Future Periods Research Articles

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.

Assessing South Indian Ocean tropical cyclone characteristics in HighResMIP simulations

AbstractSeveral damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region.

TFformer: A time–frequency domain bidirectional sequence-level attention based transformer for interpretable long-term sequence forecasting

Transformer methods have shown strong predictive performance in long-term time series prediction. However, its attention mechanism destroys temporal dependence and has quadratic complexity. This makes prediction processes difficult to interpret, limiting their application in tasks requiring interpretability. To address this issue, this paper proposes a highly interpretable long-term sequence forecasting model, TFformer. TFformer decomposes time series into low frequency trend component and high frequency period component by frequency decomposition, and forecasts them respectively. The periodic information in high-frequency component is enhanced with the sequential frequency attention, and then the temporal patterns of the two components are obtained by feature extraction. According to the period property in time domain, TFformer through periodic extension to predict the future period patterns using sequential periodic matching attention. Finally, the predicted future period pattern and the extracted trend pattern are reconstructed to future series. TFformer provides an interpretable forecasting process with low time complexity, as it retains temporal dependence using sequence-level attentions. TFformer achieves significant prediction performance in both univariate and multivariate forecasting across six datasets. Detailed experimental results and analyses verify the effectiveness and generalization of TFformer.

Red Resources + Green Ecology Painted a, New Picture of Rural Revitalization -- Based on the Field Investigation of Yangzhou Section of the Grand Canal

Promoting the comprehensive revitalization of rural areas, drawing a new picture of livability and business and beauty of rural areas, is the key task of "three rural" work in the future period. In order to grasp the current situation and actual achievements of rural revitalization in the Yangzhou section of the Grand Canal, this field survey was carried out in the first half of 2024. Through field visits and investigations, the "red + green" integration and rural revitalization status of the Yangzhou section of the Grand Canal were carefully analyzed and studied after fully grasping the rich red cultural resources and green ecological resources in the Yangzhou section of the Grand Canal. A new working idea of "red + green" integrated development in Yangzhou section of the Grand Canal to promote the overall rural revitalization of the area is given. In order to promote the common prosperity of urban and rural areas for the cities along the Grand Canal, drawing a new picture of the revitalization of the beautiful countryside to provide useful suggestions and thinking.

Glimpse on Spot and Derivative Market of Electricity and Nepal’s Hydropower

Energy plays a vital role in a country’s economic development. Nepal abundantly blessed with various renewable resources like hydropower, solar energy, biogas, and biomass can achieve prosperity both economically and socially. Further, with a significant opportunities for the regional electricity market as the country has been surrounded by big powerhouses like China and India. It has huge possibilities for regional trading of renewable energy. The present total Installed capacity of power is 3157 MW in Nepal. With 95% hydropower generation, the energy mix in the Integrated Nepal Power System makes the country a surplus of electricity during the wet season when the water in the river is very high and a shortage during the dry season when the discharge in the river is minimal. Hence, to address the surplus generation during the wet season and shortage of power during the dry season, trading in the region plays a very vital role in balancing the demand and supply of the country. Nepal has been importing power during the dry season power deficit in the market during the dry season when the demand is high leaving NEA at risk of interrupting the supply to its customers. Likewise, uncertainty about clearing a bid during the wet season when the power production is high and Nepal is in the position to export the power in the regional market, as the Hydropower plant has to compete with the cheap coal power plant resulting in financial loss and uncertainty of return on investment. Hence, to tackle the mentioned financial risks, the Electricity Derivative market can be one of the best options as derivatives markets can be used to hedge against price fluctuations in electricity spot markets. Similarly, the uncertainty of power sale can be avoided by selling or buying the electricity through a derivative market for the future time period that secures the price and the amount that can be sold in the market in the future. In a nutshell, the Electricity Derivative market can be a useful mechanism for the development of hydropower in Nepal in the future.

