Search Results (41)

Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs. Full article

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed. Full article

The purpose of this study was to evaluate the applicability of medium and long-term satellite rainfall estimation (SRE) precipitation products for drought monitoring over mainland China. Four medium and long-term (19 a) SREs, i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 post-real time Final Run precipitation products (IMF6), Global Rainfall Map in Near-real-time Gauge-calibrated Rainfall Product (GSMaP_Gauge_NRT) for product version 6 (GNRT6) and gauge-adjusted Global Satellite Mapping of Precipitation V6 (GGA6) were considered. The accuracy of the four SREs was first evaluated against ground observation precipitation data. The Standardized Precipitation Evapotranspiration Index (SPEI) based on four SREs was then compared at multiple temporal and spatial scales. Finally, four typical drought-influenced regions, i.e., the Northeast China Plain (NEC), Huang-Huai-Hai Plain (3HP), Yunnan–Guizhou Plateau (YGP) and South China (SC) were chosen as examples to analyze the ability of four SREs to capture the temporal and spatial changes of typical drought events. The results show that compared with GNRT6, the precipitation estimated by GGA6, IMF6 and 3B42V7 are in better agreement with the ground observation results. In the evaluation using SPEI, the four SREs performed well in eastern China but have large uncertainty in western China. GGA6 and IMF6 perform superior to GNRT6 and 3B42V7 in estimating SPEI and identifying typical drought events and behave almost the same. In general, GPM precipitation products have great potential to substitute TRMM precipitation products for drought monitoring. Both GGA6 and IMF6 are suitable for historical drought analysis. Due to the shorter time latency of data release and good performance in the eastern part of mainland China, GNRT6 and GGA6 might play a role for near real-time drought monitoring in the area. The results of this research will provide reference for the application of the SREs for drought monitoring in the GPM era. Full article

The current study evaluates the potential of merged satellite precipitation datasets (MSPDs) against rain gauges (RGs) and satellite precipitation datasets (SPDs) in monitoring meteorological drought over Pakistan during 2000–2015. MSPDs evaluated in the current study include Regional Weighted Average Least Square (RWALS), Weighted Average Least Square (WALS), Dynamic Clustered Bayesian model Averaging (DCBA), and Dynamic Bayesian Model Averaging (DBMA) algorithms, while the set of SPDs is Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG-V06), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and ERA-Interim (re-analyses dataset). Several standardized precipitation indices (SPIs), including SPI-1, SPI-3, and SPI-12, are used to evaluate the performances of RGs, SPDs, and MSPDs across Pakistan as well as on a regional scale. The Mann–Kendall (MK) test is used to assess the trend of meteorological drought across different climate regions of Pakistan using these SPI indices. Results revealed higher performance of MSPDs than SPDs when compared against RGs for SPI estimates. The seasonal evaluation of SPIs from RGs, MSPDs, and SPDs in a representative drought year (2008) revealed mildly to moderate wetness in monsoon season while mild to moderate drought in winter season across Pakistan. However, the drought severity ranges from mild to severe drought in different years across different climate regions. MAPD (mean absolute percentage difference) shows high accuracy (MAPD <10%) for RWALS-MSPD, good accuracy (10% < MAPD <20%) for WALS-MSPD and DCBA-MSPD, while good to reasonable accuracy (20% < MAPD < 50%) for DCBA in different climate regions. Furthermore, MSPDs show a consistent drought trend as compared with RGs, while SPDs show poor performance. Overall, this study demonstrated significantly improved performance of MSPDs in monitoring the meteorological drought. Full article

