Climate Hazards Group Infrared Precipitation With Stations Research Articles

Comparison Accuracy of CHIRPS, GSMaP V7, and GSMaP V8 Satellite Rainfall Estimation in Kalimantan

The application of satellite product rainfall estimates (SPREs) is growing in hydrometeorology due to limited rainfall measurement. This study aims to compare the accuracy of three SPRE, namely Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), Global Satellite Mapping of Precipitation (GSMaP) Moving Vector with Kalman Filtering (GSMaP-MVK), and near-real-time (GSMaP-NRT) versions 7 and 8, against daily and monthly rainfall measurements from eighteen gauges in Kalimantan from December 2021 to May 2023. Continuous validation includes root mean square error (RMSE), relative bias (RB), and correlation coefficient (CC), and categorical validation consists of a probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of SPREs. The results showed that GSMaP-MVK version 8 has the highest accuracy on a daily scale with an RMSE value of 14.31 mm/day, while the CHIRPS has the highest accuracy on a monthly scale with an RMSE of 81 mm/month. GSMaP version 8 is better than GSMaP version 7, with a difference in RMSE, CC, and RB at 14.2%, 9.7%, and 84%. Categorical validation showed that GSMaP version 8 was 2.13%, higher in POD, 3.95% in CSI, and 10.2% in FAR compared to GSMaP version 7. Keywords: Accuracy, CHIRPS, GSMaP, Kalimantan, Rain-gauge.

Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen’s slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda’s critical MAM season.

Integrating RUSLE Model with Cloud-Based Geospatial Analysis: A Google Earth Engine Approach for Soil Erosion Assessment in the Satluj Watershed

This study employed an advanced geospatial methodology using the Google Earth Engine (GEE) platform to assess soil erosion in the Satluj Watershed thoroughly. To achieve this, the Revised Universal Soil Loss Equation (RUSLE) model was integrated into the study, which was revealed through several analytical tiers, each with a unique function. The study commenced with estimating the R factor, which was carried out using annual precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS). The erodibility of the soil, which the K factor describes, was then calculated using the USDA soil texture classifications taken from the Open Land Map. The third layer emphasizes the LS factor, which analyzes slope data and how they affect soil erosion rates, using digital elevation models. To understand the impact of vegetation on soil conservation, the fourth layer presents the C factor, which evaluates changes in land cover, and the Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 data. The P factor incorporates MODIS data to assess the types of land cover and slope conditions. Combining these layers with the RUSLE model produces a thorough soil loss map, revealing different levels of soil erosion throughout the Satluj Watershed. The preliminary findings indicate that 3.3% of the watershed had slight soil loss, 0.2% had moderate loss, and 1.2% had high soil erosion rates. And 92% had severe rates of soil erosion. After a thorough investigation, the detected regions were divided into risk classifications, providing vital information for the watershed’s land management and conservation plans. The mean soil loss throughout the watershed was determined to be 10,740 tons/ha/year. This novel method creates a strong foundation for evaluating soil erosion, while also highlighting the value of the cloud-based geospatial analysis and the RUSLE model in comprehending intricate environmental processes.

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.

Analysis of rainfall and temperature variabilities in Sidama regional state, Ethiopia

The objective of the study was to examine local-scale fluctuation in precipitation and temperature in selected districts of Sidama regional state. Specifically, it focuses on three districts—Hawassa Zuriya, Wonsho, and Hula—using precipitation and temperature records obtained from the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) database which covers the period from 1981 to 2022. Various statistical measures such as mean, standard deviation, as well as coefficient of variation was employed to detect fluctuation. For trend detection, the Mann-Kendall (MK) and Sen's slope tests were also employed. Observations revealed that the average yearly precipitation spatially varied from 1331 mm in Hula, followed by 1275 mm in Wonsho, and 1013 mm at Hawassa Zuriya. Rainfall was bimodal which 53% rains in Kiremt and 33% in Belg season respectively. Annual rainfall show relatively low variability (<20%) for Hula and Wonsho districts, and moderate variability (CV˃20%) for Hawassa Zuriya respectively. The findings also revealed noticeable rising tendencies (p < 0.05) for average temperature across all three agroecosystems over the years under consideration with the highest slope at Hawassa Zuriya (0.038 °C/year), followed by Hula (0.031 °C/year), and Wonsho (0.022 °C/year) respectively.Moreover, both temperature and rainfall exhibited spatial and inter-annual variability. The results of this study necessitate farmers for systematic planning and implementing location specific crop calendar in the context of fluctuating climatic settings. Policy-makers as well as development practitioners can also utilize the finding to better devise and execute plans for adapting and minimizing the effects of climate change.

