Water Resources Management Research Articles

Sustainable groundwater regulation in typical irrigation areas of inland river basins based on ecological indicators and the MIKE-SHE hydrological model

Study regionThe study area is located in the Nukus irrigation area on the lower reaches of the Amudarya River in Uzbekistan. Study focusThe extensive agricultural land development has introduced a substantial amount of water resources into the irrigation area, leading to a significant rise in regional groundwater levels. Quantifying regional groundwater resource issues and achieving sustainable groundwater management under conditions of high spatiotemporal heterogeneity are key scientific issues for regional ecological protection. Thus, this study used the concept of ecological water level to quantify groundwater issues, used the MIKE-SHE hydrological model to conduct scenario simulations, formulated and subsequently evaluated a groundwater regulation plan with spatiotemporal attributes. New hydrological insights for the regionRegional sustainable development groundwater level should be within the range of 1.78–2.78 m. During the irrigation and non-irrigation periods, the groundwater levels in approximately 84.3 % and 72.2 % of the study area respectively exceeded the upper and lower ecological water level limits. Under the background of gradually increasing groundwater fluctuations, it will pose a serious threat to regional ecology. Therefore, this paper established a monthly-scale groundwater zoning control plan, which can make the area of regional groundwater within the ecological water level reach 87.53 %. All research not only contributes to the protection of the local ecological environment but also provides valuable insights for water resources management in other regions.

The Impact of Digital Technology on Water Resources Management: Evidence from China

Digital technology is gradually emerging as a new driving force in the field of water resources management. In this paper, we conduct a thorough analysis of panel data from 30 provinces in China spanning from 2013 to 2022. Utilizing the fixed-effects model, the mediation effect model, a panel threshold model, and a coupling coordination degree model, this study empirically examines the impact of digital technology on water resources management. The findings are as follows: (1) The direct impact of digital technology on water resources management is significantly positive at the 1% level, with notable regional variations. (2) Digital technology improves water management through green innovation. (3) In the process of digital technology promoting water resources management, green innovation exhibits a threshold effect, with an estimated threshold value of 1.840. (4) During the sample period, the national coupling coordination degree of digital technology and water resources management was barely coordinated, showing the following characteristics: Eastern China > Western China > Central China. These research conclusions will offer valuable insights and directions for advancing sustainable water resources management strategies and fostering the deep integration of digital technology and water resources management.

A Performance Comparison Study on Climate Prediction in Weifang City Using Different Deep Learning Models

Climate change affects the water cycle, water resource management, and sustainable socio-economic development. In order to accurately predict climate change in Weifang City, China, this study utilizes multiple data-driven deep learning models. The climate data for 73 years include monthly average air temperature (MAAT), monthly average minimum air temperature (MAMINAT), monthly average maximum air temperature (MAMAXAT), and monthly total precipitation (MP). The different deep learning models include artificial neural network (ANN), recurrent NN (RNN), gate recurrent unit (GRU), long short-term memory neural network (LSTM), deep convolutional NN (CNN), hybrid CNN-GRU, hybrid CNN-LSTM, and hybrid CNN-LSTM-GRU. The CNN-LSTM-GRU for MAAT prediction is the best-performing model compared to other deep learning models with the highest correlation coefficient (R = 0.9879) and lowest root mean square error (RMSE = 1.5347) and mean absolute error (MAE = 1.1830). These results indicate that The hybrid CNN-LSTM-GRU method is a suitable climate prediction model. This deep learning method can also be used for surface water modeling. Climate prediction will help with flood control and water resource management.

Slightly Saline Water Improved Physiology, Growth, and Yield of Tomato Plants in Yellow Sand Substrate

