Climate Hazards Group Infrared Precipitation With Stations Research Articles

Changing Rainfall Patterns in the Northeastern South Kivu Region, Democratic Republic of the Congo: A Detailed Analysis Using CHIRPS Rainfall Data (1981–2023)

AbstractUnderstanding changes in rainfall patterns is vital for effective water resource management and agricultural planning in climate-sensitive regions. In Northeastern South Kivu, Democratic Republic of the Congo, limited knowledge of rainfall dynamics poses challenges for local climate assessments. This study analyzed rainfall patterns using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2023 to fill this knowledge gap. The findings revealed significant spatial and temporal changes in rainfall across the regions of Kabare, Bukavu, Kalehe, and Idjwi. Mean annual rainfall ranged from 1136.92 mm to 2048.85 mm, with coefficients of variation between 8.23% and 8.45%. Monthly rainfall fluctuations were substantial, ranging from 16 to 56.35%. Standardized rainfall anomalies indicated a shift from predominantly wet conditions in the 1980s to drier conditions in later decades, with over half the years from 1990 to 2023 recording below-average rainfall. Mann-Kendall trend analysis, supported by Innovative Trend Analysis (ITA), confirmed significant decreases in annual rainfall, with rates between − 3.53 to -5.72 mm/year. Additionally, rainfall intensity indices highlighted a decline in total annual precipitation, suggested fewer extreme events. Spatial variability was observed, with regions like Kalehe and Bukavu showed higher values for indices such as R95P and RX1day compared to Kabare and Idjwi. These results underscored the urgent need for adaptive strategies to mitigate water scarcity and manage changing rainfall patterns in the region.

Geographically distributed trend and variability analysis of rainfall over the state of Haryana, India, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data dataset

Geographically distributed trend and variability analysis of rainfall over the state of Haryana, India, using Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data dataset

Spatial-temporal seasonal and annual rainfall trends and variability assessment in the Pangani Basin, East Africa

Although in situ rainfall data remains the most accurate, the gauge network density in East Africa is sparse. It lacks continuity, thus making it inadequate to assess the spatial and long-term rainfall trend and variability accurately. As such, rainfall remote sensing data are normally used instead of ground station data. This study evaluates the capabilities and limitations of remote sensing data compared with ground-based observations in Tanzania's Pangani Basin in assessing the seasonal and annual rainfall trends and variability. Data from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and twenty-three ground stations were analyzed, comprising a time series from 1990 to 2022. Trend analysis was conducted using the Mann-Kendall test, while the spatial distribution of precipitation was determined using Sen's slope method. CHIRPS annual rainfall showed good agreement with station rainfall data, with NSE, R2, slope, Pbias, MAE, and RMSE values of 0.84, 0.92, 0.92, 7.55%, 297.4, and 397.1, respectively. The coefficient of variation from station data indicated extreme variability, exceeding 30% for annual rainfall, while CHIRPS data showed moderate variability, ranging from 20% to 30%. Both station data and remote sensing data showed an increasing trend for annual and seasonal rainfall at least 10 stations. However, stations like Maji Moshi, Kiungu Primary School, Segera C. Tank, and Kibong'oto exhibited an increasing trend for vuli rainfall with CHIRPS data, while station data indicated a significant decreasing trend.The study highlights the necessity for calibration and validation to avert misinterpretations in climate trend analyses, especially at the basin level.

Influence of precipitation and temperature on burned areas in the agricultural and cattle ranching frontier of the Brazilian Amazon.

Fire occurrence, intensity, and spread are highly influenced by climatic variables. This study investigates the correlation between burned area, precipitation, and temperature in Rondônia, an agricultural frontier in the southwestern Brazilian Legal Amazon, from 2001 to 2022. The analysis utilized climatological data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and MODIS product MOD11A1.061 for temperature, along with MODIS product MCD64A1 for burned area. The study was conducted on a monthly scale, employing the cross-correlation function to determine the lagged effects of temperature and precipitation on burned areas. Trend analysis was performed using the Mann-Kendall test, with the magnitude of trends estimated by Sen's Slope. Results indicated a significant negative correlation between burned areas and precipitation, with a 2-month lag and an R2 of - 0.51. In contrast, temperature exhibited a significant positive correlation with burned areas, showing a 1-month lag and an R2 of 0.55. Trend analysis revealed a decrease in precipitation by - 0.0542mm.month-1, temperature increased by 0.006°C.month-1, while burned areas decreased by - 111.13 km2.month-1. These findings underscore the intricate relationship between climate variables and fire occurrences, highlighting the urgent need for policies addressing climate change and environmental degradation in the Amazon.

