Global Precipitation Products Research Articles

Investigating the Impact of Large Lakes on Local Precipitation: Case Study of Lake Urmia, Iran

Large lakes face considerable challenges due to human activities and climate change, impacting local weather conditions and ecosystem sustainability. Lake Urmia, Iran’s largest lake and the world’s second-largest saltwater lake, has undergone a substantial reduction in water levels, primarily due to drought, climate change, and excessive irrigation. This study focuses on the potential repercussions on local climate conditions, particularly investigating the impact of moisture sources, evaporation from lake surfaces, and evapotranspiration from agricultural activities, on local convection rainfall. The prevailing westerly winds in the basin suggest a hypothesis that this moisture is transported eastward within the basin, potentially leading to local precipitation as it ascends to higher altitudes near the eastern basin border. To validate this hypothesis, climate data from 1986 to 2017 from the Sarab meteorological station (east of the lake basin, influenced by local precipitation) and Saqez meteorological station (south of the basin, unaffected by local precipitation) were analyzed. The impact of lake water level reduction was assessed by categorizing data into periods of normal lake conditions (1986–1995) and water level reduction (1996–2017). Additionally, the MSWEP global precipitation product was used to examine the precipitation distribution in the entire basin over the entire period and sub-periods. The findings indicate Lake Urmia’s significant influence on convective rainfall in the eastern basin, especially during the summer. Despite decreasing lake levels from 1996 to 2017, convective rainfall in the eastern basin increased during the summer, suggesting intensified agricultural irrigation, particularly in hot seasons.

Assessing the Impact of Climate Change on Snowfall Conditions in Poland Based on the Snow Fraction Sensitivity Index

This study focuses on temperature and snowfall conditions in Poland, both of which were analyzed from 1981 to 2020. A 40-year record of daily snow fraction time series values was reconstructed using a unique and global multi-source weighted-ensemble precipitation (MSWEP) product, which provided a spatially and temporally consistent reference for the assessment of meteorological conditions. The average states and trends in snow fraction and temperature were analyzed across several years, focusing on the 6-month cold season (November–April). The impact of temperature on the snow fraction pattern was assessed by introducing a snow fraction sensitivity index. To predict short-term changes in snow conditions, a proxy model was established; it incorporated historical trends in the snow fraction as well as its mean state. This study provides clear evidence that the snow fraction is principally controlled by increases in temperature. A warming climate will thus cause a decline in the snow fraction, as we observed in vast lowland areas. Given the ongoing global warming, by the 2050s, snow-dominated areas may go from covering 86% to only 30% of the country’s surface; they will be converted into transient rain–snow areas. Our results demonstrate that a decline in snow water resources has already occurred, and these resources are expected to diminish further in the near future. New insights into the sensitivity of the snow fraction to climate warming will expand our collective knowledge of the magnitude and spatial extent of snow degradation. Such widespread changes have implications for the timing and availability of soil and groundwater resources as well as the timing and likelihood of floods and droughts. Thus, these findings will provide valuable information that can inform environmental managers of the importance of changing snowfall conditions, guiding them to include this aspect in future climate adaptation strategies.

Investigating twelve mainstream global precipitation datasets: Which one performs better on the Tibetan Plateau?

Acquiring precipitation information is a critical yet challenging task for the Tibetan Plateau (TP) with sparse gauges and high altitudes. Presently, there is a multitude of operational precipitation products merged from different data sources. A major concern within the TP science community is which one of these available precipitation datasets performs best on the TP. To address this concern, we selected and compared twelve widely used global precipitation datasets, including six satellite-based precipitation estimates (GSMaP-Gauge, IMERG-Final, TMPA-v7, CMORPH-BLD, PERSIANN-CDR, CHIRPS), five reanalysis data (ERA5, JRA-55, MERRA2, GLDAS, NCEP2), and one multi-source fusion product (MSWEP). Benchmarked against both automatic weather stations and independent field observations, we comprehensively investigate the spatiotemporal accuracy and regional applicability of these global precipitation products on the TP. The evaluation results indicate that almost all the datasets have a consistent tendency to overestimate lower precipitation rates and underestimate higher ones. However, four out of the twelve studied datasets (i.e., GSMaP-Gauge, IMERG-Final, ERA5, and MSWEP) significantly outperform others. Specifically, the two GPM-based satellite precipitation products, GSMaP-Gauge and IMERG-Final, can better reflect the difference between different rain intensities and perform better at moderate rain rates. In contrast, the reanalysis-based ERA5 exhibits a more clustered data series, with relatively higher accuracy at low precipitation rates. Moreover, ERA5 demonstrates a stable superiority in the mountainous areas characterized by complex local terrains. Furthermore, we find that the applicability of different precipitation datasets has an obvious region-related feature. Thus, a regional distribution map was made to clearly outline the optimal choice of current mainstream global precipitation datasets over different climate zones within the TP. This study can offer valuable insights for TP researchers, especially aiding them to select the appropriate precipitation data for their specific studies.

