Satellite-based Precipitation Research Articles

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Evaluating the accuracy of APHRODITE and CHIRPS satellite-based Precipitation products for meteorological drought monitoring

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

Drought deciles index for spatial and temporal assessment of satellite-based precipitation datasets

Obtaining precise and long-term precipitation datasets is vital for addressing hydrological and climatic concerns. Limitation of drought recurrent characterizing and its impact in Iraq have been hampered primarily by unavailability of reliable climate data sets. This work assesses the performance of four gridded precipitation products (GPDs), APHRODITE for the period (1983–2007), CHIRPS for the period (1983–2017), PERSIANN for the period (1983–2017) to evaluate their potential for drought monitoring. This was accomplished by analyzing their ability to estimate the Drought Deciles Index (DDI) at multiple time scales (1-month, 3-month, 6-month, 12-month, and 24-month) across 22 ground stations (GS) in semi-arid Iraq during the period 1970–2018. The study area was divided into three zones based on Köppen's climate classifications. The results confirmed the potential of GPDs for monitoring drought in Iraq. Their performance was stronger for shorter time scales with time scales of 1, 3, and 6 and becomes weaker for time scales of 12 and 24. Furthermore, better performance of GPD found at locations with higher annual precipitation rates and altitudes. Overall, APHRODITE and TRMM showed the greatest potential for accurately representing GS. In addition, it is revealed that the DDI's was capable for effectively analyzing and monitoring drought periods in Iraq.

Extreme precipitation detection ability of four high-resolution precipitation product datasets in hilly area: a case study in Nepal

Given Nepal's vulnerability to extreme precipitation (EP), it is imperative to conduct a comprehensive analysis to comprehend the historical trends of such events. However, acquiring precise precipitation data for EP remains challenging in mountainous countries like Nepal owing to the scarcity of densely gauged networks. This limitation impedes the dissemination of knowledge pertaining to EP variability events in Nepal. The current research on this topic is deficient for two main reasons: 1) there is a lack of studies leveraging recently released high-resolution precipitation products to identify their EP detection capabilities, which further hinders the usability of those products in data-scarce regions like Nepal, and 2) most studies have focused on the characterisation of EP events in Nepal rather than their spatial and temporal variability. To address these issues, this study evaluated the EP detection capabilities of four high-resolution precipitation product datasets (PPDs) across Nepal from 1985 to 2020. These datasets include the ERA5 Land reanalysis data, satellite-based precipitation data (PERSIANN_CCS_CDR and CHIRPS_V2.0) and a merged dataset (TPHiPr). We used various statistical and categorical indices to assess their ability to capture the spatial and temporal variability of EP events. The annual EP events were characterised by 11 indices divided into frequency and intensity categories. The TPHiPr merged dataset offered a robust depiction of monthly precipitation estimates, achieving the highest critical success index, accuracy, probability of detection and a low false alarm ratio for daily precipitation detection of 0.1 mm in Nepal. Conversely, the PERSIANN_CCS_CDR dataset exhibited poor performance. Most PPDs showed increasing trends in EP indices. However, the TPHiPr dataset showcased those trends with fewer errors and stronger correlations for many frequency (R10mm, R20mm and R25mm) and intensity (RX1day, RX5day, PRCPTOT and R99p) indices. The results indicate that TPHiPr outperformed other PPDs in accurately representing the spatial distribution of EP trends in Nepal from 1985 to 2020, particularly noting an exacerbation of EP events mostly in the eastern region of Nepal throughout the study period. While TPHiPr demonstrated superior performance in detecting various EP indices across Nepal, individual products like the ERA5 Land reanalysis dataset showed enhanced performance in the western region of Nepal. Conversely, PERSIANN_CCS_CDR and CHIRPS_V2.0 performed well in the eastern region compared to other PPDs.

