Development of hydro-social-economic-environmental sustainability index (HSEESI) in integrated water resources management.

Sustainable development is a grand challenge of the present century, with tremendous direct and indirect implications for a range of social, economic, and environmental factors. This research proposes a water-centric framework for evaluating "relative" sustainability of the status quo in a country via a new "hydro-social-economic-environmental sustainability index" (HSEESI). We test this framework across 35 countries of American continent using national-scale surveyed data for the 2005-2017 periods. HSEESI possesses four dimensions, namely economic, social development, knowledge and technology, and health sanitation and environment, and 12 related indicators for characterizing these dimensions. Based on the developed HSEESI scores, we assess the linkages between water resources and social-economic-environmental systems at the country level, using single and hybrid-artificial intelligence-gene expression programming (GEP) methods. The former method involves all the indicators, while the latter focuses only on the most effective indicators. Further, we aggregate these analyses at three spatial scales, including American continent, North American countries, and South American countries. Our analyses show that both methods lead to approximate similar results, but the latter is preferred for larger scales as it is more cost effective. Overall, results indicate that the status of water resources in North America is relatively sustainable, whereas in South America, it is relatively unsustainable. Importantly, social development, health sanitation, and environmental dimensions, in both North and South American continents, seem to have a relatively unsustainable status, indicating that water resources systems may not have enough capacity to meet the needs of those dimensions. At the country level, our analyses show that water resources systems of Uruguay, Guyana, and Venezuela may face the highest relative unsustainability, across economic, social development, and health sanitation and environment dimensions. The approach and the framework developed in this study can be applied in other regions around the world and with a more detailed representation of intra-country sustainability issues. It can inform managers and policymakers for sustainable planning and developing water resources projects across scales.

Multivariate modeling of pan evaporation in monthly temporal resolution using a hybrid evolutionary data-driven method (case study: Urmia Lake and Gavkhouni basins).

Evaporation is an important meteorological variable that has a great impact on water resources. In the current research, climatology data, and seasonal coefficient have been used to estimate monthly pan evaporation (Epan) for 2005-2018 study years at four selected stations of the Urmia Lake basin with Dsa and six selected stations of Gavkhouni basin with Bsk climate categories, in Iran. Estimation of monthly Epan was performed using data-driven methods such as artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) as well as wavelet-hybrids (WANN, WANFIS, and WGEP). Based on the evaluation criteria, the WGEP model performance was better than the other models in estimating the monthly Epan. The results indicated that WGEP and ANN are the best and poorest models for all stations without affecting the climate condition of basins. The values of RMSE for WGEP model for stations of Urmia Lake and Gavkhouni basins were varied from 15.839 to 26.727 and 20.651 to 70.318, respectively. Also, the values of RMSE for ANN model for stations of Urmia Lake and Gavkhouni basins were varied from 29.397 to 38.452 and 30.635 to 85.237, respectively. The model's performance was improved as a result of considering the data noise elimination and applying seasonal coefficient to estimate Epan of various climatic conditions. This study with presenting mathematical equations for estimating monthly Epan has a significant impact on the management and planning of water resources policymakers in the future.