Quantifying flood risks during monsoon and post-monsoon seasons: An integrated framework for resource-constrained coastal regions

Global coastal regions are transforming into “flooding hot-spots” due to the multi-faceted confluence of several flood-drivers. Unfortunately, these regions are often poorly gauged, hindering comprehensive flood hazard quantification. Besides summer-monsoon floods, post-monsoon rainfalls induced by cyclonic-depressions have escalated the flood risks in the coastal regions. For the first time, the present study captures the seasonal flood hazards by disentangling monsoon (June, July, August, and September) and post-monsoon (October, November, and December) periods of rainfall. An integrated MIKE + 1D-2D coupled model was used to generate flood hazard risks for Jagatsinghpur district (Odisha, India), a resource-constrained multi-hazard catchment in the Lower Mahanadi River Basin. Due to limited data for this coastal region, we investigated the performance of CHIRPS v2.0, a global Satellite Precipitation Product (SPP) with high spatiotemporal characteristics, for capturing extreme rainfall events and deriving flood hazards at the inundation scale. We observed higher magnitude of flood hazards are prominent in the coastal villages, emerging from a complex interaction between rainfall and storm-tide events. Three widely used Archimedean copulas were implemented to capture the rainfall and storm-tide events' joint (bivariate) behavior and their implications on population exposure, which is valuable for mapping flood hazard risks. We observed that univariate analysis (Ua) significantly underestimates the flood hazard over bivariate analysis (Ba) during both periods. Interestingly, the results are more pronounced in the population exposure estimates as ∼47 % (monsoon) and ∼54 % (post-monsoon) of the population are exposed based on univariate analysis against ∼65 % (monsoon) and ∼60 % (post-monsoon) under bivariate analysis. Our study highlights the necessity of disentangling rainfall time-series for foreseeing under-shadowed flood risks, vital for designing flood combating structures, evacuation plans, land use planning, and flood governance. The results further enhance our understanding of monsoon and post-monsoon flood hazards and their associated risks under different return periods.

On the reliability of 12 high-resolution precipitation products for process-based hydrological modeling in China

Numerous satellite- and gauge-based precipitation products (SBPs/GBPs) have emerged in recent decades, providing unprecedented opportunities for process-based hydrological modeling. However, their reliability in high-resolution hydrological modeling at continental scales, especially for variables other than streamflow, remains to be investigated. Factors influencing their hydrological performance (e.g., catchment sizes, precipitation products’ spatial resolution and raw observation information) are also under debate. Here, we provide a comprehensive analysis of the hydrological reliability of 12 state-of-the-art precipitation datasets over China using a process-based land surface model at 0.125° resolution. Simulations driven by different precipitation products are evaluated across ∼ 200 basins with different catchment sizes. Results show that all simulations well reproduce the streamflow, soil moisture, evapotranspiration, and snow-depth at monthly timescale. None of the precipitation products provides best results for all variables, but the gauge-based and infrared-microwave-merged satellite products are superior to the infrared satellite products. The gauge-based regional precipitation product based on the 0.25° China precipitation dataset and the 6-km China Land Data Assimilation System (CN051_CLDAS) provides the best performance generally, followed by two global precipitation products from the Global Precipitation Climatology Centre (GPCC V2022) and the Global Precipitation Measurement Integrated Multi-satellite Retrievals (IMERGE-F V6). However, the advantages of CLDAS_CN051 and GPCC V2022 against satellite-based precipitation products are scale-dependent and tend to disappear over large-scale catchments (>106 km2). The spatial resolutions of precipitation products show no relationship with their hydrological performance, but the number of stations used to generate/bias-correct the precipitation is positively correlated with the performance of streamflow and soil moisture simulations. Differences among the climatology of terrestrial water-cycle components caused by using different precipitation products are notable and tend to increase over the dry and cold regions. Snow depth is the variable most sensitive to precipitation datasets, followed by runoff, ET, and soil moisture. This study suggests that using more station observations is more important than increasing spatial resolutions to enhance the applications of precipitation products in high-resolution hydrological modeling.

