The cqFlow Model: Model description and course

D:\cqflow-1.0\Documents\cqflow-course.sxi Fiesta Workshop June 29 – July 1 San Jose

The cqFlow Model:Model description and course

The Fiesta project

J. Schellekens


Updated files

Go to to 04 machine in the netwerk and find the share: CD\cqflow\updates

Copy the content of this directory to: d:\cqflow\cqflow-course(nb overwrite the existing files)

Also includes this presentation as pdf


Today

0900 Introduction to today 0915 Description of the model 1000 Introduction to using the model and pcraster 1020 Coffee Start of Hands-on 1040 Look at the pre-run scenario's

We cannot run a whole year on these PC's Run the model yourself Lunch Making adjustments to parameters and landuse Questions Options -> show some data pre-processing

D:\cqflow-1.0\Documents\cqflow-short.sxi Fiesta Workshop June 29 – July 1 San Jose

Why the cqFlow model? The main purpose of the cqFlow model is to

quantify the discharge from the Rio Chiquito catchment and evaluate the consequences of several land use scenarios within the catchment

As such, it can be used to: ...integrate the results from the plot scale and sub-basin

scale studies to a large scale ...evaluate the hydrological consequences of land use

change in this cloud forest catchment (scenario runs) In addition, the model is flexible enough to allow it

to be applied to other catchments later (re-calibration will be required)


cqFlow assumptions The catchment can be modelled using hourly time

steps Translation tables between land use and soil type

maps can be used to derive model parameters Fog interception can be modelled by aspect, wind

speed, wind direction and catching efficiency. Juvic gauge information is needed to determine HP.

Fog interception can be added as a surplus precipitation to an existing interception model by adding the volume of cloud water interception to the canopy store


Status of cqFlow

A running, calibrated model written in pcraster Simple command-line user interface Model description User manual -> September 2005 Requires expert knowledge

Will be available for download from the Fiesta web-site: http://www.geo.vu.nl/users/fiesta/

Presently available at:http://www.xs4all.nl/~schj/cqflow/

The version you are using is NOT the final version. Final version available in September


The model (details)

Measured radiation corrected for shading and slope/aspect of the terrain

Precipitation corrected for aspect/slope of the ter-rain

Uses angle of incoming precipitation The two components of the Juvic gauge are needed in

this model but can be replaced by a regression equation Canopy model

Available energy divided to Ei and Et based on the wet/dry fraction of the canopy

Transpiration using Penman Montieth Interception using a modified Rutter model

Hourly timesteps Wet canopy evap via Penman Montieth


Radiation correction

First the solar geometry is determined using location in-formation and the time and day of the year.

The critical sun angle determines at what solar angles a cell in the DEM receives radiation.

Use a relation between potential radiation and meaured radiation to determine direct and diffusive radiation

After that read the measured incoming radiation and cor-rect this using the solar angle information.

# Determine critical sun angle and Shaded cells... HoriAng = horizontan(Altitude,directional(SolAzi)); HoriAng = if(HoriAng lt 0 then scalar(0) else HoriAng); CritSun = if(SolAlt gt 90 then scalar(0) else scalar(atan(HoriAng))); Shade = SolAlt gt CritSun;



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Shortwave AVGCorrected SWPotential HOR



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Daily average catchment radiation

Measure shortwaveCorrected shortwave


Precipitation correction

The first step is to correct the gauge information for wind losses. This is a site/gauge specific function that should be re-determined when applying the model to another catchment.

Next the angle (in degrees from the vertical) of the incom-ing precipitation is determined using both components of the Juvic gauge

Alternatively a regression between speed and angle may be used

PCorrFac = 0.01 * exp(4.606 - 0.0057 * WindSpeed**2.0);Precipitation = Precipitation / PcorrFac;

Ratio = if (Precipitation > 0.0 then HP/Precipitation else 0.0);PrecipAngle = if (Precipitation == 0.0 then if (HP > 0.0 then 90 else 0.0) else scalar(atan(Ratio)))


Precipitation correction

Determine the precipitation at plane at a 90 de-grees angle to the precipitation vector: the Poten-tial precipitation

Now use this and winddir information to determine the real P on each cell

Precipitation=Precipitation/scalar(cos(PrecipAngle));

