Climate Hazards Group InfraRed Precipitation Research Articles

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Elevation Dependent Climate Assessment and its Influence on Snow Cover Variability in Hindu Kush Himalayan Region Using Google Earth Engine for the Period of 2003 – 2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003-2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (-0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

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.

Satellite rainfall bias correction incorporating effects on simulated crop water requirements

ABSTRACT Satellite rainfall estimates (SRE) offer spatial-temporal rainfall representations in regions with limited ground-based gauge rainfall measurements. However, differences exist between SRE and gauge measured rainfall, which needs assessment and reduction. This study presents a method to correct errors in SRE to make their use in agro-hydrological applications and models meaningful. The main scientific objective is the determination of effective window sizes for SRE bias correction. To conclude on effective window sizes, the crop water requirement satisfaction index (WRSI) for gauged rainfall, uncorrected SRE and bias corrected SRE were estimated and propagation effects of SRE errors on respective WRSI estimates were assessed. WRSI indicates how much of the crop water needs are satisfied by rainfall. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) SRE was bias corrected using gauged rainfall data from 20 stations in the Lake Victoria basin of Kenya from 2012 to 2018. The results show that the error in WRSI can serve to determine effective window sizes for SRE bias correction rather than using SRE bias error itself. This proposed correction method resulted in improved estimates of WRSI.

Spatiotemporal assessment of rainfall and drought projection for integrated dam management in Benut River Basin, Malaysia under CMIP6 scenarios

This study assesses the influence of climate change on rainfall and drought in the Benut River Basin (BRB) to support the integrated management of Machap dam. The study seeks to reshape dam safety components in the near future through a technical focus group discussion (FGD) with key stakeholders. Utilizing the Coupled Model Intercomparison Project Phase 6 (CMIP6) dataset for the immediate future (2020–2040) under various scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), historical Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) rainfall datasets (1981 to 2021) are employed. A multi-filtration process ranks twenty-three Global Climate Models (GCMs) using seven relative importance metrics (RIMs). This work's novelty lies in a focused GCM evaluation through individual contributions quantified with RIMs, providing a refined ranking that address the limitations of traditional metrics. Then, the Compromise Programming Index (CPI) aggregates the ranked models, with the top subset selected through Jenks Optimised Classification (JOC), identifying ACCESS-ESM1–5 and CMCC-ESM2 as top models. The selected GCMs were bias-corrected using the linear scaling method and validated before ensembled, effectively replicating the probability distribution function (PDF) of CHIRPS data, as confirmed by a Taylor diagram indicating the closest agreement. The study reveals a generally wetter tendency in the northernmost area of Machap dam under all SSPs, with 5–9 % maximum increasing percentages. Conversely, downstream areas anticipate decreasing rainfall. June (6–22 %) and July (7–22 %) project higher rainfall, indicating increased water availability in a relatively dry period, while December (3–11 %) suggests increasing flood risk. The South West monsoon (SWM) anticipates lower rainfall in early March (3–7 %) and more severe dry periods in August (8–11 %). Future drought occurrences are projected homogeneously across scenarios, with drought months ranging from 15 % to 19 % of the 252 months under all severity classifications. In particular, the study enhances myDAMS by proposing continuous development of the guideline under diverse scenario for Machap dam's overall management and sustainability. Projected scenarios, coupled with outcomes from FGD, can inform basin-scale dam management, aiding risk mitigation for critical dam infrastructure.

Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan

Abstract The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.

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.

Evaluating the relative influence of climate and human activities on recent vegetation dynamics in West Bengal, India

