Gridded Precipitation Datasets Research Articles

Evaluation of GPM IMERG Early, Late, and Final Run in Representing Extreme Rainfall Indices in Southwestern Iran

The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region.

Quantile mapping technique for enhancing satellite-derived precipitation data in hydrological modelling: a case study of the Lam River Basin, Vietnam

ABSTRACT Accurate precipitation is crucial for hydrological modelling, especially in sparse gauge regions like the Lam River Basin (LRB) in Vietnam. Gridded precipitation data sets derived from satellite and numerical models offer significant advantages in such areas. However, satellite precipitation estimates (SPEs) are subject to uncertainties, especially in high variable of topography and precipitation. This study focuses on enhancing the accuracy of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Prediction Center morphing technique (CMORPH) using the Quantile Mapping (QM) technique, aligning the cumulative distribution functions of the observed precipitation data with those of the SPEs, and assessing the impact on hydrological predictions. The study highlights that the post-correction IMERG precipitation using QM performs better than other data sets, enhancing the hydrological model's performance for the LRB at different temporal scales. Nash–Sutcliffe efficiency values increased from 0.60 to 0.77, surpassing the original IMERG's 0.52 to 0.74, and correlation coefficients improved from 0.79 to 0.89 (compared with the previous 0.75–0.86) for hydrological modelling. Additionally,Per cent Bias (PBIAS) decreased from approximately −1.66 to −2.21% (contrasting with the initial −20.22 and 4.6%) with corrected SPEs. These findings have implications for water resource management and disaster risk reduction initiatives in Vietnam and other countries.

Evaluation and Comparison of Five Long-Term Precipitation Datasets in the Hang-Jia-Hu Plain of Eastern China

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research.

Assessing the accuracy and reliability of satellite-derived precipitation products in the Kosi River basin (India).

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash-Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets.

Orinoco revisited: Comprehensive analysis of the Orinoco River basin present and future hydroclimate

The Orinoco River basin, ranked as South America’s third-largest catchment, is pivotal in contributing to the Atlantic Ocean’s water volume. This study provides a comprehensive update on the basin’s surface water balance, examining trends using gridded precipitation and total evaporation datasets. We also explore projected changes in precipitation until the end of the 21st century, focusing on the RCP8.5 climate change scenario. To achieve this, we utilize data from regional climate models designed by the CORDEX-CORE experiment, selecting an ensemble that excels in performance across South America and Central America. We estimate the accuracy of reference datasets in capturing water balance dynamics. We identify increasing trends in precipitation and total evaporation across most of the basin, enhancing our understanding of its long-term hydrological balance. Notably, the Andean and Guianese sectors of the basin contribute equally to half of the mean surface runoff, although the latter encompasses only 30% of the total area. This underscores the key role of the Guianese shield sub-basins. In regional climate modeling, despite some underestimation, the model runs for the CORDEX South America domain simulate effectively the precipitation across the basin. Regarding climate scenarios, our analysis using the RCP8.5 scenario projects an average annual precipitation reduction of around 45% for the entire basin. These findings emphasize the urgency of adopting measures to mitigate potential adverse effects on the Orinoco River basin’s hydrological sustainability in response to evolving climate patterns.

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models.

Uncertainty estimation of hydrological modelling using gridded precipitation as model inputs in the Gandaki River Basin

Study RegionFour subbasins in the Gandaki River Basin in the southern Himalayan region. Study FocusIn the southern Himalayan region, gridded precipitation datasets are often used to complement sparse gauge network observations in hydrological modelling for water resource assessment. However, the applicability and uncertainty of using these datasets in hydrological modelling remain to be evaluated. In this study, nine high spatial resolution gridded precipitation datasets were used to force a widely used hydrological model to simulate the daily runoff at four hydrological stations in this region. The accuracy of these datasets was evaluated, and the model calibration results and modelling uncertainty were analyzed. New Hydrological Insights for the RegionOur analysis revealed large differences among the nine gridded precipitation datasets, and most datasets underestimated the annual precipitation in the low-elevation regions and overestimated that in the high Himalayas. Furthermore, through the adjustments of model parameters, the underestimations of precipitation by gridded datasets were compensated for by the overestimation of glacial melt. Therefore, the model performance was better, and the model uncertainty was lower in basins with higher glacial coverage. Notably, our findings indicated that the performances of gridded precipitation datasets were heterogeneous and that their direct use in hydrological modelling may result in unreasonable hydrological process variables; thus, these datasets should be evaluated and used with caution in water resource assessments in Himalayan regions.

