to the levels reported in Table 6-1. Unlike the "unsatisfactory" performance with both satellite products with values of E, RSR and Ev - 1.87, 1.69 and 56.59% with S1 and -3.25, 2.06 and 118.61% with S2, respectively.

Índices de eficiencia So S1 S2Nash-Stucliffe (E) 0.58 -1.87 -3.25RMSE estandarizado (RSR) 1.54 1.69 2.06Error en volu m en en % (Ev) 0 . 09 56 . 5 9 1 1 8. 6 1

Table 6-7. Efficiency index validation Contreras temporary space in the sub with So, S1 and S2. Temporary space validation period: January 1, 2003 to October 31, 2009.

From the point of view of the scattergram flows between simulated and observed (Figure 6-6) positive correlation exists in all cases. With values of 0.68 and 0.80 with To, 0.11 and 0.19 T1, T2 0.55 and 0.51, depending on the correlation test (Pearson correlation first and second Kendall value).

Figure 6-6. Scatterplot of observed and simulated flow rate of validation Contreras temporary space with sub To (left), T1 (middle) and T2 (right). Temporary space validation period: January 1, 2003 to October 31, 2009.

The hydrograph simulated rain gauges well recognized basis flow and the shape of the curve recession peak flows detected and days occurring but underestimates its maximum value at 37%. (Figure 6-7, left). However, in the hydrographs generated satellite products, it shows that it recognizes no flow basis and fails to detect the maximum flow (Figure 6-7, center and right)

Figure 6-7. Hydrographs generated validation Contreras temporary space in the sub with rain gauges (left), rain PERSIANN (center) and rain PERSIANN-CCS (right). Temporary space validation period: January 1, 2003 to October 31, 2009.

5.5 Water balance

In addition to comparing the flow hydrographs, analysis of the resulting water balance is another important tool for assessing the validity of satellite products indicator. Thus, in Table 6-8 the average values of the components of the water balance simulation with daily rain gauge (So), rain PERSIANN (S1) and rain PERSIANN-CCS (S2) in the sub-basin is collected for Pajaroncillo the analysis period January 1, 2003 to October 31, 2009.

Balance hídrico So S1 S2Lluvia (mm/d) 1.64 1.02 2.13Evapotranspiración real (mm/d) 1.32 0.58 1.71Pérdidas subterráneas (mm/d) 0.00 0.00 0.00Caudal observado (mm/d) 0.34 0.34 0.34Caudal simulado (mm/d) 0.38 0.47 0.47Almacenamiento estático (mm) 59 89 59Almacenamiento superficial (mm) 0.3 2 1Almacenamiento gravitacional (mm) 2 8 7Almacenamiento en el acuífero (mm) 82 35 67Flujo superficial (%) 17 24 28Interflujo (%) 9 11 10Flu j o base ( % ) 74 65 62

Table 5-8. Average values of flows and storage in the water balance in the watershed of Pajaroncillo with So, S1 and S2. Testing Period: January 1, 2003 to October 31, 2009.

In the water balance with rain gauges, values of 1.64 are obtained, 1.32 and 0.38 mm of rainfall, actual evapotranspiration and the simulated flow respectively. It also distributes the flow by 17% as direct runoff, interflow 9% and 74% as base flow. These results suggest that in sub Pajaroncillo, the flow is never exhausted and that recessions are short in time, that is a sub-basin with permanent flow. With satellite products, 38% less rain PERSIANN and 30% more rain PERSIANN-CCS is obtained. This clearly influences variations in runoff production mechanisms in the watershed of Pajaroncillo, as shown in Figure 5-8.

Figure 5-8. Composition runoff with rain gauge, rain and rain PERSIANN PERSIANN-CCS in the watershed of Pajaroncillo. Testing Period: January 01, 2003 to October 31, 2009.

As the hydrological model tries to maintain a flow rate similar to observed behavior (since the calibration strategy is a function of the flow and not a component of the water balance), you get the actual evapotranspiration by 56% with PERSIANN is reduced and increased by 30% PERSIANN-CCS. Similar behavior is reported in the rain evapotranspiration component PERSIANN for Bitew and Gebremichael (2011b) and Moreno et al. (2012).

In Figure 6-9 the daily evolution of flows (direct runoff, interflow and baseflow), storage (static, gravitational and aquifer), actual evapotranspiration (ETR) is seen rain. It highlights the differences in the values of rain and ETR, with higher values in PERSIANN-CCS and lower values in PERSIANN, about rain gauges.

