Climate Hazards Group Infrared Precipitation With Stations Research Articles

Weather or not? The role of international sanctions and climate on food prices in Iran

IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security.

Enhancement of RegCM4.7‐CLM precipitation and temperature by improved bias correction methods over Central Africa

AbstractPrecipitation and temperature projections from Regional Climate Models (RCMs) over Central Africa (CA) are of great importance. However, several studies have already shown that the data from RCMs cannot be directly used for climate impact studies on a local scale because of systematic biases that characterize them. Therefore, RCM simulations must be preprocessed in order to make them more representative of climate at a local scale. The present study focuses on improving temperature and precipitation simulations from the RegCM4.7 RCM over CA. For this purpose, two correction methods are used: The adjusted Linear Scaling and Variance (Va) methods. Corrected and uncorrected precipitation and near‐surface temperature are compared with Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the fifth generation of ECMWF reanalysis data (ERA5), respectively. Comparison of the performances of both methods was made during various seasons not only over the whole CA region but also over two sub‐domains (Zones 1 and 2) having different characteristics. This was done on the basis of the mean bias and Root Mean Square Error. Results show that the precipitation from RegCM4 is tainted with huge wet biases compared to CHIRPS. Overall, the analysis suggests that the Va method is the most suitable for reducing the biases of RegCM4.7 simulations, particularly for precipitation irrespective of regions or seasons. However, it has been found that both methods fail to improve temperature biases in the Inter‐Tropical Convergence Zone.

Delimitation of water areas using remote sensing in Brazil’s semiarid region

Remote sensing techniques are of fundamental importance to investigate the changes occurred in the terrestrial mosaic over the years and contribute to the decision-making by increasing efficient environmental and water management. This article aimed to detect, demarcate and quantify the hydric area of Poço da Cruz reservoir, located in Ibimirim, Pernambuco, semiarid region of Brazil, with modeling based on Landsat 8/OLI satellite multispectral images from 2015 to 2020, and to relate it with data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellites average rainfall. For this purpose, the Modified Normalized Difference Water Index (MNDWI) was modeled, being produced georeferenced theme maps and extracted only the pixels represented by positive spectral values, which represent water targets. The open-access software Quantum Geographic Information System (QGIS, version 2.18.16) was used for all stages of digital image processing and connection with complementary databases on the theme maps elaboration. In the results, changes in the spatial distribution of Poço da Cruz were evidenced and analyzed using precipitation data from the CHIRPS product, allowing a better understanding of the rainfall behavior in the region and its influence. The MNDWI was lined with the CHIRPS product, in which the spatial correlation between the rainy event and the water area’s delimitation is documented, especially in October 2017 (minimum values) and October 2020 (maximum values).

Influence of climatic phenomena and deforestation on hydroenvironmental fragility, Gurupi River watershed, Northern Brazil

In recent decades, climatic and anthropogenic pressures have caused serious environmental problems. The joint analysis of geo-environmental variables, through geoprocessing techniques, can support the estimation of the contribution of each environmental component to hydro-environmental fragility (HF). The aim of this work was to analyze the contribution of climatic phenomena and deforestation in the HF of the Gurupi River Watershed (GRW). Precipitation data were extracted from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS); land use and cover were obtained from the MapBiomas Project; drainage network was acquired from the National Water and Sanitation Agency (ANA); slope data were gathered from the National Institute for Space Research (INPE); soil data were obtained from the Brazilian Agricultural Research Corporation (EMBRAPA); geomorphological units were extracted from the Brazilian Institute of Geography and Statistics (IBGE); and rock data were based on the Geological Survey of Brazil (CPRM). For the mapping of HF, the Analytic Hierarchy Process (AHP) was adopted to weigh the importance of each variable in four extreme precipitation year scenarios (1989, 2012, 2015, and 2019). It was observed that spatial precipitation is considerably different in extreme years. Results showed that deforestation has increased over the years; and that static geo-environmental variables (drainage, slope, soils, geomorphological units, and rocks) have larger feature domains that favor the increase of HF in the GRW. The HF of the GRW showed significant differences in the analyzed scenarios. Policies and environmental conservation programs are needed in the GRW.

