Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light

Poméon, Thomas; Jackisch, Dominik; Diekkrüger, Bernd

doi:10.1016/j.jhydrol.2017.01.055

Cited by 86 publications

(87 citation statements)

References 47 publications

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“…While a full review of the validation literature is beyond the scope of this study, the robust performance of CHIRPS does appear to be evident in comparisons with TMPA and CMORPH estimates over Italy (Duan et al, ) and Madagascar (Toté et al, ). Over West Africa, CHIRPS performance was found to be similar to CMORPH, TMPA and the PERSIANN datasets (Poméon et al, ). A gauge‐based validation of CHIRPS over Brazil found high levels of skill (Paredes‐Trejo et al, ).…”

Section: Discussionmentioning

confidence: 99%

Validation of the CHIRPS satellite rainfall estimates over eastern Africa

Dinku¹,

Funk

Peterson

et al. 2018

Quart J Royal Meteoro Soc

541

316

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Long and temporally consistent rainfall time series are essential in climate analyses and applications. Rainfall data from station observations are inadequate over many parts of the world due to sparse or non‐existent observation networks, or limited reporting of gauge observations. As a result, satellite rainfall estimates have been used as an alternative or as a supplement to station observations. However, many satellite‐based rainfall products with long time series suffer from coarse spatial and temporal resolutions and inhomogeneities caused by variations in satellite inputs. There are some satellite rainfall products with reasonably consistent time series, but they are often limited to specific geographic areas. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite‐based rainfall products with relatively high spatial and temporal resolutions and quasi‐global coverage. In this study, CHIRP and CHIRPS were evaluated over East Africa at daily, dekadal (10‐day) and monthly time‐scales. The evaluation was done by comparing the satellite products with rain‐gauge data from about 1,200 stations. The CHIRP and CHIRPS products were also compared with two similar operational satellite rainfall products: the African Rainfall Climatology version 2 (ARC2) and the Tropical Applications of Meteorology using Satellite data (TAMSAT). The results show that both CHIRP and CHIRPS products are significantly better than ARC2 with higher skill and low or no bias. These products were also found to be slightly better than the latest version of the TAMSAT product at dekadal and monthly time‐scales, while TAMSAT performed better at the daily time‐scale. The performance of the different satellite products exhibits high spatial variability with weak performances over coastal and mountainous regions.

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Section: Discussionmentioning

confidence: 99%

Validation of the CHIRPS satellite rainfall estimates over eastern Africa

Dinku¹,

Funk

Peterson

et al. 2018

Quart J Royal Meteoro Soc

541

316

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“…This product has been evaluated at a regional (e.g., Poméon, et al [38]) and a global (e.g., Beck, et al [39]) Remote Sens. 2018, 10, 1316 5 of 22 scale and showed reliable performance.…”

Section: Satellite Precipitation Productsmentioning

confidence: 99%

“…TRMM aims to provide the "best" estimate of quasi-global precipitation, which is equipped with multiple rainfall sensors, including a precipitation radar, a TRMM microwave imager, and a visible and infrared radiometer [22,27]. It was developed originally for rainfall retrievals in the tropics and has been extended to a quasi-global scale [38]. TRMM provides two forms of precipitation products: a near real-time product with a delay of several hours from the observation time and a gauge-adjusted product with a delay of 2-3 months from the observation time.…”

Section: Satellite Precipitation Productsmentioning

confidence: 99%

“…The hydrological evaluations of the precipitation products are just based on the hypothesis that errors in the precipitation inputs can propagate into the hydrological simulations [26,59]. Compared with the point-to-pixel validation, evaluating precipitation products based on their predictability of streamflow is attractive, as it is performed at the basin scale and avoids the scale discrepancy problem using gauge observations for validation [38]. In this study, we compared the model performances for streamflow simulations driven by a gauge-based dataset and five precipitation products.…”

Section: Hydrological Evaluations Of the Satellite Precipitation Prodmentioning

confidence: 99%

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Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