Substantial uncertainties are associated with satellite precipitation datasets (SPDs), which are further amplified over complex terrain and diverse climate regions. The current study develops a regional blended precipitation dataset (RBPD) over Pakistan from selected SPDs in different regions using a dynamic weighted average least squares (WALS) algorithm from 2007 to 2018 with 0.25° spatial resolution and one-day temporal resolution. Several SPDs, including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42-v7, Precipitation Estimates from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), ERA-Interim (reanalysis dataset), SM2RAIN-CCI, and SM2RAIN-ASCAT are evaluated to select appropriate blending SPDs in different climate regions. Six statistical indices, including mean bias (MB), mean absolute error (MAE), unbiased root mean square error (ubRMSE), correlation coefficient (R), Kling–Gupta efficiency (KGE), and Theil’s U coefficient, are used to assess the WALS-RBPD performance over 102 rain gauges (RGs) in Pakistan. The results showed that WALS-RBPD had assigned higher weights to IMERG in the glacial, humid, and arid regions, while SM2RAIN-ASCAT had higher weights across the hyper-arid region. The average weights of IMERG (SM2RAIN-ASCAT) are 29.03% (23.90%), 30.12% (24.19%), 31.30% (27.84%), and 27.65% (32.02%) across glacial, humid, arid, and hyper-arid regions, respectively. IMERG dominated monsoon and pre-monsoon seasons with average weights of 34.87% and 31.70%, while SM2RAIN-ASCAT depicted high performance during post-monsoon and winter seasons with average weights of 37.03% and 38.69%, respectively. Spatial scale evaluation of WALS-RPBD resulted in relatively poorer performance at high altitudes (glacial and humid regions), whereas better performance in plain areas (arid and hyper-arid regions). Moreover, temporal scale performance assessment depicted poorer performance during intense precipitation seasons (monsoon and pre-monsoon) as compared with post-monsoon and winter seasons. Skill scores are used to quantify the improvements of WALS-RBPD against previously developed blended precipitation datasets (BPDs) based on WALS (WALS-BPD), dynamic clustered Bayesian model averaging (DCBA-BPD), and dynamic Bayesian model averaging (DBMA-BPD). On the one hand, skill scores show relatively low improvements of WALS-RBPD against WALS-BPD, where maximum improvements are observed in glacial (humid) regions with skill scores of 29.89% (28.69%) in MAE, 27.25% (23.89%) in ubRMSE, and 24.37% (28.95%) in MB. On the other hand, the highest improvements are observed against DBMA-BPD with average improvements across glacial (humid) regions of 39.74% (36.93%), 38.27% (33.06%), and 39.16% (30.47%) in MB, MAE, and ubRMSE, respectively. It is recommended that the development of RBPDs can be a potential alternative for data-scarce regions and areas with complex topography. Full article

Although the Tropical Rainfall Measurement Mission (TRMM) has come to an end, the evaluation of TRMM satellite precipitation is still of great significance for the improvement of the Global Precipitation Measurement (GPM). In this paper, the hydrological utility of TRMM Multi-satellite Precipitation Analysis (TMPA) 3B42 RTV7/V7 precipitation products was evaluated using the variable infiltration capacity (VIC) hydrological model in the upper Yangtze River basin. The main results show that (1) TMPA 3B42V7 had a reliable performance in precipitation estimation compared with the gauged precipitation on both spatial and temporal scales over the upper Yangtze River basin. Although TMPA 3B42V7 slightly underestimated precipitation, TMPA 3B42RTV7 significantly overestimated precipitation at daily and monthly time scales; (2) the simulated runoff by the VIC hydrological model showed a high correlation with the gauged runoff and lower bias at daily and monthly time scales. The Nash–Sutcliffe coefficient of efficiency (NSCE) value was as high as 0.85, the relative bias (RB) was −6.36% and the correlation coefficient (CC) was 0.93 at the daily scale; (3) the accuracy of the 3B42RTV7-driven runoff simulation had been greatly improved by using the hydrological calibration parameters obtained from 3B42RTV7 compared with that of gauged precipitation. A lower RB (14.38% vs. 66.58%) and a higher CC (0.87 vs. 0.85) and NSCE (0.71 vs. −0.92) can be found at daily time scales when we use satellite data instead of gauged precipitation data to calibrate the VIC model. However, the performance of the 3B42V7-driven runoff simulation did not improve in the same operation accordingly. The cause might be that the 3B42V7 satellite products have been adjusted by gauged precipitation. This study suggests that it might be better to calibrate the parameters using satellite data in hydrological simulations, especially for unadjusted satellite data. This study is not only helpful for understanding the assessment of multi-satellite precipitation products in large-scale and complex areas in the upper reaches of the Yangtze River, but also can provide a reference for the hydrological utility of the satellite precipitation products in other river basins of the world. Full article

