Technical Proposal SOWral.ucar.edu/~hopson/Satya/Technical Proposal_SOW.pdfATEC project (see Section...

Selection No 1172405_ Technical Proposal

Section D

D - Description of Approach, Methodology and Work Plan In this section, we describe the technical approach, methodology and work plan of the technical components of the assignment, “Development of Flood Forecasting for the Ganges and the Brahmaputra Basins using satellite based precipitation, ensemble weather forecasts, and remotely-sensed river widths and height”. In addition, we describe the structure and composition of our team assembled to meet this assignment’s objectives.

Technical Approach and Methodology In this sub section we explain our understanding of the objectives of the assignment and methodology for carrying out the activities and obtaining the expected outputs within the overarching objective of this assignment: to support the assessment of strategic improvements to regional flood forecasting capacity. We reproduce each Objective given in the RFP below, and give our response directly beneath each one: Objective (i) Implement long-lead time, public-access flood forecasting systems for the Ganges and Brahmaputra basins spanning India, Bangladesh and Nepal utilizing new satellite precipitation estimates and ensemble weather forecasts from multiple centers. The Climate Forecasting Applications for Bangladesh (CFAB) river flow forecasting system (Hopson & Webster, 2010), currently operational only for Bangladesh, will be extended into India, Nepal, and Bangladesh, implemented and re-calibrated basin-wide for all the tributaries and corresponding sub-basins of Ganges and Brahmaputra. The existing forecast modeling will then be improved as follows: (a) by optimally-combining additional satellite precipitation estimates (new products NOAA Hydroestimator and JAXA/EORC GSMaP products will be added to the existing utilization of NASA TRMM and NOAA CMORPH products); (b) ensemble weather forecasts from multiple centers will be used (CMA, CMC, JMA, KMA, MeteoFrance, NCEP, and UKmet in addition to the current ECMWF); (c) utilizing a higher resolution DEM; (d) routing model improvements; (e) new pre- and post-processing statistical tools (“quantile regression based) used in ensemble streamflow prediction; (f) improvements to snow modeling (SNOW-17, operational US model); (g) and finally, the system will also be extended out an additional 5 days (from the current 10 days), creating an enhanced lead-time (1-15 day) probabilistic river flow forecasting scheme.

Selection No. 1172405 Technical Proposal

Section D Response: The CFAB hydrological forecast model is a hydrologic multi-modeling system whose soil moisture states are initialized by NASA and NOAA precipitation products (e.g., TRMM 3B42, Huffman et al., 2005, 2007; CMORPH, Joyce et al., 2004), and whose states and fluxes are forecasted forward using ensemble weather forecast data products and conditionally post-processed to produce calibrated probabilistic forecasts of river discharge for key river reach locations. Already operational over the Brahmaputra and Ganges river basins but only providing operational flow forecasts at points within Bangladesh (the Ganges at Hardinge Bridge and the Brahmaputra at Bahadurabad, in particular) , this system will be extended and calibrated for subcatchments upstream in India to show the potential benefits for upstream users as well. To reach this goal, this Objective calls out very specific approaches that can be used to further improve the skill and accuracy of the CFAB model, especially as it will be used to provide flood forecasts at much smaller catchment scales than previously used (e.g. improvements such as higher DEM resolution, more quickly updated satellite precipitation estimates such as the NOAA Hydroestimator (Scofield & Kuligowski, 2003), and use of an expanded set of ensemble weather forecasts, provided freely by the Thorpex-Tigge archive (http://tigge.ecmwf.int, as opposed to sole reliance on the forecasts from ECMWF which were used previsously as part of the CFAB project for Bangladesh). If NCAR receives this consultancy, we will build each of this sublist of improvements directly into the CFAB model that was used for Bangladesh. The expected outputs from this will be web displays that provide 3-hr updated imagery of subcatchment flood risk, and clickable pop up time-series plots showing past and forecasted river flows for each subcatchment. As an example of the subcatchments to be forecasted for, previous work by the consultant Team Leader has delineated the Ganges and Brahmaputra catchments and subbasins using Hydro1k, shown below. (However, for this consultancy, we would generate more refined delineations using higher resolution SRTM data.)


Section D

Figure 2: Ganges (upper panel) and Brahmaputra (lower panel) sub-catchments individually to be forecasted, both for extreme rainfall events and main-stem flood levels. Objective (ii) Combine CFAB ensemble discharge forecasts with DFO remotely-sensed river discharge estimates to produce optimal river discharge estimates at select locations along the river course. The Dartmouth Flood Observatory (DFO), in partnership with the Joint Research Council, is providing multi-site estimates of river width for both the Ganges and Brahmaputra (see JRC-Ispra, http://www.gdacs.org/floodmerge/ and DFO,


Section D http://www.dartmouth.edu/~floods/). These estimates are publically-available at http://old.gdacs.org/flooddetection/. Actual measurements are of upstream relative changes in emissivity due to changes in river width (microwave imagery visible through cloud cover). This signal is then transformed into discharge (see Hirpa et al. 2013) and effectively “advected” to downstream forecasting locations. This river discharge system is independent of the CFAB system discussed in Objective (i) above, the results of which will be optimally combined with the CFAB system, with weighing coefficients optimized by forecast lead-time. Skill comparisons between the CFAB and DFO-based system will be made, in addition to the benefits in their optimal combination. Response: One of the drawbacks of the previous CFAB system, as designed for Bangladesh, was the lack of data availability of near-real-time river stage measurements upstream in the Ganges and Brahmaputra catchments. As such, the CFAB modeling structure treated the upstream river catchments as being “ungauged”. One approach to instead provide a “work around” on this data limitation is to explore remotely-sensed information that do not rely on in situ measurements being taken and being timely provided. The DFO RiverWatch data provide one such source of such data, especially since the imagery this system relies on (microwave) can penetrate cloud cover (which should be potentially plentiful during times of flood risk).

