TMPA 3B42V7 Research Articles

Evaluation of CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6 Satellite Precipitation Datasets in Arabian Arid Regions

Rainfall depth is a crucial parameter in water resources and hydrological studies. Rain gauges provide the most reliable point-based rainfall estimates. However, they do not have a proper density/distribution to provide sufficient rainfall measurements in many areas, especially in arid regions. To evaluate the adequacy of satellite datasets as an alternative to the rain gauges, the Kingdom of Saudi Arabia (KSA) is selected for the current study as a representative of the arid regions. KSA occupies most of the Arabian Peninsula and is characterized by high variability in topographic and climatic conditions. Five satellite precipitation datasets (SPDSs)—CMORPH, PERSIANN-CDR, CHIRPS V2.0, TMPA 3B42 V7, and GPM IMERG V6—are evaluated versus 324 conventional rain-gauges’ daily precipitation measures. The evaluation is conducted based on nine quantitative and categorical metrics. The evaluation analysis is carried out for daily, monthly, yearly, and maximum yearly records. The daily analysis revealed a low correlation for all SPDSs (<0.31), slightly improved in the yearly and maximum yearly analysis and reached its highest value (0.58) in the monthly analysis. The GPM IMERG V6 and PERSIANN-CDR have the highest probability of detection (0.55) but with a high false alarm ratio (>0.8). Accordingly, in arid regions, the use of daily SPDSs in rainfall estimation will lead to high uncertainty in the obtained results. The best performance for all statistical metrics was found at 500–750 m altitudes in the central and northern parts of the study area for all satellites except minor anomalies. CMORPH dataset has the lowest centered root mean square error (RMSEc) for all analysis periods with the best results in the monthly analyses.

Using SEEPS with a TRMM-Derived Climatology to Assess Global NWP Precipitation Forecast Skill

Abstract Monitoring precipitation forecast skill in global numerical weather prediction (NWP) models is an important yet challenging task. Rain gauges are inhomogeneously distributed, providing no information over large swathes of land and the oceans. Satellite-based products, on the other hand, provide near-global coverage at a resolution of ∼10–25 km, but limitations on data quality (e.g., biases) must be accommodated. In this paper the stable equitable error in probability space (SEEPS) is computed using a precipitation climatology derived from the Tropical Rainfall Measuring Mission (TRMM) TMPA 3B42 V7 product and a gauge-based climatology and then applied to two global configurations of the Met Office Unified Model (UM). The representativeness and resolution effects on an aggregated SEEPS are explored by comparing the gauge scores, based on extracting the nearest model grid point, with those computed by upscaling the model values to the TRMM grid and extracting the TRMM grid point nearest the gauge location. The sampling effect is explored by comparing the aggregate SEEPS for this subset of ∼6000 locations (dictated by the number of gauges available globally) with all land points within the TRMM region of 50°N and 50°S. The forecast performance over the oceanic areas is compared with performance over land. While the SEEPS computed using the two different climatologies should never be expected to be identical, using the TRMM climatology provides a means of evaluating near-global precipitation using an internally consistent dataset in a climatologically consistent way.

Evaluation gridded precipitation datasets in Indonesia

This preliminary study evaluates ten gridded precipitation datasets in Indonesia, namely APHRODITE, CMORPH, CHIRPS, GFD, SA-OBS, TMPA 3B42 v7, PERSIAN-CDR at 0.25°, moreover GSMaP_NRT V06, GPM-IMERG (Early-Run) V06, and MSWEP V2 at 0.1» in the period of 2003 to 2015. The evaluation focuses on time series bias using metrics such as Mean Error, Coefficient of Variation, Relative Change (Variability), and Kolmogorov-Smirnov test (KS-test) at daily, monthly, seasonal, and annual time scales. The statistical relationship between the precipitation datasets with reference observational data use Taylor diagrams for evaluating the relative skill of the precipitation dataset. The study aims to evaluate the uncertainty of the precipitation datasets compared to rain gauge datasets. Time series bias of SA-OBS and MSWEP have the nearest value to zero as the best score. The relative skill of monthly rainfall based on rainfall typical shows that MSWEP outperformed in regions A and B, GPM-IMERG in C region. GPM-IMERG's relative skill is outperformed than other datasets at annual time scale in Region A and B, while TMPA 3B42 in Region C. The application of existing precipitation datasets is essential to cope with the limitation of rain gauge observations. This study implicates the development of precipitation products in the Indonesia region.