Efficient utilization of saline water and yellow sand resources can enhance water and soil resource management while boosting crop yields in Xinjiang. This study conducted a two-season field experiment in Alar City, Xinjiang, from March to July 2023 and August 2023 to January 2024. The objective was to examine the effects of different irrigation water salinities (2, 3, 4, 5, and 6 g·L−1) on the physiology, growth, and yield of sand-cultured tomatoes grown in yellow sand slag. Groundwater irrigation with salinity levels of 0.8–1 g·L−1 was used as the control (CK). The results showed that the salinity of the substrate gradually increased with the salinity of irrigation water in each treatment. The salt accumulation increased by 59.5%, 82.5%, and 99.5% at the end of the experiment for T3 (4 g·L−1), T4 (5 g·L−1), and T5 (6 g·L−1), respectively, compared to CK. As the salinity of irrigation water increased, plant height, stem thickness, chlorophyll content, net photosynthesis rate, stomatal conductance, transpiration rate, and total yield of tomato showed an increasing and then decreasing trend, in which the total tomato yield of the T2 (3 g·L−1) treatment was significantly increased by 35.2% compared with that of CK between the two seasons. In contrast, as the salinity of irrigation water increased, the inter-cellular CO2 concentration of tomato leaves showed a decreasing and then increasing trend, with the T2 treatment having the lowest inter-cellular CO2 concentration. Pathway analysis revealed that appropriate salinity levels increased tomato yield by regulating inter-cellular CO2 concentration. Based on these findings, a 3 g·L−1 salinity level is recommended for irrigating sand-cultured tomatoes to maximize yellow sand resource use, address freshwater shortages, and optimize water and soil management in the Xinjiang region.

Analysis of Industrial Water Use Efficiency Based on SFA–Tobit Panel Model in China

Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.

Assessment and Modeling of Water Quality in the Niger River: A Comprehensive Study Using Water Quality Indices and Intelligent Techniques

The Niger River is essential to the livelihoods of those in Bamako and its neighboring areas, providing vital resources for drinking water, agriculture, livestock, industry, and fishing. Given its significance across these sectors, there is a pressing need to establish an effective water resource management strategy that incorporates a thorough qualitative assessment of the river's water quality. This research seeks to characterize the water quality of the Niger River by employing Water Quality Indices (WQI) and intelligent modeling techniques. To fulfil this objective, various physicochemical parameters, including pH, electrical conductivity (EC), nitrate (NO3-), nitrite (NO2-), and iron (Fe), were collected from 40 sampling points along the river during three distinct periods: December 2017, March 2018, and July 2018. The study utilized a weighted arithmetic approach to compute the WQIs, while the predictive models were developed using two of the most famous and effective modeling techniques namely Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). In order to evaluate the predictive performance of the models, the dataset was partitioned into three distinct segments, allocating 60% for training purposes, 20% for validation, and the remaining 20% for testing, with the segments organized in a sequence from upstream to downstream. The performance of both models was evaluated using metrics such as the correlation coefficient (r²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The computed Water Quality Indices (WQIs) vary from 0.44 to 1887.40, indicating a diverse range of water quality across the samples analyzed. The classification of these samples reveals that 62.5% are considered excellent, while 15% are categorized as good, another 15% as poor, 2.5% as very poor, and 5% as unsuitable for consumption. Furthermore, the results derived from ANN with five inputs, one hidden layer (13 neurons) and one output (WQI) demonstrates superior efficiency in assessing water quality.

A novel BiVO4/vermiculite Z-scheme heterojunction with enhanced removal rate of dyes through adsorption photodegradation synergistic mechanism

Water pollution, exacerbated by industrialization, poses severe threats to aquatic ecosystems and human health, particularly due to persistent organic pollutants like dyes. To mitigate this issue, the development of effective and sustainable water treatment technologies is essential. This study presents the synthesis of a novel bismuth vanadate (BVO)/vermiculite (Vt) adsorption-photocatalytic self-cycling composite using a simple hydrothermal method. The composite's Z-scheme heterojunction structure, along with the introduction of surface oxygen vacancies, significantly reduces electron-hole recombination and enhances charge separation. These modifications lead to a 1.64-fold increase in adsorption capacity compared to Vt and a 6.4-fold improvement in photocatalytic efficiency over BVO. The material maintained a 76 % removal efficiency after five usage cycles and demonstrated high efficacy in methylene blue (MB) solutions, even at concentrations up to 300 mg/L. Moreover, the composite displayed stable performance under repeated use and in high-pollutant environments, highlighting its durability and practicality. These characteristics underscore its strong potential for widespread application in environmental remediation and sustainable water resource management, offering an innovative solution to the global challenge of water pollution.