Figure 6-9. Daily evolution of flows and main variables of water balance in the watershed of Pajaroncillo with

rain gauge (left), rain PERSIANN (center) and rain PERSIANN-CCS (right). Testing Period: January 1, 2003 to

October 31 2009.

5.6 Error propagation simulated rain to flow

Nikolopoulos et al. (2010) indicate that the evaluation of error propagation is a difficult task because it is related to various factors such as: (i) own instrumental satellite product issues, (ii) basin scale, (iii) scale space -temporal rain, (iv) level of complexity and physical processes represented by the hydrological model and (v) regional characteristics.

In this thesis, the error propagation is analyzed through efficiency ratios rain and flow rate are plotted as shown in Figure 6-10, if the error displayed in the rain spreads in error equals or is runoff dimming or get worse through hydrologic modeling for different scales of aggregation basin. If the points are set to 1: 1 line, means that the error in the rain propagates in an error equal to runoff, while they are placed within the shaded areas in the figure indicate that the error is damped by hydrologic modeling.

The values of efficiency ratios E, RSR and Ev rain, correspond to those in Figure 5-16. And in the case of hydrological modeling, it was quantified through indices E, RSR and Ev calculated calibration Pajaroncillo (212 days), temporary validation Pajaroncillo (2284 days) and validation spatiotemporal in Albaida, Contreras Alarcon and Swedish (2496 days).

It was found that the error in volume of rain is dampened with both satellite products. Conversely, the failure of rain in terms of E and RSR worse hydrologic modeling, except in the smallest such as Pajaroncillo (861 km2) and Albaida (1,301 km2) that are perfectly matched with line 1 basins: 1 . In this regard, Nikolopoulos et al. (2010) report that basins with 400 km2 smaller areas are better able to buffer the error of simulated rain to flow. In addition, Wigmosta and Prasad (2005) indicate that small and medium watersheds, slope processes dominate the shape and size of the hydrograph and the residence time of water in the drainage network is small compared to the slope; so the network plays a secondary role in the hydrological response. However in large basins, the residence time in the system of channels is greater and there is a strong influence of the network of channels in the form and magnitude of the hydrograph, however, slope processes remain important because they determine the volume Water that enters the network channels.

Figure 6-10. Efficiency ratings of rain areal efficiency ratios vs simulated flow, with S1 and S2 at different levels of aggregation basin. Ev in absolute value.

5.7 Discussion of Results

Hydrologic modeling (Figure 6-3 and Figure 6-5) with rain PERSIANN get an "unsatisfactory" performance values E, RSR and Ev of 0.27, 0.85 and -10.48% in calibration; -2.02, 1.74 and 54.08% in temporal validation. Instead, rain PERSIANN-CCS "satisfactory" performance, with values of E, RSR and Ev is obtained: 0.51, 0.70 and -7.55% in calibration; and -0.90, 1.38 and 53.93% in temporal validation. In addition, the calibration simulated hydrograph play nicely base flow and detects most peak flows and days when events occur, also recognizes the maximum flow but underestimates its value by 48%.

Calibration of hydrological model parameters TETIS has raised the performance modeling. Also, various authors performed a calibration of the hydrological model to improve performance with products of satellite rainfall estimate (Stisen and Sandholt, 2010; Bitew and Gebremichael, 2011b; Bitew et al, 2011;.. Jiang et al, 2012; Moreno et al., 2012).

The results are encouraging with PERSIANN-CCS rain and it seems that better resolution raster data of rain, less FBIAS and an error of overestimation in the volume of rain, causing that this product is best suited to satellite hydrologic modeling. Similar results regarding satellite products with better spatial resolution, are reported by Nikolopoulos et al. (2010) with product KIDD (resolution 4 km) of better spatial resolution in respect of the TRMM-3B42 (resolution 0.25 °) and KIDD products (25 km resolution). By contrast, in the rain PERSIANN modeling, a coarse spatial resolution of raster data from the rain and the mistake of underestimating the volume of rain are negatively affecting the modeling, since there is insufficient rain to feed the cycle hydrological, but this dampening is possibly most likely rain detection. In this regard, Moreno et al. (2012) report on the automatic model calibration tribs rain PERSIANN, E values ranging from - 0.48 to 0.79 in four sub-basins of the Colorado River and areas of complex topography ranging from 35-350 km2.