Bai

Liu

2018

Remote Sensing

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Abstract:The sparse rain gauge networks over the Tibetan Plateau (TP) cause challenges for hydrological studies and applications. Satellite-based precipitation datasets have the potential to overcome the issues of data scarcity caused by sparse rain gauges. However, large uncertainties usually exist in these precipitation datasets, particularly in complex orographic areas, such as the TP. The accuracy of these precipitation products needs to be evaluated before being practically applied. In this study, five (quasi-)global satellite precipitation products were evaluated in two gauge-sparse river basins on the TP during the period 1998-2012; the evaluated products are CHIRPS, CMORPH, PERSIANN-CDR, TMPA 3B42, and MSWEP. The five precipitation products were first intercompared with each other to identify their consistency in depicting the spatial-temporal distribution of precipitation. Then, the accuracy of these products was validated against precipitation observations from 21 rain gauges using a point-to-pixel method. We also investigated the streamflow simulation capacity of these products via a distributed hydrological model. The results indicated that these precipitation products have similar spatial patterns but significantly different precipitation estimates. A point-to-pixel validation indicated that all products cannot efficiently reproduce the daily precipitation observations, with the median Kling-Gupta efficiency (KGE) in the range of 0.10-0.26. Among the five products, MSWEP has the best consistency with the gauge observations (with a median KGE = 0.26), which is thus recommended as the preferred choice for applications among the five satellite precipitation products. However, as model forcing data, all the precipitation products showed a comparable capacity of streamflow simulations and were all able to accurately reproduce the observed streamflow records. The values of the KGE obtained from these precipitation products exceed 0.83 in the upper Yangtze River (UYA) basin and 0.84 in the upper Yellow River (UYE) basin. Thus, evaluation of precipitation products only focusing on the accuracy of streamflow simulations is less meaningful, which will mask the differences between these products. A further attribution analysis indicated that the influences of the different precipitation inputs on the streamflow simulations were largely offset by the parameter calibration, leading to significantly different evaporation and water storage estimates. Therefore, an efficient hydrological evaluation for precipitation products should focus on both streamflow simulations and the simulations of other hydrological variables, such as evaporation and soil moisture.

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“…As such, the resulting hydrographs represent the optimal model fit to each input precipitation dataset for each catchment. We did not, however, consider a split sample calibration and validation approach as was done in [34] and in common in rainfall-runoff modeling to assess the optimality or robustness of the modeling in this current study. While this is clearly a potential limitation to our methodology, the relatively short length of the data time series available combined with the large amount of data gaps potentially precludes the applicability of a split sample evaluation.…”

Section: Calibration Of Hbv-light and Evaluation Of Utility Of Gpds Imentioning

confidence: 99%

Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania

2017

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This study explored the potential for bias correction of global precipitation datasets (GPD) to support streamflow simulation for water resource management in data limited regions. Two catchments, 580 km 2 and 2530 km 2 , in the Kilombero Valley of central Tanzania were considered as case studies to explore three GPD bias correction methods: quantile mapping (QM), daily percentages (DP) and a model based (ModB) bias correction. The GPDs considered included two satellite rainfall products, three reanalysis products and three interpolated observed data products. The rainfall-runoff model HBV was used to simulate streamflow in the two catchments using (1) observed rain gauge data; (2) the original GPDs and (3) the bias-corrected GPDs as input. Results showed that applying QM to bias correction based on limited observed data tends to aggravate streamflow simulations relative to not bias correcting GPDs. This is likely due to a potential lack of representativeness of a single rain gauge observation at the scale of a hydrological catchment for these catchments. The results also indicate that there may be potential benefits in combining streamflow and rain gauge data to bias correct GPDs during the model calibration process within a hydrological modeling framework.

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Evaluating the performance of remotely sensed and reanalysed precipitation data over West Africa using HBV light

Cited by 86 publications

References 47 publications

Validation of the CHIRPS satellite rainfall estimates over eastern Africa

Validation of the CHIRPS satellite rainfall estimates over eastern Africa

Evaluation of Five Satellite-Based Precipitation Products in Two Gauge-Scarce Basins on the Tibetan Plateau

Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania

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