PrecipAspect=scalar(WindDir);Input_angle=scalar(acos(cos(Terrain_angle)*cos(PrecipAngle)+sin(Terrain_angle)*sin(PrecipAngle)

*cos(scalar(Aspect)-scalar(PrecipAspect))));# Remove angles > 90 deg (precipitation going through the ground....)Input_angle = max (0, min(90, Input_angle));# now we can calculate the amount of rain on the sloping surface...Precipitation=Precipitation*scalar(cos(Input_angle));Precipitation=Precipitation/scalar(cos(Terrain_angle));


Effect of P corrections

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PolygonsCorrected for wind

Corrected for slope


Evapotranspiration (1)

Determine radiation using DEM and global incoming radiation. For this an existing model was adjusted.

Use measured radiation to scale the results Available energy determined using Albedo and a re-

gression equation for the Ground Heat Flux


Evapotranspiration (2)

Based on wetted part of the canopy (C/S) distribute radiant energy among:

Transpiration Evaporation from wet vegetation (Inter-

ception) Soil evaporation (neglected)

Determine evaporation components: Wet canopy evaporation using Penman-

Monteith with rs set to zero

Transpiration using Penman-Monteith according to vegetation type

Soil evaporation using Penman-Montei-th with r

s set to zero (very small compo-

nent, neglected)

Dry Canopy Wet Canopy

Soil

Radiant energy

Es

EtEi

P

HP


Evapotranspiration (3) Canopy model:

Modified Rutter model Explicit solution C <= S

In the (Rutter et al., 1971) model the change in amounts of water stored on the canopy is determined by the proportion of the rain that hits the canopy, the drainage from the canopy and evaporation of intercepted water:

dD dt p p R E D when C St w/ ( )= − − − − ≥1 and

dD dt p p R C S E D when C St w/ ( ) ( / )= − − − − <1An exponential function is used to calculate drainage from the canopy:

When the canopy is calculated as being only partially wet (C<S), the estimated evaporation rate is reduced such that:

D D eb C S= −0

( )

E C S Ereduced w= ( / )


The soil store Relatively simple but fully distributed conceptual model

based TOPOG_SBM model Implemented in Pcraster Uses a saturated and an unsaturated store


The soil

Inputs to the model: Et + Es from the canopy

model Total throughfall + stemflow

from the canopy model Determines:

In- exfiltration Lateral saturated flow Transfer between saturated

and unsaturated store Reduces Et + Es to an 'actual

evaporation' if water stress occurs (takes rooting depth into account).

Surface runoff via kinematic wave

U = TotalDepth - S

STotaldepth

transfer


The Soil

Ksat decreases expo-nentially in depth

Transfer between un-saturated and saturat-ed store based on K at that depth

Infiltration can include sub-cell parameters for % of compacted soil. Will be finished in September

U = TotalDepth - S

STotaldepth

transfer


Calibration

Model based on upscaling of small-scale studies using physical formulations

Soil parameters adjusted to better represent measured Discharge

In general: Little calibration Purpose of the model is to evaluate the hydrologi-

cal aspects of land use change, not to represent the current situation as best as possible

Currently the NS coefficient for measured vs modelled discharge is about 0.6


How to deal with land-use change in a distributed model?

Use a GIS base programming language, pcraster Why pcraster?

Allows data centric modelling Allows you to work with distributed data relatively easily Simple programming language Fast implementation Free Can be used by a Msc student after a short introduction

This is needed to be able to handle the large amount of input data needed.


The cqFlow IT Implementation

Based on PCraster Dynamic GIS environment Rapid development, using freely available existing tools.

Perfect for use with distributed data. ArcView extensions available

penalty is speed (compared to C, C++, Fortan) PCraster extensions made for Fiesta in C

Needed when iteration is required (Rutter interception) Can help relieve the speed penalty Not used in the version we are using now

User interface via Matlab/Octave C routines to read/write PCraster maps Simple, fast and using standard tools

Easily adjustable by the user Free and (partly) open source software


The PCraster way: linking model parameters to land use and soil type maps

Lookup tables are used to link model parameters to land-use (and soil type)

This produces model parameter maps

This concepts al-lows simple evalu-ation of land-use scenarios by only supplying a new land-use map

SOILFirstZoneCapacity.tblKsatHor.tblFirstZoneKsatVer.tblBeta.tblN.tblM.tblthetaS.tblthetaR.tbl....