Assessment of Precipitation Error Propagation in Discharge Simulations over the Contiguous United States

AbstractThis study characterizes precipitation error propagation through a distributed hydrological model based on the river basins across the Contiguous United States (CONUS), to better understand the relationship between errors in precipitation inputs and simulated discharge (i.e., P-Q error relationship). The NLDAS-2 precipitation and its simulated discharge are used as the reference to compare with TMPA-3B42 V7, TMPA-3B42RT V7, StageIV, CPC-U, MERRA-2, and MSWEP-2.2 for 1,548 well gauged river basins. The relative errors in multiple conventional precipitation products and their corresponding discharges are analysed for the period of 2002-2013. The results reveal positive linear P-Q error relationships at annual and monthly timescales, and the stronger linearity for larger temporal accumulations. Precipitation errors can be doubled in simulated annual accumulated discharge. Moreover, precipitation errors are strongly dampened in basins characterized by temperate and continental climate regimes, particularly for peak discharges, showing highly nonlinear relationships. Radar-based precipitation product consistently shows dampening effects on error propagation through discharge simulations at different accumulation timescales compared to the other precipitation products. Although basin size and topography also influence the P-Q error relationship and propagation of precipitation errors, their roles depend largely on precipitation products, seasons and climate regimes.

Assessment of Merged Satellite Precipitation Datasets in Monitoring Meteorological Drought over Pakistan

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.

Combining satellite-based rainfall data with rainfall - runoff modelling to simulate low flows in a Southern Andean catchment

Since 2010, Chile has been affected by a long-term drought, often referred to as the Mega-Drought. To adapt to future drought events, we need reliable hydro-climatic information based on robust observations and models. However, reliable hydro-climatic data needed for hydrological modelling of remote Andean catchments are rarely available. Our objective was therefore to identify the most suitable combination of different rainfall estimates with two rainfall-runoff models of varying complexity to reliably simulate low flows related to drought.We forced the widely used SWAT and HBV-light models with four satellite-based rainfall estimates (SREs) (CHIRPSv2, MSWEPv1.2, MSWEPv2.0, TMPA 3B42v7) and inverse distance weighted (IDW) precipitation observations for the main headwater catchment of the Imperial River basin (Araucania, 38°N). Sobol global sensitivity analysis and calibration were carried out using the hydroPSO R package, using two objective functions focused on the performance of low flows (logNSE and low flows KGE).Despite the differing amount and temporal distribution of water, for all four SREs, the Sobol sensitivity analysis resulted in robust identification of key model parameters for all SWAT optimisation runs, with most sensitivity for parameters related to deep groundwater recharge, lateral flow travel time and the SCS runoff curve number. For the HBV-light model, only parameters related to snow-melt and deep groundwater recharge were robustly identified for the wetter SRE products. For both models and the two objective functions, the best efficiencies were obtained with the wetter SREs (MSWEP v1.2 and CHIRPSv2) as well as with IDW.We found that SREs combined with rainfall-runoff modelling provide a valuable instrument to simulate daily discharges for the Upper Imperial River Basin. The resulting models are hence suitable to project low flows and water availability; information that is requested by local decision makers needed to design irrigation systems for the agricultural downstream area and the secure water supply of settlements in the context of future droughts.