Accuracy assessment of spatial interpolations methods using ArcGIS

The Inverse Distance Weight (IDW), Spline, and Ordinary Kriging (OK) methods are widely used for interpolating spatial data. Many GIS software options are available, particularly for spatial interpolation. This study aims to assess the accuracy of interpolation approaches based on Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data in ArcGIS 10.8. The research used monthly precipitation data extracted from 147 rain gauge station locations in North Sumatra Province. Additionally, eight stations from BMKG's network were excluded from the interpolation process and instead used as validation data. Validation was conducted by calculating error statistics between the predicted and actual data at these excluded points. The predicted data was generated by interpolating values from the 147 rain gauge stations, excluding the eight points corresponding to the validation data locations. The interpolation was done using ArcGIS 10.8 with the IDW, Spline, and Kriging toolbox. A comparison was performed by assessing error parameters and Pearson correlation coefficients. According to the accuracy assessment, both IDW and OK show comparable performance; both are better than spline interpolation. In addition, the box plot chart demonstrated that the spline technique had a tendency to generate outlier values along the perimeters of the study area.

Satellite-based estimation of daily suspended sediment load using hybrid intelligent models

ABSTRACT This study uses a combination of support vector regression models, particle swarm optimization, and grey wolf optimization algorithms to predict suspended sediment load. For this purpose, The Satellite Precipitation of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Global Land Data Assimilation System (GLDAS) soil moisture products are utilized as the predictors. The prediction models are evaluated based on various visual and quantitative indicators. The Taylor and radar diagrams confirm that the support vector regression-particle swarm optimization best agrees with the observed values. Moreover, the obtained quantitative indices show that the support vector regression-particle swarm optimization model offers better performance than other models used in the present study. The values of the best indices are: Pearson correlation coefficient of 0.997, relative root mean square error of 13.17, percentage bias of 4.05, and Nash-Sutcliffe efficiency of 0.995.

Historical and projected spatial and temporal rainfall status of Dar es Salaam, Tanzania, from 1982 to 2050

Dar es Salaam, like other cities in Africa, experiences flash floods during the rainfall season that destroy infrastructure due to the overflow of rivers and blocked sewage. This study investigates the historical and future variability and changes in spatial and temporal rainfall over Dar es Salaam. Station data and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) gridded data crossing 38 years (1982–2019) were used as a baseline and the Coordinated Regional Climate Downscaling Experiment (CORDEX) dataset from 2021 to 2050 was used for projection under Representative Concentration Pathway (RCP 4.5) forcing scenarios. A trend analysis of historical data was conducted at monthly, seasonal, and annual timescales. Mann–Kendall statistical tests and Sen’s slope estimator were applied to identify the current trend direction and magnitude of changes in rainfall patterns over time. A standardized anomaly index (SAI) was also employed to detect the region’s trends in wetness and dryness. The spatial distribution of rainfall in the city was investigated using an inverse distance weighted (IDW) interpolation technique. The statistical results reveal that a non-significant trend in rainfall was observed on monthly, seasonal, and annual timescales. Generally, in the future (2021–2050), the annual cycle of rainfall shows a slight decrease in monthly rainfall, especially from January to August, and an increase from September to December compared to historical (1982–2019) rainfall, for most of studied locations. Spatially, the distribution of projected rainfall shows that the southern part of the city will experience higher rainfall than other parts. The most significant findings were a decrease in annual projected rainfall by 20%, the MAM projected rainfall season increased by 42%, and an increase of 38% of the OND-projected rainfall season. The findings of this study will be useful for the improved management and planning of the city.