Lake water storage changes and their cause analysis in Mongolia

Lake is an important water resources in Mongolia, which has undergone a large variation in past decades. However, it is still challenging to monitor long-term changes in lake water storage (LWS) due to the lack of lake level monitoring and long-term satellite altimetry data for Mongolian lakes. According to the Advanced Land Observing Satellite Digital Elevation Model (ALOS DEM) and the Joint Research Centre (JRC) dataset, we estimated the LWS changes of 55 Mongolian lakes (> 10 km2) from 1991 to 2020. The results showed that the LWS increased by 40.24 km3 from 1991 to 1997, especially for northwestern Mongolia with 31.47 km3. However, the LWS decreased by 32.44 km3 from 1998 to 2010, and the lakes in the northwestern and southern decreased by 20.24 km3 (62%) and 7.38 km3 (23%), respectively, and then the LWS continued to decrease by 10.22 km3 from 2011 to 2020. The precipitation was the primary cause of lake change, which could explain 45.13% of LWS change based on Generalized Linear Model, followed by temperature (26.33%) and irrigated area (16.72%). Analyzing the changing characteristic and driving mechanisms of LWS can provide a scientific basis for local water resources management and planning.

Enhanced relationship between seasonal soil moisture droughts and vegetation under climate change over China

Climate change could intensify seasonal soil moisture droughts and subsequently degrade vegetation, but it can also facilitate vegetation growth with the rising temperature and CO2 concentrations. Therefore, the change of the vegetation-drought relationship under climate change remains elusive. Here, we investigate the effects of vegetation greening (i.e., increasing leaf area index (LAI)) on seasonal soil moisture drought trends by performing a set of land surface model simulations with the Conjunctive Surface-Subsurface Process model version 2 (CSSPv2), as well as the impact of seasonal soil moisture drought change on vegetation productivity via a statistical fitting model. Our results indicate that both the impact of increasing LAI on exacerbating seasonal droughts and the impact of increasing seasonal droughts on reducing vegetation productivity have been enhanced over China during 1961–2018. Specifically, increased LAI accounts for 48 % of the rising drought frequency through escalated evapotranspiration losses, with more pronounced effects in northern semiarid regions. The decrease in vegetation stomatal conductance caused by rising CO2 concentrations limits transpiration and alleviates 11 % of the increase in drought frequency, but it cannot fully offset the drought aggravation induced by increased vegetation water consumption under climate warming. Meanwhile, increased drought frequency and intensity have reduced the upward trend in vegetation productivity by 5 % over China, and by 8 % in water-limited northern regions. Our study shows the relationship between seasonal soil moisture droughts and vegetation over China has strengthened, indicating that vegetation greening will lead to increased water shortages and further impact vegetation growth and carbon sequestration. This challenges water resources management and environmental sustainability, especially in semiarid regions.

Coupling SWAT and Transformer Models for Enhanced Monthly Streamflow Prediction

The establishment of an accurate and reliable predictive model is essential for water resources planning and management. Standalone models, such as physics-based hydrological models or data-driven hydrological models, have their specific applications, strengths, and limitations. In this study, a hybrid model (namely SWAT-Transformer) was developed by coupling the physics-based Soil and Water Assessment Tool (SWAT) with the data-driven Transformer to enhance monthly streamflow prediction accuracy. SWAT is first constructed and calibrated, and then its outputs are used as part of the inputs to Transformer. By correcting the prediction errors of SWAT using Transformer, the two models are effectively coupled. Monthly runoff data at Yan’an and Ganguyi stations on Yan River, a first-order tributary of the Yellow River Basin, were used to evaluate the proposed model’s performance. The results indicated that SWAT performed well in predicting high flows but poorly in low flows. In contrast, Transformer was able to capture low-flow period information more accurately and outperformed SWAT overall. SWAT-Transformer could correct the errors of SWAT predictions and overcome the limitations of a single model. By integrating SWAT’s detailed physical process portrayal with Transformer’s powerful time-series analysis, the coupled model significantly improved streamflow prediction accuracy. The proposed models offer more accurate and reliable predictions for optimal water resource management, which is crucial for sustainable economic and societal development.