An Evaluation of CRA40 and ERA5 Precipitation Products over China

Precipitation datasets derived from reanalysis products play a crucial role in weather forecasting and hydrological applications. This paper aims to evaluate the performance of two distinct reanalysis precipitation products, i.e., the first-generation Chinese global land-surface reanalysis precipitation product (CRA40) and the fifth-generation European reanalysis precipitation product (ERA5), over mainland China. The evaluation is based on continuous and categorical statistical indicators with daily-scale gridded-point rain gauge data obtained from Chinese surface meteorological stations. The findings of this study can be summarized as follows: (1) CRA40 demonstrates a clear superiority over ERA5 in terms of the 13-year daily mean precipitation and seasonal daily precipitation. CRA40 exhibits better correlation coefficients (0.97), relative biases (5.25%), root mean square errors (0.34 mm), and fractional standard errors (0.05). (2) Both reanalyzed precipitation products generally exhibit an overestimation of precipitation in mainland China. The degree of overestimation is particularly pronounced in dry climatic regions (e.g., QZ, XJ), while wet regions (e.g., CJ, HN) demonstrate relatively less overestimation. (3) ERA5 shows better performance in the detection of daily precipitation. Neither CRA40 nor ERA5 can capture heavy precipitation events well. These findings are expected to advance our understanding of the strengths and limitations of the reanalysis precipitation products, CRA40 and ERA5, over China.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Comprehensive Evaluation of Global Precipitation Products and Their Accuracy in Drought Detection in Mainland China

Abstract Thorough evaluations of satellite precipitation products are necessary for accurately detecting meteorological drought. A comprehensive assessment of 15 state-of-the-art precipitation products (i.e., IMERG_cal, IMERG_uncal, GSMaP-G, CPC-Global, TRMM3B42, CMORPH-CRT, PERSIANN-CDR, PERSIANN, PERSIANN-CCS, SM2RAIN, CHIRPS, ERA5, ERA-Interim, MERRA-2, and GLDAS) is herein conducted for the period 2010–19 giving special attention to their performance in detecting meteorological drought over mainland China at 0.25° spatial resolution. The cited precipitation products are compared against China’s gridded gauge-based Daily Precipitation Analysis (CGDPA) product, derived from 2400 meteorological stations, and their quality is assessed at daily, seasonal, and annual precipitation time scales. Meteorological droughts in the datasets are determined by calculating the standardized precipitation evapotranspiration index (SPEI). The performance of the precipitation products for drought detection with respect to the SPEI is assessed at three time scales (1, 3, and 12 months). The results show that the GSMaP-G outperforms other satellite-based datasets in drought detection and precipitation estimation. The MERRA-2 and the ERA5 are on average closer to the CGDPA reference data than other reanalysis products for precipitation estimation and drought detection. These products capture well the spatial and temporal pattern of the SPEI in southern and eastern China having a probability of detection (POD) above 0.6 and a correlation coefficient (CC) above 0.65. CPC-Global, IMERG, and the ERA5 reanalysis product are ideal candidates for application in western China, especially in the Qinghai–Tibetan Plateau and the Xinjiang Province. Generally, the accuracy of precipitation products for drought detection is improved with longer time scales of the SPEI (i.e., SPEI-12). This study contributes to drought-hazard detection and hydrometeorological applications of satellite precipitation products. Significance Statement The purpose of this study is to comprehensively evaluate the quality of 15 global satellite-based, gauge-based, and reanalysis precipitation products for meteorological drought detection at 1-, 3-, and 12-month time scales. This work systematically evaluates these products’ capacity to capture precipitation occurrence and intensity in different seasons. This is followed by a comparison of the precipitation products’ performance in drought detection. This work’s findings and evaluation results will improve the ability of those who develop precipitation products in identifying error sources and further improving retrieval algorithms. This paper’s results will serve as a valuable reference for end users seeking to better understand the application of precipitation products to drought detection.

