Seasonal Rainfall Research Articles

Indian Agriculture Under Climate Change: The Competing Effect of Temperature and Rainfall Anomalies

AbstractThe latest generation of global climate models robustly projects that monsoon rainfall anomalies in India will significantly increase in the 21st century due to global warming. This raises the question of the impact of these changes on the agricultural yield. Based on annual district data for the years 1966-2014, we estimate the relationship between weather indices (amount of seasonal rainfall, number of wet days, average temperature) and the most widely grown kharif crops, including rice, in a flexible non-parametric way. We use the empirical relationship in order to predict district-specific crop yield based on the climate projections of eight evaluated state-of-the-art climate models under two global warming scenarios for the years 2021-2100. We find that the loss in rice yield by the end of the 21st century lies on average between 3 - 22% depending on the underlying emission scenario. For the sustainable scenario impacts range from an increase of 3.2% to a decrease of 12.1% for individual districts. In the worst-case scenario, all districts are negatively affected, with a predicted decrease in rice yield ranging from 34% to a decrease of 11.5% in the long run. Potential gains due to increasing rainfall are more than offset by the negative impacts of increasing temperature. Adaptation efforts in the worst-case global warming scenario would need to cut the negative impacts of temperature by 50% in order to reach the outcome of the sustainable scenario.

Tree size partially mediates the influence of climate on fruit and seed production in Ricinodendron heudelotii (Baill.) Heckel

AbstractUnderstanding environmental drivers of fruit and seed production in wild edible fruit species is essential for their valorisation. Ricinodendron heudelotii is an oilseed plant whose kernels are particularly treasured in cosmetic and medical industries. This study assessed fruits and seeds production patterns of R. heudelotii. Data were collected for three consecutive years on 30 trees in the phytodistricts of Plateau, Pobè and South Borgou where it naturally occurs. Tree dbh, total height, crown diameter, number of fruits and seed mass were measured. Climatic data were obtained from NASA Power Database. Data were analysed using linear (seed mass) and generalised linear (number of fruits) mixed models and structural equation modelling. Fruit and seed production were significantly higher in the South‐Borgou phytodistrict (1433 ± 1144 fruits, 4.26 ± 3.39 kg of Dry matter per tree) and Plateau phytodistrict (1.66 ± 0.35 g DM/seed). Tree dbh further significantly mediated the effect of minimum temperature, and relative humidity on seed mass. Tree dbh, irradiance and dry season rainfall were identified as the best‐predictors of seed mass production (kg DM). These results improved current knowledge of the fruit and seed production of R. heudelotii and are of significant importance for the exploitation of the species.

Analysis of Rainfall Variability Using Precipitation Concentration Index for a Catchment

The PCI is a critical metric to assess the distribution and concentration of precipitation over a specified period. By analyzing historical rainfall data, this research aims to identify trends and patterns in precipitation concentration, shedding light on potential implications for water resource management and hydrological systems. The methodology involves calculating the PCI for different temporal scales and regions, allowing for a comprehensive assessment of rainfall variability. The findings contribute to a deeper understanding of precipitation dynamics, facilitating informed decision-making in climate-sensitive sectors. There are simple rainfall measures that can be used to provide information on variability and, therefore, on the climate state. These mainly include annual and monthly rainfall data and averages, which can be used to assess rainfall seasonality, variability, and the frequency of extreme events.

Alpine Catchments’ Hazard Related to Subaerial Sediment Gravity Flows Estimated on Dominant Lithology and Outcropping Bedrock Percentage

