A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products

Abstract

Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.

Climate-dependent propagation of precipitation uncertainty into the water cycle

“…The climate-dependent (propagation of) precipitation uncertainties illustrates that there is no best overall product but instead a careful regional, climate-based selection can support hydrological applications. Overall, these findings highlight the usefulness of streamflow measurements capturing truly large-scale hydrological dynamics, which can then be used to make inferences on the accuracy of precipitation data sets (Behrangi et al, 2011;Thiemig et al, 2013;Beck et al, 2017aBeck et al, , 2019aEhsan Bhuiyan et al, 2019;Mazzoleni et al, 2019;Alnahit et al, 2020;Arheimer et al, 2020).…”

“…Across these data sets there are ample discrepancies in space and time, highlighting the need for comparative assessments (e.g., Koutsouris et al, 2016;Alijanian et al, 2017Alijanian et al, , 2019Balsamo et al, 2018;Sun et al, 2018;Massari et al, 2020;Brocca et al, 2019;Sharifi et al, 2019;Caroletti et al, 2019;Levizzani and Cattani, 2019;Roca et al, 2019;Fallah et al, 2020;Satgé et al, 2020;Contractor et al, 2020;Xu et al, 2020;Zhou et al, 2020). In particular, indirect evaluation of the data sets through application in hydrological modeling is a valuable alternative in this context as precipitation is translated into variables with more reliable observations, such as runoff, as long as runoff is measured in catchments with near-natural dynamics (Thiemig et al, 2013;Nerini et al, 2015;Beck et al, 2017aBeck et al, , b, 2019aFereidoon et al, 2019;Ehsan Bhuiyan et al, 2019;Mazzoleni et al, 2019;Arheimer et al, 2020;Dembélé et al, 2020). However, while this approach relies on the propagation of precipitation uncertainty into runoff, it is largely underexplored with respect to when and where this propagation pathway is active.…”

Rethinking Satellite Data Merging: From Averaging to SNR Optimization

“…Satellite datasets which have previously been considered for merging include soil moisture [40][41][42][43][44][45], sea surface temperature [18,46], precipitation [9,11,47], and others [12,13]. In most of these merging applications, the weighted average scheme [3] is used for its simplicity and compatibility with TC-based error estimation.…”

“…The key idea behind weighted averaging is to take independent information from data sources in hope of deriving an improved prediction via cancellation of random errors, the effectiveness of which depends on the independence of the separate data sources considered [3]. Following the work of Bates and Granger [3] who proposed optimal combination of forecasts using a mean square error (MSE) criterion, weighted averaging has been used in various fields of research including economics [4][5][6], ecology [7,8], hydrometeorology [9][10][11] and others [12,13]. While data merging using deep learning-based techniques have become more popular of late [14,15], weighted averaging continues to be extremely useful for its simplicity and applicability when data is limited, and interpretability of results important in decision and policy making [6,16].…”

Comment on hess-2021-42

“…In order to test and highlight the added value of integrating rainfall estimates obtained through different approaches for landslides forecasting, two additional merged products obtained through the merging of SM2R and IMERG-ER are created and used as input for determining the rainfall thresholds. The integration between these two different products has already been tested satisfactorily (Ciabatta et al, 2017;Massari et al, 2020). The integration allows to take benefit of the capabilities of each approach and to limit the drawbacks, i.e.…”