Use of an Ecological Compensation Model in Water Resource Development: A Case Study from Shaanxi Province, China

This study aims to analyze the current situation of water resource management in Shaanxi Province, study the basic principles of ecological compensation, evaluate the impact of water conservation projects on the ecological environment by establishing a model, and propose a sustainable water resource management model. Hanzhong City has certain typicality and representativeness within Shaanxi Province, and the problems it faces in water resource management and ecological environment may represent the entire province or similar regions. At the same time, Hanzhong City has rich data and research foundations. Therefore, by conducting a detailed analysis of the current water resource status in the Hanzhong City area of Shaanxi Province, the problems in current water resource management are revealed, and the basic principles of ecological compensation are intensely studied. The original ecological compensation plan in Shaanxi Province has been summarized. Guided by the concept of sustainable development, an ecological compensation model is established using algorithms, and the model is applied to sustainable water resource management. Establish a model for water conservation and water resource management through data collection, preprocessing, and cleaning, and apply it to practical cases in Hanzhong City. Through simulation and analysis of Hanzhong City, the new water resource management model effectively mitigates the adverse effects of water conservation projects on the ecological environment while improving water resource utilization efficiency. The changes in various environmental parameters indicate that the new plan has improved the ecological environment. Through the application of the model, the ecological compensation plan formulated has achieved sustainable protection of the ecological environment while promoting economic development. This study proposes a sustainable water resource management model through a comprehensive study of water resource management and ecological compensation in Shaanxi Province and verifies it in practical cases, demonstrating that the model has not only good applicability but also has significant effects in promoting economic growth and ecological environment protection.

Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity

Controlling the total amount of river pollutant discharge is an important means of water resource protection and management, and it is also a necessary condition for ensuring the normal functioning of water areas. The total amount of pollutant discharge is closely related to the water environmental capacity (WEC). Shifting from the traditional method of calculating WEC to dynamic analyses and calculations, concerning practical applications, in this paper, a dynamic adaptive calculation method is proposed for the river WEC that considers the changes in adaptive demand and hydrological conditions. In this method, the dynamic WEC is represented by intervals based on dynamic changes in different spatial and temporal scales, various calculation methods, hydrological conditions, and parameters. According to the calculation results for the WEC, a variable interval was formed. Taking the Shaanxi section of the main stream of the Wei River as the research object, with the support of an integrated platform, the dynamic adaptive calculation of the WEC in the Shaanxi section of the Wei River was realized, and a corresponding simulation system was constructed. The verification results show that (1) the dynamic calculation of WEC can be realized by freely combining different model methods and calculation conditions; (2) the WEC is described using a variable interval, which has strong applicability and operability; and (3) the simulation system can quickly adapt to the changing needs of practical applications and provide managers with visual and credible decision support. The research results provide a theoretical basis for river water environment pollution prevention and environmental management decision-making and help in the high-quality development of the river basin.

What is the status of the European Union's water resources and sanitation services when sustainable development goals are evaluated?

Even when water scarcity and quality issues are not severe in the European Union (EU) countries, the impacts of climate change and external pressures vary across EU nations, leading to different outcomes. Achieving sustainability requires prioritizing the efficient management of water resources and sanitation services. To this end, conducting studies to identify countries needing appropriate measures is essential. This research focuses on evaluating and analysing the situation of water resources and sanitation systems in the European Union, with two specific objectives in mind. The first objective is to compare disparities between Member States (MSs) in a particular year and track their progress over two periods of five and ten years concerning variables related to water resources and sanitation services. By examining these disparities, the study aims to identify which countries have made significant improvements and which require more attention and resources to enhance their water management and sanitation systems. The second objective is to identify the countries best positioned to achieve certain Sustainable Development Goals (SDG 6, SDG 1, SDG 2, SDG 8, SDG 10, SDG 11, SDG 12).The results indicate that the countries best positioned in terms of meeting the SDGs and achieving sustainability in water and sanitation are Luxembourg, the Netherlands, Finland, Sweden, Denmark and Germany. However, in contrast, Romania, Lithuania, and Slovakia face greater challenges, particularly in sanitation. Factors such as economic level and climate change, which impact southern countries more severely (Greece, Spain, Cyprus) where water scarcity is more acute, contribute to these difficulties. Additionally, the lack of information on wastewater management hinders decision-making and the proper management of these wastes.This study analyses the progress and current status of water and sanitation indicators from an environmental perspective for the first time. It introduces variables to assess sustainability in EU countries that are not listed as indicators on the United Nations website, aiming to evaluate compliance with the SDGs.