On the Performance of Satellite-Based Precipitation Products in Simulating Streamflow and Water Quality During Hydrometeorological Extremes

This study provides a comprehensive evaluation of streamflow and water quality simulated by a hydrological model using three different Satellite Precipitation Products (SPPs) with respect to observations from a dense rain gauge network over the Occoquan Watershed, located in Northern Virginia, suburbs to Washington, D.C., U.S. Eight extreme hydrometeorological events within a 5-year period between 2008 and 2012 are evaluated using SPPs, TMPA 3B42-V7, CMORPH V1. 0, and PERSIANN-CCS, which are based on different retrieval algorithms with varying native spatial and temporal resolutions. A Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model was forced with the three SPPs to simulate output of streamflow (Q), stream temperature (TW), and concentrations of total suspended solids (TSS), orthophosphate phosphorus (OP), total phosphorus (TP), ammonium-nitrate (NH4-N), nitrate-nitrogen (NO3-N), dissolved oxygen (DO), and biochemical oxygen demand (BOD) at six evaluation points within the watershed. Results indicate fairly good agreement between gauge- and SPP-simulated Q for TMPA and CMORPH, however, PERSIANN-simulated Q is lowest among SPPs, due to its inability to accurately measure stratiform precipitation between intense periods of precipitation during an extreme event. Correlations of water quality indicators vary considerably, however, TW has the strongest positive linear relationship compared to other indicators evaluated in this study. SPP-simulated TSS, a flow-dependent variable, has the weakest relationship to gauge-simulated TSS among all water quality indicators, with CMORPH performing slightly better than TMPA and PERSIANN. This study demonstrated that the spatiotemporal variability of SPPs, along with their algorithms to estimate precipitation, have an influence on water quality simulations during extreme hydrometeorological events.

Blending multi-satellite, atmospheric reanalysis and gauge precipitation products to facilitate hydrological modelling

Satellite-retrieved and atmospheric reanalysis precipitation can bridge the spatiotemporal gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in hydrological monitoring and modelling. To correct precipitation occurrence and intensity simultaneously, this study develops a three-stage blending approach to integrate three multi-satellite precipitation datasets (IMERG Final, TMPA 3B42V7 and PERSIANN-CDR), the ERA5 atmospheric reanalysis product and a gauge dataset within a dynamic framework. Firstly, the systematic biases of the four members were individually corrected by combining the local intensity scaling and ratio bias correction methods. Then, the “state weights” used for determining wet/dry events were optimized by evaluating a score function of the four bias-corrected members. Thirdly, the “intensity weights” were optimized using the cuckoo search (CS) algorithm and the Bayesian Model Averaging (BMA) method, respectively. The three-stage blending approach produced dynamic weights varying both spatially and temporally, and the performance was thoroughly evaluated over mainland China. Results show that the three-stage dynamic scheme performs better than individual datasets and two-stage blending methods in terms of all eight statistical metrics, and the CS algorithm outperforms the BMA method in the third stage. By randomly sampling validation sites using K-fold experiments, the developed algorithm also demonstrates a superior performance in ungauged regions. After interpolating and normalizing blending parameters of all gauges to entire domain using ordinary kriging, a new blended precipitation dataset with a daily 0.25° scale was produced. Four hydrological models are forced by blended and primary precipitations in 238 catchments over China, further confirming that the developed approach can facilitate hydrological modelling demonstrated by improving the Kling-Gupta efficiency of simulated streamflow by 12–35%.

Applicability of two satellite-based precipitation products for assessing rainfall erosivity in China

Soil erosion has become one of the most serious environmental problems worldwide, and rainfall is considered a crucial factor in water erosion. Rainfall erosivity is defined as the ability of precipitation to trigger soil erosion. The accurate assessment of rainfall erosivity is essential before taking appropriate measures to stop or slow down water erosion. In this study, we calculated the rainfall erosivity in China using the Xie model and two satellite-based precipitation products (SPPs). Gauge-based data from 2417 stations in China were used for a comparison of the results. We also proposed a procedure to assess the performance of the two SPPs using four statistical metrics and provided recommendations for different sub-regions at different time scales. The results showed that the annual rainfall erosivity based on the IMERG-F and TMPA 3B42-V7 products and the in situ gauge stations were 2014, 1954, and 2138 MJ·mm/(hm2·h·yr), respectively. The spatial correlation between IMERG-F and situ gauge stations is 0.944 and that between the TMPA 3B42-V7 product and situ gauge stations is 0.909. The variation trends of the two were highly similar to those of the gauge-based rainfall erosivity at all time scales. The TMPA 3B42-V7 product is recommended for estimating rainfall erosivity in Haihe River Basin and Huaihe River Basin at monthly scale, in Haihe River Basin and China at seasonal scale, in the Haihe River Basin, Huaihe River Basin, Yellow River Basin at annual scale; while the IMERG-F is recommended for the remaining regions except Continental Basins at the three time scales. Generally, the IMERG-F has broader applicability than the TMPA 3B42-V7 product for estimating rainfall erosivity in China. The results of this study provide a reference for selecting suitable SPPs for rainfall erosivity estimates.