CanopyLeafAreaIndex.tblMaxCanopyStorage.tblCanopyGapFraction.tblCatchEff.tblRootingDepth.tbl....

cq_prepvars

Model parametersSoilFirstZoneCapacity.mapKsatHor.mapFirstZoneKsatVer.mapBeta.mapN.mapM.mapthetaS.mapthetaR.map....

CanopyLeafAreaIndex.mapMaxCanopyStorage.mapCanopyGapFraction.mapCatchEff.mapAlbedo.mapRoughnessLength.mapWindSpeedHeigth.mapDisplacementheigth.mapRootingDepth.map....

land usemap. Somescenario


From land use map...


...to Albedo map.


Pcraster and the LDD

Pcraster can create and LDD from a dem. An LDD can be used to route water downstream

It can also be used to automate catchment delini-ation


cqFlow Workflow

Resample basemaps from different sources to appropriate resolution. Determine catchment boundary from DEM

Construct derived maps Construct input maps from lookup tables Perform dynamic model calculations Post-process the results (area averages etc.)

cq_mkbasemaps

cq_prepvars

cq_run

cq_*


cq_mkbasemapsgauge location

raw demmap

meteostation location

raw landusemap

processeddemmap

drainagemap

temp correctionmap

meteoareamap

land usemap

soil typemap

gaugesmap

User inputmap reduction factordata directoriesetc..


cq_prepvarsdrainagemap

land usemap

soil typemap

processeddemmap

gaugesmap

SOILbeta.tbl...

CanopyMaxCanopyStorage.tbl...

Model parametersSoilEvapFraction.map...

CanopyAlbedo.map...

Model (initial) state variablesUstoreVolume.map...

Command line parameters

data directoriesetc..


cq_runModel parameters

LeafAreaIndex.mapMaxCanopyStorage.mapCanopyGapFraction.mapCatchEff.mapAlbedo.map

Model (initial) state variablesSoilMoisture.map

CanopyStorage.map

temp correctionmap

meteoareamap

Timeseriesrain.tsstemp.tsswindspeed.tsswinddir.tssrh.tssradiation.tss

Results Time-series(average, and at selected points)...

Model state vari-ablesat end of run...

Model results (maps)...

Command line parameterstimestepsdata directoriesetc..


Model steps/Components (1) Input

Read forcing timeseries (apply altitude corrections) Correct Precipitation for wind loss (P coming at degrees

> 85 treated as fog) Determine P angle Use angle winddirection and slope/aspect to correct P

input Calculate solar position and use to correct radiation in-

put Vegetation

Calculate Penman-Montieth (Wet Canopy, Transpira-tion, Soil Evap)

Ad fog interception to Canopy storage for P angles > 85 degrees

Running Canopy water budget model (derived from Rut-ter)


Model steps/Components (2)

Soil Infiltration into unsat zone Et extraction via rooting depth (First from unsat zone; if

demand not met try Sat zone taking into account rooting depth)

Deep percolation (leakage) Transfer water from Unsat to Sat store Lateral transport of saturated water via Darcy Return flow to surface water Runoff via kinematic wave


Provided output maps

Maps Cumulative P based on polygons Cumulative P after wind loss correction Cumulative P after inclined P correction Et and Ei cumulative Cumulative leakage ...


Scenarios

1. Scenarios1. Current land-use (baseline, used for limited calibration)2. Land use of 1975 landsat image; mostly forest3. Conversion of all land to pasture4. Conversion of all land above 1400m to pasture

2. Options1. HP Catching efficiency of pasture is 15 %2. HP Catching efficiency of pasture is 10 %3. HP Catching efficiency of pasture is 5 %

3. Subcatchment1. All the combinations above for a 11km^2 subcatchment

with outlet at 1321 m. This is a cloud forest catchment


Cloud forest sub catchment


Pasture > 1400 m

Current 1975

Pasture


Current land-use

Used to check how well the model can predict current streamflow

Used for limited calibration Used to compare the other scenario's with Based on 2001 classified LandSat map