Thus, Figs. 3 and 4 indicate what has been entirely missing but is now feasible for South Asia. It provides a subscene of the present DFO River Watch 2 map view (automated, updated daily) of current river flow severity status of monitored sites (Fig. 3), and also a sample

time-series of the existing present and historic status output at one site (Fig. 4). This Objective, then calls for building upon previous work that has explored “advecting” these signals downstream to provide flood forecast information (Hirpa et al 2013, for which key personnel on this consultancy were co-authors on), and calls for combining flood forecasts derived by this approach with the skillfully-proven CFAB approach (whose skill is derived independently through the time-lags in the transport of observed satellite precipitation estimates through watersheds, lead-times provided by weather forecasts, and time-series


Section D

Figure 5 Daily time series of observed river discharge (solid) and model nowcast (dash) based on the satellite-based river discharge estimates for Ganges River at Hardinge Bridge ground station in Bangladesh. Satellite-derived information at locations with distances ranging from 63 KM to 1828KM upstream Hardinge Bridge station were used. (From Hirpa et al., 2013.)

forecasting methods). To reach the goals of this objective, we will utilize a “new and improved” (improvments made via the steps of Objective (i)) CFAB system (which our personnel have extensive knowledge of), with the similar technologies used as discussed in the Hirpa 2013 study. In addition, the Hirpa study will need to be “operationalized” (the study was done in “hindcast mode”), and the CFAB and Hirpa results will also be combined using a “quantile regression” approach that has been already developed as part of leveraged technologies stemming from NCAR’s ATEC project (see Section B.1.6). Note that the benefits of utilizing quantile regression for this task are that optimal combinations can be generated for each quantile (10%, 20%, etc.) of a forecasted probability distribution, such that a best estimate and “error bounds” on the possible uncertain range is also immediately provided. We would expect to report on how much skill can additionally be provided through their optimal combination of these two approaches, as part of this consultancy. Objective (iii) Transform forecast flood discharges into inundation extent maps, using analysis of past microwave and optical sensor imagery of actual inundation extent. A unique enhancement to river flow forecasting will be to transform modelled forecast flood discharges into (likely) inundation extent maps using historic flood imagery. This provides accurate geo-location of flooding important for disaster relief efforts in complex terrain, where numerical inundation modeling would normally fail. This will be accomplished using analogue approaches to select pairings of archived, remotely sensed maps of inundation extent matched with river discharges similar to model predictions. Response: Providing flood forecasts of discharge magnitude are certainly vital to effectively mitigate flood hazards for vulnerable citizens. If extensive flood mapping surveys have been carried out, then the spatial extent of oncoming flood waters can be a priori determined. However, for most regions of the world, such hydraulic modeling and spatial mapping have not been carried out in sufficient detail. To get around this limitiation, historical context could be used: if the discharge magnitudes can be placed into the context of historical flows, a sense of the flood return period can be estimated (10yr flood, 25yr flood, etc.), and citizens with intimiate knowledge of their region could have context for the spatial extent the flood waters will occupy. However, preferable to this latter approach would be to relate forecasted flood discharges to archived imager over the region of interest that occurred during periods of similar river flows, thus giving citizens and relief workers more specific a priori knowledge of the extent oncoming flood waters could


Section D

Figure 6. The DFO record of flooding in this portion of the Ganges Basin is shown as light blue (2000 to 20111), light red was flooding in the 10 days prior to map update date, and red was current flooding. The numbers indicate River Watch discharge measurement sites. This is a small subscene of the complete Surface Water Record display for this region.

Figure 7. Left: MODIS imaging and mapping of 2003 flooding along the Ganges River between river measurement sites 200 and 201. At site 200 (uncalibrated) peak discharge was 8500 m3/sec. Right: mapping of 2004 flooding. The uncalibrated peak discharge here is only ~3500 m3/sec.

occupy, without reliance on detailed surveys. Note, however, one of the limitations of this approach is the reliance on the assumption of geomorphological consistency – past and future river channel characteristics (e.g. in crosssection, conveyance, and river channel location) need to remain similar for this approach to be accurate. Thus, this method (or any of the three methods discussed, actually) will be less accurate on rivers that are highly geomorpholocially active.

To meet this Objective, we will utilize the DFO archive of catalogued imagery. NASA’s orbital technology has been used at DFO extensively, since the launch of the twin MODIS sensors in early 2000 and 2002, to map flooding in South Asia. Unlike other remote sensing-based organizations active in flood response, DFO maintains a large and growing archive of such map data, in digital (GIS) format, and for use in making comprehensive regional displays indicating the history of inundation as well as on-going

flooding (Fig. 6). The archival flood information is exceptionally valuable, providing as it does a view of flood hazard. This large archive of such mapped inundation resident at DFO will allow production of this

innovative flood prediction product. As illustrated in Fig. 7, past inundation extent can be matched to the corresponding remote sensing-derived discharge values (the same approach can be used for any ground station sites for which data output is available publicly). Linkage to the appropriate inundation map can be provided at the individual site displays: when a particular discharge and


Section D flood threshold is predicted, the user can call up the inundation that resulted, historically, from the same values. Similarly, mapping inundation maps to the ensemble of river forecasts produced by the CFAB model could then produce a range of possible inundation extent scenarios. Objective (iv) Validate Radar Altimetry Satellite Data for Operational Flood Forecast. Synthetic aperture radar (satellite-based microwave (sic) sensor) altimetry data will be collected and synthesized over river measurement sites to monitor changes in river water heights to test their degree of accuracy for flood forecasting applications. These measurements have the potential of being converted into river flow measurements to supplement in-situ discharge data and are used to assess river heights at the regional scale across all sub-basins. In addition, this approach will also test monitoring of reservoir levels, to remotely-assess water release schedules of management agencies. Response: Satellite radar altimeters have a certain capacity for monitoring the variations in surface water level over the world’s largest lakes, reservoirs, wetlands and river channels. As such the altimeters have played a role in basic research for the last two decades, and they are also currently contributing to applied programs via the delivery of near real time products to meet operational objectives. Just as with the DFO RiverWatch measurements discussed above, satellite altimetry measurements can also be employed to measure upstream river conditions (where river stage gauges are not available) whose information can then be used (due to advective travel-time delays) to forecast the onset of flood waves downstream. The benefits of satellite altimetry are its greater accuracy as compared to the RiverWatch data (very roughly a factor of 10), but its drawbacks are its lower sampling frequency (roughly every 10-days as compared to daily for RiverWatch at a fixed location). However, the sampling frequency for a given river (as opposed to fixed location) is much higher, and in particular, we believe combining both the RiverWatch and altimetry approaches together can have real power to monitor upstream conditions of rivers with no real-time river stage reporting. An important anticipated finding of this objective will be to test how far upstream (as river widths diminish) the altimetry approach can retain some level of accuracy to benefit flood forecasting, and over what types of terrain and at what sampling frequency and spatial intervals. For this Objective, Sub Consultant Dr. Charon Birkett at the Earth System Science Interdisciplinary Center (ESSIC) of the University of Maryland will be employed. Dr. Birkett is the Principal Investigator of the G-REALM program, the Global Reservoir and Lake Monitor, is a NASA/USDA funded program that ingests raw altimetric data sets and delivers water-level variations products for the world’s largest lakes and reservoirs. The products are utilized by USDA/FAS regional analysts to determine i) short-term agricultural drought conditions, and ii) long-term hydrological drought status. They also help assess irrigation potential in basins for which in situ data is often sporadic or has restricted access. The technique of deriving lake water-level variations is similar for rivers, floodplains and wetland regions, and the G-REALM system could equally be used to output products for identified river channel crossings.


