Your search found 5 records
1 Rientjes, T. H. M.; Haile, A. T.; Gieske, A. S. M.; Maathuis, B. H. P.; Habib, E.. 2011. Satellite based cloud detection and rainfall estimation in the Upper Blue Nile Basin. In Melesse, A. M. (Ed.). Nile River Basin: hydrology, climate and water use. Dordrecht, Netherlands: Springer. pp.93-107.
Remote sensing ; Satellite observation ; Clouds ; Rain ; Estimation ; River basins / Ethiopia / Lake Tana / Upper Blue Nile River Basin
(Location: IWMI HQ Call no: 551.483 G136 MEL Record No: H044024)
2 Habib, E.; Haile, Alemseged Tamiru; Sazib, N.; Zhang, Y.; Rientjes, T. 2014. Effect of bias correction of satellite-rainfall estimates on runoff simulations at the source of the Upper Blue Nile. Remote Sensing, 6(7):6688-6708. [doi: https://doi.org/10.3390/rs6076688]
Rain ; Runoff ; Satellites ; River basins ; Hydrology ; Simulation models ; Calibration ; Catchment areas ; Stream flow / Africa / Ethiopia / Upper Blue Nile Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046873)
(608 KB)
Results of numerous evaluation studies indicated that satellite-rainfall products are contaminated with significant systematic and random errors. Therefore, such products may require refinement and correction before being used for hydrologic applications. In the present study, we explore a rainfall-runoff modeling application using the Climate Prediction Center-MORPHing (CMORPH) satellite rainfall product. The study area is the Gilgel Abbay catchment situated at the source basin of the Upper Blue Nile basin in Ethiopia, Eastern Africa. Rain gauge networks in such area are typically sparse. We examine different bias correction schemes applied locally to the CMORPH product. These schemes vary in the degree to which spatial and temporal variability in the CMORPH bias fields are accounted for. Three schemes are tested: space and time-invariant, time-variant and spatially invariant, and space and time variant. Bias-corrected CMORPH products were used to calibrate and drive the Hydrologiska Byråns Vattenbalansavdelning (HBV) rainfall-runoff model. Applying the space and time-fixed bias correction scheme resulted in slight improvement of the CMORPH-driven runoff simulations, but in some instances caused deterioration. Accounting for temporal variation in the bias reduced the rainfall bias by up to 50%. Additional improvements were observed when both the spatial and temporal variability in the bias was accounted for. The rainfall bias was found to have a pronounced effect on model calibration. The calibrated model parameters changed significantly when using rainfall input from gauges alone, uncorrected, and bias-corrected CMORPH estimates. Changes of up to 81% were obtained for model parameters controlling the stream flow volume.
3 Haile, Alemseged Tamiru; Yan, F.; Habib, E.. 2015. Accuracy of the CMORPH satellite-rainfall product over Lake Tana Basin in eastern Africa. Atmospheric Research, 163:177-187. [doi: https://doi.org/10.1016/j.atmosres.2014.11.011]
Rain ; Satellites ; River basins ; Lakes ; Remote sensing ; Spatial distribution ; Wet season ; Dry season / Eastern Africa / Lake Tana Basin
(Location: IWMI HQ Call no: e-copy only Record No: H046880)
In this study, we assessed the accuracy of rainfall occurrence, amount and distribution over the Lake Tana basin in Ethiopia, Eastern Africa, as represented in the NOAA satellite-based Climate Prediction Center Morphing technique (CMORPH) rainfall product. This analysis is carried out at high spatial and temporal resolutions (8 × 8 km2 and daily) using observations from rain gauges as a reference for the period covering January 2003 to December 2006. Graphical comparisons and several statistical metrics such as bias, correlation coefficient, and standard deviation of rainfall differences are used to perform the evaluation analysis. Spatial maps of these statistical metrics were developed to assess the spatial dependency in the CMORPH accuracy. The bias is decomposed into different components, hit, missed, and false, in order to gain additional insight into the possible sources of systematic deviations in CMORPH. Overall, CMORPH was able to capture the seasonal and spatial patterns of rainfall over the basin, but with varying degrees of accuracy that depend on topography, latitude and lake-versus-land conditions within the basin. The results show that CMORPH captured rain occurrence relatively well in both wet and dry seasons over the southern part of the basin but it significantly overestimated those over the lake and its southern shore. The bias of CMORPH in the study area is characterized by seasonal and spatial variations (-25 to 30% in wet season and -40 to 60% in dry season). False as well as missed rains contribute significantly to the total rainfall amounts over the basin. Significant levels of the differences are observed at the daily resolution of CMORPH. The relation between CMORPH and gauge rainfall amounts is stronger (correlationmostly N0.4) in thewet season than in the dry (mostly b0.4).
4 Bhatti, H. A.; Rientjes, T.; Haile, Alemseged Tamiru; Habib, E.; Verhoef, W. 2016. Evaluation of bias correction method for satellite-based rainfall data. Sensors, 16(6):1-16. [doi: https://doi.org/10.3390/s16060884]
Satellite observation ; Rain ; Remote sensing ; Catchment areas ; Runoff water ; Hydrology ; Precipitation ; Meteorology ; Spatial distribution / Ethiopia / Gilgel Abbey Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H047948)
(3.11 MB)
With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the eld of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, ... , 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coef cient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the ef ciency of our bias correction approach.
5 Tessema, K. B.; Haile, Alemseged Tamiru; Amencho, N. W.; Habib, E.. 2022. Effect of rainfall variability and gauge representativeness on satellite rainfall accuracy in a small upland watershed in southern Ethiopia. Hydrological Sciences Journal, 67(16):2490-2504. (Special issue: Hydrological Data: Opportunities and Barriers) [doi: https://doi.org/10.1080/02626667.2020.1770766]
Rainfall patterns ; Rain gauges ; Satellites ; Weather data ; Evaluation ; Watersheds ; Highlands ; Precipitation ; Observation ; Estimation ; Meteorological stations ; Models / Ethiopia / Southern Nations, Nationalities, and Peoples' Region (SNNPR) / Upper Gana Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H049792)
(3.30 MB)
The actual accuracy of satellite rainfall products is often unknown due to the limitation of raingauge networks. We evaluated the effect of gauge representativeness error on evaluation of rainfall estimates from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) rainfall product. The reference data were collected using an experimental raingauge network within a small watershed of 1690 ha, which is comparable to the CHIRPS resolution. The study applied a total bias approach, decomposed into hit, missed and false biases, and an error-variance separation method to evaluate gauge representativeness error at the scale of CHIRPS pixel size, as well as modeled the spatial correlation field of daily rainfall with a three-parametric exponential model. The results indicate that the gauge representativeness error is still too large to ignore in evaluating satellite rainfall. However, it is significantly affected by sample size and caution should be exercised when the rainfall data has a small sample size.
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