Integrated water resource management (IWRM) is facing great challenges due to growing uncertainties caused by climate change (CC), rapid socio-economic and technological changes, and population growth. In the present study, we have developed different indices to assess the availability of water using an IWRM approach. These indices evaluate supply to demands, surface availability, groundwater availability, reservoirs, and environmental flow. Moreover, reliability, resilience, and vulnerability were determined. Sustainability index (SI) and sustainability index by groups (SG) were determined based on the five indices (all indices vary from 0 to 1). The impacts of climate change affect surface and groundwater availability, as do the agricultural, urban, and industrial requirements on the different supplies. We used the generalized AQUATOOL Decision Support System Shell (DSSS) to evaluate the IWRM in the Rio Grande Basin (Morelia, México). Various emission scenarios from representative concentration pathways (RCPs) were applied to the basin for the years 2015–2039 and 2075–2099. The results indicate increases in agricultural and urban demand, and decreases in surface runoff, as well as groundwater recharge. The proposed indices are useful for different approaches (decision-makers, water policy, and drought risks, among others). CC significantly affects the different proposed indices and indicates a decrease of the SI, SG1, and SG2 (i.e., less availability). For example, we found that SG2 decreased from 0.812 to 0.195 under the RCP 8.5 2075–2099 scenario, and SG2 equal to 0.252 and 0.326 for the RCP 6.0 2075–2099 and RCP 4.5 2070–2099 scenarios, respectively (values close to 0 indicate worst drought conditions).

In recent years, due to various anthropogenic activities, such as agriculture and livestock, the presence of nitrogen-associated contaminants has been increasing in surface- and groundwater resources. Among these, the main compounds present in groundwater are ammonia, nitrite, and nitrate. However, it is sometimes difficult to assess such effects given the scarcity or lack of information and the complexity of the system. In the current study, a methodology is proposed to assess nitrate in groundwater from diffuse sources considering spatiotemporal patterns of hydrological systems using a coupled SWAT/MODFLOW/MT3DMS model. The application of the model is carried out using a simplified simulation scheme of hydrological and agricultural systems because of the limited spatial and temporal data. The study area includes the Cuitzeo Lake basin in superficial flow form and the Morelia–Querendaro aquifer in groundwater flow form. The results within the methodology are surface runoff, groundwater levels, and nitrate concentrations present in surface- and groundwater systems. The results indicate that the historical and simulated nitrate concentrations were obtained within acceptable values of the statistical parameters and, therefore, are considered adequate.

Evapotranspiration is one of the most significant variables used to determine runoff, hydrological balances, and climate change studies. In semi-arid conditions, there is a need to evaluate various alternatives to establish reference evapotranspiration (ETo), given that climate change scenarios sometimes do not provide information on diverse climate variables. Several alternatives for obtaining evapotranspiration are analyzed in this study and compared with the Penman-Monteith method, modified by FAO (PMFAO56). Due to limited daily climate information, it is necessary to consider different options for determining reference evapotranspiration. In the present work, methods based on temperature (Hargreaves, Hargreaves 1, Hargreaves 2, and Baier-Robertson) and radiation (Caprio, Irmark 1, Irmark 2, Irmark 3, Makkink, Priestley-Taylor and Hasen) were investigated. The best performance for the semi-arid Jucar River Basin was determined by Hargreaves methods. Regional calibration of Hargreaves methods, Hargreaves 1 and Hargreaves 2, were performed for each sub-basin.

La disponibilidad del recurso hídrico es de vital importancia para la planificación hidrológica, este depende de múltiples causas como la variabilidad climática y la cobertura vegetal. La precipitación, temperatura y evapotranspiración han sufrido cambios en diferentes partes de España. Para la cuenca del río Júcar además de otras cuencas en España se ha producido un significativo cambio en el valor promedio de las aportaciones hidrológicas desde 1980. El presente estudio se centra en analizar cómo han afectado los cambios en las variables climáticas a las aportaciones hidrológicas y en qué grado explican las reducciones registradas en las mismas. Adicionalmente se considera la posibilidad de que otros factores como cambios en la cobertura vegetal también puedan haber influido en la reducción de escorrentía. Para modelar la hidrología de la cuenca del Júcar se utilizó el modelo lluviaescorrentía HBV. El modelo fue calibrado para periodo anterior a la fecha de cambio considerada (1950-1979) y se simuló y adaptó para los años posteriores a 1980 (1980-2007) ajustando parámetros que puedan ser explicados por cambios en las condiciones de la cuenca. La zona más susceptible es la cabecera del Júcar donde se genera la mayor cantidad del recurso hídrico y se presenta la mayor disminución en las aportaciones. En la cuenca media del Júcar se presenta una menor disminución en las aportaciones, sin embargo en la cuenca baja del Júcar se registra un incremento en las aportaciones. Los cambios en las variables hidrológicas (precipitación y temperatura) explican una parte de los cambios en las aportaciones. Otras causas posibles como los cambios en la cobertura vegetal pueden completar la explicación del cambio en las aportaciones. Para probarlo se ha recalibrado el modelo hidrológico alterando solo uno o dos parámetros relacionados con esta condición.

Temperature is one of the most influential weather variables necessary for numerous studies, such as climate change, integrated water resources management, and water scarcity, among others. The temperature and precipitation are relevant in river basins because they may be particularly affected by modifications in the variability, for example, due to climate change. We developed a stochastic model for daily precipitation occurrences and their influence on maximum and minimum temperatures with a straightforward approach. The Markov model has been used to determine everyday occurrences of rainfall. Moreover, we developed a multisite multivariate autoregressive model to represent the short-term memory of daily temperature, called MASCV. The reduction of parameters is an essential factor addressed in this approach. For this reason, the normalization of the temperatures was performed through different nonparametric transformations. The case study is the Jucar River Basin in Spain. The multisite multivariate stochastic model of two states and a lag-one accurately represents both occurrences as well as maximum and minimum temperature. The simulation and generation of occurrences and temperature is considered a continuous multivariate stochastic process. Additionally, time series of multiple correlated climate variables are completed. Therefore, we simplify the complexity and reduce the computational time for the simulation.

Precipitation is influential in determining runoff at different scales of analysis, whether in minutes, hours, or days. This paper proposes the use of a multisite multivariate model of precipitation at a daily scale. Stochastic models allow the generation of maximum precipitation and its association with different return periods. The modeling is carried out in three phases. The first is the estimation of precipitation occurrence by using a two-state multivariate Markov model to calculate the non-rainfall periods. Once the rainfall periods of various storms have been identified, the amount of precipitation is estimated through a process of normalization, standardization of the series, acquisition of multivariate parameters, and generation of synthetic series. In comparison, the analysis applies probability density functions that require fewer data and, consequently, represent greater certainty. The maximum values of surface runoff show consistency for different observed return periods, therefore, a more reliable estimation of maximum surface runoff. Our approach enhances the use of stochastic models for generating synthetic series that preserve spatial and temporal variability at daily, monthly, annual, and extreme values. Moreover, the number of parameters reduces in comparison to other stochastic weather generators.