Calibration of conceptual rainfall–runoff models (CRRM) for water-resource assessment (WRA) is a complicated task that contributes to the reliability of results obtained from catchments. In recent decades, the application of automatic calibration techniques has been frequently used because of the increasing complexity of models and the considerable time savings gained at this phase. In this work, the traditional Rosenbrock (RNB) algorithm is combined with a random sampling method and the Latin hypercube (LH) to optimize a multi-start strategy and test the efficiency in the calibration of CRRMs. Three models (the French rural-engineering-with-four-daily-parameters (GR4J) model, the Swedish Hydrological Office Water-balance Department (HBV) model and the Sacramento Soil Moisture Accounting (SAC-SMA) model) are selected for WRA at nine headwaters in Spain in zones prone to long and severe droughts. To assess the results, the University of Arizona’s shuffled complex evolution (SCE-UA) algorithm was selected as a benchmark, because, until now, it has been one of the most robust techniques used to solve calibration problems with rainfall–runoff models. This comparison shows that the traditional algorithm can find optimal solutions at least as good as the SCE-UA algorithm. In fact, with the calibration of the SAC-SMA model, the results are significantly different: The RNB algorithm found better solutions than the SCE-UA for all basins. Finally, the combination created between the LH and RNB methods is detailed thoroughly, and a sensitivity analysis of its parameters is used to define the set of optimal values for its efficient performance.

The aim of this study is to evaluate the effect of climate change on the future contributions of the Jucar River Basin (eastern Spain) and the uncertainty associated to them, which in this case is referred to that derived from the decision taken in the handling of climate projections during the entire evaluation process. For this purpose, meteorological variables were selected from 9 Regional Climate Models belonging to the Representative Concentration Pathways 4.5 and 8.5. Then, they were bias corrected and introduced into a hydrological model for the simulation of flows. Finally, the effect of climate change was evaluated by comparing future flows with those of the historical period. Depending on the historical data we manage, the change in future flows can range from an average of -8% to -28% by the end of the century. However, if the possibility of comparing them with a period that represents the current situation of the basin is considered, this change is estimated between -8% and -13%, thus allowing more concrete conclusions about the future of water resources in this basin.

The agricultural and urban water requirements in the Angulo River basin (Mexico) along with land-use change and deforestation have caused great pressure on the region's groundwater availability. The potential climate change impact on groundwater levels of the Zacapu and Pastor Ortiz aquifers is evaluated in this study, using the climate model CMIP5, the hydrological module EVALHID, and the groundwater module AQUIVAL. The regional outputs from three Representative Concentration Pathway (RCP) climate scenarios (RCP 4.5, 6.0, and 8.5), for near future (2015–2039) and distant future (2075–2099) projections, indicate that the average annual temperature is expected to increase while a decrease in precipitation is projected over the basin. The assessment of infiltration and water level evolution in these groundwater systems indicates an infiltration reduction between 1.5 and 23%, producing a static level drop between 20 and 30 m in both aquifers.

Climate change intensifies hydroclimatic extremes such as droughts and excess precipitation, posing growing challenges for urban resilience. This study develops an objective framework to assess climate vulnerability and propose adaptation measures in Morelia City, Mexico, integrating climatic and socioeconomic indicators. A four-stage methodology was applied: (1) analysis of the study area, (2) data collection and validation, (3) vulnerability assessment using the Standardized Precipitation Index (SPI) and Principal Component Analysis (PCA), and (4) formulation of adaptation measures.The PCA enabled objective weighting of indicators, reducing expert bias and revealing that the lack of adaptive capacity contributes 65% of total vulnerability. Historical data (1969–2019) and projections under RCP 4.5 and 8.5 scenarios for three horizons (2015–2039, 2045–2069, 2075–2099) indicate that the city will experience increased risks of drought and excess precipitation by the end of the century, especially under RCP 8.5.The proposed methodology provides a transparent, replicable tool for evaluating climate vulnerability in data-scarce urban regions. By combining SPI-based risk mapping with statistically derived weights, the study advances quantitative approaches to local adaptation planning. The findings highlight that strengthening health, education, and water infrastructure will be critical to improving Morelia's adaptive capacity to future climate impacts.

Las olas de calor son un fenómeno climatológico de gran impacto en la sociedad y medio ambiente debido a los riesgos que representan. En el presente trabajo se evalúa su impacto en la ciudad de Morelia, ya que en los últimos años se ha presentado un aumento de la temperatura, afectando a diferentes sectores. Para la evaluación de las olas de calor, existen diferentes tipos de criterios. Con base en la información disponible, se propuso la aplicación de umbrales relativos en un periodo histórico, así como la aplicación de índices que permitieron estimar el grado de severidad en la zona de estudio. Se pudo observar que el municipio de Morelia se encuentra entre un grado muy bajo y un grado alto de severidad, existiendo un grado medio a alto en la zona sur del municipio. Por otra parte, la zona centro se encuentra a un grado muy bajo a bajo, dejando la zona norte y en este caso la ciudad de Morelia en un grado de severidad alto. Esto contribuye al conocimiento sobre las zonas con mayor incidencia para establecer medidas de mitigación o adaptación.