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, SG 1 , and SG 2 (i.e., less availability). For example, we found that SG 2 decreased from 0.812 to 0.195 under the RCP 8.

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.