Sediment gravity flows (SGFs) cause serious damage in the Alpine regions. In the literature, several methodologies have been elaborated to define the main features of these phenomena, mainly considering the rheological features of the flow processes by laboratory experiments or by flow simulation using 2D or 3D propagation models or considering a single aspect, such as the morphometric parameters of catchments in which SGFs occur. These very targeted approaches are primarily linked to the definition of SGFs’ propagation behavior or to identify the predisposing role played by just one feature of catchments neglecting other complementary aspects regarding phenomena and the environment in which SGFs can occur. Although the research aimed at the quantification of some parameters that drive the behavior of SGFs provides good results in understanding the flow mechanisms, it does not provide an exhaustive understanding of the overall nature of these phenomena, including their trigger conditions and a complete view of predisposing factors that contribute to their generation. This paper presents a research work based on the collection and cross-analysis of lithological, geomechanical, geomorphological and morphometrical characteristics of Alpine catchments compared with sedimentological and morphological features of SGF deposits, also taking in to account the rainfall data correlation with historical SGF events. A multidisciplinary approach was implemented, aiming at quantifying SGF causes and characteristics starting from the catchments’ features where the phenomena originate in a more exhaustive way. The study used 78 well-documented catchments of Susa Valley (Western Italian Alps), having 614 historical flow events reported, that present a great variability in geomorphological and geological features. As the main result, three catchment groups were recognized based on the dominant catchment bedrock’s lithology characteristics that influence the SGFs’ rheology, sedimentological and depositional features, triggering rainfall values, seasonality, occurrence frequency and alluvial fan architecture. The classification method was also compared with the catchments’ morphometry classification, demonstrating that the fundamental role in determining the type of flow process that can most likely occur in a given catchment is played by the bedrock outcropping percentage, regardless of the results provided by the morphometric approach. The analysis of SGF events through the proposed method led to a relative estimate of the hazard degree of these phenomena distinguished by catchment type.

SARIMA Model-Based Monte Carlo Simulation of Option Contract Design for Maize Seasonal Heavy Precipitation in Shenyang

Agricultural production is highly dependent on weather conditions such as temperature, light, water and heat. The huge loss caused by extreme disaster weather makes the global insurance and reinsurance market overwhelmed, and seriously affects the enthusiasm of agricultural investment and the development of agricultural economy. With unique topography and vast territory, China is vulnerable to complex and diverse climate disasters. In recent years, the annual average economic loss caused by extreme weather disasters has reached about 200-300 billion yuan, among which floods caused by extreme heavy rainfall are the main agricultural disasters in China. In order to enhance the "farmer - insurance company - government" interest association to resist the risk of flood disaster caused by extreme heavy rainfall, financial hedging derivatives of weather disaster risk have gradually become a new hedging tool besides traditional insurance and reinsurance. This paper takes Shenyang, the main grain producing area in northeast China, as the sample of the study area, and uses the SARIMA model to obtain the distribution characteristics of seasonal rainfall time series. Different seasonal rainfall index option products (call option and put option contracts) are designed respectively, and the final option pricing is obtained by Monte Carlo stochastic simulation. The seasonal rainfall call option and put option contracts have opened up a new hedging model of agricultural extreme weather catastrophe outside the insurance and reinsurance market, which has enriched the varieties of weather financial derivatives market in China, reduced the severe impact of extreme weather disasters on agriculture, and enhanced the ability of agricultural stakeholders to resist the risk of extreme weather disasters.

Crop planning using innovative trend analysis of 62-years rainfall data

The success of climate-smart agriculture in high rainfall zone lies in understanding the rainfall trend and planning or modifying the cropping system for maximum yield. Moisture stress in critical crop growth stages is detrimental to the crop and drastically reduces the yield. Udhagamandalam region in Western Ghats is a high rainfall area and is largely cultivated by vegetable crops. Rainfall trend based crop planning would enhance the crop yield without water stress. A study was carried out at ICAR-Indian Agricultural Research Institute, Dhemaji, Assam focused on assessing the long-term seasonal and monthly rainfall trends of Udhagamandalam region, Tamil Nadu using non-parametric tests and Innovative Trend Analysis (ITA). Daily rainfall of 62 years from 1960–2021 was analyzed with non-parametric tests, viz. Mann-Kendall and modified Mann-Kendall and ITA to find the seasonal rainfall characteristics. Mann- Kendall (3.055) and modified Mann-Kendall (3.055) tests showed a significantly increasing trend in the annual and seasonal monsoonal rainfall. ITA revealed either a significant positive or a negative trend in all the months except February, with the highest trend in June (2.625). In contrast to standard non-parametric tests, ITA detected a significant positive trend in all seasons and annual rainfall, except in cold winters where the trend is negative. The long-term trend analysis results suggest that the ITA is more precise for rainfall trend analysis than standard non-parametric tests and can be used to evaluate hidden variations of rainfall trends. Hence, ITA is recommended for analyzing rainfall trends for crop planning in high-rainfall regions. IT analysis of 62 years of rainfall data of Udhagamandalam suggested that vegetable crop planning can be done by farmers from August–November months as the rainfall trend during this period is assured as an increasing trend of rainfall pattern was observed.