A downscaling-calibrating framework for generating gridded daily precipitation estimates with a high spatial resolution

Accurate precipitation datasets with a high spatiotemporal resolution are crucial for regional hydrological simulation and water resources management. Although various global satellite precipitation products (SPPs) are available, they cannot be directly applied to small-scale research due to their low spatial resolution and significant bias. To solve these problems, based on the consideration of spatiotemporal relationships, this paper presented a comprehensive downscaling-calibrating framework that consists of spatial downscaling procedures and calibrating processes. Specifically, a geographically weighted regression (GWR) model, coupled with the environmental variables selected by the Geodetector model, was applied to generate uncorrected daily precipitation data with high-spatial resolution (1 km × 1 km). Then, the preliminary downscaled daily precipitation was calibrated using a bidirectional long short-term memory (Bi-LSTM) network. The proposed framework was applied to downscale and calibrate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product of the Jiulong River Basin in southeast China from 2010 to 2016. The results indicate that: (1) the spatial downscaling procedures improved the spatial resolution of the SPPs while maintaining its accuracy; (2) the calibrating processes can effectively correct the bias of preliminary downscaled daily precipitation. Meanwhile, the accuracy evaluation showed that the proposed GWR_Bi-LSTM framework outperformed the comparative methods, and the generated precipitation dataset exhibited a better performance under various evaluation conditions. Generally, based on the proposed framework, gridded daily precipitation data with high quality and spatial resolution can be generated for hydrological and meteorological research.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Performance variations of global precipitation products in detecting drought episodes in three wet seasons in Ethiopia: Part II—statistical analysis

AbstractThe need to evaluate global climate data has increased in recent times. In this study, we evaluate the ability of global precipitation products to monitor drought during three wet seasons (Belg/March–May, Kiremt/June–September and Autumn/September–November) and associated rainfall regions in Ethiopia. We employed statistical methods to quantify and evaluate precipitation products based on probability of drought detection (POD), the extent of false alarms (FAR) and the critical success index (CSI) to see the overall performance of the studied precipitation products. The majority of the studied precipitation datasets were relatively better in capturing the Autumn drought in southern Ethiopia, and 18 out of 21 precipitation products captured accurately more than 50% of observed droughts. The CSI scores for this season are also above 0.5 for 14 precipitation products. On the other hand, 15 and 14 precipitation products accurately captured more than 50% of the seasonal drought in Kiremt and Belg rainfall seasons in north‐eastern Ethiopia. In contrast, most precipitation products do not clearly represent the drought conditions of the Kiremt season in north‐western Ethiopia. Only 8 of the 21 precipitation products accurately captured more than 50% of the observed drought in this region, and only 6 precipitation products had a CSI score greater than 0.5. The results can facilitate the selection of precipitation products for drought monitoring purposes, for use in specific wet seasons and regions of Ethiopia.

Drought monitoring performance of global precipitation products in three wet seasons in Ethiopia: Part I—Quasi‐objective examination

AbstractThe need for representative and accurate climate data such as precipitation useful for drought monitoring has been rapidly increasing among policymakers and practitioners to tackle climate‐change‐induced drought events. Hence, the objective of this article is to evaluate the drought monitoring performance of global precipitation products for the three wet seasons and rainfall regions in Ethiopia. Drought indices were calculated using the Standardized Precipitation Index (SPI) at 3‐month timescale for Belg (March–May) and Autumn (September–November) seasons and at 4‐month timescale for the Kiremt (June–September) seasons. Data products were evaluated for their accuracy in representing drought magnitude, geographical coverage and frequency using quasi‐objective (visual inspection), and frequency and correlation analysis methods. The performance of gridded precipitation products was compared against the SPI value computed for 126 reference stations and the Ethiopian satellite‐gauge merged precipitation data. Precipitation products showed different levels of performance in representing the magnitude, frequency and geographical coverage of drought events for the three wet seasons and rainfall regions. None of the data products outperformed in representing the occurrence of drought for all three wet seasons and the corresponding three rainfall regions. However, the Ethiopian merged precipitation, Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) precipitation products are relatively better than others. The study results generally indicate that no single data outperform the other precipitation products in representing the complex spatiotemporal characteristics of drought events in a mountainous region like Ethiopia.