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.

Rainfall trends and spatiotemporal patterns of meteorological drought in Menna watershed, northwestern Ethiopia

Understanding the spatiotemporal patterns of drought is crucial for planning, disaster preparedness, vulnerability assessment, impact evaluation, and policy formulation to mitigate drought-induced effects. The purpose of this study was to assess rainfall trends and spatiotemporal patterns of meteorological drought using geospatial techniques in Menna watershed. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall, and station-based observed rainfall were the datasets used. The station-based rainfall was used to confirm the accuracy of CHIRPS rainfall data. The Mann-Kendall (MK) test and Sen's slope estimator were utilized to assess trends and ascertain the extent of change. To characterize meteorological droughts, percent of normal (PN), standardized anomaly index (SAI), and standardized precipitation index (SPI) were computed during the crop growing seasons (2000–2022). The validation result confirmed a strong agreement between the observed and CHIRPS rainfall data (R2 = 0.88). Based on the MK test, an increasing trend has been observed in annual (3.7 mm/year) and belg (3.4 mm/year) rainfall, which was significant at p < 0.05. But the kiremt season was slightly decreasing (−0.7 mm/year). The PN, SAI, and SPI values detected that 2002, 2004, 2009, 2011, 2014, 2015, and 2019 were drought years in the area. Even only 1.4, 0.2, and 0.5% of the watershed were free from drought in 2009, 2014, and 2015, respectively, due to extremely high rainfall deficiency. Conversely, 2001, 2010, and 2016 were notable for having the highest amounts of rainfall compared to the other years. Generally, the region could be classified as an area highly susceptible to meteorological drought in northwestern Ethiopia. There was no even a single year free from drought in the entire study period. To that extent, about 86% of it had repeatedly encountered extreme rainfall deficit (7–23 times) during the study period. Thus, the population has always been repeatedly smashed down by the frequent droughts. To tackle existing challenges and mitigate upcoming risks, continual droughts monitoring and implementation of efficient early warning systems are vital for the region.

A comprehensive investigation of three long‐term precipitation datasets: Which performs better in the Yellow River basin?

AbstractThe fifth generation European Centre for Medium‐Range Weather Forecasts Reanalysis on global land surface (ERA5‐Land), the Multi‐Source Weighted‐Ensemble Precipitation (MSWEP), and the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) are three representative precipitation estimates with quasi‐global coverage, high‐resolution and long‐term record. This study concentrates on investigating, for the first time, the long‐term spatiotemporal accuracy and regional applicability of these precipitation estimates at a daily scale in the Yellow River basin (YRB) using 39 complete years of data record (1981–2019), with a special focus on their capability on monitoring the extreme precipitation events with short duration and the continuous heavy precipitation events. Results indicate that MSWEP generally performs better than ERA5‐Land and CHIRPS in almost all seasons and subregions, with the highest Pearson correlation coefficient and critical success index, lowest root mean square error and false alarm ratio. ERA5‐Land presents a severe overestimation of precipitation amount, particularly in the plateau climate region (BIAS = 52.27%), but well reflects its spatial–temporal patterns in the YRB. As for the detecting capability, MSWEP shows the best accuracy in detecting extreme precipitation, particularly in maximum consecutive 5‐day precipitation (RX5day). The MSWEP better represents the spatial distribution of maximum 1‐day precipitation and maximum consecutive 5‐day precipitation in the YRB, but it shows a significant overestimation in zone Southern Qinghai. MSWEP and CHIRPS have better performance of temporal variation consistency in annual precipitation with ground reference than ERA5‐Land, while ERA5‐Land performs well in capturing extreme precipitation temporal variation, especially for continuous heavy precipitation events. This study can provide useful guidance when choosing long‐term precipitation products for hydrometeorological applications and climate‐related studies in the YRB.