Spatial variability of seasonal rainfall onset, cessation, length and rainy days in Rwanda

Spatial variability of seasonal rainfall onset, cessation, length and rainy days in Rwanda

Evapotranspiration, water use efficiency, and yield for film mulched maize under different nitrogen-fertilization rates and climate conditions

The biodegradable film, as an ideal substitute for plastic film, has broad application prospects. However, it is uncertain in maize actual evapotranspiration (ETac) components, yield, and water use efficiency (WUE) of biodegradable and plastic films during the different rainfall seasons. Therefore, a 4-year field trial with three mulching patterns (FNM: flat planting with non-mulching, RPM: ridge-furrow with plastic film mulching, and RBM: ridge-furrow with biodegradable film mulching) and two N-fertilization levels (0 and 180 kg N ha–1) was conducted. The results showed that the machine-learning models could accurately estimate maize ETac and its partitioning, and the random forest and artificial neural networks models had the highest accuracy and the least input variables after optimization. Compared to FNM, RBM and RPM increased Et by 10.8 mm, 14.0 mm in the dry season, 9.1 mm, 11.2 mm in the normal season, and 4.0 mm, 7.5 mm in the wet season, respectively, but decreased Es by 75.8 mm, 82.7 mm in the dry season, 48.6 mm, 56.7 mm in the normal season, 67.1 mm, and 74.9 mm in the wet season, respectively. Therefore, RBM and RPM decreased ETac by 65.0 mm, 68.8 mm in the dry season, 39.5 mm, 45.6 mm in the normal season, and 53.1 mm, 67.5 mm in the wet season, respectively, compared to FNM. Nitrogen application had a similar effect on Es and Et but only increased ETac by 13.3 mm in the dry season, 2 mm in the normal season, and 4.3 mm in the wet season, respectively, compared to N0. Furthermore, RBM and RPM under different nitrogen-fertilizations increased maize yield by 4.0 %, 3.6 % in the dry season, 3.0 %, 3.3 % in the normal season, and 5.3 %, 5.9 % in the wet season, respectively, also increased maize WUE by 23.3 %, 24.1 % in the dry season, 12.9 %, 15.0 % in the normal season, and 21.1 %, 23.4 % in the wet season, respectively, compared to FNM. This study proved that RPM could be replaced by RBM under 180 kg N ha–1 in the different rainfall seasons in terms of reducing ETac, increasing maize yield, and improving WUE. The optimized machine learning models in this study also provided a low-cost method for computing regional maize ETac.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

Assessment of behavioural response to climate forecasts and climate change adaptation by small-holder farmers in Nambale sub-county of Busia county, Kenya

Under climate change and unpredictable rainfall variability, rainfed agriculture has often been characterised with high degree of crop failure, especially when rainfall seasons portray anomalies in rainfall onsets and cessation. This study sought to establish the level of utilisation of climate forecasts and behavioural response to such products by the smallholder farmers in Nambale, Busia, Kenya. A total of 30 subsistence farmers were interviewed. Results showed that about 4 in every 5 farmers are largely unconscious of climate change and its effects and did not adhere to the climatic forecasts, instead, farming was subjected to the year-to-year routine practices, dependent on the traditional approaches. About 60% of the farmers lacked knowledge of climate change and believed that crop failure is beyond human understanding. 100% of the farmers planted maize in the two main planting seasons of March-April-May (MAM) and August-September-October (ASO), with about 86.7% of them least practicing crop diversification or intercropping. Furthermore, climate change adaptation interventions such as planting of early maturing, drought resistant crops and crop irrigation were unlikely among the farmers. Crop performance on an experimental farm established by the Nambale Science Place-Centre for Community Engagement on Climate Risk Interpretation (NSP-CCECRI), showed potential for enhanced farm production if farming activities are adhered to timely utilisation of climate forecasts. Based on these findings, and considering limited climate change adaptation strategies, there is need for sensitising smallholder farmers in enhancing knowledge on climate change and adaptation for enhanced food security, building community climate change resilience and alleviating poverty levels.