The Potential Use of Global Evapotranspiration Products and Models to Estimate Lake Residence Time at Large Scale

AbstractWater residence time (WRT) is an important lake attribute that influences almost all in‐lake nutrient processes. However, information on WRT is often missing or outdated, especially in regions lacking lake inflow or outflow observations. Recent advances in global precipitation and evapotranspiration (ET) products and calibration‐free ET models provide the possibility of estimating lake WRT at large scales using estimated runoff from the water balance over the lake catchment. Here, we tested four global ET products and two calibration‐free ET models for estimating the mean annual lake outflow and WRT of 67 globally distributed lakes using precipitation inputs from four global precipitation products. The results show that the WRT estimated using the water balance method and global ET estimates generally correlates well with reference WRT (R = 0.90 ∼ 0.99) when using reliable precipitation inputs and is slightly more accurate than the estimates from the existing global WRT dataset and methods (R = 0.79 ∼ 0.94). Despite the large relative error (RE) in the estimates of the WRT, the RE of the phosphorus retention derived from the estimated WRT is acceptable (RE < ±30%). The simplicity and versatility of this approach underline its potential applications to large‐scale studies that require a fair estimation of WRT.

A Generalized Regression Neural Network Model for Accuracy Improvement of Global Precipitation Products: A Climate Zone-Based Local Optimization

The ability to obtain accurate precipitation data from various geographic locations is crucial for many applications. Various global products have been released in recent decades for estimating precipitation spatially and temporally. Nevertheless, it is extremely important to provide reliable and accurate products for estimating precipitation in a variety of environments. This is due to the complexity of topographic, climatic, and other factors. This study proposes a multi-product information combination for improving precipitation data accuracy based on a generalized regression neural network model using global and local optimization strategies. Firstly, the accuracy of ten global precipitation products from four different categories (satellite-based, gauge-corrected satellites, gauge-based, and reanalysis) was assessed using monthly precipitation data collected from 1896 gauge stations in Iran during 2003–2021. Secondly, to enhance the accuracy of the modeled precipitation products, the importance score of effective and auxiliary variables—such as elevation, the Enhanced Vegetation Index (EVI), the Land Surface Temperature (LST), the Soil Water Index (SWI), and interpolated precipitation maps—was assessed. Finally, a generalized regression neural network (GRNN) model with global and local optimization strategies was used to combine precipitation information from several products and auxiliary characteristics to produce precipitation data with high accuracy. Global precipitation products scored higher than interpolated precipitation products and surface characteristics. Furthermore, the importance score of the interpolated precipitation products was considerably higher than that of the surface characteristics. SWI, elevation, EVI, and LST scored 53%, 20%, 15%, and 12%, respectively, in terms of importance. The lowest RMSE values were associated with IMERGFinal, TRMM3B43, PERSIANN-CDR, ERA5, and GSMaP-Gauge. For precipitation estimation, these products had Kling–Gupta efficiency (KGE) values of 0.89, 0.86, 0.77, 0.78, and 0.60, respectively. The proposed GRNN-based precipitation product with a global (local) strategy showed RMSE and KGE values of 9.6 (8.5 mm/mo) and 0.92 (0.94), respectively, indicating higher accuracy. Generally, the accuracy of global precipitation products varies depending on climatic conditions. It was found that the proposed GRNN-derived precipitation product is more efficient under different climatic conditions than global precipitation products. Moreover, the local optimization strategy based on climatic classes outperformed the global optimization strategy.

“A comprehensive assessment of suitability of Global Precipitation Products for hydro-meteorological applications in a data-sparse Himalayan region”

Observation rainfall networks in developing countries such as Nepal face many challenges (like availability and quality). Global Precipitation Products (GPPs) could be an alternative to gauge-based observed rainfall (GOR) in hydro-meteorological studies. However, GPPs performance across the Himalayan regions is still unknown and is influenced by several factors such as spatial and temporal resolutions, primary data sources, etc. We have comprehensively assessed the suitability of the latest GPPs using categorical and continuous variable performance metrics for the Gandak River Basin in the Nepalese Himalayas. We then ranked GPPs for the first time using the Multicriteria Decision-Making technique. 11 out of 12 GPPs considered underestimated the annual rainfall in the basin. Performance of GPPs was also inconsistent for monthly/annual and daily timescales. At longer timescales, CHIRPS and IMERG_Final are better at representing the spatial and temporal pattern of the rainfall (spatial correlation of 0.75) and least percentage bias (PBIAS < 12%). At a daily timescale, IMERG_Final, ERA5, and PERSIANN_CDR stand out for probability of detection (POD) of rainfall, while all GPPs perform poorly in false alarm ratio (FAR). Although all GPPs have relatively high RMSE (6-14 mm/day), correlation (CC) with observed rainfall was high for IMERG_Final, ERA5, and MERRA_2 in most of the sub-basins. With elevation, the performance of all GPPs is reduced, as evidenced by higher PBIAS, and lower CC. Although there is plenty of room for improvements in rainfall estimation by GPPs, among the existing dataset, IMERG_Final scored best in the majority of the performance indicators and ranked first in five out of six sub-basins. It would be relatively the better choice in the data sparse Himalayan region when daily rainfall data is required. For applications that require monthly/annual rainfall, both CHIRPS and IMERG_Final are equally suitable. The method proposed in the study for assessing GPPs can be readily applied in other river basins and at sub-daily timescales.