Evaluating Geospatial Data Adequacy for Integrated Risk Assessments: A Malaria Risk Use Case

International policy and humanitarian guidance emphasize the need for precise, subnational malaria risk assessments with cross-regional comparability. Spatially explicit indicator-based assessments can support humanitarian aid organizations in identifying and localizing vulnerable populations for scaling resources and prioritizing aid delivery. However, the reliability of these assessments is often uncertain due to data quality issues. This article introduces a data evaluation framework to assist risk modelers in evaluating data adequacy. We operationalize the concept of “data adequacy” by considering “quality by design” (suitability) and “quality of conformance” (reliability). Based on a use case we developed in collaboration with Médecins Sans Frontières, we assessed data sources popular in spatial malaria risk assessments and related domains, including data from the Malaria Atlas Project, a healthcare facility database, WorldPop population counts, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation estimates, European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation forecast, and Armed Conflict Location and Event Data Project (ACLED) conflict events data. Our findings indicate that data availability is generally not a bottleneck, and data producers effectively communicate contextual information pertaining to sources, methodology, limitations and uncertainties. However, determining such data’s adequacy definitively for supporting humanitarian intervention planning remains challenging due to potential inaccuracies, incompleteness or outdatedness that are difficult to quantify. Nevertheless, the data hold value for awareness raising, advocacy and recognizing trends and patterns valuable for humanitarian contexts. We contribute a domain-agnostic, systematic approach to geodata adequacy evaluation, with the aim of enhancing geospatial risk assessments, facilitating evidence-based decisions.

Comparison of different rainfall products with gauge-based measurements over Narmada River Basin, India.

The present study evaluates and compares the performance of different rainfall products, namely, Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), India Meteorological Department (IMD) gridded, Prediction of Worldwide Energy Resource (POWER), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record (PERSIANN-CDR) with gauge-based measurements over Narmada River basin, India. The ground-based daily rainfall data (1981-2020) of 11 gauging stations have been collected from the Water Resources Department, Madhya Pradesh and the evaluation of rainfall product has been accomplished on a point-to-grid basis (nearest neighbor method) at annual and seasonal scales with the help of continuous and categorical statistical metrics. The results reveal a strong positive correlation (> 0.75) between rainfall estimates of different products and gauge-based measurements at annual scale demonstrating higher similarity in rainfall estimates and observed data, whereas seasonal estimates have exhibited comparatively weaker relationship. Likewise, percent bias (PBIAS) demonstrates least bias in annual and monsoon rainfall estimates and high in other seasons. These findings reveal that rainfall estimates tend to improve with increasing time scale (season to annual). However, majority of the rainfall products have overestimated the low rainfall (western region) and underestimated the high rainfall (eastern and southeastern regions). Further, the values of critical success index (CSI) indicate IMD gridded product outperforms in detecting rainfall events accurately followed by POWER, PERSIANN-CDR, and CHIRPS. These results suggest that IMD gridded estimates provide the best alternate to ground-based rain measurements. However, rainfall estimates from POWER, PERSIANN-CDR and CHIRPS can also be used in various hydrometeorological investigations over Narmada River basin.

Rainfall Deviation of India from the Last 40 Years Data Using Remote Sensing

This research investigates precipitation deviations using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset within the Google Earth Engine (GEE) platform. CHIRPS, developed by the Climate Hazards Group at the University of California, Santa Barbara, in collaboration with the U.S. Geological Survey, provides high-resolution daily precipitation data from 1981 to the present. Leveraging its spatial resolution of 0.05 degrees, we analyzed precipitation patterns over a specified region from 1982 to 2022. The methodology involves comprehensive data preprocessing, including loading, subsetting, and handling missing values using JavaScript in GEE. Climatology was calculated by aggregating the data monthly to establish a baseline of expected precipitation patterns. Deviations from this baseline were computed to identify anomalies. The CHIRPS dataset’s integration into GEE enabled robust spatial and temporal visualization and statistical analysis of precipitation anomalies. This study underscores CHIRPS’s crucial role in enhancing our understanding of precipitation variability, informing climate adaptation strategies, and improving hydrological, agricultural, and disaster risk management practices. The findings contribute valuable insights into precipitation trends and their implications in the context of global climate change.