Pasture Secondary forest Primary forest


Summary of results

Current land useCatchEff Catchment P HP Et Ei Q Leakage Storage OutflowPasture mm mm % of P mm mm mm mm mm % of P

0,05 Whole 5429 246 4,53 751 284 3714 891 35 85,470,1 Whole 5429 282 5,19 750 286 3743 896 36 86,11

0,15 Whole 5429 318 5,86 750 287 3773 900 37 86,760,05 SubCatch 4697 767 16,33 536 328 3533 1022 45 97,93

0,1 SubCatch 4697 795 16,93 537 329 3551 1028 47 98,490,15 SubCatch 4697 823 17,52 537 329 3571 1034 49 99,08

For the whole catchment HP is less than 6% if the P input

However, for a sub-catchment > 1321 m this increas-es to 16 %

At the same time Et + Ei is smaller in the cloud forest resulting in more outflow of water


Average radiation after correction


Transpiration


Horizontal precipitation/Cloud water interception

Based on the Ju-vic gauge input and spatial distri-bution via:

Aspect Windspeed

May be improved upon later on

Vegetation pa-rameters


Soil water

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Storage/runoff plot for run: default

Saturated store

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Hands-on

Using the model...


How to run the model?

Use the pcrshell environment This is basically a DOS box in which you type com-

mands NB A linux version of pcraster is also available

Use the nutshell environment Use Octave/Matlab as a shell

A simple interface has been set up in Octave Advantage -> powerfull processing Disadvantage -> need to learn Matlab/Octave

Pcraster models can also be run from within Ar-cView provided you have spatial analist and have the pcraster ArcView extension

Not set up for this course


How to install

Download pcraster from http://www.pcraster.nl Also download the update for pcraster Download nutshell (link at pcraster site) Download mapedit (link at pcraster site)

Install pcraster using the setup Copy the updated pcraster files to \pcraster\apps Install nutshell using the setup Copy mapedit.exe \pcraster\apps


How to view results

Maps Static maps -> display or aguila Timeseries of maps (mapstacks) -> display or aguila;

may use animation Time series

Timeplot Read into Matlab/octave and use plot Import into Excel

Viewing can also be used from within Nutshell In windows link .map files to display and .tss files

to timplot


What we will do Start up nutshell Display timeseries Display maps In addition we will look at the project scenario runs us-

ing nutshell

Run the default model Edit a landuse map using mapedit for myrun Re-run the model Compare results with previous run Adjust some model parameters and re-run the model

We will run 240 timsteps (hours) and not a full year because this takes to long (The P input in the exam-ples has been edited)


NutshellTree for navigation, shows the work-dir. Set the workdir in the treeview to the model you want to run

List of files. Select dem.map and set the aguila basemap(needed for 3D maps)


Nutshell

Display as a timeseries

Display map in 'display'

Display map in aguilaDisplay map (3d) in aguila

Edit a mapSet a file filterEdit a model


Runs and directories

Each run/model for cqflow resides in its own directory

Within that directory there are another 6 sub-diectories:

Basemaps -> DEM, Landuse and other static maps Inmap -> model parameter and state maps Intbl -> lookup tables to create the model maps Intss -> input time series (wind, rain etc.) Outmap -> output maps Outtss -> output time series

To run a model, the directory (default/myrun) must be set as the working directory


1. Nutshell setting up

1. Start up the nutshell pro-gram itself

2. Navigate to the cqflow-course\bin directory and load the cqflow.pcr and the cqflowmkpars model using the [edit model] but-ton

cqflow.pcr modelEdit model button

Navigation tree


2. Looking at the resultstime series

The results of the model run are saved in the outtss (time series) and outmap (maps) directory of the cqflow-results dir

Navigate to the outtss directory and make graphs of at least the following time series:

DischargeCubic.txt Evaporation.txt WaterCatch.txt -> Cloud water interception

To compare scenario's first open the .tss.,txt in the first scenario by selecting it and using the timeplot button. After that use the open column file. Do this for the default and deforestation sce-nario's

Also check out a few others (e.g. ActualStorage)


3. Looking at the resultsSingle maps

Output maps are stored in the outmap dir of each scenario Display the map CumEvap.map (Cumulative evaporation)

by either double clicking on it or by selecting it and press-ing the display button. Do this for at least two scenario's and compare the maps

Repeat this for a number of maps of interest

Also display some of the maps in the basemaps directory (e.g. lan-duse.map, dem.map...)