Extreme precipitation return levels for multiple durations on a global scale

Quantifying the magnitude and frequency of extreme precipitation events is key in translating climate observations to planning and engineering design. Past efforts have mostly focused on the estimation of daily extremes using gauge observations. Recent development of high-resolution global precipitation products, now allow estimation of global extremes. This research aims to quantitatively characterize the spatiotemporal behavior of precipitation extremes, by calculating extreme precipitation return levels for multiple durations on the global domain using the Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. Both classical and novel extreme value distributions are used to provide insight into the spatial patterns of precipitation extremes. Our results show that the traditional Generalized Extreme Value (GEV) distribution and Peak-Over-Threshold (POT) methods, which only use the largest events to estimate precipitation extremes, are not spatially coherent. The recently developed Metastatistical Extreme Value (MEV) distribution, that includes all precipitation events, leads to smoother spatial patterns of local extremes. For durations of 5 and 10 days, however, there are less events per year to fit the distribution (37 and 22 on average, respectively), leading to larger inter-annual variability and possible overestimation of the extremes. While the GEV and POT methods predict a consistent shift from heavy to thin tails with increasing duration, the MEV method predicts a relatively constant heaviness of the tail for any precipitation duration, opening up an important research question on what is the ‘correct’ tail behavior of extreme precipitation for different durations. The generated extreme precipitation return levels and corresponding parameters are provided as the Global Precipitation EXtremes (GPEX) dataset. These data can be useful for studying the underlying physical processes causing the spatiotemporal variations of the heaviness of extreme precipitation distributions.

Merging precipitation scheme design for improving the accuracy of regional precipitation products by machine learning and geographical deviation correction

Global satellite precipitation products (SPPs) effectively obtain spatial precipitation information but frequently fail to meet application requirements in small-scale areas due to low accuracy. This study aims to design a merging precipitation scheme based on remote sensing and surface parameters to address the inaccuracy of regional precipitation. The scheme data are based on daily IMERG-FR, CHIRPS, and PDIR-Now satellite precipitation products from 2011 to 2018, combined with WorldClim model climate data and 39 rain gauge observations. The scheme generates merged precipitation with high-accuracy and high-resolution (0.04°) through the streamlined operation of multiple methods in the Songhua River basin in northeast China. First, the stacking algorithm was employed to perform preliminary merging of SPPs and decrease data noise errors (mean absolute error and standard deviation were reduced by 13.80% and 14.44%, respectively). Second, the correlation of merged and observed precipitation was improved by 1.18%–4.46% in different seasons after geographically weighted regression. Finally, the merged data were subjected to local intensity scaling, which reduced the precipitation error by an average of 3.14%. When compared to the original SPPs, the final merged precipitation (FMP) improved the correlation (increased by 0.19) and reduced the errors (root mean square error and relative error decreased by 1.71 mm and 0.08 mm, respectively). FMP performed well under precipitation event cases, with an average difference of 0.24 mm from observed precipitation. The study realized the merging analysis of remote sensing precipitation data with varying precision and spatial resolution. Furthermore, a systematic merging precipitation scheme coupled with multiple algorithms of machine learning, geographic regression, and mathematical statistics was formed. This study provides a reference for merging precipitation at a regional scale, which can be applied to other study areas as a process-oriented scheme.