Evaluation and Correction of Satellite-Based Rainfall Products (GPM) in the Upstream Part of Bengawan Solo Basin using MCCV Parameterization

In conservative manner, water managers utilize ground station dataset for hydrology analysis. Due to spatially sparse and unreliable precipitation gauges, many have shifted to the application of remote sensing products or satellite-based precipitation products (SBPPs). One of few notable SBPPs is the Global Precipitation Measurement (GPM), which has shown excellent performances. Despite its significant progress, systematic biases are inevitable in the process of recording precipitation. Consequently, a simple yet effective correction process is necessary to be carried out prior to SBPPs’ wide applications. This study proposes a non-complex transformation using Monte Carlo-Cross Validation (MCCV) for its function parameterization. The rainfall products in focus are the monthly GPM 3IMERGDF V06 in the Upper Bengawan Solo Basin, in Central Java. Evaluation using several statistical functions demonstrate that the GPM IMERG is capable in capturing the monthly rainfall, supporting by the mean correlation of 0,76. After the correction process, the GPM IMERG slightly improves its performance, indicated by the reduced error of approximately 16%. This correction procedure also allows for correction of the monthly rainfall overestimation, particularly those exceeding 600 mm/month.

Validation of TRMM and GPM Satellite Data Using Daily Precipitation Observations

Accurate precipitation data holds immense significance in hydrological analysis. A common challenge in this field often stems from the lack of comprehensive data availability. High-resolution satellite-based precipitation measurements covering large areas offer a potential solution. However, disparities in the resolution of observed rainfall data can impact data accuracy. The main goal of this study is to evaluate the accuracy of rainfall data obtained from the TRMM and GPM satellites in the Kuranji watershed. The evaluation was conducted on the performance of the GPM IMERG-F from the Integrated Multi-satellite Retrievals for the GPM mission and the TRMM 3B42RT on a daily scale spanning from 2015 to 2019 over the Kuranji watershed. The daily precipitation measurements were validated using three widely used statistical metrics (R, RMSE, and RB). The precipitation detection capability (POD, FAR, and CSI) was also considered in this assessment. The findings demonstrate that both satellite estimations exhibit a substantial correlation coefficient (0.68 for GPM, 0.62 for TRMM) with the measurements obtained from gauges, along with an inclination to overestimate precipitation. GPM IMERG-F and TRMM 3B42RT manifest a consistent spatial pattern in daily precipitation distribution, effectively representing the observed precipitation distribution. The greater probability of detection (POD), critical success index (CSI), and lower false alarm ratio (FAR) exhibited by GPM IMERG-F at varying rainfall intensities suggests its superior performance in accurately identifying observed precipitations. This finding supports the preference for GPM IMERG-F data over TRMM 3B42RT data across various applications, hydrology, and related disciplines in the future.

Investigating the Relationship between Precipitation and Vegetation Dynamics with Emphasis on Agricultural Land Cover in the Atrak Basin Area

The present study aimed to analyze the dynamics of vegetation within the Atrak catchment area, as well as its interplay with precipitation patterns. Moreover, additional emphasis was placed on exploring the impact of these dynamics on agricultural land cover type. To achieve this objective, the Enhanced Vegetation Index (EVI) derived from MODIS data and the Comprehensive Historical and Real-Time Satellite-based Precipitation (CHRIPS) data were utilized for the period from 2003 to 2021. Additionally, the Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI) were employed to discern various degrees of drought and pluvial years within the Atrak basin. The study revealed that the years 2008, 2014, 2017, and 2021 exhibited the lowest vegetation coverage, while the years 2010, 2016, and 2019 showcased the most extensive vegetation extent. Notably, it was revealed from the VCI index that the year 2008 was the driest, and the year 2016 was the wettest. Furthermore, based on the SPI index findings, the years 2007, 2019, and 2020 were identified as pluvial years, while in the years 2008, 2014, and 2021 drought conditions occurred. All other years were classified as exhibiting normal conditions. Regarding seasonality, the observations ascertain that the spring season substantiates the most extensive vegetation cover, and a high correlation between spring precipitation and vegetation coverage was observed. Additionally, the anomaly detection outcomes indicate that the eastern regions of the basin have experienced an upward trend compared to the average of the first decade of the studied period.

Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions

Exploring the spatio-temporal variability of four satellite-based precipitation products (SPPs) in northern Morocco: a comparative study of complex climatic and topographic conditions

Evaluation of ERA5 and CHIRPS rainfall estimates against observations across Ethiopia

Satellite-based precipitation estimates and global reanalysis products bear the promise of supporting the development of accurate and timely climate information for end users in sub-Sharan Africa. The accuracy of these global models, however, may be reduced in data-scarce regions and should be carefully evaluated. This study evaluates the performance of ERA5 reanalysis data and CHIRPS precipitation data against ground-based measurements from 167 rain gauges in Ethiopia, a region with complex topography and diverse climates. Focusing over a 38-year period (1981–2018), our study utilizes a point-to-pixel analysis to compare daily, monthly, seasonal, and annual precipitation data, conducting an evaluation based on continuous and categorical metrics. Our findings indicate that over Ethiopia CHIRPS generally outperforms ERA5, particularly in high-altitude areas, demonstrating a better capability in detecting high-intensity rainfall events. Both datasets, however, exhibit lower performance in Ethiopia's lowland regions, possibly the influence of sparse rain gauge networks informing gridded datasets. Notably, both CHIRPS and ERA5 were found to underestimate rainfall variability, with CHIRPS displaying a slight advantage in representing the erratic nature of Ethiopian rainfall. The study’s results highlight considerable performance differences between CHIRPS and ERA5 across varying Ethiopian landscapes and climatic conditions. CHIRPS’ effectiveness in high-altitude regions, especially for daily rainfall estimation, emphasizes its suitability in similar geographic contexts. Conversely, the lesser performance of ERA5 in these areas suggests a need for refined calibration and validation processes, particularly for complex terrains. These insights are essential for the application of satellite-based and reanalysis of rainfall data in meteorological, agricultural, and hydrological contexts, particularly in topographically and climatically diverse regions.

Regional Precipitation Regimes and Evaluation of National Precipitation Datasets against Satellite-Based Precipitation Estimates, Republic of Georgia

Abstract Analyzing water resources in areas with few hydrometeorological stations, such as those in post-Soviet countries, is difficult due to station closures after 1989. In Caucasus, evaluations often rely on outdated data from nearby rivers. We evaluated one national-level precipitation dataset, the Water Balance of Georgia (WBG) with two satellite-based precipitation products from 1981 to 2021, including the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data and CHIRPS blended with a dense rain gauge network (geoCHIRPS). We modeled mean annual precipitation from geoCHIRPS as a function of coastal distance and elevation. CHIRPS underestimated precipitation in the cold and wet seasons (R2 = 0.74, r = 0.86) and overestimated dry season precipitation, while geoCHIRPS performed well in all seasons (R2 = 0.86, r = 0.92). Distance from the coast was a more important predictor of precipitation than elevation in western Georgia, while precipitation correlated positively with elevation in the east. At four hydroelectric plants, the underperformance as a percentage of capacity (∼37%) corresponds with the percentage difference between differences in precipitation products (∼38%), suggesting that plants designed based on WBG may be systematically overdesigned, but further work is needed to determine the reasons for the underperformance of the plants and frequency. We conclude that 1) the existing WBG does not accurately reflect elevation–precipitation relationships near the coast, and 2) for accurate analysis of spatiotemporal precipitation variability and its impacts on hydropower generation and environmental and sustainable water resource management, it is essential to calibrate satellite-based precipitation estimates with additional rain gauge data.