A Statistical Analysis of Drought and Fire Weather Indicators in the Context of Climate Change: The Case of the Attica Region, Greece

As warmer and drier conditions associated with global warming are projected to increase in southern Europe, the Mediterranean countries are currently the most prone to wildfire danger. In the present study, we investigated the statistical relationship between drought and fire weather risks in the context of climate change using drought index and fire weather-related indicators. We focused on the vulnerable and long-suffering area of the Attica region using high-resolution gridded climate datasets. Concerning fire weather components and fire hazard days, the majority of Attica consistently produced values that were moderately to highly anti-correlated (−0.5 to −0.9). This suggests that drier circumstances raise the risk of fires. Additionally, it was shown that the spatial dependence of each variable on the 6-months scale Standardized Precipitation Evapotranspiration Index (SPEI6), varied based on the period and climate scenario. Under both scenarios, an increasing rate of change between the drought index and fire indicators was calculated over future periods versus the historical period. In the case of mean and 95th percentiles of FWI with SPEI6, abrupt changes in linear regression slope values were observed, shifting from lower in the past to higher values in the future periods. Finally, the fire indicators’ future projections demonstrated a tendency towards an increasing fire weather risk for the region’s non-urban (forested and agricultural) areas. This increase was evident from the probability distributions shifting to higher mean and even more extreme values in future periods and scenarios. The study demonstrated the region’s growing vulnerability to future fire incidents in the context of climate change.

Projecting Irrigation Water and Crop Water Requirements for Paddies Using WEAP-MABIA under Climate Change

Monitoring future irrigation water demand as a part of agricultural interventions is crucial to ensure food security. In this study, the impact of climate change on paddy cultivation in Brunei is investigated, focusing on the Wasan rice scheme. This research aims to project irrigation water requirement (IWR) and crop water requirement (CWR) or the main and off season using the WEAP-MABIA model. Historical data analysis over the past 30 years and future projections up to 2100 are employed to assess potential impacts. An ensemble of statistically downscaled climate models, based on seven CMIP6 GCMs under shared socioeconomic pathways (SSPs) (SSP245, SSP370, and SSP585), was utilised to project the IWR and CWR. Using downscaled CMIP6 data, three future periods were bias-corrected using quantile delta mapping (QDM) for 2020–2046 (near future), 2047–2073 (mid future), and 2074–2100 (far future). The WEAP-MABIA model utilises a dual crop coefficient approach to evaluate crop evapotranspiration (ETc), a critical factor in computing IWR. Results indicate that changes in future temperatures will lead to higher average ETc. Consequently, this results in elevated demands in irrigation water during the off season, and it is especially prominent in high-emission scenarios (SSP370 and SSP585). While the main season experiences a relatively stable or slightly increasing IWR trend, the off season consistently shows a decreasing trend in IWR. Moreover, the off season benefits from substantial rainfall increases, effectively reducing IWR despite the rise in both maximum and minimum temperatures. This study also highlights some recommendations for implementing possible improvements in irrigation management to address the effects of climate change on rice cultivation in the region. Future investigation should focus on enhancing crop yield predictions under climate change by integrating a dynamic crop growth model that adjusts for changing crop coefficient (Kc) values.

The social cost of carbon falling on the wealthy

Under the Paris Agreement, the responsibility for reducing greenhouse gas emissions lies with individual countries, which submit pledges to the United Nations Framework Convention on Climate Change (UNFCCC) outlining their ambitions and timelines for emission reductions. Within countries, emissions are unevenly distributed among individuals, with the wealthy known to emit disproportionately more. Since greenhouse gas emissions represent a social cost, this paper investigates the economic implications of the lifestyles of the wealthy. The social cost of carbon (SCC) attributed to millionaires—defined based on their assets, including property—is calculated using national emissions data, distributional data, and SCC assessments for both historical (1990–2022) and future periods (2023–2050). The results suggest that only 1.3% of the global SCC, estimated at US$8.7 trillion, was covered by any form of carbon pricing in 2022. Of this amount, 16.5% (US$1.4 trillion) is attributable to millionaires, who represent just 0.8% of the world's population. The most cost-effective climate policy would be to internalize the SCC, ensuring that the carbon price is equal to the SCC. If the SCC remains externalized, the wealthiest individuals could benefit by an estimated US$25.1 trillion over the period from 2023 to 2050. Other market-based mechanisms will be necessary to curb the carbon-intensive consumption patterns of the wealthy.