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

Many fields have identified an increasing need to use global satellite precipitation products for hydrological applications, especially in ungauged basins. In this study, we conduct a comprehensive evaluation of three Satellite-based Precipitation Products (SPPs): Integrated Multi–satellitE Retrievals for GPM (IMERG) Final run V6, Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT) V1.5, and Multi-Source Weighted-Ensemble Precipitation (MSWEP) V2.2 for a subbasin of the Mekong River Basin (MRB). The study area of the Srepok River basin (SRB) represents the Central Highland sub-climatic zone in Vietnam under the impacts of newly built reservoirs during 2001–2018. In this study, our evaluation was performed using the Rainfall Assessment Framework (RAF) with two separated parts: (1) an intercomparison of rainfall characteristics between rain gauges and SPPs; and (2) a hydrological comparison of simulated streamflow driven by SPPs and rain gauges. Several key findings are: (1) IMERGF-V6 shows the highest performance compared to other SPP products, followed by SM2RAIN-ASCAT V1.5 and MSWEP V2.2 over assessments in the RAF framework; (2) MSWEP V2.2 shows discrepancies during the dry and wet seasons, exhibiting very low correlation compared to rain gauges when the precipitation intensity is greater than 15 mm/day; (3) SM2RAIN–ASCAT V1.5 is ranked as the second best SPP, after IMERGF-V6, and shows good streamflow simulation, but overestimates the wet seasonal rainfall and underestimates the dry seasonal rainfall, especially when the precipitation intensity is greater than 20 mm/day, suggesting the need for a recalibration and validation of its algorithm; (4) SM2RAIN-ASCAT had the lowest bias score during the dry season, indicating the product’s usefulness for trend analysis and drought detection; and (5) RAF shows good performance to evaluate the performance of SPPs under the impacts of reservoirs, indicating a good framework for use in other similar studies. The results of this study are the first to reveal the performance of MSWEP V2.2 and SM2RAIN-ASCAT V1.5. Additionally, this study proposes a new rainfall assessment framework for a Vietnam basin which could support future studies when selecting suitable products for input into hydrological model simulations in similar regions.

Characteristics and physical mechanisms of a rainstorm in Hotan, Xinjiang, China

Owing to global warming, extreme precipitation events in the arid regions of Central Asia have increased, resulting in significant consequences for water resources and ecosystems. Hence, to address the features and corresponding physical mechanisms of these rainstorms, we examined the rainstorm that occurred in Hotan, Xinjiang in June 2021 as a case study. We employed multiple datasets, including meteorological stations, sounding observations, satellite precipitation data, and reanalysis datasets. The results indicate that the Global Precipitation Measurement satellite precipitation product accurately captured the temporal and spatial variations in this rainstorm, as verified against hourly in situ observation data. Some meteorological stations recorded values greater than twice their historical records, such as Luopu, Pishan, Moyu, and Hotan. Moreover, the duration of the precipitation was longer than 2 days. For the physical mechanisms of this rainstorm, the water vapor in this rainstorm is sourced from the 45°–65°N region of the North Atlantic Ocean crosses the Ural Mountains and the West Siberian Plain to southern Xinjiang. The low-pressure levels (e.g., 700 hPa and 850 hPa) have the more water vapor flux and specific humidity than the high-pressure levels. Our findings can aid the understanding of extreme precipitation events in Central Asia and provide a reference for dealing with meteorological disasters, including extreme precipitation, in the context of global climate change.

Spatio-temporal performance evaluation of 14 global precipitation estimation products across river basins in southwest Iran

Study regionKarkheh and Karun basins (29°−35°N, 46°−52°E) are two large river basins (area 51,000 and 67,000 km2, respectively) with complex topography in southwest Iran. Study focusAccess to spatio-temporally consistent precipitation data is a key requirement for hydrological studies, especially in data-scarce regions. This study evaluated 14 global precipitation products against gauge observations from 2003 to 2012 in Karun and Karkheh basins, southwest Iran. Different categorical and statistical indices at varying spatial and temporal resolution, including Kling-Gupta Efficiency (KGE), bias, correlation coefficient, and variability ratio, were used to evaluate the products. New hydrological insights for the regionFor daily time steps, TMPA-3B42V7.0, MERRA-2, and CMORPH-BLDV1.0 outperformed all other products, with KGE > 0.3. TMPA-3B42V7.0, MERRA-2, and PERSIANN-CDR were the best-performing products for monthly time steps, with KGE> 0.5. ERA5-Land showed the highest positive bias (bias>1.5) compared with in-situ observations, particularly for mountainous southeastern parts of Karun basin. Overall, bias-adjusted products obtained by merging ground-based observations in the estimations outperformed the unadjusted versions. The spatial distribution of statistical error metrics indicated that almost all products showed their greatest uncertainties for mountainous regions due to complex precipitation processes in these regions. These results can contribute significantly to hydrological and water resources planning measures in the study region, including early flood warning systems, drought monitoring, and optimization of dam operation.