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Search Results (114)

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Keywords = PERSIANN-CDR

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21 pages, 6342 KiB  
Article
Enhancing Transboundary Water Governance Using African Earth Observation Data Cubes in the Nile River Basin: Insights from the Grand Ethiopian Renaissance Dam and Roseries Dam
by Baradin Adisu Arebu, Esubalew Adem, Fahad Alzahrani, Nassir Alamri and Mohamed Elhag
Water 2025, 17(13), 1956; https://doi.org/10.3390/w17131956 - 30 Jun 2025
Viewed by 554
Abstract
The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations [...] Read more.
The construction of the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile has heightened transboundary water tensions in the Nile River Basin, particularly affecting downstream Sudan and Egypt. This study leverages African Earth Observation Data Cubes, specifically Digital Earth Africa’s Water Observations from Space (WOfS) platform, to quantify the hydrological impacts of GERD’s three filling phases (2019–2022) on Sudan’s Roseires Dam. Using Sentinel-2 satellite data processed through the Open Data Cube framework, we analyzed water extent changes from 2018 to 2023, capturing pre- and post-filling dynamics. Results show that GERD’s water spread area increased from 80 km2 in 2019 to 528 km2 in 2022, while Roseires Dam’s water extent decreased by 9 km2 over the same period, with a notable 5 km2 loss prior to GERD’s operation (2018–2019). These changes, validated against PERSIANN-CDR rainfall data, correlate with GERD’s filling operations, alongside climatic factors like evapotranspiration and reduced rainfall. The study highlights the potential of Earth Observation (EO) technologies to support transparent, data-driven transboundary water governance. Despite the Cooperative Framework Agreement (CFA) ratified by six upstream states in 2024, mistrust persists due to Egypt and Sudan’s non-ratification. We propose enhancing the Nile Basin Initiative’s Decision Support System with EO data and AI-driven models to optimize water allocation and foster cooperative filling strategies. Benefit-sharing mechanisms, such as energy trade from GERD, could mitigate downstream losses, aligning with the CFA’s equitable utilization principles and the UN Watercourses Convention. This research underscores the critical role of EO-driven frameworks in resolving Nile Basin conflicts and achieving Sustainable Development Goal 6 for sustainable water management. Full article
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25 pages, 8903 KiB  
Article
Comparative Analysis of Satellite-Based Rainfall Products for Drought Assessment in a Data-Poor Region
by Hansini Gayanthika, Dimuthu Lakshitha, Manthika Chathuranga, Gouri De Silva and Jeewanthi Sirisena
Hydrology 2025, 12(7), 166; https://doi.org/10.3390/hydrology12070166 - 27 Jun 2025
Cited by 1 | Viewed by 438
Abstract
Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in [...] Read more.
Drought is one of the most impactful natural disasters, and it significantly impacts three main sectors of a country: the environment, society, and the economy. Therefore, drought assessment and monitoring are essential for reducing vulnerability and risk. However, insufficient and sparse long-term in situ rainfall data limit drought assessment in developing countries. Recently developed satellite-based rainfall products, available at different temporal and spatial resolutions, offer a valuable alternative in data-poor regions like Sri Lanka, where rain gauge networks are sparse and maintenance issues are prevalent. This study evaluates the accuracy of satellite-based rainfall estimates compared to in situ observations for drought assessment within the Mi Oya River Basin, Sri Lanka. We assessed the performance of various satellite-based rainfall products, including IMERG, GSMaP, CHIRPS, PERSIANN, and PERSIANN-CDR, by comparing them with ground-based observations over 20 years, from 2003 to 2022. Our methodology involved checking detection accuracy using the False Alarm Ratio (FAR), Probability of Detection (POD), and Critical Success Index (CSI), and assessing accuracy through metrics such as Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC), Percentage Bias (PBias), and Nash–Sutcliffe Efficiency (NSE). The two best-performing satellite-based rainfall products were used for meteorological and hydrological drought assessment. In the accuracy detection metrics, the