Characterization of extreme rainfall changes and response to temperature changes in Guizhou Province, China

Different geographical zones have regional heterogeneity in underlying earth surface structure and microclimate which result in different evolution trends and their response to climate change varies in extreme rainfalls in these zones. In the Guizhou province of China, there are complex landforms, which lead to spatial redistribution of rainfall, frequent extreme rainfall, and disasters high risk of geologic disasters. Research on extreme climate in Guizhou mostly paid attention to its spatio-temporal characteristics and modeling, but lack of analysis on its characteristics of extreme rainfall variability and response to temperature changes under different subsurface conditions. This study investigated the characteristics of the extreme rainfall spatiotemporal and recurrence periods in Guizhou province and discussed the relationship between the response of extreme rainfall to temperature change. Daily rainfall data from 1990 to 2020 and 2021–2100 at 31 meteorological observation stations throughout the province were collected to calculate extreme precipitation. This research had the following results. (1) Both historical and future periods show an upward trend in extreme rainfall in Guizhou province, with a spatial distribution pattern of “high in the south and low in the north, high in the east and low in the west” and “high in the southeast and low in the northwest”, respectively; the spatial distribution of extreme rainfall under each recurrence period is consistent with the non-recurrence period. (2) Both historical and future periods show an upward trend in temperature in Guizhou province, with a spatial distribution consistent with that of the extreme rainfall in the corresponding period. (3) The change in extreme rainfall intensity with increasing temperature is almost always greater than the C–C rate for different periods and underlying earth surface structure; Extreme rainfall has a Hook response structure to temperature change, and the climate response structure shifts to the right with climate warming. The results of the study can provide a basis for decision-making on regional disaster prevention and mitigation in the context of temperature change.

Association of precipitation extremes and crops production and projecting future extremes using machine learning approaches with CMIP6 data.

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980-2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985-2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025-2049, F2: 2050-2074, F3: 2075-2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

SARS-CoV-2 variant replacement constrains vaccine-specific viral diversification.

Coronavirus disease 2019 (COVID-19) vaccine breakthrough infections have been important for all circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant periods, but the contribution of vaccine-specific SARS-CoV-2 viral diversification to vaccine failure remains unclear. This study analyzed 595 SARS-CoV-2 sequences collected from the Military Health System beneficiaries between December 2020 and April 2022 to investigate the impact of vaccination on viral diversity. By comparing sequences based on the vaccination status of the participant, we found limited evidence indicating that vaccination was associated with increased viral diversity in the SARS-CoV-2 spike, and we show little to no evidence of a substantial sieve effect within major variants; rather, we show that rapid variant replacement constrained intragenotype COVID-19 vaccine strain immune escape. These data suggest that, during past and perhaps future periods of rapid SARS-CoV-2 variant replacement, vaccine-mediated effects were subsumed with other drivers of viral diversity due to the massive scale of infections and vaccinations that occurred in a short time frame. However, our results also highlight some limitations of using sieve analysis methods outside of placebo-controlled clinical trials.

Assessing the impact of climate change on agricultural production in central Afghanistan