Assessing the relative importance of climate change and human activities is important in developing sustainable management policies for regional land use. In this study, multiple remote sensing datasets, i.e. CHIRPS (Climate Hazard Group InfraRed Precipitation with Station Data) precipitation, MODIS Land Surface Temperature (LST), Enhanced Vegetation Index (EVI), Potential Evapotranspiration (PET), Soil Moisture (SM), WorldPop, and nighttime light have been analyzed to investigate the effect that climate change (CC) and regional human activities (HA) have on vegetation dynamics in eastern India for the period 2000 to 2022. The relative influence of climate and anthropogenic factors is evaluated on the basis of non-parametric statistics i.e., Mann-Kendall and Sen's slope estimator. Significant spatial and elevation-dependent variations in precipitation and LST are evident. Areas at higher elevations exhibit increased mean annual temperatures (0.22 °C/year, p < 0.05) and reduced winter precipitation over the last two decades, while the northern and southwest parts of West Bengal witnessed increased mean annual precipitation (17.3 mm/year, p < 0.05) and a slight cooling trend. Temperature and precipitation trends are shown to collectively impact EVI distribution. While there is a negative spatial correlation between LST and EVI, the relationship between precipitation and EVI is positive and stronger (R2 = 0.83, p < 0.05). Associated hydroclimatic parameters are potent drivers of EVI, whereby PET in the southwestern regions leads to markedly lower SM. The relative importance of CC and HA on EVI also varies spatially. Near the major conurbation of Kolkata, and confirmed by nighttime light and population density data, changes in vegetation cover are very clearly dominated by HA (87%). In contrast, CC emerges as the dominant driver of EVI (70–85%) in the higher elevation northern regions of the state but also in the southeast. Our findings inform policy regarding the future sustainability of vulnerable socio-hydroclimatic systems across the entire state.

Potential impacts of climate change on the sudan-sahel region in West Africa – Insights from Burkina Faso

The Sudan-Sahel region has long been vulnerable to environmental change. However, the intensification of global warming has led to unprecedented challenges that require a detailed understanding of climate change for this region. This study analyzes the impacts of climate change for Burkina Faso using eleven climate indices that are highly relevant to Sudan-Sahelian societies. The full ensemble of statistically downscaled NEX-GDDP-CMIP6 models (25 km) is used to determine the projected changes for the near (2031–2060) and far future (2071–2100) compared to the reference period (1985–2014) for different SSPs. Validation of the climate models against state-of-the-art reference data (CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and ERA5 (ECMWF Reanalysis v5)) shows reasonable performance for the main climate variables with some biases. Under the SSP5–8.5, Burkina Faso is projected to experience a substantial temperature increase of more than 4.3 °C by the end of the century. Rainfall amount is projected to increase by 30 % under the SSP5–8.5, with the rainy season starting earlier and lasting longer. This could increase water availability for rainfed agriculture but is offset by a 20 % increase in evapotranspiration. The country could be at increased risk of flooding and heavy rainfall in all SSPs and future periods. Due to the pronounced temperature increase, heat stress, and cooling degree days are expected to strongly increase under the SSP8.5 scenarios, especially in the western and northern parts. Under the SSP1–2.6 and SSP2–4.5, the projected changes are much lower for the country. Thus, timely implementation of climate change mitigation measures can significantly reduce climate change impacts for this vulnerable region and strengthen population resilience for a sustainable future.

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.

Spatiotemporal Drought Assessment Based on Gridded Standardized Precipitation Index (SPI) in Vulnerable Agroecosystems

Drought is one of the most critical environmental hazards for the viability and productive development of crops, especially in a climate change environment. To this end, drought assessment is a process of paramount importance to make vulnerable agricultural regions more resilient. The primary aim of this paper is an integrated drought assessment through time and space in one of the most susceptible (in terms of water availability limitations) and agriculturally productive regions in Greece and the Mediterranean, namely, the Thessaly region. Supplementary objectives consist of the determination of the two most extreme years in terms of drought and wetness, so that we may reveal any potential climatological cycles/patterns from 1981 to 2020. Additionally, the methodology includes the annual and seasonal analysis using one of the most widely used drought indices, namely, the Standardized Precipitation Index (SPI), so that consistent measurements are available across a large study area, avoiding the possible scarcity/deficiency of data coming from a sparse land weather network. The innovative element of this paper is the integrated spatiotemporal drought assessment in multiple time scales through the estimation of the SPI making use of remotely sensed data, such as CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data). The outcomes highlight that the study area faced two severe years of drought in 1988 and 1989, which led to moderate and extreme drought conditions, respectively. In contrast, extremely wet conditions were observed in 2002–2003, whereas 2009–2010 experienced moderately wet conditions. The central and western part of the region tends to suffer the most in terms of drought severity, especially at the most extreme years. The validity of the results has been confirmed by the adoption of R2 where the index is approaching 0.67 despite the large size of the pixels (5 × 5 km). In this context, the mapping of spatial and seasonal variability across the study area permits more targeted measures (e.g., precision farming) instead of horizontal policies.