results indicate that while products like IMERG and GSMaP generally provide reliable rainfall estimates, others like PERSIANN and PERSIANN-CDR tend to overestimate rainfall. For instance, IMERG shows a CSI range of 0.04–0.25 for moderate and heavy rainfall and 0.10–0.30 for light rainfall. On a monthly scale, IMERG and CHIRPS showed the highest performance, with CC (NSE) values of 0.81–0.94 (0.53–0.83) and 0.79–0.86 (0.54–0.74), respectively. However, GSMaP showed the lowest bias, with a range of −17.1–13.2%. Recorded drought periods over 1981–2022 (1998–2022) were reasonably well captured by CHIRPS (IMERG) products in the Mi Oya River Basin. Our results highlighted uncertainties and discrepancies in the capability of different rainfall products to assess drought conditions. This research provides valuable insights for optimizing the use of satellite rainfall products in hydrological modeling and disaster preparedness in the Mi Oya River Basin. Full article
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17 pages, 1235 KiB  
Article
Bias Correction Methods Applied to Satellite Rainfall Products over the Western Part of Saudi Arabia
by Ibrahim H. Elsebaie, Atef Q. Kawara, Raied Alharbi and Ali O. Alnahit
Atmosphere 2025, 16(7), 772; https://doi.org/10.3390/atmos16070772 - 24 Jun 2025
Cited by 1 | Viewed by 469
Abstract
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed [...] Read more.
Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments. Full article
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39 pages, 31656 KiB  
Article
Assessment of Satellite and Reanalysis Precipitation Data Using Statistical and Wavelet Analysis in Semi-Arid, Morocco
by Achraf Chakri, Nour-Eddine Laftouhi, Lahcen Zouhri, Hassan Ibouh and Mounsif Ibnoussina
Water 2025, 17(11), 1714; https://doi.org/10.3390/w17111714 - 5 Jun 2025
Viewed by 703
Abstract
Climate change, marked by decreasing rainfall and increasing extreme events, represents a major challenge for water resources, particularly in semi-arid regions. To estimate aquifer recharge, it is essential to assess the fraction of precipitation contributing to groundwater recharge and to implement a water [...] Read more.
Climate change, marked by decreasing rainfall and increasing extreme events, represents a major challenge for water resources, particularly in semi-arid regions. To estimate aquifer recharge, it is essential to assess the fraction of precipitation contributing to groundwater recharge and to implement a water balance model. However, the limited number of rainfall stations has led researchers to rely on satellite and reanalysis rainfall products. The accuracy of these datasets is essential for reliable hydrological modeling. In this study, we evaluated five rainfall products—CHIRPS, ERA5_Ag, CFSR, GPM, and PERSIANN-CDR—by comparing them to ground measurements from gauging stations in the central Haouz region of Marrakech. The evaluation was conducted at three temporal scales: daily, monthly, and annual. Statistical metrics, including RMSE, MAE, NSE, Bias, and Pearson correlation, as well as classification metrics (accuracy, F1 score, recall, precision, and Cohen’s Kappa), and wavelet analysis, were applied to assess the accuracy of the products. The results identified ERA5_Ag and GPM as the most accurate products in capturing rainfall events. Nevertheless, ERA5_Ag showed a high bias. After applying the quantile mapping method to correct the bias, the product exhibited greater accuracy. The corrected datasets from these two products will be used to estimate recharge over the last 30 years, contributing to the development of a hydrogeological model for groundwater dynamics. Full article
(This article belongs to the Special Issue Hydrogeological and Hydrochemical Investigations of Aquifer Systems)
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7 pages, 4672 KiB  
Proceeding Paper
Performance Evaluation of the ERA5, MERRA-2, and PERSIANN-CDR Gridded Products in the Tambo Basin
by Cristhian Apaza-Vilca, Maria Liz Mamani-Yupanqui and Efrain Lujano
Environ. Earth Sci. Proc. 2025, 32(1), 17; https://doi.org/10.3390/eesp2025032017 - 22 Apr 2025
Viewed by 402
Abstract
Gridded meteorological data help to address the scarcity of data in sparse hydrometeorological networks, but their validation is crucial. This study evaluated the performance of ERA5, MERRA-2, and PERSIANN-CDR gridded products in the Tambo basin, comparing their data with meteorological stations and basin-wide [...] Read more.