Afghanistan has faced extreme climatic crises such as drought, rising temperature, and scarce precipitation, and these crises will likely worsen in the future. Reduction in crop yield can affect food security in Afghanistan, where the majority of population and economy are completely dependent on agriculture. This study assessed the interaction between climate change and crop yield in Kabul of Afghanistan during the reference (1990–2020) and future (2025–2100) periods. Climate data (1990–2020) were collected from four meteorological stations and three local organizations, and wheat yield data (1990–2020) were acquired from the United States Agriculture Department. Data during the reference period (1990–2020) were used for the validation and calibration of the statistical downscaling models such as the Statistical Downscaling Model (SDSM) and Long Ashton Research Station Weather Generator (LARS-WG). Furthermore, the auto-regression model was used for trend analysis. The results showed that an increase in the average annual temperature of 2.15°C, 2.89°C, and 4.13°C will lead to a reduction in the wheat yield of 9.14%, 10.20%, and 12.00% under Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5 during the future period (2025–2100), respectively. Moreover, an increase in the annual maximum temperature of 1.79°C, 2.48°C, and 3.74°C also causes a significant reduction in the wheat yield of 2.60%, 3.60%, and 10.50% under RCP2.6, RCP4.5, and RCP8.5, respectively. Furthermore, an increase in the annual minimum temperature of 2.98°C, 2.23°C, and 4.30°C can result in an increase in the wheat yield of 6.50%, 4.80%, and 9.30% under RCP2.6, RCP4.5, and RCP8.5, respectively. According to the SDSM, the decrease of the average monthly precipitation of 4.34%, 4.10%, and 5.13% results in a decrease in the wheat yield of 2.60%, 2.36%, and 3.18% under RCP2.6, RCP4.5, and RCP8.5, respectively. This study suggests that adaptation strategies can be applied to minimize the consequences of climate change on agricultural production.

Improving Lameness Detection in Cows: A Machine Learning Algorithm Application

The deployment of diverse data-generating technologies in livestock farming holds the promise of early disease detection and improved animal well-being. In this paper, we combine routinely collected dairy farm and herd data with weather and high frequency sensor data from 6 farms to predict new lameness events in various future periods, spanning from the following day to 3 weeks. A Random Forest classifier, using input features selected by the Boruta Algorithm, was used for the prediction task; effects of individual features were further assessed using partial dependence plots. We achieve precision scores of up to 93% when predicting lameness for the next 3 weeks and when using information from the last 3 weeks, combined with a balanced accuracy of 79%. Removing sensor data results have tendency to reduce the precision for predictions, especially when using information from the last one,2 or 3 weeks. Moving to a larger data set (without sensor data) of 44 farms keeps the similar balanced accuracy but reduces precision by more than 30%, revealing a substantial a trade-off in model quality between false positives (false lameness alerts) and false negatives (missed lameness events). Sensor data holds promise to further improve the precision of these models, but can be partially compensated by high resolution data from other systems, such as automated milking systems.

Precipitation and temperature timings underlying bioclimatic variables rearrange under climate change globally.

Modeling how climate change may affect the potential distribution of species and communities typically utilizes bioclimatic variables. Distribution predictions rely on the values of the bioclimatic variable (e.g., precipitation of the wettest quarter). However, the ecological meaning of most of these variables depends strongly on the within-year position of a specific climate period (SCP), for example, the wettest quarter of the year, which is often overlooked. Our aim was to determine how the within-year position of the SCPs would shift (SCP shift) in reaction to climate change in a global context. We calculated the deviations of the future within-year position of the SCPs relative to the reference period. We used four future time periods, four scenarios, and four CMIP6 global climate models (GCMs) to provide an ensemble of expectations regarding SCP shifts and locate the spatial hotspots of the shifts. Also, the size and frequency of the SCP shifts were subjected to linear models to evaluate the importance of the impact modeler's decision on time period, scenario, and GCM. We found ample examples of SCP shifts exceeding 2 months, with 6-month shifts being predicted as well. Many areas in the tropics are expected to experience both temperature and precipitation-related shifts, but precipitation-related shifts are abundantly predicted for the temperate and arctic zones as well. The combined shifts at the Equator reinforce the likelihood of the emergence of no-analogue climates there. The shifts become more pronounced as time and scenario progress, while GCMs could not be ranked in a clear order in this respect. For most SCPs, the modeler's decision on the GCM was the least important, while the choice of time period was typically more important than the choice of scenario. Future predictive distribution models should account for SCP shifts and incorporate the phenomenon in the modeling efforts.