ANALISIS INTENSITAS CURAH HUJAN DAN KURVA IDF (INTENSITY-DURATION-FREQUENCY) METODE MONONOBE DI KOTA SALATIGA

Extreme rainfall in Indonesia has led to natural disasters in many areas including Salatiga City. Many human activities depend on the amount of rainfall that falls on the earth's surface. In the field of civil engineering, research on rainfall is very common because many activities related to civil engineering use rainfall data such as for water resource management, drainage development, dam construction and other building construction. This study aims to determine the amount of rainfall intensity and IDF curves in Salatiga City for return periods of 2 years, 5 years, 10 years, 25 years, 50 years and 100 years. The data used in this study is the maximum daily rainfall data collected from Salatiga Central Bureau of Statistics (BPS). The type of distribution selected in this study is the Log Pearson Type III probability distribution. The results of the analysis show that the highest rainfall intensity occurs at a short duration (5 minutes) in the 100-year return period of 106.66 mm/hour and the results of the IDF curve show that the shorter the rainfall time, the higher the rainfall intensity while the longer the rainfall time, the smaller the rainfall intensity at each return period T.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

Salinity challenges and adaptive strategies in salinization-affected coastal Bangladesh: Implications for agricultural sustainability and water resource management

Abstract Salinization has become a rising global concern due to its notable effects on agriculture and freshwater resources. Coastal region of Bangladesh has been struggling with elevated levels of soil and water salinity, exacerbated by storm surges and rising sea levels. We assessed nutrient and salinity contents in agricultural and homestead lands, and the level of salinity in pond and canal water in six sub-districts in coastal Bangladesh. Finally, using household (HH) survey, focus group discussion (FGD) and key informant interview (KII), we explored the adaptive practices and challenges of salinity issues in agriculture and drinking water management. Soil nitrogen, phosphorus, and potassium contents exhibited significant variations across the sub-districts, which reflect the diversity of agricultural practices and soil management strategies. However, there was no notable difference in soil salinity across the sub-districts, which underscores the commonality of soil salinity as a pressing concern. Shyamnagar (13.99 dS m−1) recorded the highest level of pond water salinity, followed by Assasuni (13.96 dS m−1), Dacope (13.91 dS m−1), Koyra (13.58 dS m−1), Morrelganj (13.33 dS m−1), and Mongla (13.19 dS m−1) sub-districts, which highlights that water salinity decreased from exposed coast to the landward areas. Respondents in HH surveys, FGDs and KIIs identified salinity as a major challenge in agriculture and drinking water. Furthermore, climate-related stresses were recognized as significant challenges impacting crop productivity. The research highlights the feasibility of rainwater harvesting, with 89%–100% of HHs harvest rainwater in HH tanks, as an effective adaptive practice for managing drinking water. The study emphasizes the positive impact of vermicompost in reducing soil salinity levels, which is demonstrated by the 43%–88% of HHs using this practice, indicating its potential as a nature-based solution to address soil salinization. The findings underscore the need for resilient agricultural systems and sustainable water management approaches to tackle these challenges.

Environmental Assessment and Restoration of the Hunjiang River Basin Based on the DPSIR Framework

The Hunjiang River, a vital water system in northeastern China, has suffered severe ecological damage due to overexploitation. This study analyzes the basin’s environmental conditions from 2016 to 2020, identifies key restoration factors, and examines practical restoration projects. Investigating five major pollutants (permanganate index, chemical oxygen demand (COD), biochemical oxygen demand, ammonia nitrogen, total phosphorus) in eight sections, the study finds the Xicun section most polluted, mainly from Baishan City’s industrial and domestic discharges. The ammonia nitrogen concentration at the Zian section also shows deterioration. Using a DPSIR (Driving forces, Pressures, State, Impacts, Responses) framework, the study elucidates the relationship between environmental and socio-economic issues. Results indicate that population changes, industrial development, and water resource management have complex ecological impacts. Evaluating the urban water resource carrying capacity with the entropy weight method and correlation coefficient weighting method, the study finds that increasing forest coverage, improving wastewater treatment efficiency, and reducing COD emissions are crucial. Quantitative assessment of integrated protection and restoration projects involving mountains, rivers, forests, farmlands, lakes, and grasslands demonstrates their positive impact. This research reveals the interplay between the ecological environment and social factors, proposes practical restoration measures, and clarifies project effects, providing reliable decision-making schemes for policymakers.

