Rainfall Variability Research Articles

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Rainfall variability and welfare of agricultural households: Evidence from rural Niger

AbstractVariable weather continues to be the major source of vulnerability to chronic hunger and poverty in many developing countries due to the strong dependency of livelihood strategies on rainfed farming. Quantifying the effect of climatic parameters on agricultural households is, therefore, necessary to help policymakers understand the benefits of climate policies, improve the allocation of the scarce resources dedicated to adaptation and prioritize among adaptation strategies. This article investigates the empirical relationship between the welfare of rural households and rainfall variability in the semi‐arid tropics, using household panel data and high‐resolution remotely sensed rainfall data from Niger. We find that a standard deviation increase in rainfall variability is associated with a reduction of real food consumption by 11.13%. Results also indicate that a standard deviation increase in rainfall variability reduces expenditures for cereal‐based products, animal‐based products and processed foods by 11.96%, 21.31%, and 16.23%, respectively. Our results are consistent across a battery of robustness checks. Finally, we find geographical‐based differences in terms of the effect and that access to cereal banks cushions the negative effect of rainfall variability. Policy interventions aiming at improving the well‐being of rural households should therefore emphasize improving climate adaptation strategies.

Application of GIS & RS in Rainwater Harvesting for an Arid Region

In dry areas with variable rainfall, water shortages severely affect the livelihoods of local populations. Rainwater harvesting from land sites and roof-tops is a readily available solution to this problem. This study presents a GIS & RS-based methodology to identify potential land sites and roof-top rainwater harvesting potential using easily obtainable and freely available data. Slope, land cover, soil, and drainage maps were used to identify suitable rainwater harvesting land sites, which were further analyzed for surface runoff using the curve number method. This research also incorporated obtainable socio-economic factors in identifying the suitable locations of dams considering the implementation stage. Based on the proposed criteria, 44 suitable locations for dams were identified and validated using a validation map and Google Earth images. CROPWAT model analysis revealed that paddy water demand in Batticaloa would increase threefold if all available land for paddy cultivation was utilized. Therefore, abandoned lands can be utilized by erecting dams at those identified locations. Analysis of Google Earth images also revealed that roof rainwater harvesting would be sufficient to meet the basic water demand in the area. This study demonstrates that rainwater harvesting can be an effective strategy for addressing water scarcity in dry areas.

The Role of the Subtropical and Extratropical Southern Hemisphere Anomalous Sea Surface Temperature in the Trans‐Seasonal Influence of Southern Annular Mode on Rainfall Over Southeastern South America

AbstractThe current study explores the linkage and possible mechanisms of the Southern Annular Mode (SAM) with rainfall variability over southeastern South America (SESA). The findings indicate that the preceding May SAM can modulate the subsequent rainfall over the SESA region in September‐October‐November (SON), independent of the El Niño‐Southern Oscillation (ENSO) signal. Further analysis suggests that the SAM‐rainfall relationship is mainly due to the response of sea surface temperature (SST) in the subtropical and extratropical Southern Hemisphere to SAM variability via air‐sea interactions. Such SST anomalies persist to the subsequent SON and stimulate an upstream wave train extending into the study region from the South Pacific. Such wave patterns form a cyclone anomaly over SESA, indicating strong subsidence and reduced convection over the region. Meanwhile, the low‐level anomalous cyclone and anticyclone located over the subtropical South Atlantic (on the coast of the study region) are associated with easterly winds to the South (North) of the anomalous cyclone (anticyclone), which advect colder and drier air into the study region due to the cold SST over the subtropical South Atlantic and leading to increased moisture scarcity and decreased rainfall over SESA. Besides, model results based on the joint climate indices (i.e., SAM, ENSO and Indian Ocean Dipole (IOD)) revealed a satisfactory estimate of the SON rainfall compared with either single index, indicating that the SAM can be one of the precursors of the SESA rainfall, besides ENSO and IOD.

Urbanizational impact on climatic variables and geographical analysis of physical land use land cover variation of a city using Remote Sensing.