Earnings Management Trend in Manufacturing Companies Sub-Sector Food and Beverage Listed on the Indonesia Stock Exchange Year 2019-2023

This study aims to analyze the trend of earnings management in manufacturing companies operating in the food and beverage sub-sector listed on the Indonesia Stock Exchange. Earnings management trends encompass methods used by companies to manipulate or manage their financial statements. Factors influencing earnings management include tax planning, income tax expenses, and profitability. The management's goal is to enhance corporate earnings to reflect stable profits, thus accurately portraying the company's financial performance in future periods. The study population consists of 45 companies, with a sample of 9 companies selected using purposive sampling. Data collection utilized secondary data from quarterly financial reports of manufacturing companies listed on the Indonesia Stock Exchange from 2019 to 2023. The research employed descriptive analysis to assess earnings management trends as predictors of profit increases. The findings reveal significant fluctuations in earnings management practices among food and beverage manufacturing companies listed on the Indonesia Stock Exchange during the period from 2019 to 2023.

Evaluation of future wind climate over the Eastern Mediterranean Sea

The aim of this study is to evaluate wind climate characteristics in the Eastern Mediterranean Sea until the end of the 21st century using the wind fields simulated by the EURO-CORDEX Regional Climate Models (RCM), namely the Rossby Centre regional atmospheric model (version RCA4). Long-term averages, long-term trends, and long-term variability in wind characteristics under two Representative Concentration Pathway (RCP 4.5 and 8.5) scenarios were assessed for two future periods: the near future for the years 2021–2060 and the middle future for the years 2061–2100. Relative to the historical period, the most remarkable changes in the mean wind speeds were projected under the RCP 8.5 scenario in the middle future in the central and southern Aegean Sea (exceeding 5 % increase) and in the eastern and northern Levantine basin (up to 5 % decrease), compatible with the definition of the climate scenarios as the RCP8.5 scenario assumes a rising radiative forcing until 2100. The changes in extreme wind characteristics are more noteworthy in agreement with the indicators of climate change (i.e., an increase in the intensity and the frequency of extreme events). 100-year return period wind speed increases by about 29 % in the western Levantine basin under the RCP 8.5 scenario while the most drastic decrease in 100-year extreme wind speed was found in the central Levantine basin with a reduction of exceeding −29 % for the middle future period under the RCP 8.5 scenario. These decreasing and increasing behaviors in extreme wind speeds are expected in the study area as the Mediterranean is affected by many climate systems.

Building and system design's impact on thermal resilience to overheating during heatwaves: An uncertainty and sensitivity analysis

Rising heatwaves necessitate thermally resilient buildings to mitigate occupant heat-stress and mortality, ensuring habitable conditions during thermal shocks. This study's aim and novelty is to identify key design parameters affecting three thermal resilience aspects -(i) shock impact or SET-Dh i.e., degree hours above standard effective temperature threshold of 28 °C (ii) absorptivity time (tabs) and (iii) recovery time (trec) across varying degrees of shock intensities (doS-low, medium and high). A reference Belgian apartment is simulated with varying design parameters (building orientation, envelope and glazing properties, glazing percentage, airtightness, operation and control of passive strategies, cooling systems) during three types of heatwaves. Uncertainty analysis quantified the impact of design variations on thermal resilience, while global sensitivity analysis pinpointed key parameters affecting resilience during different shocks. The study shows that while 87 % of design variations remain thermally resilient during shorter shocks, only 21 % do so during longer shocks and that WWR, cooling capacity, and passive strategies (operation of natural night ventilation and solar shading) have twice the impact on thermal resilience compared to building orientation and glazing properties. While tabs is affected by heat build-up factors (e.g., WWR, solar shading), trec is influenced by heat removal factors (e.g., cooling capacity, NNV). Parameters such as heavy thermal mass extend heat absorption but also delays heat dissipation during prolonged heatwaves. This study guides designers and architects to focus on interventions that enhance buildings' thermal resilience during both short and long heatwaves, aiding in climate adaptation and the ability to withstand future extreme heat periods.