Spatial–Temporal and Risk Assessment of Microplastics in the Surface Water of the Qinhuai River during Different Rainfall Seasons in Nanjing City, China

Microplastics (MPs) are increasingly becoming recognized as worldwide environmental contaminants, exerting a substantial impact on the safety of city rivers. This study explored the temporal variance in MPs in different rainfall seasons, including spring, plum, and autumn rains. The Qinhuai River has large spatial fluctuations in MPs at six sampling sites, with an average concentration of 466.62 ± 153.69 items/L, and higher MP abundance was found downstream of spring rain and upstream of autumn rain. Among the different rainfall seasons, the variations in microplastics at various sampling sites were more stable in the plum rain season, with an average concentration of 473.67 ± 105.17 items/L, while the concentrations of TP and TN in the plum rain season were higher than those in other rain seasons. Transparent MPs had the highest abundance at many sampling sites in all seasons, and large-sized MPs (270–5000 μm) occurred more in the autumn rain season. PVC was more prevalent in autumn, but PET decreased in the plum rain season. Interestingly, more fibers, PET, and large-sized MPs were found in the autumn rain. The index of hazard scores of plastic polymers (H) revealed that the studied river was at a severe pollution level (IV), which was highly influenced by PVC and PC. In addition, the pollution load index (PLI) value in different rain seasons indicated slight pollution (I). At the same time, it was higher in autumn rains than in other seasons due to the higher variance in MPs. Therefore, the ecological risk of microplastics in the Qinhuai River should be seriously considered, along with seasonal variance and the PVC and PC polymers. Our research is expected to provide valuable assistance in improving the management of urban rivers.

Evaluation of INSAT-3DR Hydro-Estimator product for monsoon season rainfall at block-level and its utility in forecast verification: A case study in Karnal district, India

Evaluation of INSAT-3DR Hydro-Estimator product for monsoon season rainfall at block-level and its utility in forecast verification: A case study in Karnal district, India

Microplastics in Hong Kong's marine waters: Impact of rainfall and Pearl River discharge

This study provided a systematic investigation of microplastics in Hong Kong's surface marine waters during the pandemic from 2019 to 2021. Microplastics (2.07 ± 4.00 particles/m3) exhibited significant temporal variations with higher abundance in the wet season, without a consistent trend after the mandatory mask-wearing requirement was announced. The impact of pandemic restrictions on microplastic distribution was found to be relatively minor. However, significant correlations between microplastic abundances and rainfall highlighted the substantial contribution of local emissions through surface runoff. Notably, sites in closer proximity to the Pearl River Delta exhibited higher microplastic abundances, indicating their association with emission sources. The influence of rainfall and adverse weather on marine microplastic loads demonstrated different sensitivities among various locations but can generally last for one month. These results revealed the impact of seasonal rainfall on coastal microplastics and emphasized the need for efforts to reduce microplastic discharge from land-based sources.

CONSTRAINTS FACED BY FARMERS IN ADAPTING TO CLIMATE CHANGE

Some constraints faced by the farmers due to climate variability, be it the delay in seasonal rainfall; heavily or less occur rainfall, its unexpected arrival during the reproductive phase of the crops, or even the increase in night temperature, continue to strike the farmers financially, forcing them to rethink farming as a viable proposition. The present study involving a sample size of 240 farmers from Gujarat, India, employed the Garrett'smean scoreto determine the constraints to climate change. The findings revealed that among the Among the constraints in managing the adverse implications of climate change, lack of finance was found to be the most critical constraint faced by the farmers in the study area. With a Garrett mean score (GMS) of 57.10. The constraint was ranked two in the overall study area with a Garrett mean score of 55.76. High cost of adaptation is one constraint which is not perceived to be a serious issue across the study area as the perceptions of farmers with regards to its severity was found to be mixed. Only in the districts of Jamnagar and Junagadh, the constraint was ranked second in terms of severity with a Garrett mean score of 56.80 and 55.67, respectively.