<p>Climate change has serious implications foron the rise and variation of the average earth’s temperature, and intensified rainfall. In urban areas, temperatures and intensified rainfall variation are noticeably seenin the city which disrupts the normal life of a living things. Also, it affects the storage of rainfall runoff water in tanks and lakes. Green cover reduction due to the increase ofagriculture, industrialization, population explosion and urbanization have a direct impact on the climate pattern.The average temperature of a city is increasing in every decade due to a drastic reduction in the green cover.This is visible today from the abnormal melting of glaciers. The rainfall pattern is highly intensified, and the frequency of rainfall is on the a decreasing trend. This intensified rainfall creates high surface runoff during rainfall and disrupts the normal life in a city. The Standard Precipitation Index (SPI) for a city indicates a noticeable shift in its climatic pattern, particularly with consistent rainfall during the southwest monsoon. Over the span of four decades, LULC research has revealed an increase in residential areas and a decrease in green cover. This urbanization process is associated with changes in LULC, resulting in decreased vegetation and diminished storage water bodies.Variations in the temperature of a city are on an increasing trend. Urban forestry or the dense green coverof an area reduces the temperature and creates cool pockets in the city. The regression model for temperature and vegetation indicates the negative correlation of temperature with high vegetation growth. Keywords: Urbanization, Climate change, Rainfall-runoff, Temperature, Green Cover</p>

Rainfall Assessment Using Weighted Interval Type-2 Fuzzy Inference System

This study proposes an integrated model for assessing rainfall in Chennai using the Interval type-2 fuzzy reasoning technique and the Analytic Hierarchy Process (AHP). Interval Type-2 Fuzzy Inference System (IT2FIS) is employed to describe complex relationship between the rainfall variables and rainfall rate by considering individual weights of those variables through the aggregation of criteria weights using AHP. The suggested approach has an edge over its predecessors in terms of modeling the inter-personal and intra-personal uncertainty involved in rainfall rate classification. Finally, the developed rainfall model (RM) is applied on the historical dataset collected from Indian Meteorological Department (IMD). The actual data and the IT2FIS model’s performance are compared. Correlation coefficient ([Formula: see text]) and Bland–Altman Plot (BAP) have been used to evaluate the performance metrics of the proposed model. The outcome suggests that the AHP-IT2FIS model can accurately estimate rainfall over time, indicating its potential for long-term use.

Re-evaluating soil moisture-based drought criteria for rainfed crops in peninsular India

Background: Peninsular India, being completely under the influence of monsoonal climate, suffers crop yield variability due to rainfall distribution-induced soil moisture constraints. Timely and appropriate assessment of this rainfall and soil moisture-induced crop yield variability serves as a key for exemplary relief assistance. Per cent available soil moisture (PASM) is one among several drought declaration indices followed by stakeholders in India for declaration of drought, needs re-evaluation as the existing criteria in unable to capture the yield loss due to ineffective classification of PASM categories. This study attempts to revise the agricultural drought classes by PASM based on relationships established between yield of major rainfed crops of the study region and PASM.Methods: Analysis of yield variability due to PASM was carried out based on long term observations in experiments conducted at five dry farming locations (Akola, Parbhani, Kovilpatti, Ananthapuramu and Bengaluru) of peninsular India. The average yield for each category of PASM was calculated and tabulated for regression analysis. The PASM versus yield in each group was correlated and regression equations were developed if significant positive correlations were established.Results: The range of available soil moisture to obtain at least 50 percent of optimum yield in cereals (maize: 26 and finger millet: 52.9 PASM), pulses (pigeon pea: 37.2 PASM), oilseeds (soybean: 26.8 to 30.5, groundnut: 53.8 to 61.7 PASM) and commercial crops (cotton: 26.3 PASM) was 26–61 percent.Conclusion: The revised PASM-based drought classes (0–50 severe; 51–75 mild and 76–100 no drought) would help in drought declaration and precise identification of drought-hit areas for meaningful relief assistance. However, there is further investigation is needed to include a soil component for further fine-tuning of the criteria.

Precipitation patterns and their variability in the southern region of Brazil

Climate change is a long-term transformation of climate patterns, which can be natural or anthropogenic. Signs of climate change can manifest themselves in different ways: through the occurrence of extreme events, rising ocean levels, melting glaciers, among others. Negative impacts are produced by climate change and are one of humanity's biggest concerns, since its effects reach the entire planet with different consequences. Considering the need for actions aimed at contributing to the process of mitigating climate change, the importance of knowing changes in regional climate patterns is emphasized. This study aims to analyze changes in precipitation patterns over time in the southern region of Brazil, including variability, trends, anomalies, droughts and excess rainfall. Daily precipitation data from 1961 to 2020 were used, collected at 15 meteorological stations in the region. Various statistical analyzes were carried out, such as averages, maximum and minimum values, seasonal and annual anomalies, frequency distribution, Mann-Kendall trend tests and the Normalized Precipitation Index (SPI). The results indicate that climate change trends are positive in terms of increasing rainfall volumes, altering rainfall dispersion patterns (volume). In other words, rainfall is more irregular in terms of distribution throughout the seasons. The big highlight was Florianópolis -SC, which during the summer had the highest volume of rain among the meteorological stations studied.