Section D The G-REALM water level products (in graphical and text format) are created using a suite of altimeter data sets. Current operational products are based on IGDR data from the NASA/CNES Jason-2/OSTM mission, and in the near future, from the ISRO/CNES SARAL mission. The two figures below provide preliminary analysis from the use of this system to meet this particular Objective of this consultancy.

Figure 8: Google Earth imagery showing the location of the satellite radar altimeter “Jason-2/OSTM” ground track crossings over the Ganges River. These crossings have a temporal resolution or repeat period of 10-days. For example, satellite passes 003, 079, and 155 etc. ascend over the river from south-west to north-east. Descending passes 192, 014, 090 etc. descend over the river from the Himalayas. Time series of water height variations can be constructed at each channel-crossing site.


Section D Figure 9 (below): Preliminary altimetric time series depicting water level variations for specific ascending pass channel crossings on the Ganges River. Data source: Jason-2/OSTM satellite mission (2008 to present day), GDR data.


Section D Work Plan In this sub section we discuss the main activities of the assignment, including their content and duration, and dates of the milestones to be reached. A list of the final documents, reports, and trainings to be delivered is also provided. With reference to the key tasks to be undertaken please refer to Figure 1 above (the flow chart of CFAB system forecast production). With reference to the timing and duration of these tasks, please also see Annex 3 – Work Schedule. As with the previous subsection, we reproduce each Task as given in the RFP below, and give our response to each directly beneath each one, providing anticipated task begin date relative to the beginning of the consultancy period of performance (PoP) and duration (in weeks). These tasks are:

Task 1: GIS Basin Delineation

• Delineate sub-basins and river networks using SRTM (30m horizontal resolution) data. The delineated sub-basins using the USGS Hydro1k (1km resolution stream networks and flow connectivity) will be used, but use of the STRM data will be employed to repeat the process at higher resolution.

Response: Our team has technical experience delineating watersheds across the globe, including for the Ganges and Brahmaputra (see Figure 2 above). Given the foundational importance of this task, we anticipate completing this task, including ancillary outputs necessary for improved river routing, within the first week of this consultancy: Duration/start date: 1week beginning week 1 of PoP

• Calculate grid-weighting per sub-basin for gridded rainfall observation, estimates, and forecast products. This process will generate (fractional) weights that will efficiently combine precipitation values from neighboring grids to produce sub-basin average rainfall, based on the grid-box spatial overlap with the basin.

Response: At the finest subcathment scale, basins will be treated as single units with routing routines linking and accumulating the subcatchment flows downstream. As such, subbasin (spatially-)averaged rainfall needs to be calculated from gridded rainfall products (observations or forecasts). To accomplish this, weights of each grid (of the precipitation products) as applied to each subcatchment need to be calculated. Our team has developed scripts that will be modified for generating subbasin areal-based weighting for each grid point and for each subbasin (with a unique set of weights for each gridded products’ grid-spacing, aka for each unique Thorpex-TIGGE weather center grid spacing weather forecasts and satellite precipitation estimates grid spacing). Given our team’s technical experience delineating watersheds and its foundational importance, and we anticipate completing this task in one week, starting the 2nd week of the PoP: Duration/start date: 1week beginning week 2 of PoP Reported Milestones: we would provide the World Bank figures showing the newly delineated subcatchment for which we will forecast for.


Section D

Task 2: Rain Gage and Stream Gage Data Collection and Processing

• Collect archived data – for use in model calibration and analogue-technique selection Response: The data outlined in Task 2 are essential for data assimilation and verification of the consultancy forecast outputs. Our team will update our archives of raingauge (gridded 0.5X0.5 degree data from NOAA and the World Meteorological Organization (WMO) global telecommunication system (GTS)), along with river stage information for the downstream boundary condition locations of the Ganges (Hardinge Bridge in Bangladesh) and the Brahmaputra (Bahadurabad in Bangladesh), including any observed river discharge data for these gaging locations collected from the Bangladesh Flood Forecasting and Warning Center (FFWC), along with any other data provided by the World Bank. Duration/start date: 2 weeks beginning week 1 of PoP

• Derive rating curves from observations – available river discharge and stage measurements will be used to generate rating curves at select sites.

Response: where available, we will derive our own rating curves, using our own developed algorithms, for each location that we have both stage and observed discharge records. Duration/start date: 2 weeks beginning week 2 of PoP

• Automate real-time download of available rain and river stage gage sites. Response: we have download scripted templates which will be modified for each of the products listed above (including any new possible data provided by the World Bank). In addition, we will automate the capture of stage measurements available upstream in India provided at the website www.india-water.gov.in/ffs (Figure 2 below). Duration/start date: 1 week beginning week 4 of PoP. Reported Milestones: provide the World Bank access to view the updated stage measurements over the web.