Spatio-temporal dynamics of fairy circles in Namibia are driven by rainfall and soil infiltrability

ContextNamibia’s fairy circles (FCs) form an extraordinary vegetation pattern along the Namib Desert. Recent evidence from multiple fieldwork activities is increasingly supporting the view that FCs result from biomass-water feedbacks and plant self-organization.ObjectivesTo shed light on these biomass-water feedbacks, we focused here on a temporal analysis of the spatial FC patterns with regard to vegetation response after rainfall events.MethodsWe analyzed the distribution of FCs in 10 drone-mapped study plots of the Namib and related their spatial patterns to the soil infiltrability. Additionally, we mapped three plots repeatedly during the rainfall seasons 2020–2023 to assess how the emerging grasses within FCs changed the FC patterns after rainfall.ResultsWe found that the most regular, spatially periodic, FCs occurred in areas with deep aeolian sands where rain water infiltrates very quickly and homogenously across the study plot, which enables the most symmetric competitive interactions between the grasses. After ample rainfall following a drought period, between 58 and 34% of all mature FCs revegetated. These 1092 closing FCs were 73 times more than the 15 new FCs that formed during the same time. The closing FCs occurred in areas where there was locally a higher density of FCs, which act as underground water sources for the surrounding grasses.ConclusionsOur study shows that the dynamic vegetation response to rainfall and soil water is the key driver of the FC patterns. Overall, the research underlines that Namibia’s fairy circles are a self-organized emergent vegetation pattern that is driven by biomass-water feedbacks and the competition of grasses for limiting water resources.

Hot and fast: seed ecology for restoration relevant species in the Argyle region of the east Kimberley, Australia

This study investigates the germination requirements of 12 plant species native to the Argyle region of the east Kimberley, a biodiverse monsoonal tropical region characterized by high temperatures, high evaporation, and episodic seasonal rainfall. The research involved quality assessment of mature seeds, followed by dormancy alleviation and laboratory‐based germination to determine the responses of seeds to a range of temperatures (5–40°C) in terms of germination speed (T10), mean germination time, and maximum germination proportion. Data were then modeled to calculate the optimal temperature to support germination for each species. The results showed that germination rapidly commences in response to a wide range of temperatures typical of the wet season (November–February) in the east Kimberley, though germination for most species was still high (>50%) after exposure to temperatures as low as 15°C. Mean optimal temperature for germination across all species was 25.8 ± 1.5°C, with minimal variation between most species, the exception being Dodonaea physocarpa, which preferred cooler temperatures (Topt = 14.0°C). The speed of germination was also rapid (T10 = 1–3 days) across all species at the optimal germination temperature. The findings suggest that temperature is not a limiting factor for germination in this region and that the onset and intensity of the wet season are the most significant factors determining successful germination, emergence, and seedling establishment. The study underscores the importance of species‐specific understanding of environmental temperatures required for seed germination in seed‐based restoration efforts and informs the planning of direct seeding works, thus enhancing restoration outcomes.

Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations

Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. It is unclear whether SSC is predictable using macroscopic environmental attributes and forcings. This study tested the feasibility of deep-network-based models to predict daily SSC at basin outlets given only basin-averaged forcings, readily-available physiographic attributes, and streamflow (from observation or model). We trained long short-term memory (LSTM) deep networks both separately for each of the 377 sites across the conterminous United States (CONUS) (termed “local models”), and on all the sites collectively (Whole-CONUS). The Whole-CONUS and local models presented median coefficient of determination (R2) values of 0.63 and 0.52, respectively. This comparison agrees with previously acknowledged “data synergy” effects for LSTM models, where more data from more sites can help improve predictions overall. Furthermore, the continental-scale analysis provided a wealth of insights about SSC patterns. Both local and Whole-CONUS models tended to be more successful where SSC-streamflow correlations (Rs-q) were high − typically in the humid Eastern US − and with lower SSC. Low Rs-q basins were often found in the arid Southwest with higher SSC. The highly-nonlinear SSC-streamflow relationships seem related to seasonality, basin size, and heterogeneity of land cover and rainfall within the basins, suggesting these basins need to be simulated at higher spatial resolution and may require additional inputs related to SSC induced by seasonality. The Whole-CONUS model also performed well for spatial extrapolation (basins not included in the training dataset, median R2 = 0.55), supporting large-scale modeling efforts. These state-of-the-art results using only minimal inputs suggest data-driven approaches can exploit the natural coevolution of sediment processes and the environment to support sediment modeling.