Development of Intensity–Duration–Frequency (IDF) Curves over the United Arab Emirates (UAE) Using CHIRPS Satellite-Based Precipitation Products

The recent flooding events in the UAE have emphasized the need for a reassessment of flood frequencies to mitigate risks. The exponential urbanization and climatic changes in the UAE require a reform for developing and updating intensity–duration–frequency (IDF) curves. This study introduces a methodology to develop and update IDF curves for the UAE at a high spatial resolution using CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. A bias correction was applied to the CHIRPS data, resulting in an improved capture of extreme events across the country. The Gumbel distribution was the most suitable theoretical distribution for the UAE, exhibiting a strong fit to the observed data. The study also revealed that the CHIRPS-derived IDF curves matched the shape of IDF curves generated using rain gauges. Due to orographic rainfall in the northeastern region, the IDF intensities were at their highest there, while the aridity of inland regions resulted in the lowest intensities. These findings enhance our understanding of rainfall patterns in the UAE and support effective water resource management and infrastructure planning. This study demonstrates the potential of the CHIRPS dataset for IDF curve development, emphasizes the importance of performing bias corrections, and recommends tailoring adjustments to the intended application.

Dynamic rainfall-induced landslide susceptibility: A step towards a unified forecasting system

The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both in space and in time. This is the starting point of this work, which combines the traditional strengths of the susceptibility framework together with the strengths typical of landslide early warning systems. Specifically, we model landslide occurrences in the norther sector of Vietnam, using a multi-temporal landslide inventory recently released by NASA. A set of static (terrain) and dynamic (cumulated rainfall) covariates are selected to explain the landslide presence/absence distribution via a Bayesian version of a binomial Generalized Additive Models (GAM). Thanks to the large spatiotemporal domain under consideration, we include a large suite of cross-validation routines, testing the landslide prediction through random sampling, as well as through stratified spatial and temporal sampling. We even extend the model test towards regions far away from the study site, to be used as external validation datasets. The overall performance appears to be quite high, with Area Under the Curve (AUC) values in the range of excellent model results, and very few localized exceptions.This model structure may serve as the basis for a new generation of early warning systems. However, the use of The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) for the rainfall component limits the model ability in terms of future prediction. Therefore, we envision subsequent development to take this direction and move towards a unified dynamic landslide forecast. Ultimately, as a proof-of-concept, we have also implemented a potential early warning system in Google Earth Engine.

Spatiotemporal rainfall variability and its relationship to flash flood risk in Northeastern Sylhet Haor of Bangladesh

Abstract The variability and distribution of rainfall are uniquely significant for climatic risk prediction. This study aims to assess spatiotemporal rainfall variability and flash flood intensity events in the Sylhet haor region of Bangladesh by analyzing rainfall data from April for the period 1995–2022. For this, we used both Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data and Bangladesh Meteorological Department (BMD) daily rainfall data. Rainfall patterns were studied using zonal statistics in ArcGIS and graphical illustration. The results revealed that the rainfall pattern was erratic and showed a range of spatiotemporal variability. If the average rainfall exceeds 250 mm in Meghalaya and Assam and 400 mm in Sylhet, severe flash floods may occur in the Sylhet haor region. An increase in pre-monsoon rainfall and its shift from May to April may increase the intensity of flash floods and consequently damage the rice crop. This finding might help flood management agencies to develop flood management strategies, prepare flood contingency plans, provide real-time and advanced warnings to strengthen flood warning and forecasting systems, and schedule seasonal agricultural activities.

Spatiotemporal Evaluation of Satellite-Based Precipitation Products in the Colorado River Basin

Abstract Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in the Upper Colorado River basin (UCRB) for the period 2000–20. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3–4 mm day−1 differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November–April) than in the dry season (May–October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50%–60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features.

Assessing Spatio-temporal Variations and Trends of Rainfall over Somalia from 1991 to 2020

This study aims to examine the spatiotemporal variability and rainfall trends over Somalia using the Mann-Kendall (MK) test and Sen’s slope estimator to investigate the trend and magnitude of the rainfall changes and the arithmetic mean and coefficient of variation (CV) to examine the rainfall variability. To achieve this aim, the gridded monthly satellite rainfall datasets from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) for the period between 1991 and 2020 were used. Results show that annual rainfall generally exhibits bimodal rainfall distribution, March to May (Gu) and September to November (Deyr), though southern coastal regions receive extended Gu rains into July and northwestern regions experience early onset of Deyr rains. The study further showed that a low and moderate degree of inter-annual variability (CV&lt;30%) is observed in most parts of the country, while on a seasonal basis, the short rainy season (Deyr) has a higher rainfall variability (CV&gt;30%) compared to the primary rainy season (Gu) and most regions in the south receiving highest amounts of rainfall experience highest variability (CV&gt;50%) during Deyr season. Furthermore, the trend analysis of the annual rainfall showed a non-significant increasing trend. On a seasonal basis, the trends of Gu rainfall decreased in some regions while trends for the Deyr season increased in most regions. However, rainfall trends observed were not statistically significant throughout the country at a significance level of 0.05.

Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.

Robustness of gridded precipitation products for vietnam basins using the comprehensive assessment framework of rainfall

The use of satellite–based precipitation products (SPPs) have become increasingly prevalent as key inputs to provide regional rainfall for improving hydrological simulations in data–spare regions. This study introduces a new approach – Comprehensive Assessment Framework of Rainfall (CAFR) to evaluate six satellite–based precipitation products (SPPs) for eleven basins with different sizes across Vietnam (2007–2015). These SPPs include the Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final run Version 6 (GPM IMERGF V6), Multi–Source Weighted–Ensemble Precipitation (MSWEP) V2.2, Soil Moisture to Rain (SM2RAIN) – Advanced SCATterometer (ASCAT) V1.5, Asian Precipitation–Highly–Resolved Observational Data Integration Towards Evaluation (APHRODITE) V1901, Climate Hazards group Infrared Precipitation with Stations (CHIRPS) V2.0, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – Climate Data Record (CDR) V1.0. With the proposed CAFR: (1) IMERGF is suggested to have the best performance overall, especially when simulating flood peaks; (2) SM2RAIN–ASCAT demonstrates the best skills in metrics related to the dry season; (3) For streamflow simulations, SPPs' performance is sensitive to basin size, with larger basins showing better performance skills. In this study, we demonstrate the capability of our proposed framework to better understand SPP applications in hydrological modeling.

Defining the Angolan Highlands Water Tower, a 40 plus-year precipitation budget of the headwater catchments of the Okavango Delta

Angola is a source of many major rivers in southern Africa and is referred to as the “water tower” of the region. The lack of a defined area delineating the Angolan Highlands water tower (AHWT) limits the conservation of this important freshwater source. This study hydrologically defines the boundary of the AHWT as areas > 1274 m above mean sea level within the Central Bié Plateau of Angola. Using the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data, this study provides a 41-year precipitation budget of the AHWT and surrounding basins. Between 1981 and 2021, the average annual precipitation over the AHWT was 1112 mm and the gross annual average precipitation volume was approximately 423 km3 over an area of 380,382 km2. The AHWT is the southern source of the Congo Basin, the western source of the Zambezi Basin, and the sole water source of the endorheic Okavango Basin and Okavango Delta, a UNESCO World Heritage Site. On average, approximately 133 km3 (92.36%) of the gross annual precipitation volume for the headwater Cuito and Cubango catchments of the Okavango River is lost before reaching the Okavango Delta. Estimates of the annual flooding of the Okavango Delta during a 35-year period (1985–2019) were correlated to precipitation in the headwater catchments. Correlation coefficients are stronger for the entire rainfall season (0.76) and early rainfall season (0.62) for the combined Cuito–Cubango catchment in comparison to late rainfall season (0.50), which suggests that the antecedent conditions (first and second flood pulse) during the early rainfall season allows for greater Okavango Delta flood inundation. The correlation coefficients between the Cubango (0.72) and Cuito (0.78) Rivers and annual flood inundation are not significantly different (P > 0.05); however, these rivers have fundamental hydrological differences that influence the functioning of the Okavango Delta. The Cubango River, described as a flushing system, has much steeper gradient, more compact and shallow soils and flows faster with significant rapids, whereas the peatland rich, absorbent, seepage-driven baseflow of the Cuito River sustains the Okavango Delta during the dry season. The dynamics of seasonal precipitation, hydrology and climate change in the AHWT have important repercussions on water budgets, food security and biodiversity throughout southern Africa, requiring continued collaboration between countries to ensure that future development is sustainable.

Statistical blending of global-gridded climatological products: an approach to inverse hydrological model

Abstract The growing use of global-scale environmental products in hydro-climatic modeling has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending of these products to achieve optimal modeling. The proposed statistical blending in this study includes five large-scale and satellite precipitation (CHIRPS, ERA5-Land of ECMWF, GPM (IMERG), TRMM, and Terra) and evapotranspiration (GLEAM, SSEBop, MODIS, Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on in situ values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well.