Section D

Figure 2: imagery of stage measurements available for automated download through the website

www.india-water.gov.in/ffs

Task 3: Satellite Precipitation Estimates

• Collect archived data from NOAA, NASA and JAXA/EORC for calibration purposes Response: To calibrate hydrologic models, the best estimate of historical precipitation over the watersheds need to be used. In raingauge data sparse regions, these data are often provided by satellite precipitation estimates. Previous work on the CFAB project by the Team Leader has archived NOAA CMORPH and NASA TRMM precipitation estimates. However, these archives will need to be updated, along with new archives for the NOAA HydroEstimator and JAXA products (new products to be tested) would be generated. Duration/start date: 2 weeks beginning week 3 of PoP.

• Automate real-time download of these products and combine for operational hydrologic model and in-stream flow initialization

Response: The developed CFAB codes of the Team Leader Hopson will be used as templates for automatic download (for operational updating) of the NOAA, NASA, and JAXA satellite


Section D precipitation products, which are essential as part of the objective of producing an automated river forecast system. Duration/start date: 1 week beginning week 5 of PoP

• Optimal combination of products, based on available rain gage values Response: where reporting raingauge data are available, we will use them to calibrate the satellite rainfall estimates for: 1) locations where no raingauge data is available, but still required for hydrologic model calibration, and for 2) for quicker responding operational flood forecasting, given that satellite data availability times are typically significantly quicker than rain gauge reports. The calibration will be done for: 1) probability of precipitation, and for 2) the rainfall amount, given a rainfall event occurs. Duration/start date: 2 weeks beginning week 5 of PoP Reported Milestones: We would provide the World Bank links to web-based near-real-time plots of the combined rainfall products showing current rainfall conditions for each subcatchment relative to its own historical climatology, providing a “return period” contextual estimate of the severity of the rainfall event.

Task 4: Thorpex-TIGGE NWP Ensembles

• Collect archived data – It will require downloading from ECMWF data Response: Download scripts will be developed by NCAR’s Computational Information Systems Laboratory (CISL) will be developed to assist this project, downloading these data from the ECMWF Thorpex-TIGGE portal (based at Reading, UK). Duration/start date: 2 weeks beginning week 4 of PoP

• Automate real-time download Response: Download scripts will be automated for 6-times-daily automated download of 6 weather forecast center ensemble data products (for a total of over 200 dynamic ensemble members forecasting potential rainfall at 6-hour time-steps over the Ganges and Brahmaputra catchments). Duration/start date: 1 week beginning week 5 of PoP

• Pre-process and optimal combination of products, based on available rain gauge values, utilizing a quantile-regression and analog based approach, generating calibrated probability distribution function inputs into hydrologic model.

Response: Previous calibration algorithm templates developed by Team Leader Hopson as part of the CFAB, Google, and ATEC projects (subsections B.1.1, B.1.2, and B.1.6 above) will be leveraged to combine precipitation forecasts for this consultancy. Just as for the satellite precipitation estimates, the ensemble precipitation forecast products will also need to be calibrated relative to raingauge values where available, before ingesting them into hydrologic models (otherwise creating the potential for biased river discharge forecasts).


Section D Duration/start date: 2 weeks beginning week 5 of PoP Reported Milestones: Similar to what is proposed for satellite-derived rainfall estimates (Task 3), we would provide the World Bank links to web-based near-real-time plots showing current rainfall conditions for each subcatchment relative to its own historical climatology, providing a “return period” contextual estimate of the severity of the forecasted rainfall event. Task 5: Hydrologic Modeling and Multi-modeling

• Implement lumped and semi-distributed 2-layer models for each sub-basin and gauging locations (maximum of three).

Response: The CFAB hydrologic forecast system used here consists of both “lumped” and “semi-distributed” modeling components (see Figure 1 above). The lumped model treats everything upstream of the forecast location as “lumped” (aka a single spatially-averaged rainfall value). For this consultancy, each location along the Ganges and Brahmaputra rivers where we have river stage measurements (see triangles in Figure 2 above) could be treated as a distinct implementation of the “lumped” modeling approach (aka some hundreds of points), using each reported stage value ingested as part of the algorithm. Although the consultancy specifies a maximum of three staging locations be done, we will consider implementing the approach at more than just three sites within the Ganges and Brahmaputra catchments, depending on the computational demands of each process and the computational resource still available. Duration/start date: 3 weeks beginning week 7 of PoP

• River routing using o Constant travel time in lower reaches. o Muskinghum-Cunge in upper catchments of the Ganges and Brahmaputra.

Response: As part of the implementation of the semi-distributed modeling component of the CFAB forecasting system, both a constant travel time (simplest) and Muskinghum-Cunge (more computationally demanding) routing scheme would be implemented and the results compared at different stage observation locations to see the added (if any) benefits of the Muskinghume-Cunge over the constant travel time, and whether the former justifies the extra computational burden. Duration/start date: 4 weeks beginning week 10 of PoP Reported Milestones: The results of this comparison study would be made available to World Bank staff at the completion of this task.

• Automate model calibration. Response: Both the CFAB lumped and semi-distributed hydrologic schemes have calibration coefficients as part of their algorithms. We will implement automated calibration (CRON computer background) processes to optimize these calibrations as more data comes in – this is especially important, since at the beginning of this consultancy, very little upstream stage data


Section D would be available from online stage data sites (see Figure 2), but will continue to be generated over time, continually improving hydrologic calibrations. Duration/start date: 1 week beginning week 10 of PoP

• Implement auto-regressive corrections to each model, before combining into optimal multi-model

Response: An important error correction component to the CFAB forecasting system are auto-regressive error corrections applied to the forecast time series. These autoregression corrections rely on near-real-time stage (or discharge) measurements. This algorithm would be applied to the river forecast location where stage readings are available (Figure 2). Duration/start date: 2 weeks beginning week 13 of PoP

• Optimal combination of model outputs, combine models for each lead time and post-process and combine using quantile-regression and analogue based-approach generating calibrated final probability distribution function.

Response: One of the strengths of using more than one hydrologic model (in this case, the lumped and semi-distributed approaches), is that they can optimally be combined and weighted to essentially mitigate random errors of each, and improve overall foreasting skill. Here we propose to utilize a quantile regression calibration system (similar as discussed in Tasks 3 and 4), that generates an ensemble of forecasts, where each ensemble represents a specific optimized quantile of the forecast output probability distribution function. Duration/start date: 3 week beginning week 14 of PoP Reported Milestones: As each modeling component (“lumped”, semi-distributed, etc., becomes available, we would provide web-based plots of forecasted discharge outputs as they become available at the completion of each sub-task listed above, for near-real-time display.

