Search Results (85)

Satellite and reanalysis rainfall estimates (SREs) are valuable alternatives to gauge data in data-scarce regions; however, their reliability in areas with complex terrain and variable precipitation remains uncertain. This study evaluated six SREs (CHIRPS v2, ERA5, ERA5-Land, IMERG v07, MSWEP v2.8, and TRMM 3B42) against gauge observations over the period 2005 to 2019. The evaluation was conducted using multiple statistical, categorical, and distributional metrics at daily to seasonal timescales. Terrain-based classification and rainfall intensity categories were used to explore the influence of topography and event magnitude on product performance. The accuracy of SREs improves with temporal aggregation, the monthly scale offering the highest reliability for water resource management. However, their tendency to overestimate light and underestimate heavy daily rainfall requires careful bias adjustment in flood and extreme event analysis. MSWEP, CHIRPS, and IMERG provided balanced and consistent performance across all metrics, rainfall intensities, and terrain zones. Notably, ERA5 and ERA5-Land consistently overestimated average rainfall. All SREs identified dry days well, and their performance declined with increasing intensity. No significant performance variation was observed across different altitudes. This study provides valuable insights into the selection of rainfall products, supporting climate and hydrological studies in data-scarce areas of the Ethiopian highlands. Full article

Drought poses significant challenges to agricultural productivity and water resource sustainability in Henan Province, emphasizing the need for effective monitoring approaches. This study investigates the suitability of the TRMM 3B43V7 satellite precipitation product for drought assessment, based on monthly data from 15 meteorological stations during 1998–2019. Satellite-derived precipitation was compared with ground-based observations, and the Standardized Palmer Drought Index (SPDI) was calculated to determine the optimal monitoring timescale. Statistical metrics, including Nash–Sutcliffe Efficiency (NSE = 0.87) and Pearson correlation coefficient (PCC = 0.88), indicate high consistency between TRMM data and ground measurements. The 12-month SPDI (SPDI-12) was found to be the most effective for capturing historical drought variability. To support integrated drought management, a regionally adaptive framework is recommended, balancing agricultural demands and ecosystem stability through tailored strategies such as enhanced irrigation efficiency in humid regions and ecological restoration in arid zones. These findings provide a foundation for implementing an operational drought monitoring and response system in Henan Province. Full article

Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan–Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan–Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan–Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan–Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan–Hami region showed an insignificantly increasing trend. Full article

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 potential of satellite precipitation products (SPPs) in monitoring and mitigating hydrometeorological disasters caused by extreme rainfall events has been extensively demonstrated. However, there is a lack of comprehensive assessment regarding the performance of SPPs over the Qinghai-Tibet Plateau (QTP). Therefore, this research aimed to evaluate the effectiveness of five SPPs, including CMORPH, IMERG-Final, PERSIANN-CDR, TRMM-3B42V7, and TRMM-3B42RT, in identifying variations in the occurrence and distribution of intense precipitation occurrences across the QTP during the period from 2001 to 2015. To evaluate the effectiveness of the SPPs, a reference dataset was generated by utilizing rainfall measurements collected from 104 rainfall stations distributed across the QTP. Ten standard extreme precipitation indices (SEPIs) were the main focus of the evaluation, which encompassed parameters such as precipitation duration, amount, frequency, and intensity. The findings revealed the following: (1) Geographically, the SPPs exhibited better retrieval capability in the eastern and southern areas over the QTP, while displaying lower detection accuracy in high-altitude and arid areas. Among the five SPPs, IMERG-Final outperformed the others, demonstrating the smallest inversion error and the highest correlation. (2) In terms of capturing annual and seasonal time series, IMERG-Final performs better than other products, followed by TRMM-3B42V7. All products performed better during summer and autumn compared to spring and winter. (3) The statistical analysis revealed that IMERG-Final demonstrates exceptional performance, especially concerning indices related to precipitation amount and precipitation intensity. Moreover, it demonstrates a slight advantage in detecting the daily rainfall occurrences and occurrences of intense precipitation. On the whole, IMERG-Final’s ability to accurately detect extreme precipitation events on annual, seasonal, and daily scales is superior to other products for the QTP. It was also noted that all products overestimate precipitation events to some extent, with TRMM-3B42RT being the most overestimated. Full article