Evaluation of the convection permitting regional climate model CNRM-AROME on the orographically complex island of Corsica

AbstractMeteorological processes over islands with complex orography could be better simulated by Convection Permitting Regional Climate Models (CP-RCMs) thanks to an improved representation of the orography, land–sea contrasts, the combination of coastal and orographic effects, and explicit deep convection. This paper evaluates the ability of the CP-RCM CNRM-AROME (2.5-km horizontal resolution) to simulate relevant meteorological characteristics of the Mediterranean island of Corsica for the 2000–2018 period. These hindcast simulations are compared to their driving Regional Climate Model (RCM) CNRM-ALADIN (12.5-km horizontal resolution and parameterised convection), weather stations for precipitation and wind and gridded precipitation datasets. The main benefits are found in the representation of (i) precipitation extremes resulting mainly from mesoscale convective systems affected by steep mountains during autumn and (ii) the formation of convection through thermally induced diurnal circulations and their interaction with the orography during summer. Simulations of hourly precipitation extremes, the diurnal cycle of precipitation, the distribution of precipitation intensities, the duration of precipitation events, and sea breezes are all improved in the 2.5-km simulations with respect to the RCM, confirming an added value. However, existing differences between model simulations and observations are difficult to explain as the main biases are related to the availability and quality of observations, particularly at high elevations. Overall, better results from the 2.5-km resolution, increase our confidence in CP-RCMs to investigate future climate projections for Corsica and islands with complex terrain.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Characterizing the performances of different observational precipitation products and their uncertainties over Africa

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.

A high-resolution map of diffuse groundwater recharge rates for Australia

Abstract. Estimating groundwater recharge rates is important to understand and manage groundwater. Numerous studies have used collated recharge datasets to understand and project regional- or global-scale groundwater recharge rates. However, recharge estimation methods all have distinct assumptions, quantify different recharge components and operate over different temporal scales. We use over 200 000 groundwater chloride measurements to estimate groundwater recharge rates using an improved chloride mass balance (CMB) method across Australia. Groundwater recharge rates were produced stochastically using gridded chloride deposition, runoff and precipitation datasets. After filtering out groundwater recharge rates where the assumptions of the method may have been compromised, 98 568 estimates of recharge were produced. The resulting groundwater recharge rates and 17 spatial datasets were integrated into a random forest regression algorithm, generating a high-resolution (0.05°) model of groundwater recharge rates across Australia. The regression reveals that climate-related variables, including precipitation, rainfall seasonality and potential evapotranspiration, exert the most significant influence on groundwater recharge rates, with vegetation (the normalised difference vegetation index or NDVI) also contributing significantly. Importantly, the mean values of both the recharge point dataset (43.5 mm yr−1) and the spatial recharge model (22.7 mm yr−1) are notably lower than those reported in previous studies, underscoring the prolonged timescale of the CMB method, the potential disparities arising from distinct recharge estimation methodologies and limited averaging across climate zones. This study presents a robust and automated approach to estimate recharge using the CMB method, offering a unified model based on a single estimation method. The resulting datasets, the Python script for recharge rate calculation and the spatial recharge models collectively provide valuable insights for water resource management across the Australian continent, and similar approaches can be applied globally.