Rainfall and Temperature Trends and Variability in Arid and Semi-arid Lands of Kitui County, Kenya

A study was carried out to analyse rainfall and temperature trends and variability in selected agro-ecological zones in Kitui County. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset was used while temperature data was obtained from the Climatic Research Unit gridded Time Series (CRU TS) dataset. The results indicated that there was a non-significant decreasing trend (p<0.05) in average annual rainfall in all the four agro-ecological zones (p<0.05) for a 30-years period (1988-2018) in the study area. A decreasing trend in March-April-May (MAM) seasonal rainfall trend was reported in the arid and semi-arid agro-ecological zones while an increasing trend was recorded in the transitional and semi-humid zones. For the October-November-December (OND) seasonal rainfall, a non-significant decreasing trend was reported in all the four agro-ecological zones. Additionally, a higher annual rainfall variability was recorded in the drier (arid) and wetter (semi-humid) zones compared to that in semi-arid and transitional zones. Moreover, the study established that there was a spatial variation in both MAM and OND seasonal rainfall variability and that rainfall variability was higher in the OND seasonal rainfall than that of the MAM seasonal rainfall in all the agro-ecological zones. With reference to temperature trends, a statistically significant increasing trend in annual and OND seasonal average maximum and minimum temperatures was reported in all the four agro-ecological zones. Further, the study noted a non-significant increasing trend in maximum and minimum temperatures for the MAM season in all the agro-ecological zones. In regard to temperature variability, the study deduced that there was low temperature variability compared to rainfall variability in all the four agro-ecological zones. The study recommends that location-specific rainfall and temperature analysis should guide planning and implementation of adaptation strategies for effective response to climate variability. Keywords: Agro-ecological Zones, Mann-Kendall Test, Coefficient of Variation, CHIRPS, CRU TS DOI: 10.7176/JEES/12-12-05 Publication date: December 31 st 2022

Groundwater storage change estimation using GRACE data and Google Earth Engine: A basin scale study

Groundwater is the main source of drinking and irrigation water available in the majority of the Ghaghara River basin (GRB), which is a trans-boundary river basin and characterized by a sub-humid climate that possesses a lot of uncertainty due to non-precise measurement of water and energy flux. The basin is classified as an upper mountainous basin, geographical coverage of the GRB covers countries like China, Nepal, and India. The study is intended to estimate variation in groundwater storage based on satellite observations of the time-varying Earth's gravitational field from the Gravity Recovery and Climate Experiments (GRACE). In this study, soil moisture has been estimated from the TerraClimate data and rainfall from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) using Google Earth Engine (GEE). The long-term trends and spatio-temporal variation of groundwater have been analyzed from April 2002 to January 2017. The groundwater trend has shown a drastic decline in storage level. On an average 2.28 cm of decline in groundwater storage per year and a total of 36.61 cm of decline in groundwater storage from 2002 to 2017 has been observed. The GRACE data showed that the southwest region of the GRB has a high storage level. The present research findings outline that implementation of suitable and a feasible water conservation scheme is necessary for improved management of water resources in the entire GRB.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices.

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1month (SPI1) and 3months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

Multiple gridded‐based precipitation products' performance in Sudan's different topographical features and the influence of the Atlantic Multidecadal Oscillation on rainfall variability in recent decades