Evaluating satellite-based products is vital for precipitation estimation for sustainable water resources management. The current study evaluates the accuracy of predicting precipitation using four remotely sensed rainfall datasets—Tropical Rainfall Measuring Mission products (TRMM-3B42V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Records (PERSIANN-CDR), Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), and National Centers for Environmental Prediction (NCEP)-Climate Forecast System Reanalysis (CFSR)—over the Haraz-Gharehsoo basin during 2008–2016. The benchmark values for the assessment are gauge-observed data gathered without missing precipitation data at nine ground-based measuring stations over the basin. The results indicate that the TRMM and CCS-CDR satellites provide more robust precipitation estimations in 75% of high-altitude stations at daily, monthly, and annual time scales. Furthermore, the comparative analysis reveals some precipitation underestimations for each satellite. The underestimation values obtained by TRMM CDR, CCS-CDR, and CFSR are 8.93 mm, 20.34 mm, 9.77 mm, and 17.23 mm annually, respectively. The results obtained are compared to previous studies conducted over other basins. It is concluded that considering the accuracy of each satellite product for estimating remotely sensed precipitation is valuable and essential for sustainable hydrological modelling. Full article

In the arid areas of Northwest China, especially in the Tianshan Mountains, the scarcity of meteorological stations has brought some challenges in collecting accurate information to describe the spatial distribution of precipitation. In this study, the applicability of TRMM3B42, GPM IMERG, and MSWEP V2.2 in different regions of Tianshan Mountain is comprehensively evaluated by using ten statistical indicators, three classification indicators, and variation coefficients at different time–space scales, and the mechanism of accuracy difference of precipitation products is discussed. The results show that: (1) On the annual and monthly scales, the correlation between GPM and measured precipitation is the highest, and the ability of three precipitation products to capture precipitation in the wet season is stronger than that in the dry season; (2) On the daily scale, TRMM has the highest ability to estimate the frequency of light rain events, and MSWEP has the highest ability to monitor extreme precipitation events; (3) On the spatial scale, GPM has the highest fitting degree with the spatial distribution of precipitation in Tianshan Mountains, MSWEP is the closest to the precipitation differentiation pattern in Tianshan Mountains; (4) The three satellite products generally perform best in low and middle longitude regions and middle elevation regions. This study provides a reference for the selection of grid precipitation datasets for hydrometeorological simulation in northwest arid areas and also provides a basis for multi-source data assimilation and fusion. Full article

The tropical Rainfall Measuring Mission TRMM 3B42 V7 product and its successor, the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM IMERG high-resolution product GPM IMERG V5, have been validated against rain gauges precipitation in an arid mountainous basin where ground-based observations of precipitation are sparse, or spatially undistributed. This paper aims to evaluate hydro-statically the performances of the TRMM 3B42 V7 and GPM IMERG V05 satellite precipitations products SPPs, at multiple temporal scales, from 2014 to 2017. SPPs are compared with the gauge station and show good results for both statistical and contingency metrics with notable values R > 0.94. Moreover, the rainfall-runoff events implemented on the hydrological model were performed at 3-hourly time steps and showed satisfactory results based on the obtained Nash–Sutcliffe criteria ranging from 94.50% to 57.50%, and from 89.3% to 51.2%, respectively. The TRMM product tends to underestimate and not capture extreme precipitation events. In contrast, the GPM product can identify the variability of precipitation at small time steps, although a slight underestimation in the detection of extreme events can be corrected during the validation steps. The proposed method is an interesting approach for solving the problem of insufficient observed data in the Mediterranean regions. Full article