Task 6: Incorporate DFO Measurements

• Increase the number of daily-updated River Watch river flow measurement sites to 90 locations while receiving input from regional specialists and collaborators in locating these sites; and test accuracy to available local ground station data.

Response: Previous work by our team (Feyera et al. 2013) utilized approximately 26 RiverWatch sites along each of the two rivers of this consultancy (aka Ganges and Brahmaputra). Skill could potentially be further improved beyond that shown in this past work by the provision of more data, to further improve the skill of downstream advective forecasting, and also as data that could be used in data assimilation algorithms to update upstream hydrologic model states above the RiverWatch sites. Duration/start date: 6 weeks beginning week 1 of PoP

• Implement new routing routine in CFAB model to overlap DFO measurement sites with model grid points.


Section D Response: The benefits of having a consistent set of hydrologic model routing sites and DFO measurement sites are that this allows the DFO RiverWatch data to be available for directly updating (via data assimilation) the river flow states in the CFAB semi-distributed model routing routine. Duration/start date: 3 weeks beginning week 13 of PoP

• Create real time data links, from DFO to NCAR, to enable their planned discharge measurements assimilation task.

Response: Real time data links are essential for operational flood forecasting, and being timely enough allow data assimilation into hydrologic model initial states before forecast generation ensues. Duration/start date: 2 weeks beginning week 15 of PoP

• Develop assimilation algorithm to update CFAB model flow estimates with River Watch measurements.

Response: Given funding levels and time constraints of this consultancy, the data assimilation algorithm would be based on simple “optimal interpolation” of instream hydrologic model routing states, which potential future consultancies can build from to implement more complex data assimilation schemes (as well as ingest satellite altimetry data as well). Duration/start date: 3 weeks beginning week 15 of PoP

• Automate all data ingest and data assimilation processes. Response: As above, crucial to near real-time operational forecast generation over the scale required of this consultancy (aka roughly 1.5 million square kilometers), all tasking of the forecast system implementation will need to be automated. Here we will build upon experience and scripts developed from the original CFAB forecast system implementation. Duration/start date: 2 weeks beginning week 16 of PoP

• Ensure DFO-only river forecast systems are produced periodically. Ensure combined CFAB-DFO River forecast systems are also produced periodically

Response: A stand-along DFO/RiverWatch advection-based hydrologic forecast will be daily produced (essentially, operationalizing the system shown in hindcasts from Feyera et al. 2013, but using the expanded RiverWatch sites proposed by this consultancy), and optimal combination of both the CFAB and DFO systems will be combined together, again, utilizing similar quantile-regression algorithms as discussed in Tasks 3, 4, and 5 above. Duration/start date: 5 weeks beginning week 18 of PoP Reported Milestones: Web-based plots of these forecasts will be made available to the World Bank for viewing at the completion of this task. Task 7: Transforming forecasts into informative visualizations and Dissemination


Section D • Couple the flood discharge estimates, on a selected site and reach basis, to the MODIS

observed inundation, to produce online libraries of measured or predicted discharge and matching floodplain inundation maps.

Response: To provide the linkage between the DFO MODIS imagery and the CFAB ensemble discharge forecasts, our first step will be to run the CFAB model over the historical period that the archive corresponds to, associating a modeled discharge to an archived image of a given reach (note that this process will have to be carried out on a reach-by-reach basis, to the spatial extent of the image itself). In realtime mode, each CFAB forecast ensemble (for each river reach) will search over its own historical model discharges, find the dates for which the “nearest neighbors” occurred, and then pair the ensemble with the DFO imagery occurring on the same date. Duration/start date: 4 weeks beginning week 7 of PoP

• Devise satellite-based microwave sensor altimetry virtual data points to collect river measurement sites to monitor changes in river water heights to compute river flow for validations.

Response: The work will be undertaken by Dr. Charon Birkett. Due to finanancial and time limiations, we view this task as a somewhat separate “stand-alone” project, laying the foundation for potential further consultancies to assimilate these measurements into the CFAB model for improved hydrologic prediction. As such the timeline for this will be set by the sub contractor (in consultation with Hopson), with a final report due six months after the beginning of the period of performance. More detailed tasking is provided below:

Ø Investigation of radar altimetry data sets over the Ganges/Brahmaputra Rivers and over a selection of lakes/reservoirs in India. Data will be Jason-2/OSTM GDR (primarily) and SARAL GDR (secondary), i.e. archival (not operational) data and the number of river channel and lake/reservoir crossings to be investigated will be as time (FTE) permits;

Ø Creation and validation of altimetric time series utilizing in situ gauge data sets; Ø Summary of potential virtual stations and lake/reservoirs for operational monitoring; Ø Deliverables will include i) (static) water-level variation products in both graphical

and ascii text format, ii) Google imagery displaying satellite ground track locations, iii) a summary report which will include details of instrument performance, data set limitations, and recommendations for further analysis and transition to operations.

Duration/start date: 4 weeks beginning week 7 of PoP

• Establish real-time data links between forecast model outputs and the River Watch site displays, such that predicted discharge are provided as well as the present discharge values.

Response: Real-time data links are essential if the CFAB ensemble forecasts are to be timely linked to imagery, showing where potential inundation areas are forecasted to occur, and if these areas are to have timely forewarning. Duration/start date: 3 weeks beginning week 20 of PoP


Section D • Create graphical displays providing: (i) public access to both predicted and present

river discharge status of a relatively dense network of measurement points, and (ii) to estimated and forecasted inundation maps.