Vulnerability of geoheritage sites in South Africa to climate change: Examples from the Eastern Cape Province

Climate change and variability have potential to impact on all types of heritage sites globally, through increasing weathering and erosion of geological and built heritage, changes in biome and ecosystem viability, sea-level rise and coastal flooding, and increased climate hazard events. This study examines the most likely impacts of climate change on different heritage sites in South Africa, with a particular regional focus on sites of potential geoheritage interest in the Eastern Cape Province of the country. The diverse examples described in this study are: (1) Klasies River cave, which is a Middle Stone Age archaeological site; (2) different localities along the coast where faunal footprints are preserved within aeolianite that dates mainly to the last interglacial period; (3) different localities inland where rock art panels are present within caves or rock shelters; and (4) Cape St Francis, which is a coastal foreland where different phases of late Quaternary coastal dunes with archaeological interest are preserved. Results show that many of these very varied sites of geoheritage interest are not protected as National or Provincial Heritage Sites under current legislation, which is weak and inconsistently applied across the country. This means that no specific management plans, under national policy guidelines, are in place for mitigating against the impacts of present and future climate change and variability, either regionally or nationally. These climate changes include: sea-level rise and increased coastal storminess and wave attack; and changes in weathering and erosion regimes as a result of increased temperatures and likely decreased rainfall or higher rainfall variability. A better appreciation of the interconnections that exist between geology and natural and cultural heritage may lead to valuing landscape and site geoheritage more highly at the local, national and international level.

Runoff generation in a semiarid environment: The role of rainstorm intra-event temporal variability and antecedent soil moisture

Rainfall intensity and antecedent soil moisture are key variables affecting the initiation and accumulation of runoff, and therefore, impact on the occurence flood events. We used time series of rainfall intensities characterizing rainstorms with different mean, standard deviation, and skewness to simulate runoff response of a sealed loamy soil profile under various initial soil moisture conditions. We found that shifting from wet to dry initial soil moisture leads to a 300 % increase in ponding time and a 350 % decrease in total runoff. Intense rainfall events catalyze runoff development and runoff occurs earlier with increasing mean, maximum intensity, and variance of rainfall. Higher variance in intra-event storm-intensity temporal distribution yields more runoff. Earlier runoff formation aligned with negatively skewed storm structures, but largest cumulative runoff resulted from storms displaying near-zero skewness values. The combination between rainfall intensification and extended dry periods between storms might reduce potential runoff. These insights improve our understanding of anticipated impacts of climate variations on floods and may also contribute to ecosystem health evaluations, and bolster predictive capabilities of surface processes.

Association between exposure to ambient air pollution, meteorological factors and atopic dermatitis consultations in Singapore—a stratified nationwide time-series analysis

Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting approximately 20% of children globally. While studies have been conducted elsewhere, air pollution and weather variability is not well studied in the tropics. This time-series study examines the association between air pollution and meteorological factors with the incidence of outpatient visits for AD obtained from the National Skin Centre (NSC) in Singapore. The total number of 1,440,844 consultation visits from the NSC from 2009 to 2019 was analysed. Using the distributed lag non-linear model and assuming a negative binomial distribution, the short-term temporal association between outpatient visits for AD and air quality and meteorological variability on a weekly time-scale were examined, while adjusting for long-term trends, seasonality and autocorrelation. The analysis was also stratified by gender and age to assess potential effect modification. The risk of AD consultation visits was 14% lower (RR10th percentile: 0.86, 95% CI 0.78–0.96) at the 10th percentile (11.9 µg/m3) of PM2.5 and 10% higher (RR90th percentile: 1.10, 95% CI 1.01–1.19) at the 90th percentile (24.4 µg/m3) compared to the median value (16.1 µg/m3). Similar results were observed for PM10 with lower risk at the 10th percentile and higher risk at the 90th percentile (RR10th percentile: 0.86, 95% CI 0.78–0.95, RR90th percentile: 1.10, 95% CI 1.01–1.19). For rainfall for values above the median, the risk of consultation visits was higher up to 7.4 mm in the PM2.5 model (RR74th percentile: 1.07, 95% CI 1.00–1.14) and up to 9 mm in the PM10 model (RR80th percentile: 1.12, 95% CI 1.00–1.25). This study found a close association between outpatient visits for AD with ambient particulate matter concentrations and rainfall. Seasonal variations in particulate matter and rainfall may be used to alert healthcare providers on the anticipated rise in AD cases and to time preventive measures to reduce the associated health burden.