Remote sensing precipitation products provide rich data for ungauged basins. Evaluating the accuracy and detection capability of remote sensing precipitation products is crucial before application. In this study, an index system in terms of quantitative differences, capturing capacity and precipitation distribution was constructed to evaluate three precipitation products, TRMM 3B42 V7, GPM IMERGE Final and CMORPH V1.0, at various temporal and spatial scales on the Tibetan Plateau from 2001 to 2016. The results show that the correlations among the three products were larger at the monthly scale than at the annual scale. The lowest correlations between the products and observation data were found in December. GPM performed the best at the monthly and annual scales. Particularly, the GPM product presented the best capability of detection of both precipitation and non-precipitation events among the three products. All three precipitation products overestimated 0.1~1 mm/day precipitation, which occurred most frequently. An underestimation of precipitation at 10~20 mm/day was observed, and this intensity accounted for the majority of the precipitation. All three precipitation products showed an underestimation in terms of the annual maximum daily precipitation. The accuracy of the same product varied in different regions of the Tibetan Plateau, such as the south, the southeast, eastern–central region and the northeast, and there was a certain clustering of the accuracies of neighboring stations. GPM was superior to TRMM and CMORPH in the southern Tibetan Plateau, making it recommended for applications. Full article

The acquisition of the precise spatial distribution of precipitation is of great importance and necessity in many fields, such as hydrology, meteorology and ecological environments. However, in the arid and semiarid regions of Northwest China, especially over mountainous areas such as the Heihe River basin (HRB), the scarcity and uneven distribution of rainfall stations have created certain challenges in gathering information that accurately describes the spatial distribution of precipitation for use in applications. In this study, the accuracy of precipitation estimates from eight high-resolution gridded precipitation products (CMORPHv1-CRT, CRU TSv.4.05, ERA5, GSMaP_NRT, IMERG V06B-Final, MSWEPv2.0, PERSIANN-CDR and TRMM 3B42v7) are comprehensively evaluated by referring to the precipitation observations from 23 stations over the HRB using six indices (root mean square error, standard deviation, Pearson correlation coefficient, relative deviation, mean error and Kling–Gupta efficiency) from different spatial and temporal scales. The results show that at an annual scale, MSWEP has the highest accuracy over the entire basin, while PERSIANN, CRU and ERA5 show the most accurate results in the upper, middle and lower reaches of the HRB, respectively. At a seasonal scale, the performance of IMERG, CRU and ERA5 is superior to that of the other products in all seasons in the upper, middle and lower reaches, respectively. Over the entire HRB, PERSIANN displays the smallest deviation in all seasons except for spring. TRMM shows the highest accuracy in spring and autumn, while MSWEP and CRU show the highest accuracy in summer and winter, respectively. At a monthly scale, TRMM is superior to the other products, with a relatively stronger correlation almost every month, while GSMaP is inferior to the other products. Moreover, MSWEP and PERSIANN perform relatively best, with favorable statistical results around almost every station, while GSMaP shows the worse performance. In addition, ERA5 tends to overestimate higher values, while GSMaP tends to overestimate lower values over the entire basin. Moreover, the overestimation of ERA5 tends to appear in the upper reach area, while that of GSMaP tends to appear in the lower reach area. Only CRU and PERSIANN yield underestimations of precipitation, with the strongest tendency appearing in the upper reach area. The results of this study display some findings on the uncertainties of several frequently used precipitation datasets in the high mountains and poorly gauged regions in the HRB and will be helpful to researchers in various fields who need high-resolution gridded precipitation datasets over the HRB, as well as to data producers who want to improve their products. Full article

Borneo Squall Line (BSL) is a disaster risk associated with intense rain and wind gust that affect the activities and residence near the northern coast of Borneo. Using 3-hourly rainfall from Tropical Rainfall Measuring Mission (TRMM) 3B42V7 during southwest monsoon season (May–September) from 1998–2018, a total of 629 squall days were identified. Their monthly and annual average was 6 and 30 days, respectively, with July representing the month with the highest number of squall line days. BSL is frequently initiated during midnight/predawn and terminated in the morning. Composite analyses of BSL days using the daily winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim revealed that lower tropospheric wind convergence is a crucial controlling factor for BSL formation. The position of the monsoon trough closer to the equatorial South China Sea (SCS), and strong westerly and south-westerly winds played an important role in creating this wind convergence region. Analyses of tropical cyclone (TC) data from the Regional Specialized Meteorological Centre (RSMC), Tokyo showed that nearly 72% of BSL occurred with the presence of TC. Spectral analysis exhibited prominent frequencies mainly in the 3–4- and 6-year time scale, which likely reflected the influence of interannual modulation of El-Niño Southern Oscillation (ENSO). Correlation coefficient between squall days and Sea Surface Temperature (SST) anomalies indicated that BSL increased after La-Niña events. This study is expected to have implications for real-time squall line forecasting in Malaysia and contributes toward a better understanding of BSL. Full article

