Journal of Water & Climate Change最新文献

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This study analyses the annual maximum (AM) rainfall series (1991–2022) in Khon Kaen City, Thailand. The AM rainfall series ranging from 3 to 24 h was best fitted to the Log-Pearson Type-III distribution. Notably, our findings reveal linear relationships between the moments of rainfall intensities and durations establishing the practicality of the simple scaling method for disaggregating 24-h AM rainfall data. Additionally, the results of this method are influenced by factors such as sample size, rai

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To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the opt

The north-west region of Algeria, pivotal for the nation's water resources and agriculture, faces challenges from changing precipitation patterns due to climate change. In response, our study introduces a robust forecasting tool utilizing artificial neural networks (ANNs) to predict monthly precipitation over a 12-month horizon. We meticulously evaluated two normalization methods, ANN-SS and ANN-MM, and assessed four distinct approaches for selecting input variables (no selection, ANN-WO, ANN-CO, and ANN-VE) to optimize model performance. Our research contributes significantly to the field by addressing a critical gap in understanding the impact of evolving precipitation patterns on water resources. Among the innovations, this study uniquely focuses on medium-term precipitation forecasting, an aspect often marginalized in previous research. Noteworthy outcomes include correlation coefficients of 0.48 and 0.49 during the validation phase, particularly with the Endogen variables and correlation-optimized models using Min-Max normalization. Additionally, the Min-Max normalized technique achieves an impressive 67.71% accuracy in predicting the hydrological situation based on the Standard Precipitation Index.

Climate change research uses an ensemble of general circulation model runs (GCMs-runs) to predict future climate under uncertainties. To reduce computational costs, this study selects representative GCM-runs (RGCM-runs) for Western North America (WNA) based on their performance in replicating historical climate conditions from 1981 to 2005 and projecting future changes from 1981–2010 to 2071–2100. This evaluation is conducted under two representative concentration pathways (RCPs) scenarios, RCP4.5 and RCP8.5, from the Coupled Model Intercomparison Project 5. By using an envelope-based selection technique and a multi-objective distance-based approach, we identify four RGCM-runs per RCP representing diverse climatic conditions, including wet-warm, wet-cold, dry-warm, and dry-cold. Compared to the full-set, these selected runs show a decreased mean absolute error (MAE) between the reference and RGCM-runs concerning the monthly average mean air temperature (T̄) and precipitation (P̄). For RCP4.5, T̄ MAE is 0.45 (vs. 0.58 in the full-set) and P̄ MAE is 0.31 (vs. 0.42). For RCP8.5, T̄ MAE is 0.51 (vs. 0.75) and P̄ MAE is 0.25 (vs. 0.36). The lower MAE values in the RGCM-run set indicate closer alignment between predicted and reference values, making the RGCM-run suitable for climate impact assessments in the region.

Climate change and global warming are expected to affect water resources management and planning, requiring adaptations to changing conditions. Therefore, it is very important, especially for decision-makers, to identify demand deficits due to less water availability with climate change that may occur in the existing water supply system in advance. FEHEM, a hydroeconomic optimization model of the integrated reservoir system of the Upper Euphrates Basin, which is the largest and main basin providing water flow to the Euphrates River, is developed. Using a 45-year historical hydrological dataset, water management and hydroelectric operations are evaluated with a linear programming model at monthly time steps. The effects of climate change on the Upper Euphrates Basin are evaluated under low and high carbon emission scenarios. According to the average of the different climate scenarios studied in the model, the average decrease in flows is 37.5%. With climate change, peak flows will occur about 1–2 months earlier on average. As a result of these hydrological changes, the total amount of energy production in the basin will decrease by about 54% and energy revenue by the same percentage.

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Climate change has made weather patterns more extreme, causing floods in Nigeria. Flooding is the most frequent and serious natural hazard in the confluence region of Rivers Niger and Benue, impacting lives, agriculture, and socio-economic activities significantly. Advancements in satellite technology and computational capabilities have enhanced rapid information about flood extent for monitoring, mitigation, and planning. However, there is a dearth of information based on time series analysis of flo

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Climate change has led to heightened variability in global rainfall patterns, resulting in greater unpredictability and inconsistency, and it has led to the origin of meteorological drought situation. This has amplified the frequency of droughts or drought-like conditions worldwide. India, being primarily agrarian, faces significant challenges due to drought, affecting various regions intermittently. Given the urgency of addressing recurring drought issues, it is crucial to determine specific ‘drough

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The assessment of ecosystem health at the scale of a large river basin is currently an important direction in environmental science and landscape ecology research. This study focuses on the ecological health assessment of the Qinghe River Basin. Following the Guidelines for Eco-health Assessment of Basin (Trial), a framework was designed to construct an assessment system. The aquatic and terrestrial systems of the Basin were selected, and the ecological pattern, ecological function,

Gundlakamma sub-basin faces challenges with increasing water demand and climate change impacts, requiring innovative solutions for sustainable water management. The study was conducted to improve the long-term utilization of water resources in Andhra Pradesh. To accomplish this, the study attempts to estimate LULC change detection and its impact on water resources by analyzing the performance of the soil and water assessment tool (SWAT) model. From 2005 to 2021, the amount of cropland decreased while built-up land increased, indicating urban growth. The SWAT model identifies hydrological processes and assesses the temporal and spatial distribution of water resources in the watershed. Statistical parameters results reveal that a good match was found between actual and modeled flows with Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R²) greater than 0.75 for both calibration and validation periods. The area has average annual precipitation, surface runoff, water yield, and actual evapotranspiration of 949.96, 215.6, 469.24, and 429.15 mm, respectively. The SWAT model's fascinating outcomes demonstrate that it could be a promising decision support tool for predicting water balance and water yield in other watersheds of Andhra Pradesh for sustainable water management of water resources where water quality and quantity are critical issues.

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The escalating frequency of climate change-induced droughts poses a severe threat to rainfed maize cultivation in Thailand's upper Nan River Basin (NRB). Utilizing the standardized precipitation evapotranspiration index, this study comprehensively examines spatial and temporal drought patterns and their potential agricultural impact. Findings indicate a significant shift in precipitation patterns with wetter wet seasons, drier dry seasons and rising temperatures. The upper NRB experiences prolonged a