In the water balance with PERSIANN-CCS (Table 6-8) average values of 2.13, 1.71 and 0.47 mm / d of rainfall, actual evapotranspiration and the simulated flow respectively are obtained. Satellite With this product you get 30% more rain than rain gauges; unlike that gets a PERSIANN 38% less rain than rain gauges. This overestimation and underestimation of rain, is clearly influencing changes in runoff production mechanisms in the watershed of Pajaroncillo. So you get with rain PERSIANN-CCS values of 28, 10 and 62% in direct runoff, interflow and baseflow respectively that can be seen in Figure 6-8.

Another interesting aspect of the water balance is found in the different play evapotranspiration. As the hydrological model tries to maintain a flow similar to observed behavior (since the calibration strategy is a function of the flow and not a component of the water balance), you get the corrective factor evapotranspiration is reduced to 71% PERSIANN and increases by 32% PERSIANN-CCS to finally get a 56% evapotranspiration with PERSIANN is reduced and 30% with PERSIANN-CCS (Figure 6-9). Similar behavior is reported in evapotranspiration component with rain PERSIANN understatement, for Bitew and Gebremichael (2011b) and Moreno et al. (2012).

With respect to error propagation estimation rain simulation to hydrological (Figure 6-10), the error in volume of rain is damped through the process of rain-runoff transformation. Unlike the mistake of rain in terms of E and RSR, which worsen with hydrological modeling, except in smaller basins as Pajaroncillo (861 km2) and Albaida (1,301 km2).

Chapter 6

CONCLUSIONS AND FUTURE WORK.

6.1 Conclusions

Currently, the estimated satellite rainfall is subject to various errors due to instrumental problems, nature of the measuring system, theoretical simplifications and complex relationships between observed variables and rain, among other reasons (Nikolopoulos et al, 2010;. Semire et al ., 2012); This limits their use in hydrological applications, reducing to very controlled experimental environments or areas where there are no other possibilities of observation. Therefore, reducing the error is key to their widespread hydrological application. In this study, two rain Estimated products with different spatial resolution satellite, PERSIANN (0.25 °) and PERSIANN-CCS (0.04º) were evaluated through a distributed hydrological model as an extratropical basin is the basin of the river Júcar reasonably well orchestrated.

The specific to the area of study, results indicate that spatial correlations between the estimated satellite from rain and rain reference is acceptable, less acceptable annual level on a monthly basis, but poor on a daily scale. In winter the daily correlation is weaker, because the rains are more concentrated in the mountainous areas and perhaps, this orographic effect is not well detected by satellites. By contrast, in summer the opposite pattern, with significant positive correlation was observed, possibly due to the increased presence of rainless days (zero). This is reflected in higher with the Pearson coefficient values in summer, since the presence of zeros favors greater correlation; instead coefficient Kendall best represents these cases, as it resists the effect of extreme values (minimum values in this case). Errors with high rainfall and maximum frequency of light rain overestimation is also obtained.

In general, rain-CCS PERSIANN overestimates while PERSIANN different scales underestimate aggregation basin. In addition, PERSIANN is more likely to detect rain, but also of false alarms. Rain detection is lower in the basin of the river Albaida (coastal area with torrential rains and likely fall SCM) in the sub-basin Pajaroncillo (mountainous area with orographic rainfall). Ie these differences in the two products detection satellite, are being influenced by climatic and physiographic features of the area, which coincides with that reported by Hossain and Huffman (2008).

The error in volume (V) of rain, for all levels of aggregation basin underestimated and overestimated PERSIANN with PERSIANN-CCS. The Albaida (1301 km2) basin have better performance in terms of efficiency rating of Nash-Sutcliffe (E) in the estimation of the rain with both satellite products; instead the smaller Pajaroncillo basin (861 km2) has better performance but only with Ev PERSIANN-CCS product.

Calibration of hydrological model parameters TETIS has raised the performance modeling. Also, various authors performed a calibration of the hydrological model to improve performance with products of satellite rainfall estimate (Stisen and Sandholt, 2010; Bitew and Gebremichael, 2011b; Bitew et al, 2011;.. Jiang et al, 2012; Moreno et al., 2012). Thus, in hydrological modeling, "unsatisfactory" PERSIANN yields are obtained, whereas yields PERSIANN-CCS become "satisfactory". The results are encouraging with rain PERSIANN-CCS and it seems that better resolution raster data of rain, less FBIAS and an error of overestimation in the volume of rain, causing that this product is best suited satellite in hydrologic modeling. Similar results regarding satellite products with better spatial resolution, are reported by Nikolopoulos et al. (2010) with product KIDD (4 km) of better spatial resolution in respect of the TRMM-3B42 (0.25 °) and Kidd (25 km) product. By contrast, in the rain PERSIANN modeling, a coarse spatial resolution of raster data from the rain and the mistake of underestimating the volume of rain are negatively affecting the modeling, since there is insufficient rain to feed the cycle hydrological, but this is possibly dampening with the highest probability of detection PERSIANN rain.