Spatiotemporal evaluation of five satellite-based precipitation products under the arid environment of Saudi Arabia

In arid regions like Saudi Arabia, accurate precipitation data are crucial for water resource management and climate studies. However, satellite-based precipitation products (SPPs) can contain uncertainties, impacting their reliability. This study evaluated the accuracy of five high-resolution SPPs [IMERG-V06 variants, Tropical Rainfall Measuring Mission (TRMM)-3B42V7, and Soil Moisture to Rain (SM2RAIN)-Advanced SCATterometer (ASCAT)] over Saudi Arabia. We compared daily, monthly, and yearly precipitation estimates from SPPs with in situ rain gauge data (2010–2022) using both continuous and categorical metrics. The evaluation encompassed point-to-pixel comparisons, regional analysis, and national assessments. All SPPs effectively captured the spatiotemporal patterns of precipitation across the country. Notably, monthly estimates showed stronger agreement with rain gauge data than daily estimates, as indicated by higher correlation coefficients. IMERG products generally outperformed SM2RAIN-ASCAT and TRMM, with IMERG-LR exhibiting superior performance in estimating monthly precipitation. However, underestimation of light precipitation events (<2 mm/day) was observed across all SPPs. In addition, their ability to detect moderate and heavy precipitation events remained uncertain, requiring further investigation. While IMERG-FR showed reduced bias and root mean square error compared to IMERG-ER and IMERG-LR, its capability for precipitation event detection did not exhibit significant improvement. This study highlights the need for bias correction of IMERG-LR and IMERG-FR monthly estimates for improved application in hydrometeorological studies in Saudi Arabia. Our findings contribute valuable insights for both data users and SPP algorithm developers, aiming to enhance the accuracy and reliability of satellite-derived precipitation data in arid environments.

Statistical evaluation of satellite-based CHIRPS precipitation data averaged over the midland and highland regions of Kidangoor sub-catchment, Kerala

The steep topographical setting of Kerala, traversing from Western Ghats in the east to the sandy beaches on the west, demands the use of precipitation data at a very fine spatio-temporal resolution for a range of hydrological and hydrometeorological studies. The limitation of the existing rain gauge network data in representing the variability in the monsoon showers received, across the physiographic divisions of the state, could be overcome using satellite rainfall dataset offered at a finer resolution. In this paper, a statistical evaluation of the satellite derived CHIRPS (Climate Hazards Group Infrared Precipitation with Stations) precipitation data for the Kidangoor sub-catchment was performed by comparing it with station rainfall data and IMD gridded data sets. The homogeneity test at 95 % confidence level classified the station data under ‘useful’ category. Additionally, the statistical performance matrices suggested that the CHIRPS data slightly underestimated the observed station rainfall data. However, the coefficient of determination R2 values (0.95-0.97) in the monthly series and (0.37 - 0.64) in the annual series demonstrated a strong to moderate positive correlation between the datasets. To summarize, the quantitative statistical performance matrices, evaluated for the first time in the study area, proposed that the CHIRPS rainfall estimates could very well reproduce the ground-based monthly rainfall datasets and could also serve as a good replacement for IMD gridded data.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

Efficiency of global precipitation datasets in tropical and subtropical catchments revealed by large sampling hydrological modelling

Satellite-based and reanalysis precipitation products are widely adopted as complementary information to in situ measurements for estimating river discharge using hydrological modelling. However, there is still a notable research gap in the literature associated with assessing the accuracy of satellite-based or reanalysis products in different tropical and sub-tropical catchments at large-sampling hydrological modelling with sensitivity analysis. We investigated the accuracy of precipitation, hydrological model performance and parameter sensitivity related to seven precipitation data sets based on satellite and reanalysis products, i.e., CHIRPS, TRMM, GLDAS, IMERG, MERRA-2, PERSIANN-CDR, and ERA5 over 714 contrasting tropical and subtropical catchments located in Brazil. We used the Génie Rural Journalier 4 (GR4J) hydrological model to simulate the hydrological processes of the different catchments with two approaches for the calibration: using measured ground-based precipitation data (approach I) or using each individual satellite/reanalysis precipitation products (approach II) to calibrate the models. The results showed that the precipitation products tend to overestimate precipitation, with the exception of ERA5 and MERRA-2. CHIRPS is the only product that produces unbiased precipitation estimates for most catchments. The model calibration using each precipitation product individually improved the hydrological model performance. CHIRPS, IMERG, and MERRA-2 showed good accuracy in terms of both, precipitation estimation and hydrological simulation performance in the calibration period. In the validation period, the best products in terms of KGE were CHIRPS, IMERG and TRMM (KGE > 0.64). The errors in precipitation products are better compensated via hydrological modelling in wet regions. The model parameter sensitivity varies according to precipitation input, climate, and catchment aridity. Overall, all seven products exhibited their worst hydrological performance in arid regions. This study helps to improve our understanding of the catchment response in tropical and subtropical regions while also providing key insights into the reliability of satellite/reanalysis rainfall products for streamflow simulation. This study is valuable in hydrometeorological applications, climate change assessment, water resources and disaster management, especially in regions with a relatively sparse density of precipitation stations.