Assessing the influence of land use/land cover dynamics and climate change on water resources in Upper Blue Nile, Ethiopia

ABSTRACT This study evaluates the impact of climate and land use changes on the Lake Tana Basin's hydrology, using datasets on land use, weather patterns, topography, soil characteristics, and discharge. Future climate data were obtained from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) and generated using the Weather Generator (LARS-WG) tool from the Long Ashton Research Station with five distinct GCMs. Land use changes were projected using the Markov chain model based on cellular automata (CA). The Soil and Water Assessment Tool (SWAT) model was used to assess changes in hydrological elements between reference and future periods, with calibration and validation ensured by the Integrated Parameter Estimation and Uncertainty Analysis Tool (IPEAT). Projections indicate a 4.9 °C increase in ensemble mean annual temperature and a 16% rise in precipitation by the end of the 21st century under Representative Concentration Pathway (RCP) 8.5. Additionally, average annual hydrological components, including water yield, soil water, percolation, lateral flow, runoff, and actual and potential evapotranspiration, are expected to increase due to combined climate and land use changes. Therefore, it is crucial to fully understand these cumulative impacts before formulating and implementing water resource management strategies in the basin.

Impact of projected climate variability on agricultural water demand of key crops in drought-prone central India region

ABSTRACT Water scarcity predominantly affects agriculture production. It is mandatory to save future water to prevent its shortage due to variations in climate. Variation in climate is a significant factor responsible for frequent droughts over the Bundelkhand region in central India. It is necessary to obtain early warning studies in this region to manage and arrange future agricultural water. Out of best Regional Climate Models (RCMs) under the Coordinated Regional Downscaling Experiment (CORDEX) and their driving Coupled Model Intercomparison Project Phase 5 (CMIP5), General Circulation Models (GCMs) for India, the most effective model has been found based on the observed multiscale data for the region. The daily projected data (2021–2100) of best GCM, Earth System Model, ESM-2M have been used to evaluate the future water requirements for major rabi and hot weather crops grown in the region. The climate change effect is seen in the majority, especially in the upper Bundelkhand part. Most of the crops will need more water than the present condition, especially from 2071 to 2100. The findings will also help researchers and policymakers to utilize the future projected data to propose suitable water management strategies for agriculture.

Unveiling the future water pulse of central asia: a comprehensive 21st century hydrological forecast from stochastic water balance modeling

This study uses a new dataset on gauge locations and catchments to assess the impact of 21st-century climate change on the hydrology of 221 high-mountain catchments in Central Asia. A steady-state stochastic soil moisture water balance model was employed to project changes in runoff and evaporation for 2011–2040, 2041–2070, and 2071–2100, compared to the baseline period of 1979–2011. Baseline climate data were sourced from CHELSA V21 climatology, providing daily temperature and precipitation for each subcatchment. Future projections used bias-corrected outputs from four General Circulation Models under four pathways/scenarios (SSP1 RCP 2.6, SSP2 RCP 4.5, SSP3 RCP 7.0, SSP5 RCP 8.5). Global datasets informed soil parameter distribution, and glacier ablation data were integrated to refine discharge modeling and validated against long-term catchment discharge data. The atmospheric models predict an increase in median precipitation between 5.5% to 10.1% and a rise in median temperatures by 1.9 °C to 5.6 °C by the end of the 21st century, depending on the scenario and relative to the baseline. Hydrological model projections for this period indicate increases in actual evaporation between 7.3% to 17.4% and changes in discharge between + 1.1% to –2.7% for the SSP1 RCP 2.6 and SSP5 RCP 8.5 scenarios, respectively. Under the most extreme climate scenario (SSP5-8.5), discharge increases of 3.8% and 5.0% are anticipated during the first and second future periods, followed by a decrease of -2.7% in the third period. Significant glacier wastage is expected in lower-lying runoff zones, with overall discharge reductions in parts of the Tien Shan, including the Naryn catchment. Conversely, high-elevation areas in the Gissar-Alay and Pamir mountains are projected to experience discharge increases, driven by enhanced glacier ablation and delayed peak water, among other things. Shifts in precipitation patterns suggest more extreme but less frequent events, potentially altering the hydroclimate risk landscape in the region. Our findings highlight varied hydrological responses to climate change throughout high-mountain Central Asia. These insights inform strategies for effective and sustainable water management at the national and transboundary levels and help guide local stakeholders.