As the hydrological model tries to maintain a flow similar to observed behavior (since the calibration strategy is a function of the flow and not a component of the water balance), you get the corrective factor evapotranspiration is reduced to 71% PERSIANN and increases by 32% PERSIANN-CCS to finally obtain evapotranspiration is reduced and increases with PERSIANN PERSIANN-CCS. Similar behavior is reported in evapotranspiration

component with rain PERSIANN understatement, for Bitew and Gebremichael (2011b) and Moreno et al. (2012).

With respect to error propagation estimating the hydrological rain simulation, the error in volume of rain is damped through the process of rain-runoff transformation. Unlike the mistake of rain in terms of E and RSR, which worsen with hydrological modeling, except in smaller basins as Pajaroncillo (861 km2) and Albaida (1,301 km2).

In order to improve the possibilities of practical use of satellite rain, a Bayesian model was implemented to combine information from rain gauges PERSIANN-CCS with different densities in the mountainous rain gauges Pajaroncillo subbasin. The specific to the area of study, results indicate that the average value of the rain-CCS PERSIANN estimated with improved lower densities from 100 km2 / gauge. By contrast, for densities greater than 100 km2 / gauge, the average value worsens in the range of 20 to 200%, according to increase the density of the network of rain gauges. Similar behavior with other statistical found. Thus, it is clear a significant improvement in the statistics for less than 100 km2 / density gauge, an increase of POD, CSI, PC and HSS, and reduction of FAR. In addition, a significant improvement is observed in all FBIAS densities gauges, except for the density of 45 km2 / gauge. Efficiency ratings rain E, RSR and Ev stabilize at a lower 100 km2 / density gauge.

With regard to the hydrologic modeling using Bayesian model combination of rain, "good" yields are obtained "very good" with lower densities to 100 km2 / rain gauge, obtaining the best performance for a density of 72 km2 / rain gauge which suitably it reproduces the basis hydrogram flow and curve shape of recession detects most days and maximum flows occurring, but underestimates its maximum value on a 37%. Do not rule out that this could be due to underestimation in mountainous regions such as Pajaroncillo, rainfall stations tend to be in the valleys and thus underestimate the orographic rain (Ebert et al., 2007; Alvarez, 2011). Regarding the error propagation of rain, it is that the error in volume of rain is cushioned in all densities gauge (except with a density of 431 km2 / gauge), but worse in terms of E and RSR, except for lower densities to 172 km2 / gauge.

As a final conclusion we can say that the new product estimate rain PERSIANN-CCS, and increase its spatial resolution, also improvement in reliability for use in hydrologic modeling, especially when combined with data from rain gauge, becoming the starting point for future research.

6.2 Future research

Future research Based on the development of this thesis the following future research topics arise:

Despite the good results of modeling with rain PERSIANN-CCS in the case of application to Júcar River Basin, it is not to recommend their implementation on a practical level in engineering work to support decision making in the context of environmental management and watershed management. To have a high level of confidence in the performance of this product from TV, it is necessary to combine the rain rain rain gauge PERSIANN-CCS on a large number of well instrumented experimental basins under different climatic conditions.

It is necessary to compare the results with other product-specific satellite for the Iberian Peninsula, such as RRC algorithm "Convective Rainfall Rate" and its improved version, which estimates the rain with satellite information MSG-RSR north and south of Europe , and are calibrated with data from radar AEMET and BALTRAD and correction factors growth rate of cloud temperature gradient correction "parallax" orographic correction and algorithm for lightning (Luque et al., 2006; SAFNWC, 2012 ).

The classification of the rain depending on its intensity and according to the season, could help in the discussion and characterize the error from these views. So, Labo (2012) evaluates three satellite products and reports its results in three categories rain rates: low, moderate and high. In addition, Hossain and Huffman (2008) indicate that satellite estimates of rainfall depends on the resolution of the satellite, region, season and rain threshold, so it is necessary to assess the rain to these different conditions and thus better understand the sources of error in the estimated satellite rain. And based on this type of analysis you could develop a probabilistic error model to simulate space-time stochastic realizations satellite rainfall estimate and quantify their impact on the uncertainty of hydrological simulation.