A Spatiotemporal Assessment of the Precipitation Variability and Pattern and an Evaluation of the Predictive Reliability of Global Climate Models over Bihar

Climate change is significantly altering precipitation patterns, leading to spatiotemporal changes throughout the world. In particular, the increased frequency and intensity of extreme weather events, leading to heavy rainfall, floods, and droughts, have been a cause of concern. A comprehensive understanding of these changes in precipitation patterns on a regional scale is essential to enhance resilience against the adverse effects of climate change. The present study, focused on the state of Bihar in India, uses a long-term (1901–2020) gridded precipitation dataset to analyze the effect of climate change. Change point detection tests divide the time series into two epochs: 1901–1960 and 1961–2020, with 1960 as the change point year. Modified Mann–Kendall (MMK) and Sen’s slope estimator tests are used to identify trends in seasonal and annual time scales, while Centroidal Day (CD) analysis is performed to determine changes in temporal patterns of rainfall. The results show significant variability in seasonal rainfall, with the nature of pre-monsoon and post-monsoon observed to have flipped in second epoch. The daily rainfall intensity during the monsoon season has increased considerably, particularly in north Bihar, while the extreme rainfall has increased by 60.6 mm/day in the second epoch. The surface runoff increased by approximately 13.43% from 2001 to 2020. Further, 13 Global Climate Models (GCMs) evaluate future scenarios based on Shared Socioeconomic Pathways (SSP) 370 and SSP585. The suitability analysis of these GCMs, based on probability density function (PDF), monthly mean absolute error (MAE), root mean square error (RMSE) and percentage bias (P-Bias), suggests that EC-Earth3-Veg-LR, MIROC6, and MPI-ESM1-2-LR are the three best GCMs representative of rainfall in Bihar. A Bayesian model-averaged (BMA) multi-model ensemble reflects the variability expected in the future with the least uncertainty. The present study’s findings clarify the current state of variability, patterns and trends in precipitation, while suggesting the most appropriate GCMs for better decision-making and preparedness.

Utilizing Hybrid Machine Learning Techniques and Gridded Precipitation Data for Advanced Discharge Simulation in Under-Monitored River Basins

This study addresses the challenge of utilizing incomplete long-term discharge data when using gridded precipitation datasets and data-driven modeling in Iran’s Karkheh basin. The Multilayer Perceptron Neural Network (MLPNN), a rainfall-runoff (R-R) model, was applied, leveraging precipitation data from the Asian Precipitation—Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE), Global Precipitation Climatology Center (GPCC), and Climatic Research Unit (CRU). The MLPNN was trained using the Levenberg–Marquardt algorithm and optimized with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Input data were pre-processed through principal component analysis (PCA) and singular value decomposition (SVD). This study explored two scenarios: Scenario 1 (S1) used in situ data for calibration and gridded dataset data for testing, while Scenario 2 (S2) involved separate calibrations and tests for each dataset. The findings reveal that APHRODITE outperformed in S1, with all datasets showing improved results in S2. The best results were achieved with hybrid applications of the S2-PCA-NSGA-II for APHRODITE and S2-SVD-NSGA-II for GPCC and CRU. This study concludes that gridded precipitation datasets, when properly calibrated, significantly enhance runoff simulation accuracy, highlighting the importance of bias correction in rainfall-runoff modeling. It is important to emphasize that this modeling approach may not be suitable in situations where a catchment is undergoing significant changes, whether due to development interventions or the impacts of anthropogenic climate change. This limitation highlights the need for dynamic modeling approaches that can adapt to changing catchment conditions.

Developing high resolution monthly gridded precipitation dataset for Afghanistan

Developing high resolution monthly gridded precipitation dataset for Afghanistan

Integrated Evaluation and error decomposition of four gridded precipitation products using dense rain gauge observations over the Yunnan-Kweichow Plateau, China

ABSTRACT Evaluating the precision and applicability of high-quality precipitation products in the distinctive terrain and intricate climate of the Yunnan-Kweichow Plateau (YKP) is pivotal for climate research. This study comprehensively assesses four gridded precipitation datasets (AERA5-Asia, AIMERG, ERA5-Land, and IMERG-Final) using the China Meteorological Administration’s surface precipitation data. It employs eight statistical indicators and error decomposition methods at various spatiotemporal scales. The main findings are as follows: (1) AERA5-Asia, AIMERG, and IMERG-Final show similar precipitation patterns, with ERA5-Land overestimating. While all display minor seasonal variations, AERA5-Asia underestimates summer rain. ERA5-Land tends to overstate, whereas AIMERG and IMERG-Final are generally accurate but slightly undervalued in southern YKP. (2) Hourly analysis reveals AERA5-Asia leads in performance metrics (CC: 0.23, MAE: 0.49 mm/hour, RMSE: 0.18 mm/hour, CSI: 0.27). In contrast, ERA5-Land lags, marked by the lowest BIAS (35.39%), FAR (0.74), and FBI (2.85). AIMERG and IMERG-Final display comparable results but underperform in CC (0.16, 0.13), POD (0.31, 0.30), and CSI (0.19, 0.18). (3) False bias significantly contributes to the total bias of precipitation products. AERA5-Asia and AIMERG mitigate total bias and enhance false precipitation situations through calibration algorithms, albeit introducing missed bias in the central region of YKP. The study findings offer valuable insights into YKP precipitation, informing the development of grid-based fusion algorithms in the region’s complex terrain.