AbstractThe selection of a reliable long‐term rainfall dataset is deemed appropriate for hydroclimatic assessments, especially in regions with limited distribution of rain gauge stations like Sudan. The study presents an initial investigation for gridded‐based rainfall products and satellite‐based products over Sudan using standardized statistical methods. Overall, the monthly evaluation revealed reasonable statistical agreement with in situ observations. Seasonal analysis shows that summer rainfall is detected more accurately than yearly rainfall, especially in mountainous areas across the country. Generally, underestimation was observed in all products over most regions on an annual, seasonal, and monthly scale, respectively. Results for the Taylor skill score (TSS) indicated relatively low skills for ECMWF Reanalysis 5th Generation (ERA5), moderate to strong skills for Climate Research Unit (CRU) and Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), against the reference rain gauges among the regions. The regional comparison using TSS when considering monthly and seasonal timescales showed CHIRPS and CRU had the highest performance with an average percentage of 89.6 and 92% in the west (Nyala, Al Geniena, and Al Fasher), 89 and 84.6% at the central part (Khartoum, Wad Madani, and Al Obied), and 92.5 and 86.5% in the south (Al Damazine and Kadogli). At 95% confidence level, a significant increasing trend was observed over the highlands at the centre (Al Obied) and western areas (Al Geniena and Nyala), while most stations showed positive trends. Further analysis on temporal correlation with Atlantic Multidecadal Oscillation (AMO) demonstrated a strong correlation between the AMO PC1 and JJAS rainfall PC1 (82%). The west recorded the highest response to AMO (90%), while the east had the lowest response (74%), with the north, central, and south obtaining 77, 76, and 76%, respectively. Our findings provide useful information about rainfall in Sudan, suggesting CHIRPS data for monitoring rainfall variability and extreme events which should encourage its use in areas with scarce stations.

Predictability of Intra-Seasonal Descriptors of Rainy Season over Senegal Using Global SST Patterns

Seasonal forecasting of the rainfall characteristics in Sahel is of crucial interest in determining crop variability in these countries. This study aims to provide further characterization of nine rainfall metrics over Senegal (Onset, cessation, LRS, CDD, CDD7, CDD15, NR90p, NR95p, NR99p) and their response to global SST patterns from 1981 to 2018. The Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) dataset and the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) were used. The results showed strong spatio-temporal variability with a pronounced south–north gradient for all metrics. The earliest onset was observed in the south of the country from 4 July and the latest onset in the north from 9 August. Since 2012, a new regime is observed with an increase in both long dry spells and extreme wet events. Furthermore, SST forcing has shown that the North tropical Atlantic and the East Equatorial Pacific are better able to explain the interannual variability of the intraseasonal descriptors. However, the prediction of metrics is earlier for the most remote basin (Pacific) compared to the most local basin (Atlantic). These results have implications for the seasonal forecasting of Sahel’s intraseasonal variability based on SST predictors, as significant predictability is found far from the beginning of the season.

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Performance evaluation of satellite-based rainfall estimates for hydrological modeling over Bilate river basin, Ethiopia

PurposeThe purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.Design/methodology/approachSelected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.FindingsThe spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R2 = 0.72) is better as compared to MSWEP (NSE = 0.69; R2 = 0.7), TRMM (NSE = 0.67, R2 = 0.68) and CHIRPS (NSE = 0.58 and R2 = 0.62).Practical implicationsCalibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.Originality/valueThis research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

Comprehensive evaluation of mainstream gridded precipitation datasets in the cold season across the Tibetan Plateau

Study regionThe Tibetan Plateau (TP). Study focusThis study evaluated the accuracy of six mainstream gridded precipitation products, including the Asian precipitation dataset by calibrating the GPM-era IMERG (AIMERG), Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), China Meteorological Forcing Dataset (CMFD), ECMWF Re-Analysis version 5 (ERA5-Land), Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), in the cold season across the TP. New hydrological insights for the regionThe observations and these six precipitation products all revealed high precipitation occurs in the eastern and southeastern TP, and low precipitation occurs in the Qaidam Basin. But in the western regions with sparse gauge networks, the spatial patterns revealed by these six products were different. The precipitation occurrences in the cold season detected by CMFD had the highest accuracy, followed by AIMERG and IMERG, while CHIRPS had the worst accuracy. CMFD, IMERG and AIMERG outperformed CHIRPS, ERA5-Land and PERSIANN-CCS-CDR against observations at the annual and monthly scale, ERA5-Land and PERSIANN-CCS-CDR overestimated while AIMERG, CHIRPS, CMFD and IMERG underestimated cold season precipitation. CMFD had the highest accuracy when using the total adjusted observations as a reference at monthly scale, while IMERG had the highest accuracy when only using the adjusted precipitation by Geonor gauge as a reference.

