Search Results (4)

Satellite precipitation products (SPPs) have emerged as an alternative to estimate rainfall erosivity. However, prior studies showed that SPPs tend to underestimate rainfall erosivity but without reported bias-correction methods. This study evaluated the efficacy of two SPPs, namely, GPM_3IMERGHH (30-min and 0.1°) and GPM_3IMERGDF (daily and 0.1°), in estimating two erosivity indices in mainland China: the average annual rainfall erosivity (R-factor) and the 10-year event rainfall erosivity (10-yr storm EI), by comparing with that derived from gauge-observed hourly precipitation (Gauge-H). Results indicate that GPM_3IMERGDF yields higher accuracy than GPM_3IMERGHH, though both products generally underestimate these indices. The Percent Bias (PBIAS) is −55.48% for the R-factor and −56.38% for the 10-yr storm EI using GPM_3IMERGHH, which reduces to −10.86% and −32.99% with GPM_3IMERGDF. A bias-correction method was developed based on the systematic difference between SSPs and Gauge-H. A five-fold cross validation shows that with bias-correction, the accuracy of the R-factor and 10-yr storm EI for both SPPs improve considerably, and the difference between two SSPs is reduced. The PBIAS using GPM_3IMERGHH decreases to −0.06% and 0.01%, and that using GPM_3IMERGDF decreases to −0.33% and 0.14%, respectively, for the R-factor and 10-yr storm EI. The rainfall erosivity estimated with SPPs with bias-correction shows comparable accuracy to that obtained through Kriging interpolation using Gauge-H and is better than that interpolated from gauge-observed daily precipitation. Given their high temporal and spatial resolution, and timely updates, GPM_3IMERGHH and GPM_3IMERGDF are viable data products for rainfall erosivity estimation with bias correction. Full article

Gridding precipitation datasets for climate information services in the semi-arid regions of West Africa has some advantages due to the limited spatial coverage of rain gauges, the limited accessibility to in situ gauge data, and the important progress in earth observation and climate modelling systems. Can accurate information on the occurrence of heavy precipitation in this area be provided using gridded datasets? Furthermore, what about the future of heavy rain events (HRE) under the shared socioeconomic pathways (SSP) of the Inter-Sectoral Impact Model Intercomparison Project (i.e., SSP126 and SSP370)? To address these questions, daily precipitation records from 17 datasets, including satellite estimates, interpolated rain gauge data, reanalysis, merged products, a regional climate model, and global circulation models, are examined and compared to quality-controlled in situ data from 69 rain gauges evenly distributed across West Africa’s semi-arid region. The results show a consensus increase in the occurrence of HRE, between observational and gridded data. All datasets showed three categories of HRE every season, but these categories had lower intensities and an overstated frequency of occurrence in gridded datasets compared to in situ rain gauge data. Eight out of 17 databases (~47%) show significant positive trends and only one showed a significant negative trend, indicating an increase in HRE for all categories in this region. The future evolution of HRE considered under the shared socioeconomic pathways SSP1-2.6 and SSP3-7.0, showed a trend toward the intensification of these events. In fact, the mean of the ensemble of the models showed significant changes toward higher values in the probability distribution function of the future HRE in West Africa, which may likely trigger more floods and landslides in the region. The use of gridded data sets can provide accurate information on the occurrence of heavy precipitation in the West African Sahel. However, it is important to consider the representation of heavy rain events in each data set when monitoring extreme precipitation, although in situ gauge records are preferred to define extreme rainfall locally. Full article

Floods, storms and hurricanes are devastating for human life and agricultural cropland. Near-real-time (NRT) discharge estimation is crucial to avoid the damages from flood disasters. The key input for the discharge estimation is precipitation. Directly using the ground stations to measure precipitation is not efficient, especially during a severe rainstorm, because precipitation varies even in the same region. This uncertainty might result in much less robust flood discharge estimation and forecasting models. The use of satellite precipitation products (SPPs) provides a larger area of coverage of rainstorms and a higher frequency of precipitation data compared to using the ground stations. In this paper, based on SPPs, a new NRT flood forecasting approach is proposed to reduce the time of the emergency response to flood disasters to minimize disaster damage. The proposed method allows us to forecast floods using a discharge hydrograph and to use the results to map flood extent by introducing SPPs into the rainfall–runoff model. In this study, we first evaluated the capacity of SPPs to estimate flood discharge and their accuracy in flood extent mapping. Two high temporal resolution SPPs were compared, integrated multi-satellite retrievals for global precipitation measurement (IMERG) and tropical rainfall measurement mission multi-satellite precipitation analysis (TMPA). The two products are evaluated over the Ottawa watershed in Canada during the period from 10 April 2017 to 10 May 2017. With TMPA, the results showed that the difference between the observed and modeled discharges was significant with a Nash–Sutcliffe efficiency (NSE) of −0.9241 and an adapted NSE (ANSE) of −1.0048 under high flow conditions. The TMPA-based model did not reproduce the shape of the observed hydrographs. However, with IMERG, the difference between the observed and modeled discharges was improved with an NSE equal to 0.80387 and an ANSE of 0.82874. Also, the IMERG-based model could reproduce the shape of the observed hydrographs, mainly under high flow conditions. Since IMERG products provide better accuracy, they were used for flood extent mapping in this study. Flood mapping results showed that the error was mostly within one pixel compared with the observed flood benchmark data of the Ottawa River acquired by RadarSat-2 during the flood event. The newly developed flood forecasting approach based on SPPs offers a solution for flood disaster management for poorly or totally ungauged watersheds regarding precipitation measurement. These findings could be referred to by others for NRT flood forecasting research and applications. Full article

Satellite precipitation products (SPPs) are critical data sources for hydrological prediction and extreme event monitoring, especially for ungauged basins. This study conducted a comprehensive hydrological evaluation of six mainstream SPPs (i.e., TMPA 3B42RT, CMORPH-RT, PERSIANN-RT, TMPA 3B42V7, CMORPH-CRT, and PERSIANN-CDR) over humid Xixian basin in central eastern China for a period of 14 years (2000–2013). The evaluation specifically focused on the performance of the six SSPs in capturing precipitation and streamflow extremes. Results show that the two post-real-time research products of TMPA 3B42V7 and CMORPH-CRT exhibit much better performance than that of their corresponding real-time SPPs for precipitation estimation at daily and monthly time scales. By contrast, the newly released post-real-time research product PERSIANN-CDR insignificantly improves precipitation estimates compared with the real-time PERSIANN-RT does at daily time scale. The daily streamflow simulation of TMPA 3B42V7 fits best with the observed streamflow series among those of the six SPPs. The three month-to-month gauge-adjusted post-real-time research products can simulate acceptable monthly runoff series. TMPA 3B42V7 and CMORPH-CRT present good performance in capturing precipitation and streamflow extremes, although they still exhibit non-ignorable deviation and occurrence time inconsistency problems compared with gauge-based results. Caution should be observed when using the current TMPA, CMORPH, and PERSIANN products for monitoring and predicting extreme precipitation and flood at such medium-sized basin. This work will be valuable for the utilization of SPPs in extreme precipitation monitoring, streamflow forecasting, and water resource management in other regions with similar climate and topography characteristics. Full article