The Performance of Satellite Precipitation Products for Water Resources Applications in the Upper Blue Nile Basin

Transcription

1 The Performance of Satellite Precipitation Products for Water Resources Applications in the Upper Blue Nile Basin Emmanouil Anagnostou Department of Civil and Environmental Engineering University of Connecticut Photo by:

2 Blue Nile Study Basin Study Area

3 Blue Nile Study Basin Basin runoff coefficient is < 5% Upper Blue Nile runoff coefficient is > 15%

4 Irrigation and Hydropower Schemes 2.5 millions ha of potential large and medium-scale irrigation schemes; 526,000 ha was found feasible 33 hydropower production sites

5 Precipitation Products ECMWF TMV7 TMCCA CMCT PNCT GSCT TMRT CM PN GS ERA Interim reanalysis (0.5 deg/hourly); gauge adjusted TRMM 3B42 V7 (0.25 deg/3-hourly); gauge adjusted TRMM 3B42 Climatological gauge adjusted (0.25 deg/3-hrly) CMORPH (0.25 deg/3-hourly); gauge adjusted PERSIANN (0.25 deg/3-hourly); gauge adjusted GSMaP (0.1 deg/hourly); gauge adjusted TRMM 3B42 real time (0.25 deg/3-hourly) CMORPH (0.25 deg/3-hourly) PERSIANN (0.25 deg/3-hourly) GSMaP (01 deg/hourly)

6 Regional Analysis

7 PDF (%) PDF (%) Daily Rainfall (mm) Frequency Distribution TMV7 TMCCA CMCT PNCT GSCT TMRT CM PN GS Blue Nile

8 Normalized missed rainfall volume & Normalized false alarm rainfall volume Rainfall Detection Statistics 55 Blue Nile NMRV NMRV and NFASRV NFARV

9 (unitless) Mean Relative Error 1 Blue Nile All Q MRE

10 Blue Nile Correlation Coefficient Correlation CORR All Q99

11 Hydrologic Error Analysis Gilgel Abbay: 1,664 km 2 Abbay Basin: 172,254km 2

12 Streamflow Simulations

13 Hydrologic Model Performance Statistics Period NSCE Bias CC Gauge RF CMORPH TRMM ECMWF Period NSCE Bias CC Gauge RF CMORPH TRMM ECMWF

14 Predicted Frequency Analysis Period Period Reference Reference

15 Bias Adjustment Effect Forcing Precipitation CMORPH TRMM ECMWF Keeping raw satellite precipitation data NSCE = 0.67 Bias(%) = CC = 0.86 NSCE = 0.59 Bias(%) = CC = 0.86 NSCE = 0.54 Bias(%) = CC = 0.85 Adjusting for rainfall bias Bias adjustment = 1.14 NSCE = 0.72 Bias(%) = CC = 0.87 Bias adjustment = 1.15 NSCE = 0.72 Bias(%) = CC = 0.86 Bias adjustment = 1.33 NSCE = 0.71 Bias(%) = CC = 0.86

16 Conclusions There is no clear benefit from gauge adjustment in satellite precipitation products. CMORPH (passive microwave-only product) exhibits the best performance over Blue Nile basin. Gauge-adjusted atmospheric reanalysis performs similarly with the high-resolution satellite precipitation products; reanalysis can support water resources assessment studies. Satellite precipitation driven hydrologic simulations can achieve efficiency scores similar to a dense rain gauge network. Calibration is not needed as long as the satellite precipitation datasets are corrected for biases due to complex terrain low level orographic enhancement processes.

17 NSF-PIRE Food Security Project in Ethiopia ( ) US Institutions: University of Connecticut, University of Wisconsin, University of Oklahoma, International Food Policy Research Institute, USGS, NOAA International Organizations: Addis Ababa University, Bahir Dar University, University of Kassel- Germany, University Joseph Fourier-France, University of Reading-UK, Sunmoon Univ.-S. Korea Advance human security of climate-vulnerable low income communities of sub-saharan Africa, by improving seasonal hydrologic and crop yield forecasts and the uptake of this scientific information by farmers and water managers? I. a novel framework that integrates ensemble seasonal precipitation forecasts with regional land data assimilation, climate, watershed and crop yield and economic models; II. a field experiment program that will identify and reduce barriers to effective forecast uptake; III. train a new generation of global experts who recognize the political and climate-ecological dimensions of food-energy-water problems.