Response: Through the NCAR-DFO partnership, the results of the collaborative work will be incorporated and published on the DFO web site, which is currently a well-used international portal for the dissemination of both automated discharge measurements and flood inundation mapping products. Through this website, as well as a mirrored NCAR site, we will disseminate project results to World Bank staff and national and subnational level practitioners (with the prior approval of World Bank staff), as well as via interactive engagements such as trainings and workshops. Forecast dissemination will also occur at different levels. Since the probabilistic (ensemble) forecasts can be presented in different ways, during the dissemination and input workshops, we will work with end-users and prospective end users to determine the most useful ways to present “best estimate” and uncertainty flood forecast information. The Dartmouth Flood Observatory (DFO) River Discharge Measurements web site is a model format on which we can build.1 “River Watch 2” is an existing automated processor supported by NASA Earth Science Research and Applications Programs. In support of our proposed work, the University of Colorado is making available extensive space on River Watch 2 for South Asia-focused displays to provide a prototype portal for both satellite-based present status information and the model-based discharge prediction and flood warning information. Duration/start date: 3 weeks beginning week 22 of PoP Reported Milestones: The displays discussed above will be made available to the World Bank for viewing at the completion of each sub-task. Task 8: Training and Reporting

• Conduct workshops on current technologies and future developments and operational management of full system;

Response: There are two workshops called for in this RFP: one 5 months after the beginning of the period of performance (PoP), and one 6 months after PoP. Because of the complexity and range of the technologies being employed in this consultancy, we propose the first workshop be devoted to training strictly on the technologies themselves, with the 2nd workshop devoted to presentations on the forecast outputs, displayed information and visuals, technical findings on forecast skill, and steps forward for potential future consultancies. The first workshop will rely on material Team Leader Hopson has presented at 3 previous week-long workshops in India covering long-lead flood forecasting (Patna, Bihar, August 2009; National Water Academy, Pune, June 2012; National Institute of Hydrology, Roorkee, November 2013), but also include more extensive discussions on RiverWatch, DFO, and satellite altimetry technologies. Duration/start date: 2 weeks beginning week 20 (1st week prep, workshop given week 21) of PoP (1st week prep, workshop given week 21); and 2 weeks beginning week 25 of PoP (1st week prep, workshop given week 26)

1 See: http://floodobservatory.colorado.edu/CriticalAreas/DischargeAccess.html


Section D Reported Milestones: This reporting milestone is self-evident.

• Deliver final report and prepare peer-reviewed papers for a joint publication; Response: The final report will summarizing our findings with respect to the implementation of the CFAB system for subcatchments within India, utilization of DFO RiverWatch for forecast skill enhancements and inundation imagery displays, and the potential benefits of utilizing satellite altimetrysatellite pre, and will be due 6 months after the beginning of the PoP. Duration/start date: 4 weeks beginning week 22 of PoP Reported Milestones: This reporting milestone is self-evident.

• First draft of peer-reviewed journal article on research findings completed.

Response: In addition to the delivery of the final report, a peer-reviewed journal article is also called for, with rough draft due 9 months after the PoP beginning. The overall PoP duration of 12 months allows additional time for paper revisions to be completed and payment of publishing fees. Finally, results will also be disseminated via traditional, academic-conference presentations (with prior consent of the World Bank). Duration/start date: 13 weeks beginning week 27 of PoP Reported Milestones: This reporting milestone is self-evident. Deliverables and Schedule The expected deliverables and their schedule are provided as below:

Deliverables

Description Timing (months

after signing)

Inception Report Brief approach, methodology, detailed work plan, Description of models to be used, overall data compilation for Tasks 1 and 2.

1st month

Satellite estimates, NWP ensembles, and Multi-model compilation report

Technical document describing initial results from setting up of hydrological model and calibration; outline of assumptions and challenges faced (Task3-5).

4th month

Completion of flood forecasting for the Ganges and Brahmaputra at sub-basin levels

Completion of the major tasks involved in producing sub-basin river forecasts.

5th month

Training on technologies developed as part

Also by the 5th month, a training will be conducted (in Delhi or at a smaller center) to provide hydrologists and users an overview of Long Lead Flood Forecasting technologies being implemented as part of this consultancy

5th month

Final Report and training -- Draft report on flood forecast development and sub-basins level flood forecast results as described

6th month


Section D in overall tasks, including results from setting up of hydrological model and calibration; outline of assumptions and challenges faced (Task3-5). -- Training on the overall system will also be provided in India

finalization of 1st draft of peer-review paper

First draft of peer-reviewed journal article on research findings completed.

9th month

Relation to Prior Research – leveraging previous resources This assignment will leverage technologies developed under previous similar work at NCAR and the DFO, some of which include: US National Science Foundation base funding (Hopson: Advanced Study Program postdoc, 2006-8); USAID Grant USAID/OFDA AOT-A-00-00-00262-00 (2000–03) (PI Peter Webster; Hopson) that provided Bangladesh with operational forecasts of severe flooding at 1- to 10-day lead-times from 2003-2008; US Army Test and Evaluation Command (ATEC) funding (Knievel and Hopson), which has developed effective ensemble probability calibration tools; NASA funding (Brakenridge (PI)) through a research feasibility project designed to define the pathway for sustainable implementation of a flood mapping processor provided to the DFO; and the US Bureau of Reclamation funding, where developed GIS tools for mapping numerical weather prediction outputs to river catchment areal domains would be utilized.

Organization and Staffing In this sub section we describe the structure and composition of our team, listing the main disciplines of the assignment, the key expert responsible, and proposed technical and support staff. We refer to the Senior Personnel provided below, as well as the Objectives and Tasking discussed above. Further details are referred to Section B – Consultants’s Experience, Annex 1 listing of Team composition, Task assignments, and Level of Effort, and Annex 2 which provides a listing of the Curriculum Vitae (CV) of these key personnel. Senior Personnel NCAR Hopson, Thomas. Team Leader / Prominent Researcher / Scientist: National Center for

Atmospheric Research Hacker, Joshua. Prominent Researcher / Scientist: National Center for Atmospheric Research Yates, David. Prominent Researcher / Scientist: National Center for Atmospheric Research Wilhelmi, Olga. GIS program team leader / GIS specialist: National Center for Atmospheric

Research Dumont, Arnaud. Analyst / Software engineer: National Center for Atmospheric Research Knievel, Jason. Researcher / Scientist: National Center for Atmospheric Research Sub Consultant - Dartmouth Flood Observatory Brakenridge, G. Robert. Collaborator / Consultant / Prominent Researcher / Director