Evaluation of High-Resolution Precipitation Products over Southwest China

AbstractThe evaluation of gridded high-resolution precipitation products (HRPPs) is important in areas with complex topography, because rain gauges that are unevenly and sparsely distributed over an area cannot effectively reflect the spatial variabilities of the precipitation and related extremes in detail. In this study, the applicability of six satellite-based precipitation products (TMPA 3B42V7, IMERG, GSMaP-Gauge, CMORPH-CRT, PERSIANN-CDR, and GPCP) and five gauge-based precipitation products (APHRODITE, CN05.1, GPCC-D, GPCC-M, and CRU) over southwest China from 1998 to 2016 is evaluated by performing a comparison with meteorological station observations. The results show that GPCC-M exhibits the best performances for annual, seasonal, and monthly precipitation, which is supported by the lowest root-mean-square errors (RMSEs) for annual and seasonal precipitation and the lowest normalized root-mean-square error (NRMSE) for monthly precipitation. According to the NRMSE and critical success index (CSI), CN05.1 outperforms the other HRPPs at detecting daily precipitation; however, CN05.1 tends to overestimate the frequencies of light precipitation and underestimate the frequencies of heavy precipitation, which is reflected by the probability density function (PDF) for daily precipitation. The bias ratio (BIAS) and extreme precipitation indices show that IMERG shows numerous advantages over the other HRPPs in detecting extreme precipitation and estimating the precipitation intensity. Such results are helpful for future research on precipitation/extremes and related hydrometeorological disasters that occur throughout southwest China.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

Performance evaluation of satellite- and model-based precipitation products over varying climate and complex topography

Accuracy assessment of precipitation retrievals is a pre-requisite for many hydrological studies as it helps to understand the source and the magnitude of the uncertainty in hydrological response variables, particularly over regions with complex topography. This study evaluates GPM IMERGv05, TMPA 3B42V7, ERA-Interim, and ERA5 precipitation products using 256 ground-based gauge stations between 2014 and 2018 over Turkey known to have complex topography and varying climate. Error statistics, categorical performance indices, and intensity-frequency distribution of the precipitation products are investigated over varying wetness and terrain slope classes. Results show that while the monthly correlation coefficient averaged over all the products is 0.79 for regions having slopes between 5 and 10%, this value drops to 0.74 when the slope is above 15%. IMERG, TMPA and ERA-Interim products underestimate the observed precipitation over relatively wetter regions, while they overestimate over relatively drier regions and relatively higher slopes. ERA5 consistently overestimates the observed precipitation over all the wetness and slope classes. Overall, TMPA shows the smallest wet bias (0.1 mm/day), while ERA5 has the largest wet bias (0.5 mm/day). IMERG has the highest monthly correlation (i.e., 0.82 when averaged for all 256 stations) and ERA-Interim has the lowest correlation (0.77) against the ground-based observations. The successors (IMERG and ERA5) show smaller ErrSD and higher CC compared to their predecessors (TMPA and ERA-Interim) at both daily and monthly time scales. The results also show that the studied satellite-based products perform better in matching the frequency of daily precipitation intensities, whereas the studied model-based products perform better in terms of categorical performance indices for lighter precipitation events.