Gridded meteorological data help to address the scarcity of data in sparse hydrometeorological networks, but their validation is crucial. This study evaluated the performance of ERA5, MERRA-2, and PERSIANN-CDR gridded products in the Tambo basin, comparing their data with meteorological stations and basin-wide averages using the Pearson correlation coefficient (CC), percent bias (PBIAS), and root mean square error (RMSE). PERSIANN-CDR showed the best performance (CC: 0.84–0.94, PBIAS: 6.90–83.10%, RMSE: 21.97–38.78 mm/month). MERRA-2 underestimated precipitation, while ERA5, despite its high correlation (CC: 0.83–0.94), overestimated it. PERSIANN-CDR is the recommended product for the region, providing a better representation of precipitation for hydrological studies and water resource management. Full article
(This article belongs to the Proceedings of The 8th International Electronic Conference on Water Sciences)
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22 pages, 4618 KiB  
Article
Understanding Climate Change Impacts on Streamflow by Using Machine Learning: Case Study of Godavari Basin
by Ravi Ande, Chandrashekar Pandugula, Darshan Mehta, Ravikumar Vankayalapati, Prashant Birbal, Shashikant Verma, Hazi Mohammad Azamathulla and Nisarg Nanavati
Water 2025, 17(8), 1171; https://doi.org/10.3390/w17081171 - 14 Apr 2025
Viewed by 1104
Abstract
The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, [...] Read more.
The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R2 assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R2, and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m3/s. The test results were 0.681, 0.91, 0.828, and 41.237 m3/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts. Full article
(This article belongs to the Special Issue Climate Change and Hydrological Processes, 2nd Edition)
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18 pages, 3156 KiB  
Article
Integrating Satellite-Based Precipitation Analysis: A Case Study in Norfolk, Virginia
by Imiya M. Chathuranika and Dalya Ismael
Eng 2025, 6(3), 49; https://doi.org/10.3390/eng6030049 - 6 Mar 2025
Viewed by 849
Abstract
In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data [...] Read more.
In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then identifies the most effective methods based on statistical indicators, detection capability indices, and graphical methods. Bias adjustment methods include additive bias correction (ABC), which subtracts systematic errors; multiplicative bias correction (MBC), which scales satellite data to match observed data; and distribution transformation normalization (DTN), which aligns the statistical distribution of satellite data with observations. Additionally, the study addresses the uncertainties in SbPPs for estimating precipitation, preparing practitioners for the challenges in practical applications. The additive bias correction (ABC) method overestimated mean monthly precipitation, while the PERSIANN-Cloud Classification System (CCS), adjusted with multiplicative bias correction (MBC), was found to be the most accurate bias-adjusted model. The MBC method resulted in slight PBias adjustments of 0.09% (CCS), 0.10% (CDR), and 0.15% (PERSIANN) in mean monthly precipitation estimates, while the DTN method produced larger adjustments of 21.36% (CCS), 31.74% (CDR), and 19.27% (PERSIANN), with CCS, when bias corrected using MBC, identified as the most accurate SbPP for Norfolk, Virginia. This case study not only provides insights into the technical processes but also serves as a guideline for integrating advanced hydrological modeling and urban resilience strategies, contributing to improved strategies for climate change adaptation and disaster preparedness. Full article
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31 pages, 1004 KiB  
Article
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
by Matteo Bodini
Signals 2024, 5(4), 659-689; https://doi.org/10.3390/signals5040037 - 10 Oct 2024
Cited by 2 | Viewed by 2455
Abstract
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning [...] Read more.
Reliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area. Full article
(This article belongs to the Special Issue Rainfall Estimation Using Signals)
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22 pages, 4478 KiB  
Article
Evaluation of Climatological Precipitation Datasets and Their Hydrological Application in the Hablehroud Watershed, Iran
by Hossein Salehi, Saeid Gharechelou, Saeed Golian, Mohammadreza Ranjbari and Babak Ghazi
Water 2024, 16(7), 1028; https://doi.org/10.3390/w16071028 - 2 Apr 2024
Cited by 3 | Viewed by 1673
Abstract
Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation [...] Read more.
Hydrological modeling is essential for runoff simulations in line with climate studies, especially in remote areas with data scarcity. Advancements in climatic precipitation datasets have improved the accuracy of hydrological modeling. This research aims to evaluate the APHRODITE, PERSIANN-CDR, and ERA5-Land climatic precipitation datasets for the Hablehroud watershed in Iran. The datasets were compared with interpolated ground station precipitation data using the inverse distance weighted (IDW) method. The variable infiltration capacity (VIC) model was utilized to simulate runoff from 1992 to 1996. The results revealed that the APHRODITE and PERSIANN-CDR datasets demonstrated the highest and lowest accuracy, respectively. The sensitivity of the model was analyzed using each precipitation dataset, and model calibration was performed using the Kling–Gupta efficiency (KGE). The evaluation of daily runoff simulation based on observed precipitation indicated a KGE value of 0.78 and 0.76 during the calibration and validation periods, respectively. The KGE values at the daily time scale were 0.64 and 0.77 for PERSIANN-CDR data, 0.62 and 0.75 for APHRODITE precipitation data, 0.50 and 0.66 for ERA5-Land precipitation data during the calibration and validation periods, respectively. These results indicate that despite varying sensitivity, climatic precipitation datasets present satisfactory performance, particularly in poorly gauged basins with infrequent historical datasets. Full article
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43 pages, 37454 KiB  
Article
Comparing Remote Sensing and Geostatistical Techniques in Filling Gaps in Rain Gauge Records and Generating Multi-Return Period Isohyetal Maps in Arid Regions—Case Study: Kingdom of Saudi Arabia
by Ahmed M. Helmi, Mohamed I. Farouk, Raouf Hassan, Mohd Aamir Mumtaz, Lotfi Chaouachi and Mohamed H. Elgamal
Water 2024, 16(7), 925; https://doi.org/10.3390/w16070925 - 22 Mar 2024
Cited by 2 | Viewed by 5826
Abstract
Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation [...] Read more.
Arid regions are susceptible to flash floods and severe drought periods, therefore there is a need for accurate and gap-free rainfall data for the design of flood mitigation measures and water resource management. Nevertheless, arid regions may suffer from a shortage of precipitation gauge data, whether due to improper gauge coverage or gaps in the recorded data. Several alternatives are available to compensate for deficiencies in terrestrial rain gauge records, such as satellite data or utilizing geostatistical interpolation. However, adequate assessment of these alternatives is mandatory to avoid the dramatic effect of using improper data in the design of flood protection works and water resource management. The current study covers 75% of the Kingdom of Saudi Arabia’s area and spans the period from 1967 to 2014. Seven satellite precipitation datasets with daily, 3-h, and 30-min temporal resolutions, along with 43 geostatistical interpolation techniques, are evaluated as supplementary data to address the gaps in terrestrial gauge records. The Normalized Root Mean Square Error by the mean value of observation (NRMSE) is selected as a ranking criterion for the evaluated datasets. The geostatistical techniques outperformed the satellite datasets with 0.69 and 0.8 NRMSE for the maximum and total annual records, respectively. The best performance was found in the areas with the highest gauge density. PERSIANN-CDR and GPM IMERG V7 satellite datasets performed better than other satellite datasets, with 0.8 and 0.82 NRMSE for the maximum and total annual records, respectively. The spatial distributions of maximum and total annual precipitation for every year from 1967 to 2014 are generated using geostatistical techniques. Eight Probability Density Functions (PDFs) belonging to the Gamma, Normal, and Extreme Value families are assessed to fit the gap-filled datasets. The PDFs are ranked according to the Chi-square test results and Akaike information criterion (AIC). The Gamma, Extreme Value, and Normal distribution families had the best fitting over 56%, 34%, and 10% of the study area gridded data, respectively. Finally, the selected PDF at each grid point is utilized to generate the maximum annual precipitation for 2, 5, 10, 25, 50, and 100-year rasters that can be used directly as a gridded precipitation input for hydrological studies. Full article
(This article belongs to the Special Issue Remote Sensing-Based Study on Surface Water Environment)
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27 pages, 8061 KiB  
Article
Applicability of Precipitation Products in the Endorheic Basin of the Yellow River under Multi-Scale in Time and Modality
by Weiru Zhu and Kang Liang
Remote Sens. 2024, 16(5), 872; https://doi.org/10.3390/rs16050872 - 29 Feb 2024
Viewed by 1301
Abstract
Continuous and accurate precipitation data are critical to water resource management and eco-logical protection in water-scarce and ecologically fragile endorheic or inland basins. However, in typical data-scarce endorheic basins such as the endorheic basin of the Yellow River Basin (EBYRB) in China, multi-source [...] Read more.