Dartmouth Flood Observatory: Dartmouth Flood Observatory, University of Colorado


Section D Sub Consultant – University of Maryland Birkett, Charon. Collaborator / Consultant / Prominent Researcher / Scientist: University of

Maryland Sub Consultant Islam, A.K.M. Saiful. Collaborator: Bangladesh University of Engineering and Technology Team leadership for this consultancy requires knowledge of the underlying science and primary technologies required to reach the objectives and complete the tasking, along with management capability, and teaching experience and technology transfer for conducting effective workshops on developed technologies (the latter called out in the RFP as required outputs). Team Leader Hopson has over 15 years experience both with the science and technologies required to reach the objectives and tasking, having been the primary architect of the CFAB river flow forecasting sytem (Hopson and Webster 2010) specified in Objective (i), as well as the engineer of the operational system implementing of the modeling components, which provided Bangladesh with operational forecasts of severe flooding at 1- to 10-day lead-times from 2003-2008 (in 2009 these technologies were transferred to South Asian partners for sustainable operations). He recently also conducted three World-Bank funded training workshops in India to engineers and scientists working on long-lead river forecasting; as such he will be responsible for Task 8 above. Expertise with satellite precipitation estimates is also required for this consultancy (Task 3). Hopson also has experience in operationally implementing and ingesting satellite precipitation estimates from NOAA (“CMORPH”) and NASA (“TRMM 3B42”) into the CFAB system. With colleagues Prof Mekonnen Gebremichael (UCLA) and Dr. Feyera Aga Hirpa (EU Joint Research Council) investigated multiple satellite precipitation products for hydrologic applications for East Africa (Hirpa et al. 2010). As such, Hopson (with assistance from a post-doc) will be responsible for Task 3 above. The consultancy also requires experience with operational ensemble forecast systems. In addition to the CFAB system, Hopson has expertise working with ensemble weather forecasts from multiple centers (provided via the Thorpex-Tigge project, see Section B.1.2), providing the World Health Organization and African National Health Agencies with operational ensemble humidity forecasts over the meningitis belt of Africa to inform meningitis transmission models about optimal allocation of vaccine, which is important for Task 4 for which he will be responsible for. Both Dr. Jason Knievel (ATEC Team Leader, Section B.1.6) and Hopson (supporting scientist) have experience providing multi-model forecasts to the US Army with operational ensemble weather forecasts across the U.S. via the ATEC project, and also Hopson via the multi-hydrologic model of the CFAB system. As such, Knievel will provide supervision on the operational aspects of this consultancy, and both Hopson and Knievel will be responsible for generating the multimodel forecasts given in Task 5. Implementation of improved river routing for the CFAB model is also called out for in the consultancy. Dr. David Yates will provide supervision for this tasking, given his expertise in implementing improved routing technology in the official released version of the NCAR WRF-Hydro modeling system, as called out for in Task 5.


Section D Via Objective (ii) and Task 6, the consultancy also requires expertise with generating remotely-sensed river width variations (as surrogates for discharge variations), for which DFO Director Brakenridge has extensive knowledge (Brakenridge et al. 2005, 2007). The consultancy also requires (Objective (ii) and Task 6) knowledge of utilizing these measurements for flood foreasting purposes. Related to this, Hopson and Brakenridge tested the potential of remotely-sensed river widths derived from microwave imagery data to forecast Ganges and Brahmaputra river floods downstream of the imagery locations (Hirpa et al 2012), and will be responsible for this tasking. Dr. Hacker is renowned expert in data assimiliation, and he will provide supervision for the incorporation of DFO measurements into the CFAB modeling system, as called out for in Task 6. With respect to Objective (iii) and Task 7, Brakenridge at the DFO has archived past microwave and optical sensor imagery of actual inundation extent, that could be linked to CFAB ensemble river discharge forecasts by selecting images with past similar analogues of river flow compared to current forecasts. Analyst Dumont has expertise in generating effective web-based visualizations, and will also be utilized for generating displays of these products. Both Brakenridge, Hopson, and Dumont will be responsible for this tasking With respect to Objective (iv), Collaborator Dr. Charon Birkett will be responsible for providing the analysis and reporting required to meet this objective (in consultation with Team Leader Hopson). Dr. Birkett is the Principal Investigator of the G-REALM program, the Global Reservoir and Lake Monitor, which is a NASA/USDA funded program that ingests raw altimetric data sets and delivers water-level variations products for the world’s largest lakes and reservoirs. The technique of deriving lake water-level variations is similar for rivers, floodplains and wetland regions, and the G-REALM system would be used to output products for identified river channel crossings to test the potential of satellite radar altimetry data for monitoring river heights for operational flood forecasting purposes. GIS and visualization capabilities are also critical for consultancy technology developments and outputs (see Task 1 in particular). NCAR GIS Program team leader Olga Wilhelmi’s team has extensive expertise in delineating river catchments, in developed GIS tools for mapping numerical weather prediction outputs to river catchment areal domains (subtask of Task 1), and visualization of GIS-derived outputs (see Section B.1.10 and B.1.11). She and her team will be responsible for Tasks 1 and assist with Task 7. Analyst Dumont also has a proven track record of scripted automated downloads and archiving of data, and he and Hopson (with assistance from a postdoc) will be responsible for Task 2. As well, expertise in processing and archiving of the multimodel ensemble weather forecasts (Thorpex-TIGGE) is important, and here we will utilize the computer engineering expertise of NCAR’s Douglass Schuster working with NCAR’s Computational Information Systems Laboratory (CISL) (Doug was responsible for maintaining NCAR’s Thorpex-TIGGE archive from 2007-2015), which is important for Task 4. Computational demands in generating operational production of forecasts provides its own challenges. For this, we will utilize the system administration expertise of NCAR software