Implications of uncontrolled water withdrawal and climate change on the water supply and demand gap in the Lake Tana sub-basin

This study evaluates the implications of multiple climatic and non-climatic factors on the water resource of the Lake Tana sub-basin, Ethiopia. The study focuses on three drivers: land use change, irrigation expansion, and climate change (CC), and their impact on the current and future water availability across the sub-basin. The study uses a random forest (RF) machine learning classifier in the Google Earth Engine (GEE) platform to detect land use land cover change (LULC) and for mapping the actual irrigated-area. The Climate Hazards Group InfraRed Precipitation and temperature data were used with station data to evaluate the implication of CC on the water availability of Lake Tana sub-basin. The supply-demand relationship was done for Gumara catchment within Tana sub-basin as an experimental site. Based on the LULC analysis, plantation, cropland, bare lands, built-up, and wetland showed an increasing trend, while forest, bush land, and grassland showed a decreasing trend. The increasing rate of crop land in the expense of natural forest, shrub land, grassland may cause runoff/flood, high soil erosion, and lower rate of groundwater recharge. The actual irrigated area mapping analysis shows a widespread irrigation near the Lake Shore and upstream parts of the sub-basin. The climate change analysis shows an increasing trend of potential evapotranspiration during the irrigation period attributed to increase in maximum temperature. The rising of potential evapotranspiration during the irrigation period creates a water shortage in the study and future periods as the crops need more water for growth. The estimated stream flow for Gumara catchment shows an increasing trend for the future period. The supply-demand relationship of the catchment shows an uneven distribution of water in the Gumara catchment both in the current and future periods resulting in unbalance abstraction of irrigation water. The study reveals that climate change, land cover change, and uncontrolled expansion of irrigated land area are likely to increase the unmet demand by increasing irrigation water demand, and this can cause conflict of interest between the users. Therefore, sustainable water resource management practices such as land management practices, and adaptive management of irrigation should be applicable to prevent or reduce the occurrence of conflict over the water demand.

Climate Smart Agriculture and its Implication on Climate Change Adaptation Measures within Smallholder Farming Systems in Gatundu South, Kenya

The impacts of climate change and the need for implementing adaptation and mitigation measures continues to dominate global environmental dialogue, with the Africa Climate Summit 2023 and Conference of Parties 28 being the most recent in this series. A hitherto marginalised aspect is the level of adoption of climate-smart agriculture practices in smallholder production systems. This study explored this dimension using Gatundu South as a case study. Rainfall data was obtained from the Climate Hazards Group Infrared Precipitation with Station data. Socio-economic data targeting 384 respondents was collected using questionnaires. Standard procedures were used to analyse these data. Results showed that farmers are generally aware of climatic variability especially as evidenced by changes in rainfall patterns. Farmers adapt and attempt to mitigate effects of climate change and variability by using practices that deliver direct economic benefits and not necessarily the climate-smartness of the practices. Farmers did not associate their adaptation measures with the need to reduce emission of greenhouse gasses. To smallholder farmers, direct economic benefits are the primary incentives for the adoption of climate-smart practices. Further, the link between climate change and the invisible greenhouse gases is a knowledge gap among smallholder farmers. Therefore, adoption of climate smart agriculture practices can be enhanced if the narrative shifts to emphasise the negative contribution of greenhouse gases to farmers’ health and the concomitant medical costs, through their role in exacerbating air pollution

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.

Long-term climate change and anthropogenic activities together with regional water resources and agricultural productivity in Uganda using Google Earth Engine

Uganda with its fragile ecosystem, large-scale human activities, and increasing population pressure, all of which combined, make this region increasingly susceptible to climate variation. This study examined the long-term trends of annual, seasonal, and monthly distributions of rainfall and temperature from 2001 to 2021 together with crop-wise agricultural productivity. For the analysis, we obtained CHIRPS-V2.0 (Climate Hazards Group InfraRed Precipitation with Station Data version 2.0) rainfall, Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), DMSP nighttime lights, ESA land cover attribution, and international crop production assessment records. Subsequently, several non-parametric statistical applications were applied to check the long-term spatio-temporal trends of climate parameters and their inter-relationship at higher significance using the Google Earth Engine platform. The investigation reveals an annual increase in LST, averaging 0.01 °C/year along with decreasing rainfall (1.89 mm/year). However, regional climate trends are largely elevation-dependent, which are predominantly subjected to the northern part of the study area witnessing a slight decrease in LST and thereby increased rainfall. Moreover, the long-term spatial nexus estimation divulges a potent inverse association between rainfall and temperature in the north and northeastern regions of the study area. Concurrently, changing patterns also have led to a decline in crop production and deterioration in water availability, which is accompanied by various other abnormalities, including the scarcity of water resources and anthropogenic activities. Changing climate has had significant implications on crop production, largely on corn and soybean as long-term shifts influence it in average rainfall and temperature, yearly fluctuations, and disturbances during various growth stages.