Continuous and accurate precipitation data are critical to water resource management and eco-logical protection in water-scarce and ecologically fragile endorheic or inland basins. However, in typical data-scarce endorheic basins such as the endorheic basin of the Yellow River Basin (EBYRB) in China, multi-source precipitation products provide an opportunity to accurately capture the spatial distribution of precipitation, but the applicability evaluation of multi-source precipitation products under multi-time scales and multi-modes is currently lacking. In this context, our study evaluates the regional applicability of seven diverse gridded precipitation products (APHRODITE, GPCC, PERSIANN-CDR, CHIRPS, ERA5, JRA55, and MSWEP) within the EBYRB considering multiple temporal scales and two modes (annual/monthly/seasonal/daily precipitation in the mean state and monthly/daily precipitation in the extreme state). Furthermore, we explore the selection of suitable precipitation products for the needs of different hydrological application scenarios. Our research results indicate that each product has its strengths and weaknesses at different time scales and modes of coupling. GPCC excels in capturing annual, seasonal, and monthly average precipitation as well as monthly and daily extreme precipitation, essentially meeting the requirements for inter-annual or intra-annual water resource management in the EBYRB. CHIRPS and PERSIANN-CDR have higher accuracy in extreme precipitation assessment and can provide near real-time data, which can be applied as dynamic input precipitation variables in extreme precipitation warnings. APHRODITE and MSWEP exhibit superior performance in daily average precipitation that can provide data for meteorological or hydrological studies at the daily scale in the EBYRB. At the same time, our research also exposes typical problems with several precipitation products, such as MSWEP’s abnormal assessment of summer precipitation in certain years and ERA5 and JRA55’s overall overestimation of precipitation assessment in the study area. Full article
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17 pages, 5691 KiB  
Article
Estimating the CSLE Biological Conservation Measures’ B-Factor Using Google Earth’s Engine
by Youfu Wu, Haijing Shi and Xihua Yang
Remote Sens. 2024, 16(5), 847; https://doi.org/10.3390/rs16050847 - 28 Feb 2024
Cited by 5 | Viewed by 1736
Abstract
The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and [...] Read more.
