Reliability of <scp>SM2RAIN</scp> precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Satgé, Frederic; Hussain, Yawar; Molina‐Carpio, Jorge; Zolá, Ramiro Pillco; Laugner, Coralie; Akhter, Gulraiz; Bonnet, Marie‐Paule

doi:10.1002/joc.6704

Cited by 11 publications

(14 citation statements)

References 85 publications

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“…Under the influence of multiple factors, the inversion data of SM2RAIN precipitation is relatively high in summer. [34]. Besides, through time comparison and analysis of runoff simulation results, it can be seen that SM2RAIN still has a certain application potential in the Qaraqash River basin.…”

Section: Discussionmentioning

confidence: 91%

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Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Wang

Chen

et al. 2020

Remote Sensing

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Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

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Section: Discussionmentioning

confidence: 91%

“…SM2RAIN is a product that estimates precipitation based on soil moisture. At present, studies have shown that SM2RAIN can well capture the precipitation characteristics under drought conditions [33,34]. Brocca et al also pointed out that the performance of SM2RAIN in some areas lacking precipitation data is better than IMERG and GPCC [36].…”

Section: Introductionmentioning

confidence: 99%

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Wang

Chen

et al. 2020

Remote Sensing

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“…In contrast, similar β and Vr values were observed for both wet and dry months. This seasonal variation is linked to the monthly precipitation total distribution [45]. Across Nigeria, the monthly precipitation totals are well (poorly) distributed between the maximum and minimum values during the dry season (wet season) (Figure 1).…”

Section: Spp Reliability On the National Scalementioning

confidence: 97%

“…However, such action prevents a robust comparison, as SPP sensitivity to the regional meteorological, topographic, and land cover characteristics induce substantial space and time reliability variation [26,43,44]. A recent study comparing seven satellite-based precipitation datasets over two distinct areas with similar precipitation patterns (i.e., South American Andean Plateau and Pakistan) shows that the most reliable SPP from one region to another is different [45].…”

Section: Precipitation Monitoring Across Remote Regionsmentioning

confidence: 99%

From TRMM to GPM: How Reliable Are Satellite-Based Precipitation Data across Nigeria?

et al. 2020

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In this study, 16 satellite-based precipitation products (SPPs) comprising satellite, gauge and reanalysis datasets were assessed on a monthly time step using precipitation data from 11 gauge stations across Nigeria within the 2000–2012 period as reference. Despite the ability of some of the SPPs to reproduce the salient north–south pattern of the annual rainfall field, the Kling–Gupta efficiency (KGE) results revealed substantial discrepancies among the SPP estimates. Generally, the SPP reliability varies spatially and temporally, with all SPPs performing better over part of central Nigeria during the dry season. When we compared the real-time and adjusted satellite-based products, the results showed that the adjusted products had a better KGE score. The assessment also showed that the reliability of integrated multi-satellite retrievals for Global Precipitation Mission (IMERG) products was consistent with that of their predecessor Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). Finally, the best overall scores were obtained from multi-source weighted-ensemble precipitation (MSWEP) v.2.2 and IMERG-F v.6. Both products are therefore suggested for further hydrological studies.

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“…At the catchment outlet, the best models produced values of KGE of $0.65 and PBIAS of $6% for the MSWEP forcing dataset, KGE of $0.68 and PBIAS of $7%, for the IMERG dataset, and KGE of $0.66 and PBIAS of À1.7% for ERAforced models. Given the complex hydrological conditions of the basin and the large-scale gridded datasets used to force the hydrological model(Bain et al, 2023;Barkhordari et al, 2023;Beck et al, 2020;Satgé et al, 2021), these evaluation results suggest that calibrated DRYP models are skilful at simulating streamflow at the catchment outlet of the Ewaso Ng'iro basin.The calibrated DRYP models well capture the temporal variation of streamflow at the basin outlet (Archer's post, Figure1a) for all three forcing datasets (Figure2d), for example, during the large flow events in March 2003, December 2007 and December 2012, although they fail to well capture other flow events (e.g., December 2004, March 2010). Overall, there are high correlations between modelled and measured streamflow for all rainfall forcing datasets (Q MSWEP : r = 0.70, p < 0.001; Q ERA : r = 0.78, p < 0.001, and Q IMERG : r = 0.74, p < 0.001).…”

mentioning

confidence: 98%

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

Quichimbo,

Singer,

Michaelides

et al. 2023

Hydrological Processes

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Precipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.

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Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions

Cited by 11 publications

References 85 publications

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

From TRMM to GPM: How Reliable Are Satellite-Based Precipitation Data across Nigeria?

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

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