ANALISIS POLA CURAH HUJAN DI WILAYAH SUMATERA BAGIAN UTARA MENGGUNAKAN FAST FOURIER TRANSFORM (FFT)

This research was conducted in North Sumatra, namely at Iskandar Muda, Malikussaleh and Deli Serdang stations in the period 1885 to 2019. The purpose of the study was to analyze seasonal and interannual rainfall patterns. The method used is Fast Fourier Transform (FFT) and correlation between rainfall, sea surface temperature and wind. The results of the study showed that the rainfall pattern at Iskandar Muda, Malikussaleh and Deli Serdang stations had two peaks of rainfall. The peak rainfall of the three stations occurs in May and the second peak has differences between Deli Serdang and Iskandar Muda and Malikussaleh stations. Where the peaks of high periodization are only in the 6-month and 12-month periods. There is also a peak periodogram from each station seen in 18-30 months. Rainfall periodogram analysis shows the magnitude of data frequency for each phase cycle and its frequency. The correlation between rainfall, wind and sea surface temperature (SST) at the three stations has a different pattern. At Deli Serdang station, there is an influence of ENSO and IOD, while at Iskandar Muda and Malikussaleh stations there is no influence of ENSO and IOD. Then in the September, October, November (SON) season at the three stations there is no visible influence of ENSO and IOD.

A new antshrike (Aves: Thamnophilidae) endemic to the Caatinga and the role of climate oscillations and drainage shift in shaping cryptic diversity of Neotropical seasonal dry forests

AbstractThe Caatinga is the largest patch of Seasonal Dry Tropical Forest in the Neotropics, located in northeastern Brazil and characterized mainly by deciduous vegetation and extreme rainfall seasonality. It has historically been treated as a biologically impoverished domain, but recent studies uncovered new diversification patterns and several new taxa of frogs, mammals, insects, and fishes. Here we employed a dense sampling regime to evaluate whether the São Francisco River (SFR) would have promoted genetic diversification and fixed phenotypic differences and how Quaternary climatic oscillations shaped distribution and population sizes in a Caatinga endemic species, the Silvery‐cheeked Antshrike (Sakesphoroides cristatus). We adopted an integrative approach using multilocus genetic, plumage, vocal data, and ecological niche modelling (ENM) to characterize evolutionary units and niche suitability in past scenarios. We recovered strong genetic structure across the SFR that was congruent with plumage and vocal variation, revealing a yet undescribed species named herein as Sakesphoroides niedeguidonae, sp. nov. (urn:lsid:zoobank.org:act:2B9FC637‐008A‐4E9A‐B92B‐69ED7FE23823s). The splitting time estimated between the newly described species and S. cristatus is consistent with the establishment of the modern course of SFR, with a more recent course shift apparently promoting the secondary contact between the two species in the Raso da Catarina region. After their split, both species experienced increases in population sizes and range sizes at similar times during the Last Glacial Maximum. We expect other Caatinga avian endemic lineages to show similar patterns of genetic differentiation across the SFR that were enhanced by Quaternary climatic oscillations.

Assessing climate change impacts on irrigation water requirements in the Lower Mahanadi Basin: A CMIP6-based spatiotemporal analysis and future projections

ABSTRACT Climate change heightens India's agricultural risks, particularly in nations like India heavily reliant on farming. Previous studies focused on Coupled Model Intercomparison Project Phase (CMIP3) and (CMIP5) scenarios for large river basins, but the heightened risk of local climate changes poses a significant threat to smaller basins, notably affecting crops. This study investigates the spatiotemporal dynamics of climate change impacts on paddy crop irrigation in India's Lower Mahanadi Basin, utilizing the latest general circulation models (GCMs) from the CMIP6, focuses on two emission scenarios, SSP585 and SSP370. Thirteen models were analysed, top six were selected based on statistical criteria like PBIAS, NSE, R2, RSR, and RMSE. Models project climate changes for near- (2025–2050), mid- (2051–2075), and far-future (2076–2100) periods against a baseline (1981–2014), investigating spatiotemporal variations in rainfall, temperature, and irrigation water requirements (IWRs) in the region. In both scenarios, future mean seasonal rainfall is expected to increase compared with the baseline. SSP370 projects a 23.7% rise in minimum rainfall, while maximum rainfall varies by 11.5%. SSP585, on the other hand, projects a 9.53% decrease in maximum IWR and a 28.9% increase in maximum rainfall compared with the baseline. Both scenarios anticipate a 3–4 °C temperature increase in the far-future.