Climate change shocks and crop production: The foodgrain bowl of India as an example

Global warming is causing climate change (CC) characterized by increased frequency of heatwaves, droughts, erratic rains, hailstorms, cloudbursts, floods, landslides etc. The CC has already adversely affected ecosystems. In spite of efforts to mitigate greenhouse gas (GHG) emissions, which lead to warming, the global temperature during 2011–20 was 1.1°C above that during pre-industrial era. The projections are that warming will continue to increase and adverse effects will intensify particularly in developing countries like India. In India a number of studies have recorded wide spatial variability in rainfall, though, many reported a general overall negative trend since mid-20th century. Further, varying pattern of rainfall has been recorded in three agroclimatic regions of Punjab state, the granary of India. Unseasonal rains followed by spiked temperature during winter (rabi) season of 2021–22 reduced wheat (Triticum aestivum L.) yield in Punjab by 651 kg/ha and by 301 kg/ha in Haryana compared to 2020–21. Further, the grain was of lower quality. During rainy (kharif) season of 2022, Southern Rice Black-streaked Dwarf Virus (SRBSDV), appeared for the first time in Punjab and Haryana. Some farmers ploughed the affected fields. Adverse weather during rabi 2022–23 also, reduced the wheat yield (143–150 kg/ha) in these states. At the national level, erratic weather during rabi 2021–22 and kharif 2022 caused losses of about 3 mt of grain of each of wheat and rice (Oryza sativa L.). The projected increased adverse effects due to intensified CC include food insecurity. Thus, there is an emergent need to accelerate implementation of adaptation and mitigation strategies in agriculture. The adaptation options include cultivar improvement, conservation agriculture altering growing seasons, crop diversification and sustainable soil, and water resource management. In the process of adaptive management of crop production, adjusting sowing dates and breeding cultivars having varying duration in consonance with CC has been one of the central aspects. Shifting sowing dates to find appropriate crop cultivation season is a low-cost measure. However, cultivar development is time and resource consuming. Novel biotechnological tools enable fast cultivar development with precision, and facilitate mobilization of genes from wild-weedy relatives, which are rich in genes conferring resistance/tolerance to biotic and biotic stresses, required to combat CC challenge. In view of CC stress on water resources, improving water-use efficiency (WUE) has gained importance. Sensor-based micro-irrigation/fertigation has great potential to enhance water and fertilizer-use efficiency. Similarly, the application of other smart technologies like nanotechnology, sensor-based pesticide application, bio-fertilizers and bio-pesticides, need to be mobilised. In view of agro-ecological diversity in India, right-sized regionspecific technology packages have to be developed implying that crop research will expand exponentially. This needs strengthening of human resources and institutional infrastructure, expanding and linking basic and applied researches, and fortifying inter-disciplinary/inter-institutional collaborations to develop and diffuse technology innovations. Enabling factors include enhanced funding and international cooperation. All-out efforts are needed to have more climate-resilient agriculture.

Rainfall reliability and maize production in the Bamenda Highlands of Cameroon

The long-term average rainfall for a given period (month, season or year) scarcely indicates reliability because rainfall in low latitudes varies significantly from one year to the other. The less variable rainfall is, the more reliable it is, as the index of variability measures the likelihood of repetition in the mean amount of rainfall. This study bridges some gaps in related studies in the Bamenda Highlands, such as a study assessing climate change impacts on food security, where the Rainfall Anomaly Index was used and another study on the impact of rainfall on maize production using the Standardized Precipitation Index. The objectives of this study are to assess rainfall reliability and establish the impact of rainfall reliability on maize production. Rainfall data were collected from 15 stations, while maize output was collected for four localities. Results revealed that rainfall is still reliable for 13 stations, with a coefficient of variations of 9.62 to 18.54 %, while Ndop and Ndawara recorded unreliable rainfall of 23.14 % and 30.97 % respectively. Rainfall reliability was complemented by the Standardized Precipitation Index, which showed that only 53.45 % of rainfall episodes were normal to sustain maize production, while 46.55 % were events of rainfall deficits. Maize production has been decreasing in Ndu, Oku and Nkum while increasing in Ndop. These findings reflect the realities of other tropical mountainous regions in the world. Faced with future climatic uncertainties, farmers should embrace agroecological practices, climate-smart agriculture, conservation agriculture, Nature-based Solutions, Ecosystem-based Adaptation and diversification of production systems and livelihood sources to ensure food security.

