The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981–2016)

Awange, Joseph; Hu, K.X.; Khaki, Mehdi

doi:10.1016/j.scitotenv.2019.03.148

Cited by 95 publications

(49 citation statements)

References 60 publications

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“…A dynamical scaling function (F DS ) (cf. Demirel et al, 2018) is used to account for vegetation-climate interactions (Bai et al, 2018;Jiao et al, 2017). E p is formulated as follows:…”

Section: Hydrological Model Set-upmentioning

confidence: 99%

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Dembélé

Schaefli

Giesen

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatio-temporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River basin (VRB) in West Africa. Seventeen precipitation products based essentially on gauge-corrected satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42 and TRMM 3B42RT) and on reanalysis (ERA5, PGF, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2 and JRA-55) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall–temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in situ streamflow data and satellite remote-sensing datasets from GLEAM evaporation, ESA CCI soil moisture and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB. The top three best-performing rainfall datasets are TAMSAT, CHIRPS and PERSIANN-CDR for streamflow; ARC, RFE and CMORPH-CRT for terrestrial water storage; MERRA-2, EWEMBI/WFDEI-GPCC and PGF for the temporal dynamics of soil moisture; MSWEP, TAMSAT and ARC for the spatial patterns of soil moisture; ARC, RFE and GSMaP-std for the temporal dynamics of actual evaporation; and MSWEP, TAMSAT and MERRA-2 for the spatial patterns of actual evaporation. No single rainfall or temperature dataset consistently ranks first in reproducing the spatio-temporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets, which are increasingly becoming an alternative to in situ measurements in data-scarce regions.

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“…A dynamical scaling function (F DS ) (cf. Demirel et al, 2018) is used to account for vegetation-climate interactions (Bai et al, 2018;Jiao et al, 2017). E p is formulated as follows:…”

Section: Hydrological Model Set-upmentioning

confidence: 99%

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Dembélé

Schaefli

Giesen

et al. 2020

Hydrol. Earth Syst. Sci.

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“…Compared with MSWEP V2.1, the GWRMP scattered points in entire basin and different subareas are more concentrated near the regression line, and abnormal points that deviate significantly from the regression line are significantly reduced, indicating that the ability of GWRMP to explain the changes in surface daily precipitation is significantly higher than that of the MSWEP V2.1 without merged benchmark precipitation. MSWEP V2.1 has an underestimation of daily precipitation, especially for the heavy precipitation, which is a universal systematic error in precipitation products, and this problem has been verified in many studies [9][10][11][18][19][20]. In the process of the fusion of precipitation data by GWR model, the underestimation by MSWEP V2.1 was corrected using the precipitation at the surface rainfall gauges, but since the MSWEP V2.1 is smaller than the measured precipitation as a whole, the calculated error at the rain gauges is mainly positive error, and when GWR is used for error space interpolation, some grids may introduce unnecessary error, and when superimposed with the background field of MSWEP V2.1, a systematic error in GWRMP was introduced for some grids.…”

Section: Lake-effect On Precipitation Diagnosismentioning

confidence: 85%

“…has a higher spatial-temporal resolution (3 h, 0.1 • × 0.1 • ), longer time series (1979-present). Many pieces of research [19][20][21] on the daily precipitation accuracy of MSWEP are conducted based on the dense rain gauges in different regional areas, and the results showed that MSWEP is overall highly consistent with the surface observation precipitation and has higher precision than TRMM 3B42V7. The researches on MSWEP in different countries such as India [22], Iran [23] and China [24] under different time scales and different levels of rainfall events show that MSWEP has a slightly weaker ability of monitoring extreme precipitation, but possesses a generally higher accuracy of daily precipitation and has a great potential for the analysis of global and regional precipitation and hydrological simulation [25,26].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Lake-Effect on Precipitation in the Taihu Lake Basin Based on the GWR Merged Precipitation