Evaluation of Gridded Precipitation Datasets in Malaysia

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

Accuracy evaluation of GPM multi-satellite precipitation products in the hydrological application over alpine and gorge regions with sparse rain gauge network

Abstract With the release of Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM products, hydrologists can obtain precipitation data with higher resolution and wider coverage. However, great uncertainties still exist in the accuracy and hydrological utility of these data in alpine and gorge regions with sparse gauge stations. In this study, the Lancang River Basin in China was used as an example, and near real-time products (IMERG-E and IMERG-L) and post-processed products (IMERG-F and TMPA 3B42-V7) were evaluated. Different indexes and methods were applied to evaluate the accuracy of these products. The variable infiltration capacity hydrological model was adopted to evaluate their hydrological utility. The following findings were obtained. (1) Compared with observed precipitation data, the near real-time products tend to underestimate, while the post-processed products tend to overestimate precipitation. The performance of the four products in winter is poor. (2) IMERG products offer improvements in two aspects: first, the near real-time products achieve good accuracy and second, the detectability and the accuracy in gorge areas have been greatly improved. (3) The near real-time products have the potential for hydrological applications. The best simulation result was obtained based on IMERG-F, followed by 3B42-V7, IMERG-E, and IMERG-L. (4) The four products can provide reliable precipitation data for the hydrological application over the Lancang River Basin.

Investigating the use of satellite-based precipitation products for monitoring water quality in the Occoquan Watershed

Study RegionThe Washington D.C area. Study FocusThis work investigates the potential of using satellite-based precipitation products in a hydrological model to estimate water quality indicators in the Occoquan Watershed, located in the suburban Washington D.C area. Three (3) satellite-based precipitation products based on different retrieval algorithms (the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis, TMPA 3B42-V7; the Climate Prediction Center’s CMORPH product; and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System, PERSIANN−CCS) are compared to gauge-based records over a 5-year period across the study region. The 3 satellite-based precipitation products and the gauge-based dataset are used as input to the Hydrologic Simulation Program FORTRAN (HSPF) hydrology and water quality model. Each satellite precipitation-forced simulation is compared to the reference model simulation forced with the gauge-based observations, in terms of streamflow and water quality indicators, i.e., stream temperature (TW), total suspended solids (TSS), dissolved oxygen (DO), and biological oxygen demand (BOD). New Hydrological Insights for the RegionResults indicate that the spatiotemporal variability observed in the satellite-based precipitation products has a quantifiable impact on both modeled streamflow and water quality indicators. All 3 satellite products present moderate agreements with the reference precipitation and simulation; CMORPH presenting the best overall performance followed closely by TMPA, and PERSIANN presenting a comparatively inferior performance in terms of correlation, root-mean-square error and bias for streamflow and water quality indicators, such as TW, TSS, DO and BOD concentrations.

Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan

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.

Role of satellite and reanalysis precipitation products in streamflow and sediment modeling over a typical alpine and gorge region in Southwest China

Satellite and reanalysis precipitation products, as new and complementary data sources, are attractive for hydro-meteorological applications, especially in data-sparse areas. This study evaluates the accuracy of two satellite precipitation products (TMPA 3B42V7 and PERSIANN-CDR) and one reanalysis precipitation product (NCEP-CFSR) against gauge precipitation observations with four statistical indices over the upstream of the Lancang River Basin (ULRB), Southwest China. The reliability and applicability of these precipitation products as inputs to a hydrological model (Soil and Water Assessment Tool, SWAT) for streamflow and sediment simulations are also assessed. Furthermore, we compare the spatial plots of extreme water yield (99 percentiles) and suspended sediment yield (99 percentiles) driven by the four precipitation sources, and investigate the spatial and temporal variability of water yield and suspended sediment yield over the ULRB. Results show that for direct comparisons with gauge precipitation observations, monthly TMPA 3B42V7 precipitation product performs the best at the basin scale with the smallest error and bias, and the highest correlation, followed by NCEP-CFSR, and PERSIANN-CDR. For modeling-based indirect inference, TMPA 3B42V7 presents great capability for streamflow and sediment simulations in the SWAT model on a monthly time step at the basin outlet, and PERSIANN-CDR also performs well. NCEP-CFSR shows acceptable skills in modeling sediment but unacceptable skills in modeling streamflow. Extreme water yield presents moderate spatial variability over the ULRB while extreme suspended sediment yield presents strong spatial variability. Water yield of this basin shows a decreasing trend during 1998–2008 while there is no obvious trend in suspended sediment yield in this period.