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Managing Aflatoxin Contamination Threats in Groundnut (Arachis hypogaea L.) Production in Ghana: The role of adoption of management strategies

Efforts to raise knowledge and awareness of smallholder groundnut farmers about aflatoxin contaminations and its management continue to pose a threat to consumers health along the food value chain. This paper examines smallholder farmers’ awareness and adoption of aflatoxin management strategies using a multistage sampling technique to sample 318 groundnut farmers in Ghana. A 5-point Likert scale and Kendall’s coefficient of concordance was used to examine the perception about aflatoxin contaminations and constraints faced in managing aflatoxins in groundnut production, respectively. A multivariate probit model was further employed to analyze the factors influencing the adoption of aflatoxin management strategies. The results revealed that 92% of the farmers were aware of aflatoxin contaminations, however, only 20.7% of the farmers had received aflatoxins training. The results also showed that farmers are in agreement about aflatoxin contaminations in groundnut production. The most commonly adopted aflatoxin management strategies by farmers were timely harvesting, crop rotation, sorting, and storage in aerated and covered environment. Furthermore, the empirical results showed that education, credit access, membership in farmer based organizations, distance to farm, off-farm activity and location had a significant influence on farmers’ adoption of all aflatoxin management strategies. Pest and disease incidence, and rainfall variability were identified as major constraints faced by smallholder groundnut farmers in Ghana. There is the need for periodic public sensitizations and technical capacity building of smallholder farmers to adopt aflatoxins management strategies which could improve groundnut productivity and ensure food security in Ghana.

Simulation of climate-adaptation responses to rainfall variability on rainfed yield anomalies

Climate adaptation offers promising approaches to cope with the detrimental impacts of climate change on rainfed agriculture, intending to mitigate yield losses and safeguard food security worldwide. This is especially critical in the semi-arid tropics, characterized by highly variable and often intensified rainfall regimes. The current study explored the potential contribution of climate adaptation measures, specifically sowing windows and supplemental irrigation (SI), to offset the impacts of rainfall variability on rainfed crop yield in the Thondamuthur block, India. Under the proposed adaptation measures, this study analyzed the relationship between historical rainfall variability (1951–2019) and rice, maize, and sorghum yield anomalies. A seamlessly integrated framework was developed to link rainfall variability analysis with yield response simulations using the AquaCrop model. Rainfall classification analysis indicated an increase in rainfall availability during the Kharif season and a decrease during the Rabi season in Period III compared with the baseline. The late-sown Kharif and early-sown Rabi crops demonstrated effective climate adaptation, with the 27th and 38th SMWs identified as optimal climate-adapted sowing weeks, respectively. Significant decreases of 10% and 12% in SI requirements were observed in early-sown Rabi maize and sorghum, respectively. The results showed that substituting Rabi rice and maize with Rabi sorghum led to reductions of 90% and 75% in seasonal SI requirements, respectively. The early-sown Rabi rice and maize contributed to 88% and 12% relative yield increases, respectively, compared with the normal-sown. This study highlights the effectiveness of climate adaptation measures in enabling farmers to withstand climate-induced anomalies.

Assessment of climate change induced rainfall trend and variability with non-parametric and linear approach for Sirajganj district, Bangladesh

The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.

Future Climate Change Impacts on Rice in Uttar Pradesh, India's Most Populous Agrarian State

AbstractUttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs.

Projections of future streamflow for Australia informed by CMIP6 and previous generations of global climate models

Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts.

Analysing Spatial and Temporal Rainfall Variability of Southern Rajasthan using GIS Approach

This study focuses on analysing the behavioral patterns of rainfall in southern Rajasthan, particularly in reference to its amount and variation over time. The study made use of standard meteorological weeks (SMW) and historic weekly rainfall records. IDW (Inverse Distance Weighting) interpolation technique within the ArcGIS platform was used for spatial analysis, creating spatial variation maps. It was found that significant rainfall occurred from Standard Meteorological Week (SMW)-22 to SMW-42. The weekly mean rainfall across the study area during the monsoon period ranged from 2.1 to 68.4 mm, with corresponding standard deviations ranging from 9.3 to 79.7 mm. Notably, the standard deviation was often higher than the mean, indicating substantial variability in weekly rainfall. Trend analysis using the Mann-Kendall test revealed that most stations in the study area exhibited no significant trend in annual rainfall data. Similarly, weekly rainfall data showed a trend in SMW-30 for 44 stations, while all other weekly data series did not exhibit significant trends, except for SMW-30. Stationarity was generally accepted for most weekly data series, except for SMW-30 and SMW-32, which showed non-stationary behavior. However, homogeneity tests indicated homogeneity in weekly rainfall data series for almost all selected stations. Overall, the study highlighted the significant variability in rainfall patterns in southern Rajasthan, with no significant trend in annual rainfall, except during the peak monsoon period (SMW-30). These findings contribute to a better understanding of regional rainfall behavior, which is crucial for water resource management and agricultural planning in the region.