Evaluation of multi-satellite precipitation products in estimating precipitation extremes over mainland China at annual, seasonal and monthly scales

The wide and consistent global coverage of satellite-based quantitative precipitation estimates (QPEs) has shown great potential for monitoring precipitation (PR) at large spatial scales. Evaluation of QPEs in estimating PR extremes is vital to forecasting hydrologic extremes. Here, we present a systematic evaluation of four commonly used QPEs: (1) the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), (2) Tropical Rainfall Measuring Mission 3B42 Version 7 (TRMM 3B42 V7), (3) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and (4) Multi-Source Weighted-Ensemble Precipitation (MSWEP), in their abilities to detect PR extremes in mainland China at annual-seasonal-monthly scales. Four Standard Extreme Precipitation Indices (SEPIs) are chosen as the assessment metrics, including the maximum 1-day PR (Rx1day), the simple PR intensity index (SDII), the count of days with PR ≥ 20 mm (R20mm), and the consecutive dry days (CDD). Results indicate that PERSIANN-CDR performs best for all SEPIs, followed by CHIRPS and TRMM 3B42 V7. All QPEs (except MSWEP) perform better (worse) in capturing CDD (SDII) than other SEPIs at all three timescales. However, large differences among the performance of QPEs in estimated seasonal SEPIs are found - CHIRPS (PERSIANN-CDR) outperforms the other QPEs in spring (summer and autumn), while PERSIANN-CDR and CHIRPS outperform the other QPEs in winter. All QPEs perform better in detecting extreme PR occurrence in summer than in other seasons, and spatially most of them perform better in humid southeastern China than in arid northwestern China. CHIRPS and TRMM 3B42 V7 overestimate Rx1day, R20mm and SDII in most of China, while MSWEP notably underestimates CDD in most of China and overestimates Rx1day, R20mm and SDII in western China.

Spatial and Temporal Assessment of Remotely Sensed Land Surface Temperature Variability in Afghanistan during 2000–2021

The dynamics of land surface temperature (LST) in Afghanistan in the period 2000–2021 were investigated, and the impact of the factors such as soil moisture, precipitation, and vegetation coverage on LST was assessed. The remotely sensed soil moisture data from Land Data Assimilation System (FLDAS), precipitation data from Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), and NDVI and LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) were used. The correlations between these data were analyzed using the regression method. The result shows that the LST in Afghanistan has a slightly decreasing but insignificant trend during the study period (R = 0.2, p-value = 0.25), while vegetation coverage, precipitation, and soil moisture had an increasing trend. It was revealed that soil moisture has the highest impact on LST (R = −0.71, p-value = 0.0007), and the soil moisture, precipitation, and vegetation coverage explain almost 80% of spring (R2 = 0.73) and summer (R2 = 0.76) LST variability in Afghanistan. The LST variability analysis performed separately for Afghanistan’s river subbasins shows that the LST of the Amu Darya subbasin had an upward trend in the study period, while for the Kabul subbasin, the trend was downward.

Performance of CMIP6 HighResMIP on the Representation of Onset and Cessation of Seasonal Rainfall in Southern West Africa