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR. Full article

We present a new remote sensing based method to estimate dissolved organic carbon (DOC) flux discharged from rivers into coastal waters off the Sarawak region in Borneo. This method comprises three steps. In the first step, we developed an algorithm for estimating DOC concentrations using the ratio of Landsat-8 Red to Green bands B4/B3 (DOC (μM C) = 89.86 $·e^{0.27·(B4/B3)}$), which showed good correlation (R = 0.88) and low mean relative error (+5.71%) between measured and predicted DOC. In the second step, we used TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data to estimate river discharge for the river basins. In the final step, DOC flux for each river catchment was then estimated by combining Landsat-8 derived DOC concentrations and TMPA derived river discharge. The analysis of remote sensing derived DOC flux (April 2013 to December 2018) shows that Sarawak coastal waters off the Rajang river basin, received the highest DOC flux (72% of total) with an average of 168 Gg C per year in our study area, has seasonal variability. The whole of Sarawak represents about 0.1% of the global annual riverine and estuarine DOC flux. The results presented in this study demonstrate the ability to estimate DOC flux using satellite remotely sensed observations. Full article

Accurate precipitation measurement is very important for socio-hydrological resilience in the face of frequent extreme weather events such as cyclones. This study evaluates the performance of two satellite products: the Tropical Rainfall Measuring Mission (TRMM 3B43V7) Multi-satellite Precipitation Analysis (TMPA, hereafter: TRMM) and the Integrated Multi-satellite Retrievals for GPM (IMERG, Final Run V06, hereafter: GPM) in the Sultanate of Oman. Oman is an arid country that generally has few rainy days, but has experienced significant flash floods, tropical storms and cyclones recently, leading to the loss of lives and millions of dollars in damage. Accurate precipitation analysis is crucial in flood monitoring, hydrologic modeling, and the estimation of the water balance of any basin, and the lack of a sufficient weather monitoring network is a barrier to accurate precipitation measurement. Satellite rainfall estimates can be a reliable option in sparse network areas, especially in arid and semi-arid countries. This study evaluated monthly rainfall (hereafter: OBSERVED) levels at 77 meteorological stations from January 2016 to December 2018. The capacity of the TRMM and GPM satellite products to detect monthly rainfall amounts at varying precipitation thresholds was also evaluated. Findings included (1) overall and across the 11 Governorates of Oman, both satellite products show different spatial variability and performance to the OBSERVED at the monthly, seasonal, and annual temporal scales; (2) from the perspective of precipitation detection and frequency bias, GPM showed a similar performance to TRMM at detecting low precipitation (2 mm/month) but was poorer at detecting high precipitation (>30 mm/month) across the entire country as well as in the Northern, Interior, and Dhofar regions; (3) both products show similarities to the OBSERVED through the partitioning of their seasonal time series into a distinct number of homogenous segments; and (4) both products had difficulty reproducing OBSERVED levels in the Dhofar and Interior regions, which is consistent with studies conducted in mountainous and coastal regions. With the aim of reproducing the spatial and temporal structure of OBSERVED in a rugged terrain, the study shows that both satellite products can be used in areas of sparse rain gauges or as additional observation for studies of extreme weather events. Overall, this study suggests that for Oman, both satellite products can be used as proxies for OBSERVED with appropriate bias corrections and GPM is also a reliable replacement for TRMM as a precipitation satellite product. The findings will be useful to the country’s flood resilience and mitigation efforts, especially in areas where there is sparse rain gauge coverage. Full article

Satellite rainfall estimates (SRFE) are a promising alternative for the lack of reliable, densely distributed, precipitation data common in developing countries and remote locations. SRFE may be significantly improved when corrected based on rain gauge data. In the present study the first complete validation of the Tropical Rainfall Measuring Mission (TRMM) 3B42-based MERGE product is performed by means of ground truthing and hydrological modeling-based applications. Four distinct, highly anthropogenic watersheds were selected in the Upper Paraíba do Sul River Basin (UPSRB)—Brazil. The results show that when compared to TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 at the watershed scale, MERGE has a higher correlation with observed data. Likewise, root mean square errors and bias are significantly lower for MERGE products. When hydrologically validated, MERGE-based streamflow simulations have shown the capacity of reproducing the overall hydrological regime with “good” to “very good” results for the downstream lowland sections. Limitations were observed in the hydrological modeling of the upstream, highly anthropogenic, dammed watersheds. However, such limitations may not be attributed to MERGE precipitation since they were also obtained for the individually calibrated rain gauge-based simulations. The results indicate that the used MERGE dataset as a hydrological model input is better suited for application in the UPSRB than the TMPA 3B42V7. Full article