Section D engineer John Exby, as well as a devoted computer server (purchased through previous project funding): Dell PowerEdge R820 with an 8-core CPU configured with 128GB system RAM and an 8TB RAID. Finally, with our Collaborator A.K.M. Saiful Islam, Professor at the Bangladesh University of Engineering and Technology (BUET) and the Institute of Water and Flood Management (IWFM), with his collaborations at the Bangladesh Flood Forecasting and Warning Center (FFWC) and the Bangladesh Meteorological Department (BMD), we have the organizational capacity to disseminate the technical and skill improvements derived from this project for institutions in Bangladesh as well (called out for in the RFP, for “the need for continuity of downstream work”). Coordination of Group Effort We will have project team meetings twice a month and more frequently as needed, such as in preparing for workshops via Skype or conference call that will include key researchers on the project. There will be more frequent sub-team meetings as appropriate. Collaborations and Partnerships We view the organization of our undertaking as an interconnected collaborative partnership, beginning with the collaborative partnership which is our interdisciplinary research team at the NCAR, the DFO (Brakenridge), and the University of Maryland (Birkett). The next scale of partnership involves our international partners, including organizations engaged in generating forecasts, such as the Flood Forecasting and Warning Center of Bangladesh (Islam, BUET and FFWC). These partners are currently engaged with regional and global institutions in a manner that will allow other regions at risk of similar flood hazards to capitalize on improved flood forecasting. Finally, the next scale will be the partnership of those who engage in the project’s two workshops—including technical experts (scientists and engineers), decision makers and policymakers, with potential of directly reaching vulnerable area residents. As such, our work is intended to reach beyond our initial technical institutional partnerships, to create a dense network of expanded connections among people attempting to mitigate the flood threat across different scales throughout the area. References Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle

filters for online nonlinear/non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, 50, 174-188.

Brakenridge, G. R., Nghiem, S. V., Anderson, E., & Chien, S. (2005). Space-based measurement of river runoff. EOS, Transactions of the American Geophysical Union, 86(19), 185-188.

Brakenridge, G. R., Nghiem, S. V., Anderson, E., & Mic, R. (2007). Orbital microwave measurement of river discharge and ice status. Water Resources Research, 43, W04405. doi: 10.1029/2006WR005238

CEGIS. (2006). Sustainable end-to-end climate/flood forecast application through pilot projects showing measurable improvements. CEGIS Base Line Report, 78 pp.


Section D Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater, A. G., Schmidt, J.,

Uddstrom, M. J. (2008). Hydrocological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advanced Water Resource, 31, 1309-1324.

Hirpa, F. A., Hopson, T. M., De Groeve, T., Brakenridge, G. R., Gebremichael, M., & Restrepo, P. J. (2013). Upstream satellite remote sensing for river discharge forecasting: Application to major rivers in South Asia. Remote Sensing of Environment, 131, 140-151. doi: DOI:10.1016/j.rse.2012.11.013

Hopson, T. M., & Webster, P. J. (2010). A 1-10-Day ensemble forecasting scheme for the major river basins of Bangladesh: Forecasting severe floods of 2003-07. Journal of Hydrology, 11, 618-641.

Huffman, G. J., R. F. Adler, S. Curtis, D. T. Bolvin, and E. J.Nelkin (2005). Global rainfall analyses at monthly and 3-hr time scales. Measuring Precipitation from Space: EURAINSAT and the Future, V. Levizzani, P. Bauer, and J. F. Turk, Eds., Springer, 722 pp.

Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu, G., . . . Stocker, E. F. (2007). The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1), 38-55.

Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie (2004). CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503.

Kleuskens, M., Westerhoff, R. S., & Huizinga, J. (2011). Operational flood mapping: A pilot study in the Mekong Area. Paper presented at the Proceedings of the International Symposium of Remote Sensing of the Environment, Sydney, Australia.

Lee, H. S., Seo, D. J., Lui, Y., Koren, V., McKee, P., & Corby, R. (2012). Variatinal assimilation of streamflow into operational ditributed hydologic models: Effect of spatiotemporal scale adjustment. Hydrology and Earth System Sciences, 16, 2233-2251. doi: 10.2194/hess-16-2233-2012

Montanari, M., Hostache, R., Matgen, P., Schumann, G., Pfister, L., & Hoffman, L. (2009). Calibration and sequential updating of a coupled hydrolic-hydrualic model using remote sensing-derived water stages. Hydrology and Earth System Sciences, 13, 367-380. doi: 10.5194/hess-13-367-2009

Moradkhani, H., Hsu, K., Gupta, H. V., & Sorooshian, S. (2005). Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using particle filter. Water Resources Research, 41, W05012. doi: 10.1029/2004WR003604

Seo, D. J., Cajina, L., Corby, R., & Howieson, T. (2009). Automatic state updating for operational streamflow forecasting via variational data assimilation. Journal of Hydrology, 367, 255-275.

Song, X. Y., & Lee, S. Y. (2004). Bayesian analysis of two-level nonlinear structural equation models with continuous and polytomous data. British Journal of Mathematical and Statistical Psychology, 57, 29-52.

Syvitski, J. P. M., & Brakenridge, G. R. (2013). Causation and avoidance of catastrophic flooding along the Indus river, Pakistan. GSA Today, 23(1). doi: 10.1130/GSATG1165A.1131


Section D Webster, P. J. (2013). Improve weather forecasts for the developing world. Nature, 493, 17-

19. Webster, P. J., Jian, J., Hopson, T. M., Hoyos, C. D., Agudelo, P., Chang, H.-R., . . . Subbiah,

A. R. (2010). Extended-range probabilistic forecasts of Ganges and Brahmaputa floods in Bangladesh. Bulletin of the American Meteorological Society, 91, 1493-1514.

Weerts, A. H., & El Serafy, G. Y. H. (2006). Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models. Water Resources Research, 42, W09403. doi: 10.1029/2005WR004093

Westerhoff, R. S., Huizinga, J., Kleuskens, M., Burren, R., & Casey, S. (2010). Operational satellite-based flood mapping using the Delft-FEWS System. In Proceedings of the ESA Living Planet Symposium, Bergen, Norway, June 28-July 2, 2010. Retrieved Jan 10, 2013, from http:/kennisonline.deltares.nl/product/22381

Selection No1172405 Technical Proposal

Annex 3

Technical Proposal SOWral.ucar.edu/~hopson/Satya/Technical Proposal_SOW.pdfATEC project (see Section...

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Transcript of Technical Proposal SOWral.ucar.edu/~hopson/Satya/Technical Proposal_SOW.pdfATEC project (see Section...