Error adjustment of TMPA satellite precipitation estimates and assessment of their hydrological utility in the middle and upper Yangtze River Basin, China

Accurate and continuous precipitation amount and spatiotemporal distribution data are essential for water resource management, flood forecasting, and simulation of hydrological processes. Satellite-based precipitation products (SPPs) with high spatio-temporal resolution have been shown to represent a potential alternative data source for ungauged regions. The objective of this study was to evaluate the performance and the hydrological utility of a SPP (TMPA 3B42V7) driving two different hydrological models (Xinanjiang and Tank models) for runoff monitoring in the Yangtze River Basin in China. The results indicate that the TMPA precipitation data can be applied for runoff simulation compared to the ground-gauged data, and the adjustment of TMPA data can be helpful to improve the accuracy of rainfall data at the point and region scale. Two adjustment equations were proposed in this study (the geographical adjustment model and the additive error adjustment model), and the Bias had significantly improved, which solve the problem of data undervaluation. In addition, the TMPA precipitation data show an acceptable accuracy for driving the Xinanjiang and Tank models for the purpose of runoff monitoring. By utilizing the measured runoff data from the four main stations of the Yangtze River Basin, an excellent performance was shown in Xinanjiang model with the determined coefficient of 0.945–0.966 and 0.900–0.922 for the calibration and validation periods, respectively. This was similar for the Tank model with values of 0.943–0.963 and 0.917–0.920 for the calibration and validation periods, respectively. Based on these results, TMPA data would be helpful not only for understanding the characteristics of precipitation at high spatial and temporal resolution, but also for estimating near-real time runoff patterns.

Evaluation of TMPA 3B42V7, GPM IMERG and CMPA precipitation estimates in Guangdong Province, China

Three satellite rainfall products (3B42V7, IMERGV05/V04/V03, and China hourly Merged Precipitation Analysis product) are evaluated using measurements from a dense rain gauge network as reference in Guangdong Province, China from April 2014 to December 2016. The three products are compared with gauges in annual, monthly, daily and hourly accumulation, and at gridded, sub‐regional and regional scales. An error decomposition approach is employed to separate the total bias into Hit, Miss and False components. Overall, the CMPA estimate is the best in agreement with gauge observations. The improvement of IMERGV05 over 3B42V7 is notable, especially in reducing the hit bias and missed precipitation, resulting in better detection of the light rain and heavy rain. The enhancement of IMERG is more significant at the 3‐hr scale than daily scale. IMERGV05 shows better performance than its previous versions especially for IMERGV03, which had an abnormal blocky pattern mainly from July to September over the Pearl River Delta area. The three products have different error characteristics and show large spatial variations. 3B42V7 and IMERG have large areas of overestimation in the mountainous areas and underestimation in the coastal areas, while CMPA is characterized by an alternate distribution of small positive and negative values. The overestimation of precipitation is partially attributed to the positive hit biases that falsely estimate the precipitation from moderate intensity to heavy rainfall by the three products.

Hydrologic assessment of the TMPA 3B42-V7 product in a typical alpine and gorge region: the Lancang River basin, China

Abstract To evaluate the accuracy and applicability of the TMPA 3B42-V7 precipitation product for the Lancang River basin, we used different statistical indices to explore the performance of the product in comparison to gauge data. Then, we performed a hydrologic simulation using the Variable Infiltration Capacity (VIC) hydrological model with two scenarios (Scenario I: streamflow simulation using gauge-calibrated parameters; Scenario II: streamflow simulation using 3B42-V7-recalibrated parameters) to verify the applicability of the product. The results of the precipitation analysis show good accuracy of the V7 precipitation data. The accuracy increases with the increase of both space and time scales, while time scale increases cause a stronger effect. The satellite can accurately measure most of the precipitation but tends to misidentify non-precipitation events as light precipitation events (<1 mm/day). The results of the hydrologic simulation show that the VIC hydrological model has good applicability for the Lancang River basin. However, 3B42-V7 data did not perform as well under Scenario I with the lowest Nash–Sutcliffe coefficient of efficiency (NSCE) of 0.42; Scenario II suggests that the error drops significantly and the NSCE increases to 0.70 or beyond. In addition, the simulation accuracy increases with increased temporal scale.