Application of a reaction-based water quality model to the total dissolved solids concentration of the Pasig River.

With the goal to support effective water resource management, water quality models have gained popularity as tools for evaluating the distributions of pollutants and sediments. This work focuses on the application of the numerical solution of an advection-dispersion-reaction (ADR) water quality model for rivers and streams to a major Philippine waterway, the Pasig River. The water quality constituent is described by a system of reaction and advection-dispersion-reaction equations. The model and method are based on a previously used strategy where Guass-Jordan decomposition is applied to the matrix system and the resulting conservative form of the model is solved numerically using the fully implicit scheme and finite element method. The methodology is demonstrated by a case study in Pasig River involving the concentrations of total dissolved solids (TDS) obtained from the Department of Environment and Natural Resources (DENR) through the Pasig River Unified Monitoring Stations (PRUMS) report. Sensitivity analysis and parameter estimation are also applied to the model to assess which parameters influence the model output the most.

Quantifying Climate Change Variability for the Better Management of Water Resources: The Case of Kobo Valley, Danakil Basin, Ethiopia

Alterations in the hydrological cycle due to climate change are one of the key threats to the future accessibility of natural resources. This study used 12 GCM climate models from CMIP6 to evaluate future climate change scenarios by applying model performance measures and trend analysis in Kobo Valley, Ethiopia. The models were ranked based on their ability to analyze the historical datasets. The result of this study showed that the outputs of the FIO-ESM-2-0 CIMP6 model had a good overall ranking for both precipitation and temperature. After bias correction of the model-based projections with the observed data, the average annual precipitation in the average scenario (SSP2-4.5) decreased by 4.4% and 13% in 2054 and 2084, respectively. Similarly, in the worst-case scenario (SSP5-8.5), by the end of 2054 and 2084, decreases of 4% and 12.8%, respectively, were predicted. The average annual maximum temperature under the SSP2-4.5 scenario increased by 1.5 °C in 2054 and by 2.1 °C in 2084. The average annual maximum temperature under the worst-case (SSP5-8.5) scenario increased by 1.7 °C in 2054 and by 3.2 °C in 2084. In the middle scenario (SSP4.5), the average annual minimum temperature increased by 2.2 °C in 2054 and by 3 °C in 2084. The average annual minimum temperature under the worst-case (SSP5-8.5) scenario increased by 2.6 °C in 2054 and by 4.3 °C in 2084. The seasonal variability in precipitation in the studied valley will decrease in the winter and increase in the summer. A decrease in precipitation combined with an increase in temperature will strengthen the risk of drought events in the future.

An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method

Accurate estimation of reference evapotranspiration (ETo) is essential for effective water resources management, irrigation system design, and various hydrological and agricultural applications. This study employed extreme gradient boosting (XGBoost) model, signal decomposition techniques, and XGBoost coupled with Nelder–Mead (NM) method to enhance ETo prediction across two meteorological stations in Iran. This study proposed a novel framework which lies in its comprehensive integration of advanced techniques to create a model that is both interpretable and highly accurate for ETo estimation. For this aim, sixty meteorological variables were categorized into solar and cloud-based, temperature-based, wind and humidity-based, and pressure-based groups to analyze their effect on ETo estimation. Feature selection methods, including the Gradient Boosting Machine, Kendall’s Tau, and Relief Algorithm, were employed to identify the most influential predictors. Variables with normalized weights equal to or greater than 0.9 were selected for model input, resulting in the top 10% of variables being utilized. The XGBoost models were then developed using these selected inputs (level 1), a wavelet-based hybrid was developed according to the most effective features (level 2), and the XGBoost model was coupled by NM algorithm (level 3) for ETo estimation. Results of estimated ETo by levels 1 to 3 were compared with four common empirical approaches. The results indicated that Kendall’s Tau-based feature selection provided the most accurate predictions in Shiraz, achieving an RMSE of 0.721 (mm/day) for solar and cloud-based variables during the test phase. Additionally, the application of wavelet analysis further refined the model inputs, which enhanced ETo estimation in most variable groups. Integrating the NM algorithm with XGBoost demonstrated significant improvements in ETo estimation, where it could improve RMSE to 0.091 and 0.155 (mm/day) for testing section in Fasa and Shiraz, respectively.