Response of remote sensing-based climatic indicators to drought conditions during El Niño events and implications for water–food–energy nexus

ABSTRACT Hydrometeorological extremes, such as droughts, are a major threat to society and can have extensive damaging effects. In this study, daily rainfall estimates from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) quasi-global rainfall dataset were used to calculate the Standardised Precipitation Index (SPI) for the assessment of meteorological drought in Southern Province, Zambia. Normalised Difference Vegetation Index (NDVI) imagery (250 m resolution) from MODIS-Terra, for the period 2000–2021, were used to derive the Standardised Vegetation Index (SVI) in order to assess agricultural drought. The Mann–Kendall trend test and Sen's slope were used to determine the spatial-temporal trends and their magnitudes. This study demonstrated that the droughts of the Southern Province of Zambia can be classified into two categories: regressive and aggressive droughts. Regressive droughts are associated with moderate to strong El Niño events. Although El Niño events undermine water security, regressive droughts tend to result in resilient vegetation owing to residue soil moisture. In contrast, aggressive droughts are characterised by an increase in drought intensity as the season progresses. Water security prospects in the region should focus on climate-smart approaches, such as managed aquifer recharge, to ensure water availability even under extreme drought conditions.

Predicting agricultural and meteorological droughts using Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM).

Drought is an extended shortage of rainfall resulting in water scarcity and affecting a region's social and economic conditions through environmental deterioration. Its adverse environmental effects can be minimised by timely prediction. Drought detection uses only ground observation stations, but satellite-based supervision scans huge land mass stretches and offers highly effective monitoring. This paper puts forward a novel drought monitoring system using satellite imagery by considering the effects of droughts that devastated agriculture in Thanjavur district, Tamil Nadu, between 2000 and 2022. The proposed method uses Holt Winter Conventional 2D-Long Short-Term Memory (HW-Conv2DLSTM) to forecast meteorological and agricultural droughts. It employs Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data precipitation index datasets, MODIS 11A1 temperature index, and MODIS 13Q1 vegetation index. It extracts the time series data from satellite images using trend and seasonal patterns and smoothens them using Holt Winter alpha, beta, and gamma parameters. Finally, an effective drought prediction procedure is developed using Conv2D-LSTM to calculate the spatiotemporal correlation amongst drought indices. The HW-Conv2DLSTM offers a better R2 value of 0.97. It holds promise as an effective computer-assisted strategy to predict droughts and maintain agricultural productivity, which is vital to feed the ever-increasing human population.

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

On the Challenges of Simulating Streamflow in Glacierized Catchments of the Himalayas Using Satellite and Reanalysis Forcing Data

Abstract Hydrologic assessment of climate change impacts on complex terrains and data-sparse regions like High Mountain Asia is a major challenge. Combining hydrological models with satellite and reanalysis data for evaluating changes in hydrological variables is often the only available approach. However, uncertainties associated with the forcing dataset, coupled with model parameter uncertainties, can have significant impacts on hydrologic simulations. This work aims to understand and quantify how the uncertainty in precipitation and its interaction with the model uncertainty affect streamflow estimation in glacierized catchments. Simulations for four precipitation datasets [Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), ERA5-Land, and Asian Precipitation–Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE)] and two glacio-hydrological models [Glacio-Hydrological Degree-Day Model (GDM) and Hydrological Model for Distributed Systems (HYMOD_DS)] are evaluated for the Marsyangdi and Budhigandaki River basins in Nepal. Temperature sensitivity of streamflow simulations is also investigated. Relative to APHRODITE, which compared well with ground stations, ERA5-Land overestimates the catchment average precipitation for both basins by more than 70%; IMERG and CHIRPS overestimate by ∼20%. Precipitation uncertainty propagation to streamflow exhibits strong dependencies to model structure and streamflow components (snowmelt, ice melt, and rainfall-runoff), but overall uncertainty dampens through precipitation-to-streamflow transformation. Temperature exerts a significant additional source of uncertainty in hydrologic simulations of such environments. GDM was found to be more sensitive to temperature variations, with >50% increase in total flow for 20% increase in actual temperature, emphasizing that models that rely on lapse rates for the spatial distribution of temperature have much higher sensitivity. Results from this study provide critical insight into the challenges of utilizing satellite and reanalysis products for simulating streamflow in glacierized catchments. Significance Statement This work investigates the uncertainty of streamflow simulations due to climate forcing and model parameter/structure uncertainty and quantifies the relative importance of each source of uncertainty and its impact on simulating different streamflow components in glacierized catchments of High Mountain Asia. Results highlight that in high mountain regions, temperature uncertainty exerts a major control on hydrologic simulations and models that do not adequately represent the spatial variability of temperature are more sensitive to bias in the forcing data. These findings provide guidance on important aspects to be considered when modeling glacio-hydrological response of catchments in such areas and are thus expected to impact both research and operation practice related to hydrologic modeling of glacierized catchments.

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.