Merging satellite precipitation products tends to reduce the errors associated with individual satellite precipitation products and has higher potential for hydrological applications. The current study evaluates the performance of merged multi-satellite precipitation dataset (daily temporal and 0.25° spatial resolution) developed using the Dynamic Bayesian Model Averaging algorithm across four different climate regions, i.e., glacial, humid, arid and hyper-arid regions, of Pakistan during 2000–2015. Four extensively evaluated SPPs over Pakistan, i.e., Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Climate Prediction Center MORPHing technique (CMORPH), and Era-Interim, are used to develop the merged multi-satellite precipitation dataset. Six statistical indices, including Mean Bias Error, Mean Absolute Error, Root Mean Square Error, Correlation Coefficient, Kling-Gupta efficiency, and Theil’s U coefficient, are used to evaluate the performance of merged multi-satellite precipitation dataset over 102 ground precipitation gauges both spatially and temporally. Moreover, the ensemble spread score and standard deviation are also used to depict the spread and variation of precipitation of merged multi-satellite precipitation dataset. Skill scores for all statistical indices are also included in the analyses, which shows improvement of merged multi-satellite precipitation dataset against Simple Model Averaging. The results revealed that DBMA-MSPD assigned higher weights to TMPA (0.32) and PERSIANN-CDR (0.27). TMPA presented higher skills in glacial and humid regions with average weights of 0.32 and 0.37 as compared to PERSIANN-CDR of 0.27 and 0.25, respectively. TMPA and Era-Interim depicted higher skills during pre-monsoon and monsoon seasons, with average weights of 0.31 and 0.52 (TMPA) and 0.25 and 0.21 (Era-Interim), respectively. Merged multi-satellite precipitation dataset overestimated precipitation in glacial/humid regions and showed poor performance, with the poorest values of mean absolute error (2.69 mm/day), root mean square error (11.96 mm/day), correlation coefficient (0.41), Kling-Gupta efficiency score (0.33) and Theil’s U (0.70) at some stations in glacial/humid regions. Higher performance is observed in hyper-arid region, with the best values of 0.71 mm/day, 1.72 mm/day, 0.84, 0.93, and 0.37 for mean absolute error, root mean square error, correlation coefficient, Kling-Gupta Efficiency score, and Theil’s U, respectively. Merged multi-Satellite Precipitation Dataset demonstrated significant improvements as compared to TMPA across all climate regions with average improvements of 45.26% (mean bias error), 30.99% (mean absolute error), 30.1% (root mean square error), 11.34% (correlation coefficient), 9.53% (Kling-Gupta efficiency score) and 8.86% (Theil’s U). The ensemble spread and variation of DBMA-MSPD calculated using ensemble spread score and standard deviation demonstrates high spread (11.38 mm/day) and variation (12.58 mm/day) during monsoon season in the humid and glacial regions, respectively. Moreover, the improvements of DBMA-MSPD quantified against fixed weight SMA-MSPD reveals supremacy of DBMA-MSPD, higher improvements (40–50%) in glacial and humid regions. Full article

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA. Full article

Determining rainfall thresholds for landsliding is crucial in landslide hazard evaluation and early warning system development, yet challenging in data-scarce regions. Using freely available satellite rainfall data in a reproducible automated procedure, the bootstrap-based frequentist threshold approach, coupling antecedent rainfall (AR) and landslide susceptibility data as proposed by Monsieurs et al., has proved to provide a physically meaningful regional AR threshold equation in the western branch of the East African Rift. However, previous studies could only rely on global- and continental-scale rainfall and susceptibility data. Here, we use newly available regional-scale susceptibility data to test the robustness of the method to different data configurations. This leads us to improve the threshold method through using stratified data selection to better exploit the data distribution over the whole range of susceptibility. In addition, we discuss the effect of outliers in small data sets on the estimation of parameter uncertainties and the interest of not using the bootstrap technique in such cases. Thus improved, the method effectiveness shows strongly reduced sensitivity to the used susceptibility data and is satisfyingly validated by new landslide occurrences in the East African Rift, therefore successfully passing first transferability tests. Full article

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coefficient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates. Full article

Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000–2015 and 2007–2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil’s U, and the modified Kling–Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25° scale during 2007–2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil’s U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil’s U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil’s U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region. Full article