4. Run the default model (240 steps/10 days)

1. Set the cqflow-course\default directory as the working dir by double clicking on it (check on the top left display if this is set)

2. You can use the “run” button or “run in window button” to run this model (takes about 10 to 15 minutes). Please use “run in window”

3. While it is running please go to the next exercise


5a. looking at the results3D view

The aguila program can combine a DEM and an-other map to show the resuls in 3D

First go to the basemaps directory and select the map dem.map

Now use the aguila drop-down menu and select 'set aguila base maps' item


5b. Looking at the results3D view

After the basemap has been selected you can show any map in 3D

To do this select a map and use the aguila 3D button to show a map in 3D

Do this for a number of maps NB In aguila you can use the arrow keys to alter

the 3D view The FirstZoneDepth.map clearly shows the rela-

tion between topography and (shallow) groundwa-ter


6. Looking at the resultsmap series

Wait for the run to finish Go to the outmap directory Select the map series filter from the filter box Select a map series

Q = discharge ET = evaporation PR = Solar radiation WC = Cloud water interception

Use the display button to display the series In the display program use the animate button to

make a movie of the map series


7a. Modifying the landuse We will now use the myrun model Initially the model produces identical

results as the default run3. Go to the myrun\basemaps directory4. Select the landuse.map file and use the

map-edit button to display the map


7b. Modifying the landuse

Select an area with the free draw tool (end with a rigth mouse click)

Set the value in this area to 1 (pasture) and press on the apply button

Select a LARGE areaFill in a value here


7c. Modifying the landuse

Save the map using the save button

Selected areaSelected area after setting it to 1


7d. Modifying the land useRe-run the model

Open the cqflowmkpars.pcr model in the bin di-rectory

Make sure the working directory is set to myrun and run the model using the green arrow

This will prepare the new input maps for the actual model

Now switch to the cqflow.pcr model (by clicking on the tab) and run this

You can now comparethe result of thisrun with those ofthe default runCompare both maps and timeseries, focus on CumEvap and CumInt


7e. Modifying the land useComparing results

Compare timeseries by first double clicking on the .tss file in the default\outtss dir and using the “open column file” options in timeplot to open the same file from the myrun\outtss dir

Comparing maps can be done by simply opening the same map from each of the runs and showing them side by side.

Alternatively you can use the provide batch file to create a diff.map file that show the default map minus the myrun map. Make the cqflow-course dir the working dir and enter this in the command bar:diffmap CumEvap.mapYou can also use another valid map name

Display the CumEvap.mapdiff.map file by double click-ing on it. It can be found in the \cqflow-course dir


8. Modifying model parameters All distributed model parameters are stored in *.tbl files in the

intbl directory These are files that may be edited in the nutshell editor. They

list the values for the parameter for each land use type: 1= Pas-ture, 2= secondary forest, 3= primary forest

The example shows the vertical saturated conductivity (in mm/hr)

Open the file FirstZoneKsatVer.tbl, change the Ksat for pasture to 5 mm/hr and save the file

Now re-run the scripts using the instructions from 7dand compare the results

Do the same for the N.tbl file. N is Manning N, set if to 0.2 for all lansduse types rerun the model and compare the discharge


9. Adjusting the model

The model can easily be adjusted in the nutshell editor One simple adjustment is the save more output variables The present sstem uses the juvic gauge information. If

only normal gauges are present a regression may be used

Ratio = if (Precipitation > 0.0 then HP/Precipitation else 0.0);PrecipAngle = if (Precipitation == 0.0 then if (HP > 0.0 then 90 else 0.0) else scalar(atan(Ratio)));

#b = ln(2+0.38 * Precipitation ** 0.42);#a = 1/ln(e + 10.46 * Precipitation ** 1.24);#PrecipAngle=90- exp(4.5 - a * WindSpeed ** b);


Options

Discuss the cq_mkbasemaps.m script Discuss the Octave interface Adding C functions to pcraster

The cqFlow Model: Model description and course

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