Extreme rainfall indices and their consequences on the local farming crop calendar: An agro-climatic zone based study

&lt;p&gt;The goal of this study is to investigate the extreme rainfall indices and their consequences on the local farming crop calendar among agro-climatic zones (ACZs) of the Abiya watershed. Climate Hazards Group Infrared Precipitation (CHIRPS) provided long-term (1981–2019) rainfall data for 50 sample grid points with a spatial resolution of 5 × 5 km. Different crops are affected differently by the same extreme rainfall event depending on when it occurred and how extreme it was; this means crop calendars for a specific time may be properly governed by extreme climatic conditions. There hasn’t been a sufficient published study on extreme rainfall indices and their consequences on crop calendars by considering various ACZ in Ethiopia. The study defined local crop calendar timing and the consequences of the extreme rainfall indices through focus group discussions. INSTAT+ software was applied to calculate eight extreme rainfall indices. The indices were evaluated using Mann-Kendall’s (MK) and Sen’s slope techniques to identify trends and determine variations in the magnitude, respectively. Increase and decrease indications for various crop calendars were found in each ACZ. Further, going-up signals were seen in the highlands, midlands, and all ACZs for land preparation time (LPT), sowing and management time (SaMT), and harvesting and threshing time (HaTP), respectively. While HaTT was found to be uniform in all ACZs, some of the declining trends in the indices were detected for LPT and SaMT in the cold-highland and highland zones. The perceived trend in indices across the whole ACZ will have direct and unintended consequences for watershed crop production. The findings imply that to reduce the unfavourable consequences of these extreme rainfall indices occurrences in the agricultural sector, it is necessary to develop suitable crop varieties and drought-tolerant crops, as well as an effective early warning system.&lt;/p&gt;

Assessment of the impact of rainfall uncertainties on the groundwater recharge estimations of the Tikur-Wuha watershed, rift valley lakes basin, Ethiopia

Spatial recharge estimation uncertainty is directly proportional to uncertainty in input precipitation data Thus, the main objective of this study was to investigate the recharge uncertainty by using improved spatial rainfall observations. The physically based fully distributed hydrological model WetSpa was used to simulate 20,000 possible combinations of parameters for two model setup. The M1 model setup was developed based on the rainfall measurements obtained from rain gauge stations scattered in and around the Tikur-Wuha watershed in Ethiopia, while M2 model setup was developed using bias-corrected satellite rainfall estimates (SREs) based on Climate Hazards Group InfraRed Precipitation (CHIRP) merged with relevant ground station records. The required parameter combinations were generated using Monte Carlo simulation stratified by applying Latin Hypercube Sampling (LHS). One hundred best performing parameter combinations were selected for each model to generate spatial recharge statistics and assess the resulting uncertainty in the recharge estimates. The results revealed that enhanced spatial recharge estimates can be produced through improved CHIRP-based SREs. The long-term mean annual recharge (218.29 mm) in the Tikur-Wuha watershed was estimated. Model parameter calibration performed using discharge measurements obtained from the Wosha rain gauge station located in the subcatchment area of the Tikur-Wuha watershed had a Nash-Sutcliffe efficiency of 0.56. Seventy percent of the watershed showed a coefficient of variation (Cv) < 0.15 for M2, while 90 % of the area exhibited a Cv < 0.15 for M1. Furthermore, the study findings highlighted the importance of improving evapotranspiration data accuracy to reduce the uncertainty of recharge estimates. However, the uncontrolled irrigation water uses and the total recharge coming from the irrigation fields scattered across the Tikur-Wuha watershed were not considered in the study, which is a limitation of the study. Future studies should consider the contribution made by irrigation water to the total recharge of the watershed.