Zhao

Yang

et al. 2020

Water

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Based on the high-density gauged rainfall, the geographically weighted regression (GWR) was used to fuse the daily precipitation of rain gauges with those of Multi-source Weighted-Ensemble Precipitation V2.1 (MSWEP V2.1) and a new merged daily precipitation was generated (referred to as GWR merged precipitation, denoted by GWRMP). Then, the precipitation accuracy at 0.1° × 0.1° grid scale and the lake-effect on precipitation in the Taihu Lake Basin were investigated. Results show that GWRMP is characterized with higher precision and stronger spatial recognition ability compared with MSWEP in the whole basin at 0.1° × 0.1° grid scale, and lake area with a relatively sparse network of rain gauges is no exception. Topography is the most important influencing factor of rainfall in the Taihu Lake Basin, the Pearson correlation coefficient (r) between DEM and the main precipitation type (EOF-1) in the whole basin is 0.64, resulting in a rainy area in the southwestern mountain, and less rain at plain and lake area based on the GWRMP. The multi-year average precipitation in the lake upwind area is 8.31% lower than that in the downwind area. Different with the influence mechanism of precipitation in the southwestern mountainous area characterized by high consistency between the spatial distribution of precipitation and the climatic elements derive from the ERA5 meteorological reanalysis data (|r| > 0.6), there is a lower consistency in the lake downwind area (|r| < 0.5) and no consistency in the lake upwind area at the 0.25° × 0.25° grid scale. The southeast monsoon is deduced as the most important factor affecting the procedure of lake-effect on precipitation in the Taihu Lake Basin. The distribution of wind direction and wind speed determines the dynamic changes of surface water vapor to a certain extent, and the lake-effect on precipitation is most likely occurs in July.

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“…PERSIANN tended to underestimate rainfall by 43%, while CMORPH tends to underestimate by 11% and TMPA 3B42RT tends to overestimate by 5% [43]. A study that evaluated the MSWEP rainfall reanalysis product over Africa found that it had no obvious advantages compared to Global Precipitation Climatology Centre (GPCC), CHIRPS or Agricultural Climate Forecast System Reanalysis (AgCFSR) [44]. In particular, MSWEP was unable to capture major hydro-climate extremes over west, east and southern Africa, where it underestimated compared to CHIRPS [44].…”

Section: Box Plotsmentioning

confidence: 99%

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia

Alemu

Wimberly

2020

Sensors

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Despite the sparse distribution of meteorological stations and issues with missing data, vector-borne disease studies in Ethiopia have been commonly conducted based on the relationships between these diseases and ground-based in situ measurements of climate variation. High temporal and spatial resolution satellite-based remote-sensing data is a potential alternative to address this problem. In this study, we evaluated the accuracy of daily gridded temperature and rainfall datasets obtained from satellite remote sensing or spatial interpolation of ground-based observations in relation to data from 22 meteorological stations in Amhara Region, Ethiopia, for 2003–2016. Famine Early Warning Systems Network (FEWS-Net) Land Data Assimilation System (FLDAS) interpolated temperature showed the lowest bias (mean error (ME) ≈ 1–3 °C), and error (mean absolute error (MAE) ≈ 1–3 °C), and the highest correlation with day-to-day variability of station temperature (COR ≈ 0.7–0.8). In contrast, temperature retrievals from the blended Advanced Microwave Scanning Radiometer on Earth Observing Satellite (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) passive microwave and Moderate-resolution Imaging Spectroradiometer (MODIS) land-surface temperature data had higher bias and error. Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) rainfall showed the least bias and error (ME ≈ −0.2–0.2 mm, MAE ≈ 0.5–2 mm), and the best agreement (COR ≈ 0.8), with station rainfall data. In contrast FLDAS had the higher bias and error and the lowest agreement and Global Precipitation Mission/Tropical Rainfall Measurement Mission (GPM/TRMM) data were intermediate. This information can inform the selection of geospatial data products for use in climate and disease research and applications.

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The newly merged satellite remotely sensed, gauge and reanalysis-based Multi-Source Weighted-Ensemble Precipitation: Evaluation over Australia and Africa (1981–2016)

Cited by 95 publications

References 60 publications

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Suitability of 17 gridded rainfall and temperature datasets for large-scale hydrological modelling in West Africa

Analysis of the Lake-Effect on Precipitation in the Taihu Lake Basin Based on the GWR Merged Precipitation

Evaluation of Remotely Sensed and Interpolated Environmental Datasets for Vector-Borne Disease Monitoring Using In Situ Observations over the Amhara Region, Ethiopia

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