Estimating rainfall and water balance over the Okavango River Basin for hydrological applications

Wilk, Julie; Kniveton, Dominic; Andersson, Lotta; Layberry, Russell; Todd, Martin C.; Hughes, Denis A.; Ringrose, Susan; Vanderpost, Cornelis

doi:10.1016/j.jhydrol.2006.04.049

Cited by 101 publications

(61 citation statements)

References 18 publications

(16 reference statements)

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“…These applications include adjusting satellite QPE against gauged rainfall and implementing the QPE for streamflow simulation [93], interpolating gauged rainfall against the satellite precipitation pattern and using the interpolated rainfall for hydrologic prediction in data sparse regions [94].…”

Section: Implementation Of Satellite Precipitationmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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Abstract:Fluvial flooding is one of the most catastrophic natural disasters threatening people's lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

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Section: Implementation Of Satellite Precipitationmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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“…CMORPH exhibited the worst skills (strong positive bias), TRMM 3B42 displayed a moderate aptitude and FEWS RFE 2.0 the best performance in terms of distribution and bias. The Microwave Infra-Red Algorithm (MIRA) has been compared at a daily time scale to ground station data over Southern Africa (Layberry et al, 2006) showing better agreement in the wet months than in the drier ones, but overall quite poor skills for rainfall detection. Over the Okavango basin, a monthly dataset at 0.5 • based on the TRMM and Special Sensor Microwave Imager (SSM/I) datasets was found to overestimate the rainfall by 20 % (Wilk et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

Liechti

Matos

Boillat

et al. 2012

Hydrol. Earth Syst. Sci.

114

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Abstract. In the framework of the African DAms ProjecT (ADAPT), an integrated water resource management study in the Zambezi Basin is currently under development. In view of the sparse gauging network for rainfall monitoring, the observations from spaceborne instrumentation currently produce the only available rainfall data for a large part of the basin.Three operational and acknowledged high resolution satellite derived estimates: the Tropical Rainfall Measuring Mission product 3B42 (TRMM 3B42), the Famine Early Warning System product 2.0 (FEWS RFE2.0) and the National Oceanic and Atmospheric Administration/Climate Prediction Centre (NOAA/CPC) morphing technique (CMORPH) are analyzed in terms of spatial and temporal repartition of the precipitations. They are compared to ground data for the wet seasons of the years 2003 to 2009 on a point to pixel basis at daily, 10-daily and monthly time steps and on a pixel to pixel basis for the wet seasons of the years 2003 to 2007 at monthly time steps.The general North-South gradient of precipitation is captured by all the analyzed products. Regarding the spatial heterogeneity, FEWS pixels are much more inter-correlated than TRMM and CMORPH pixels. For a rainfall homogeneity threshold criterion of 0.5 global mean correlation coefficient, the area of each sub-basin should not exceed a circle of 2.5 • latitude/longitude radius for FEWS and a circle of 0.75 • latitude/longitude radius for TRMM and CMORPH considering rectangular meshes.In terms of reliability, the correspondence of all estimates with ground data increases with the time step chosen for the analysis. The volume ratio computation indicates that CMORPH is overestimating the rainfall by nearly 50 %. The statistics of TRMM and FEWS estimates show quite similar results.Due to its lower inter-correlation and longer data set, the TRMM 3B42 product is chosen as input for the hydraulichydrologic model of the basin.Further work will focus on the calibration of the hydraulichydrological model of the basin, including the major existing hydraulic structures with their operation rules.

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“…CC, RMSE, ME, MAE, and BIAS indicated the degree of linear correlation, the average difference, the average magnitude of the error, and the systematic bias, respectively, between WRF simulation and observation. POD, FAR, and FBI were calculated based on a contingency table approach [7], and indicated the fraction of occasions when an event occurred that was also forecast, the proportion of forecast events that failed to materialize, and the unbiased forecast where the event was forecast exactly as often as it was observed, respectively. The POD and the FAR range from 0 to 1, with a higher value indicating a better simulation, whereas the FAR is the converse.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…The spatial-temporal distribution pattern of precipitation has a large effect on the land surface hydrological flux [3,4]. Precipitation is the most important variable affecting the exchange of moisture and heat between the atmosphere and the land surface, and is of primary importance for the study of regional hydrological processes and water resources management [3][4][5][6][7]. However, obtaining high resolution and reliable precipitation forcing in complex terrain, particularly mountainous regions, to drive land surface model is still a challenging work [8].…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin

Pan

Cheng

et al. 2015

Remote Sensing

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Abstract:To obtain long term accurate high resolution precipitation for the Heihe River Basin (HRB), Weather Research and Forecasting (WRF) model simulations were performed using two different initial boundary conditions, with nine microphysical processes for different analysis parameterization schemes. High spatial-temporal precipitation was simulated from 2000 to 2013 and a suitable set of initial, boundary, and micro parameters for the HRB was evaluated from the Heihe Watershed Allied Telemetry Experimental Research project and Chinese Meteorological Administration data at hourly, daily, monthly, and annual time scales using various statistical indicators. It was found that annual precipitation has gradually increased over the HRB since 2000. Precipitation mostly occurs in summer and is higher in monsoon-influenced areas. High elevations experience winter snowfall. Precipitation is higher in the eastern upstream area than in the western upstream, area; however, the converse occurs in winter. Precipitation gradually increases with elevation from 1000 m to 4000 m, and the maximum precipitation occurs at the height of 3500-4000 m, then the precipitation slowly decreases with elevation from 4000 m to the top over the Qilian Mountains. Precipitation is scare and has a high temporal variation in the OPEN ACCESS Remote Sens. 2015, 7 9231 downstream area. Results are systematically validated using the in situ observations in this region and it was found that precipitation simulated by the WRF model using suitable physical configuration agrees well with the observation over the HRB at hourly, daily, monthly and yearly scales, as well as at spatial pattern. We also conclude that the dynamic downscaling using the WRF model is capable of producing high-resolution and reliable precipitation over complex mountainous areas and extremely arid environments. The downscaled data can meet the requirement of river basin scale hydrological modeling and water balance analysis.

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Estimating rainfall and water balance over the Okavango River Basin for hydrological applications

Cited by 101 publications

References 18 publications

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Comparison and evaluation of satellite derived precipitation products for hydrological modeling of the Zambezi River Basin

Development and Evaluation of a River-Basin-Scale High Spatio-Temporal Precipitation Data Set Using the WRF Model: A Case Study of the Heihe River Basin

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