The mean annual water balance is essential for evaluating water availability in a catchment and planning water resources. Climate change alters this balance by affecting precipitation, evapotranspiration, and overall water availability. This study analyses the impact of climate change on the mean annual water balance in the Catamayo catchment, a key water source for irrigation and hydropower in southern Ecuador and northern Peru. A Budyko-based approach was employed due to its conceptual simplicity and proven robustness for estimating long-term water balances under changing climatic conditions. Using outputs from 23 Global Circulation Models (GCMs) under CMIP6’s SSP2-4.5 and SSP8.5 scenarios, the results indicate increasing aridity, particularly in the lower and middle parts of the catchment, which correspond to arid and semi-arid zones. Water availability may decrease by 26.3 ± 12.3% to 33.3 ± 17% until 2080 due to negligible changes (statistically speaking) in average precipitation but rising evapotranspiration. However, historical precipitation analysis (1961–2020) reveals an increasing trend over this historical period which can be attributed to natural climatic variability associated to the El Nino-Southern Oscillation (ENSO), possibly enhanced by anthropogenic climate change. A novel hybrid method combining the statistics of historical precipitation deficits with GCM mean projections provides estimates of future precipitation deficits. These findings suggest potential reductions in crop yields and hydropower capacity, which (although not quantitatively assessed in this study) are inferred based on the projected decline in water availability. Such impacts could lead to higher energy costs, increased reliance on fossil fuels, and intensified competition for water. Mitigation measures, including water-saving strategies, energy diversification, and integrated water resource management, are recommended to address these challenges.

WEAP21 based modelling under climate change considerations for a semi-arid region in southern-central Chile

Polders are low-lying areas located in deltas, surrounded by embankments to prevent flooding (river or tidal floods). They rely on pumping systems to remove water from the inner rivers (artificial rivers inside the polder area) to the outer rivers, especially during storms. Urbanized polders are especially vulnerable to pluvial flooding if the drainage, storage, and pumping capacity of the polder is inadequate. In this paper, a Monte Carlo (MC) framework is proposed to evaluate the benefits of rainfall threshold-based flood warnings when mitigating pluvial flooding in an urban flood-prone polder area based on 24 h forecasts. The framework computes metrics that give the potential waterlogging duration, maximum inundated area, and pump operation costs by considering the full range of potential storms. The benefits of flood warnings are evaluated by comparing the values of these metrics across different scenarios: the no-warning, perfect, deterministic, and probabilistic forecast scenarios. Probabilistic forecasts are represented using the concept of “predictive uncertainty” (PU). A polder area located in Nanjing was chosen for the case study. The results show a trade-off between the metrics that represent the waterlogging and the pumping costs, and that probabilistic forecasts of rainfall can considerably enhance these metrics. The results can be used to design a rainfall threshold-based flood early warning system (FEWS) for a polder area and/or evaluate its benefits.

Precipitation modeling is vital for water resource management in basins with limited gauged data. In this study, the 5 km Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product was downscaled to 1 km at the annual and mean monthly scales for the Catamayo–Chira catchment, a key water source for Ecuador and Peru. Single-variable and multivariable machine learning (ML) methods were applied to data from 10 gauged stations from 2001 to 2023. Predictors included longitude (Long), latitude (Lat), altitude, Normalized Difference Vegetation Index, and Land Surface Temperature. Performance metrics were utilized to assess the methods. The results demonstrated a notable improvement after downscaling compared to the original CHIRPS estimates. The most effective single-variable methods were simple linear regression (LR) for Long and Lat, and non-linear ML methods such as support vector machine with linear kernel (SVM-lin) and with radial basis function kernel (SVM-rbf), and artificial neural networks (ANN), employing all predictors. Surprisingly, single-variable linear methods yield better results than multivariable non-linear ones. These models provided acceptable fits to annual and mean monthly observations, and their performance tended to be better during the drier months. Downscaled annual precipitation distributions successfully captured differences between “El Niño” and non-“El Niño” years. The current study could be replicated in basins with limited gauging data, thereby enhancing water resource management.