The biological conservation measures factor (B) in the Chinese Soil loss Equation (CSLE) model is one of the main components in evaluating soil erosion, and the accurate calculation of the B-factor at the regional scale is fundamental in predicting regional soil erosion and dynamic changes. In this study, we developed an optimal computational procedure for estimating and mapping the B-factor in the Google Earth Engine (GEE) cloud computing environment using multiple data sources through data suitability assessment and image fusion. Taking the Yanhe River Basin in the Loess Plateau of China as an example, we evaluated the availability of daily precipitation data (CHIRPS, ERA5, and PERSIANN-CDR data) against the data at national meteorological stations. We estimated the B-factor from Sentinel-2 data and proposed a new method, namely the trend migration method, to patch the missing values in Sentinel-2 data using three other remote sensing data (MOD09GA, Landsat 7, and Landsat 8). We then calculated and mapped the B-factor in the Yanhe River Basin based on rainfall erosivity, vegetation coverage, and land use types. The results show that the ERA5 precipitation dataset outperforms the CHIRPS and PERSIANN-CDR data in estimating rainfall and rainfall erosivity, and it can be utilized as an alternative data source for meteorological stations in soil erosion modeling. Compared to the harmonic analysis of time series (HANTS), the trend migration method proposed in this study is more suitable for patching the missing parts of Sentinel-2 data. The restored high-resolution Sentinel-2 data fit nicely with the 10 m resolution land use data, enhancing the B-factor calculation accuracy at local and region scales. The B-factor computation procedure developed in this study is applicable to various river basin and regional scales for soil erosion monitoring. Full article
(This article belongs to the Special Issue Soil Erosion Estimation Based on Remote Sensing Data)
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26 pages, 6287 KiB  
Article
Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan
by Nuaman Ejaz, Aftab Haider Khan, Muhammad Shahid, Kifayat Zaman, Khaled S. Balkhair, Khalid Mohammed Alghamdi, Khalil Ur Rahman and Songhao Shang
Water 2024, 16(4), 597; https://doi.org/10.3390/w16040597 - 17 Feb 2024
Viewed by 2294
Abstract
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of [...] Read more.
Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs. Full article
(This article belongs to the Section Hydrology)
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17 pages, 19548 KiB  
Article
Evaluation of Multi-Source Precipitation Products in the Hinterland of the Tibetan Plateau
by Min Sun, Aili Liu, Lin Zhao, Chong Wang and Yating Yang
Atmosphere 2024, 15(1), 138; https://doi.org/10.3390/atmos15010138 - 22 Jan 2024
Cited by 5 | Viewed by 1854
Abstract
High-resolution precipitation products have been crucial for hydrology, meteorology, and environmental ecosystems over the Tibetan Plateau (TP). However, these products are usually subject to systematic errors, which may vary with time and topographic conditions. The study evaluated the suitability of four satellite-derived products [...] Read more.
High-resolution precipitation products have been crucial for hydrology, meteorology, and environmental ecosystems over the Tibetan Plateau (TP). However, these products are usually subject to systematic errors, which may vary with time and topographic conditions. The study evaluated the suitability of four satellite-derived products (GPM IMERG, GSMaP, CMORPH, and PERSIANN-CDR) and four fusion precipitation products (ERA5-Land, CHIRPS, CMFD, and TPHiPr) by comparing with 22 rain gauges at a daily scale from 1 January 2014 to 31 December 2018 over the hinterland of the TP. The main findings are as follows: (1) TPHiPr and CMFD are better than the satellite-derived products, while the performance of CHIRPS is worse; (2) among the satellite-derived products, the quality of GPM IMERG is the highest on different time scales, and PERSIANN-CDR is better in the months of June to October, while GSMaP and CMORPH have poor performance; (3) the eight precipitation products have weaker detection capability for heavy precipitation events, and the quality of each product decreases with the increase in the precipitation threshold, while the rate of descent of fusion precipitation products is slower than that of satellite-derived products. This study demonstrates the performance of eight precipitation products over the hinterland of the TP, which is expected to provide valuable information for hydrometeorology applications. Full article
(This article belongs to the Topic Hydrology and Water Resources Management)
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15 pages, 1749 KiB  
Article
Performance of the PERSIANN Family of Products over the Mekong River Basin and Their Application for the Analysis of Trends in Extreme Precipitation Indices
by Claudia Jimenez Arellano, Vu Dao, Vesta Afzali Gorooh, Raied Saad Alharbi and Phu Nguyen
Atmosphere 2023, 14(12), 1832; https://doi.org/10.3390/atmos14121832 - 16 Dec 2023
Cited by 3 | Viewed by 1403
Abstract
Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information [...] Read more.
Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1 by 0.1 and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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