Long‐term variability and trends of meteorological droughts in Ukraine

AbstractThe purpose of this research is to identify changes in the spatiotemporal distribution and severity of meteorological droughts in Ukraine between 1946 and 2020. In order to determine the role of precipitation and atmospheric evaporative demand on drought severity, two drought indices were applied for comparative analysis: the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). Both indices were calculated on 3‐ and 12‐month timescales. The gridded dataset of monthly air temperature and atmospheric precipitation for Ukraine with high spatial resolution (0.1° × 0.1°), developed in the Ukrainian Hydrometeorological Institute, was used. Results of time series analysis of drought indices showed at least 20 episodes, in which more than 25% of the territory of Ukraine was affected by drought of varying intensity. Trends show an increase in drought severity determined mostly by a strong observed increase in the atmospheric evaporative demand. Tendencies towards an increase in the duration and intensity of droughts were observed mainly in the southwestern, central and northern regions of Ukraine. The main cause of the increase was droughts in spring and summer. The temporal analysis of the spatial distribution of areas affected by drought during the identified main drought episodes showed different patterns in drought propagation across the territory of Ukraine.

Assessment of 30 gridded precipitation datasets over different climates on a country scale

Assessment of 30 gridded precipitation datasets over different climates on a country scale

Multi-dimensional evaluation of four monthly long-term and high-resolution gridded precipitation datasets on the Qinghai-Tibet Plateau

The reliability assessment of gridded precipitation datasets (GPDs) is vital for various precipitation-related studies, particularly in poorly gauged regions as the Qinghai-Tibet Plateau (QTP). However, existing studies predominantly focuses more on mainstream GPDs with low spatiotemporal resolution, making it challenging to meet the requirements for widespread applications. There is an urgent need for a systematic assessment of long-term and high-resolution GPDs. In this study, we considered four GPDs (HRLT, TPHiPr, PENG, and CHIRPS) with a spatial resolution above 0.05° during 1981–2019, evaluating their monthly performance across three dimensions: spatial accuracy, temporal accuracy, and precipitation event detection ability. The results revealed the following: (1) TPHiPr exhibited the highest spatial accuracy (R2 = 0.81), and the four GPDs consistently performed the worst in arid areas (R2 = 0.59 ± 0.15) and the best in semi-humid areas (R2 = 0.80 ± 0.02). (2) PENG attained the highest temporal accuracy (NSE = −0.56), and the four GPDs performed the worst in winter (NSE = -1.06 ± 0.9) and the best in autumn (NSE = 0.08 ± 0.16). (3) HRLT and TPHiPr demonstrated superior precipitation event detection abilities compared to others. The four GPDs consistently exhibited high detection abilities for light monthly precipitation events (0–10 mm) and low detection abilities for heavy monthly precipitation events (150–200 mm). This study underscored the potential advantages of GPDs in the three dimensions, and also highlighted their limitations in arid areas, winter, and heavy precipitation events, thus providing directions for further GPDs' accuracy improvement.

