Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.

Researchers have widely applied discharge simulation using artificial neural networks (ANNs) and have gained prominence in water resources. Morphological features, watershed urbanization, and climate change influence hydrological variables. Thus, data‐driven models need to be able to identify the hydrological relationships without explicitly stating the physical processes. The main objectives of this work were (i) to evaluate an ANN Multilayer Perceptron for flood forecasting in an urban basin and its efficiency for several lead times; (ii) to evaluate discharge variation considering climate change scenarios. The study applied the methodology in a basin occupied by the Cerrado biome, with its intermediate outlet in an urban area that suffers from recurrent floods. The selection of climate change models followed from the Coupled Model Intercomparison Project Phase 6 scenarios Shared Socioeconomic Pathway (SSP)2‐4.5 and SSP5‐8.5 for two futures: 2021–2050 and 2071–2100, with the period of 1976–2019 as reference. The model obtained satisfactory results for the discharge prediction at the current time and for a horizon of up to 4 days. However, forecasts for longer lead times led to metrics deterioration. Furthermore, future projections suggest decreased discharges, more extreme events, and increased short return‐period floods. The developed model is valuable for short‐term forecasting and water resources management in the face of changing climates.

Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as ANNs, need to be able to identify the relationship between the hydrological inputs and outputs of the model, without explicitly considering the other relationships involved in physical processes. This work aimed to apply a Multilayer Perceptron (MLP) neural network for predicting flows in an urban basin subject to recurrent floods, using precipitation and flow data from previous periods as inputs. After model calibration and validation for the current state of the basin (2018–2019), its responses were analysed using input data before the basin urbanization (1985–1986) to identify the error behaviour at the output as a proxy for the basin changes effect. Its efficiency was evaluated using hydrographs, showing satisfactory results in both periods. In the urbanization period, there is more dispersion for maximum flows. For the day 4 steps back in the current forecast, NSE = 0.59 was observed, whereas in the other period, NSE = 0.70. The evaluation of the models for the current period of basin urbanization showed that the model could capture the basin's physical dynamics within the established static relationship. Also, the result found in the statistical relationships for the inputs showed once again the impact of urbanization on the basin.

Reliable urban stormwater design under nonstationary rainfall is becoming increasingly important, yet quantitative links between floods occurrence, updated design requirements, and associated costs remain limited. This study (i) characterizes the evolution and impacts of flood-related events in São Paulo State, Brazil (1991–2024), and (ii) quantifies how nonstationary rainfall projections (CMIP6 SSP2-4.5 and SSP5-8.5) affect culvert sizing and construction costs across municipalities for standardized hypothetical catchments, across multiple return periods and future horizons. Observations indicate an increase in flood occurrence, from an average of 7.8 events per year in the 1990s to 72.9 events per year in the 2010s. In the immediate future (2015–2055), SSP2-4.5 projected costs remain close to the baseline for most municipalities (for return periods ≤ 25 years, 68% show increases up to 10%), whereas for the distant future (2056–2100) 86% exceed 10% under SSP5-8.5. However, under SSP5-8.5 in the immediate future (2015–2055), and for RP > 25 years, approximately 46% of municipalities exceed 10% additional costs. Design discharges generally rise by 9–43% in the immediate future, with stronger increases under SSP5-8.5 toward the late century. Mapping required hydraulic area to commercially available nominal sizes discretizes upgrades and creates threshold behavior, with larger basins crossing size classes more often. These findings challenge the assumption of stationary design and support the adoption of nonstationary adaptation strategies to reduce the long-term probability of structural failure.

Climate change is expected to impact rainfall amount, seasonality, and dry/wet patterns, with direct implications for rainwater harvesting systems. This study aims to quantify how future rainfall may affect rainwater harvesting systems across Brazil by combining multi-model climate projections with a daily water balance model. A single-family social housing archetype (60 m² roof area; four occupants; 150 L/day/person; non-potable demand equal to 30% of total demand) was simulated for 652 Brazilian cities, using bias-corrected daily rainfall from the CLIMBra dataset and nineteen climate models. Historical conditions were compared with near-future and far-future projections under the SSP2-4.5 and SSP5-8.5 scenarios. Historically, the greater potential for potable water savings has occurred in wetter, less seasonal climates, such as those in the North. In contrast, more seasonal and drought-prone areas, such as the Northeast, showed lower reliability. In future climates, most models indicate relative reductions in the potential for potable water savings in the North, Northeast, and Centre–West, with larger reductions under SSP5-8.5 and in the far-future scenarios. The South shows the most significant divergence between models and may increase the potential for potable water savings in some projections. On the other hand, in the South, the volume of rainwater harvesting system overflow increases under future scenarios. This work contributes to the literature by delivering a national-scale, multi-model, uncertainty-aware evaluation of rainwater harvesting performance under non-stationary rainfall regimes.