The number of precipitation products at the global scale has increased rapidly, and the accuracy of these products directly affects the accuracy of hydro-meteorological simulation and forecast. Therefore, the applicability of these precipitation products should be comprehensively evaluated to improve their application in hydrometeorology. This paper evaluated the performances of six widely used precipitation products in southwest China by quantitative assessment and contingency assessment. The precipitation products were Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42 version 7 (TRMM 3B42 V7), Global Satellite Mapping of Precipitation (GSMaP MVK), Integrated Multi-satellitE Retrievals for GPM final run (GPM IMERG Final), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network—Climate Data Record (PERSIANN-CDR), Climate Hazards Infrared Precipitation with Stations version 2.0 (CHIRPS V2.0), and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). From the above six products, the daily-scale precipitation data from 2001 to 2019 were chosen to compare with the measured data of the rain gauge, and the data from the gauges were classified by river basin and elevation. All precipitation products and measured data were evaluated by statistical indicators. Results showed that (1) GPM IMERG Final and CHIRPS V2.0 performed well in the Yarlung Zangbo River (YZ) basin, while GPM IMERG Final and GLDAS V2.0 performed well in the Lantsang River (LS), Nujiang River (NJ), Yangtze River (YT), and Yellow River (YL) basins; (2) in the upper and middle reaches of the YZ basin, GPM IMERG Final and CHIRPS V2.0 were outstanding in all evaluated products; downstream of the YZ basin, all six products performed well; and upstream of the LS and NJ, GPM IMERG Final, TRMM 3B42 V7, CHIRPS V2.0, and GLDAS V2.0 can be recommended as a substitute for measured data; and (3) GPM IMERG Final and GLDAS V2.0 can be seen as substitutes for measured data when elevation is below 4000 m. GPM IMERG Final and CHIRPS V2.0 were recommended when elevation is above 4000 m. This study provides a reference for data selection of hydro-meteorological simulation and forecast in southwest China and also provides a basis for multi-source data assimilation and fusion. 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

In this study, we aimed to investigate the hydrological performance of three gridded precipitation products—CHIRPS, RFE, and TRMM3B42V7—in monthly streamflow forecasting. After statistical evaluation, two monthly streamflow forecasting models—support vector machine (SVM) and artificial neural network (ANN)—were developed using the monthly temporal resolution data derived from these products. The hydrological performance of the developed forecasting models was then evaluated using several statistical indices, including NSE, MAE, RMSE, and R². The performance measures confirmed that the CHIRPS product has superior performance compared to RFE 2.0 and TRMM data, and it could provide reliable rainfall estimates for use as input in forecasting models. Likewise, the results of the forecasting models confirmed that the ANN and SVM both achieved acceptable levels of accuracy for forecasting streamflow; however, the ANN model was superior (R² = 0.898–0.735) to the SVM (R² = 0.742–0.635) in both the training and testing periods. Full article

This study evaluates the capacity of three gridded precipitation products (MSWEP V2.2, TRMM-3B42 V7, and GPM-IMERG V6) to detect precipitation in the Min Jiang watershed, a data-scarce and mountainous region in western China. A set of statistical and contingency indices is calculated for the precipitation products and compared with rain gauge observations at 23 ground stations from July 2000 to May 2016. Consistency between gridded and ground precipitation datasets is examined at different temporal (i.e., daily, monthly, seasonally, and annually) and spatial (i.e., site level, sub-regional level, and watershed level) resolutions. We identify possible reasons for discrepancies among precipitation datasets. Our results indicate that: (1) the MSWEP product is best suited for the study of long-term mesoscale rainfall, rather than short-term light or extreme rainfall; (2) the IMERG product represents stable performance for the simulation of rainfall spatial variability and detection capability; and (3) Composition of the datasets, climatic systems, and regional topography are key factors influencing the consistency between gridded and ground precipitation datasets. Therefore, we suggest using MSWEP V2.2 and GPM-IMERG V6 as potential precipitation data sources for hydrometeorological studies over the Min Jiang watershed. The findings of this study inform future hydrometeorological and climate applications in data-scarce regions with complex terrain. Full article