Evaluation of gridded precipitation data in water availability modeling in CONUS

Water availability estimates are affected by the quality of data used. In this study precipitation data from meteorological forcing datasets specifically compiled for large-scale hydrological modeling are evaluated over the Conterminous United States (CONUS). The performance of the datasets was evaluated using Pearson Correlation (r), Nash Sutcliff Efficiency (NSE), Percent Bias (PBIAS), and Kling Gupta Efficiency (KGE) criterion. Three observation-based gridded precipitation data from the Inter-Sectoral Impact Model Intercomparison Project – ISIMIP (EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI), WATCH Forcing Data methodologyapplied to ERA-Interim data (WFDEI), and WFDE5 over land merged with ERA5 over the ocean (W5E5)) and one regional dataset (GRIDMET) were compared. The datasets were compared using more than 3300 gage precipitation observations. For grid-level comparisons, the Climate Prediction Center's Unified gage-based gridded precipitation dataset (CPC) was used as a reference dataset after verification with gage observations. The grid-to-grid comparison was made on daily, monthly, annual, and seasonal time steps. Grid runoff and discharge were simulated using the Community Water Model (CWatM) model. The flow estimations using the datasets were compared with CPC estimations and with more than 900 discharge observations distributed over the nine climate regions of CONUS. Furthermore, the per capita water availability was estimated and compared. In comparison with gage precipitation observation, CPC had superior performance, followed by GRIDMET. CPC resulted in daily KGE, NSE, and r above 0.5, and PBIAS within 25 % in 79 %, 69 %, 87 %, and 90 % of the stations, respectively. Grid-to-grid precipitation comparisons resulted in overall comparable performance between the datasets. In grid discharge estimation, GRIDMET (median KGE = 0.68) had the highest correlation with CPC. WFDEI, W5E5, and EWEMBI show close performance to each other in estimating grid discharge with median KGE values of 0.43, 0.25, and 0.45 respectively. The performance of the datasets as compared to observed stream flow gages also showed comparable performance with above zero KGE in 73, 56, 71, 60, and 70 stations by GRIDMET, EWEMBI, W5E5, and WFDEI, and CPC respectively. In estimating water availability per capita, W5E5 showed an overestimation as compared to ISMIP datasets. The estimations from CPC and GRIDMET were close to each other. This study serves as a valuable resource for guiding the selection of a suitable dataset for large-scale modeling over the CONUS. By providing performance information for the compared datasets, it assists in making informed data selection and contributes to our understanding of how the selection of input datasets affect water availability modeling.

Assessing the sensitivity of modelled water partitioning to global precipitation datasets in a data‐scarce dryland region

AbstractPrecipitation is the primary driver of hydrological models, and its spatial and temporal variability have a great impact on water partitioning. However, in data‐sparse regions, uncertainty in precipitation estimates is high and the sensitivity of water partitioning to this uncertainty is unknown. This is a particular challenge in drylands (semi‐arid and arid regions) where the water balance is highly sensitive to rainfall, yet there is commonly a lack of in situ rain gauge data. To understand the impact of precipitation uncertainty on the water balance in drylands, here we have performed simulations with a process‐based hydrological model developed to characterize the water balance in arid and semi‐arid regions (DRYP: DRYland water Partitioning model). We performed a series of numerical analyses in the Upper Ewaso Ng'iro basin, Kenya driven by three gridded precipitation datasets with different spatio‐temporal resolutions (IMERG, MSWEP, and ERA5), evaluating simulations against streamflow observations and remotely sensed data products of soil moisture, actual evapotranspiration, and total water storage. We found that despite the great differences in the spatial distribution of rainfall across a climatic gradient within the basin, DRYP shows good performance for representing streamflow (KGE >0.6), soil moisture, actual evapotranspiration, and total water storage (r >0.5). However, the choice of precipitation datasets greatly influences surface (infiltration, runoff, and transmission losses) and subsurface fluxes (groundwater recharge and discharge) across different climatic zones of the Ewaso Ng'iro basin. Within humid areas, evapotranspiration does not show sensitivity to the choice of precipitation dataset, however, in dry lowland areas it becomes more sensitive to precipitation rates as water‐limited conditions develop. The analysis shows that the highest rates of precipitation produce high rates of diffuse recharge in Ewaso uplands and also propagate into runoff, transmission losses and, ultimately focused recharge, with the latter acting as the main mechanism of groundwater recharge in low dry areas. The results from this modelling exercise suggest that care must be taken in selecting forcing precipitation data to drive hydrological modelling efforts, especially in basins that span a climatic gradient. These results also suggest that more effort is required to reduce uncertainty between different precipitation datasets, which will in turn result in more consistent quantification of the water balance.