Changes in rainfall onset and cessation dates are critical for improving decision making and adaptation strategies in numerous socio-economic sectors. An objective method of determining onset and cessation date is employed over Southern West Africa (SWA) in this study. The method is applied over 34 years of the quasi-global rainfall dataset from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and five High Resolution Model Intercomparison Project (HighResMIP) model datasets under the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiment. Generally, a strong agreement exists between CHIRPS and the HighResMIP models in capturing the behaviour of seasonal rainfall over SWA, with models able to capture the bimodal rainfall season. The ability of models in capturing onset and cessation dates as observed in CHIRPS shows the strength of these models in representing the short break between the two wet seasons that is otherwise known as the ‘Little Dry Season’. Patterns observed in the onset and cessation dates over the SWA region are consistent with the northward and southward displacement of the Intertropical Convergence Zone (ITCZ). The seasonal timing of the models shows good agreement with observations such that most mean onset/cessation dates agree within 26 days. While IPSL-CM6A-ATM-HR, a model among the five HighResMIPs used in the study, best agrees with CHIRPS in representing onset and cessation dates during the unimodal rainfall season, no one model best agrees with CHIRPS during the bimodal season, with models outperforming each other in representing onset/cessation dates with little variation.

Validation of the accuracy of the CHIRPS precipitation dataset at representing climate variability in a tropical mountainous region of South America

This study aims to validate the accuracy of the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset at representing climate variability over various time scales. The study is carried out in Antioquia, in northwestern Colombia, and uses statistically independent information provided by 75 rain gauges located at different sites from 1981 to 2018. In this study, statistical metrics are used to analyze the error structure of the CHIRPS data. Resilient methods for validation are included based on Gauss-Markov diagnostic problems and influence points, leverage, and outliers in the form of the trimmed least squares boot method and principal component analysis. To represent the performance of CHIRPS more accurately at estimating precipitation at different time scales, a comparison is made using 12 specific rain gauge located in different subregions of Antioquia, which had well-differentiated climatic characteristics. The results show that the accuracy of CHIRPS estimates is conditioned by geographic and climatic characteristics of the region in which precipitation events occur. CHIRPS works well in most weather conditions in Antioquia, even during the most intense periods of ENSO. However, it does better in the Andean subregions and underperforms in warmer regions. This study finds that CHIRPS is a rainfall data source that can be considered for analyzing seasonal and interannual variability and spatial precipitation patterns. It provides good spatio-temporal coverage and is particularly suitable for areas with few rainfall gauges. However, its accuracy is minimal for daily analysis.

Trends and spatiotemporal patterns of meteorological drought incidence in North Wollo, northeastern highlands of Ethiopia

The study was aimed at detecting and characterizing meteorological drought risk areas using remote sensing data in North Wollo, Ethiopia. The Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset was used to compute the Z score index (ZSI) and depict droughts during the meher season (2000–2019). MOD11A2 from Moderate Resolution Imaging Spectroradiometer (MODIS) image datasets was also used to generate land surface temperature (LST) and investigate its association with ZSI and precipitation. To analyze the seasonal and annual rainfall trends, the Mann–Kendall test was applied, and Sen’s slope estimator was used to fix the magnitude of change. The Mann–Kendall test result revealed that an increasing trend of annual rainfall (0.24–10.7 mm/year) has been observed in many districts, while it was decreasing (−0.43 to −3.85 mm/year) in kiremt. However, the belg (1.64–5.13 mm) and bega (0.54–2.8 mm) seasons revealed a slightly increasing trend which was significant at p < 0.05 in one station for the former and three stations for the latter. The ZSI value confirmed that 2002, 2004, 2009, 2011, 2014, 2015, and 2019 were under a rainfall deficit with extreme drought events. Nevertheless, 2009 and 2015 were the driest years, with a Z score intensity of −2.01 to −2.84. Based on the correlation and regression results, ZSI showed a positive relationship (r = 0.93–0.99) with precipitation and negative (r = −0.1 to −0.52) with LST. Thus, ZSI revealed a significant increasing trend (at p < 0.0001) with rising precipitation and a decreasing with LST. In the last 20 years, every district encountered a rainfall deficit repeatedly (15–18 times), and thus, the area is categorized as an extremely high drought risk zone. Such spatiotemporal drought risk events have an imminent threat to the rain-fed agricultural activities, imposing an immense influence on the agro-based livelihoods of the local community. Therefore, it demands continuous drought monitoring and the application of effective early warning systems.