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.

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China.

In hydrological studies, satellite and reanalysis precipitation products are increasingly being used to supplement gauge observation data. This study designed the composite simulation index (COSI), considering two factors: F1 (data accuracy assessment) and F2 (hydrological simulation performance), to compare the performance of the latest satellite-based and reanalysis-based precipitation products (IMERG, ERA5, ERA5-Land), the prior precipitation products (TRMM, CMADS), and the multi-source weighted-ensemble precipitation (MSWEP). The Soil and Water Assessment Tool (SWAT) model was then applied to compare and analyze the hydrological simulation performance of four preferred products using three data fusion methods including simple model averaging, variance-based weighted averaging, and the latest quantile-based Bayesian model averaging (QBMA). The results can be summarized as follows: (1) Reanalysis products are superior to satellite-based products in terms of F1. However, the satellite-based precipitation products exhibit less BIAS and relatively higher F2, while the MSWEP has relatively high performance on both F1 and F2. (2) Among reanalysis-based precipitation products, CMADS has the best COSI value of 0.53. Although ERA5-Land shows good performance for individual parameters, the comprehensive assessment reveals that ERA5 outperforms ERA5-Land in terms of both F1 and F2. (3) IMERG and TRMM exhibit similar spatiotemporal patterns and similar F1, but IMERG is superior in F2. (4) QBMA outperformed traditional methods in F2, improving the NS coefficient of SWAT model from 0.74 to 0.85. These findings provide a useful reference for analyzing the strengths and limitations of satellite-based and reanalysis precipitation products, and also provide valuable ideas for the combined application of multi-source precipitation products in hydrological studies.

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

Satellite-based precipitation error propagation in the hydrological modeling chain across China

Satellite-based precipitation products have great potential to promote hydrological modeling skill owing to the spatially homogeneous measurements they provide. However, satellite-based precipitation uncertainties and their hydrological impacts are poorly understood for different products and hydroclimate regions in China. This study comprehensively evaluates the error properties of ten satellite-based precipitation products and the precipitation error propagation characteristics in the hydrological modeling chain during the period 2001–2018. The results indicate that precipitation error tends to become smaller during the routing process for most satellite precipitation products; e.g., αRMAE, an error propagation ratio based on relative mean absolute error, decreased about 26% (from 0.58 to 0.43) when considering routing processes during hydrological simulation by using the PERSIANN-CDR satellite precipitation product. The linear slope of simulated streamflow against precipitation error greatly decreases (compared to that of simulated runoff) after flow routing of 366 catchments across China. This study also found that satellite-based precipitation product errors in hydrological simulations for drier and water-limited small catchments are more likely for amplify in the streamflow simulations. These findings are essential to improving understanding of the interactions between satellite-derived precipitation forcing and hydrological processes, and thus for improving the data accuracy of precipitation forcing for hydrological simulation in China.

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Comparison of satellite-based and reanalysis precipitation products for hydrological modeling over a data-scarce region

Assessment of Satellite-Based Precipitation Estimates over Egypt

Assessment of Satellite-Based Precipitation Estimates over Egypt