Analysis of the Water Demand-Supply Gap and Scarcity Index in Lower Amu Darya River Basin, Central Asia

Lower reaches of the Amu Darya River Basin (LADB) is one of the typical regions which is facing the problem of water shortage in Central Asia. During the past decades, water resources demand far exceeds that supplied by the mainstream of the Amu Darya River, and has resulted in a continuous decrease in the amount of water flowing into the Aral Sea. Clarifying the dynamic relationship between the water supply and demand is important for the optimal allocation and sustainable management of regional water resources. In this study, the relationship and its variations between the water supply and demand in the LADB from the 1970s to 2010s were analyzed by detailed calculation of multi-users water demand and multi-sources water supply, and the water scarcity indices were used for evaluating the status of water resources utilization. The results indicated that (1) during the past 50 years, the average total water supply (TWS) was 271.88 × 108 m3/y, and the average total water demand (TWD) was 467.85 × 108 m3/y; both the volume of water supply and demand was decreased in the LADB, with rates of −1.87 × 108 m3/y and −15.59 × 108 m3/y. (2) percentages of the rainfall in TWS were increased due to the decrease of inflow from the Amu Darya River; percentage of agriculture water demand was increased obviously, from 11.04% in the 1970s to 44.34% in 2010s, and the water demand from ecological sector reduced because of the Aral Sea shrinking. (3) the supply and demand of water resources of the LADB were generally in an unbalanced state, and water demand exceeded water supply except in the 2010s; the water scarcity index decreased from 2.69 to 0.94, indicating the status changed from awful to serious water scarcity. A vulnerable balanced state has been reached in the region, and that water shortages remain serious in the future, which requires special attention to the decision-makers of the authority.

The optimal allocation of water resources oriented to prioritizing ecological needs using multiple schemes in the Shaying River Basin (Henan Section), China

China is actively exploring water resources management considering ecological priorities. The Shaying River Basin (Henan Section) serves as an important grain production base in China. However, conflicts for water between humans and the environment are becoming increasingly prominent. The present study analyzed the optimal allocation of water while considering ecological priorities in the Shaying River Basin (Henan Section). The ecological water demand was calculated by the Tennant and the representative station methods; then, based on the predicted water supply and demand in 2030, an optimal allocation model was established, giving priority to meeting ecological objectives while including social and comprehensive economic benefit objectives. After solving the model, the optimal results of three established schemes were obtained. This revealed that scheme 1 and scheme 2 failed to satisfy the water demand of the study area in 2030 by only the current conditions and strengthening water conservation, respectively. Scheme 3 was the best scheme, which could balance the water supply and demand by adding new water supply based on strengthening water conservation and maximizing the benefits. Therefore, the actual water allocation in 2030 is forecast to be 7.514 billion (7.514 × 109) m3. This study could help basin water management departments deal with water use and supply.

Sensitivity analysis and prediction of water supply and demand in Shenzhen based on an ELRF algorithm and a self-adaptive regression coupling model

Given that sensitive feature recognition plays an important role in the prediction and analysis of water supply and demand, how to conduct effective sensitive feature recognition has become a critical problem. The current algorithms and recognition models are easily affected by multicollinearity between features. Moreover, these algorithms include only a single learning machine, which exposes large limitations in the process of sensitive feature recognition. In this study, an ensemble learning random forest (ELRF) algorithm, including multiple learning machines, was proposed to recognize sensitive features. A self-adaptive regression coupling model was developed to predict water supply and demand in Shenzhen in the next ten years. Results validate that the ELRF algorithm can effectively recognize sensitive features compared with decision tree and regular random forest algorithms. The model used in this study shows a strong self-adaptive ability in the modeling process of multiple regression. The water demand in Shenzhen will reach 2.2 billion m3 in 2025 and 2.35 billion m3 in 2030, which will exceeded the water supply ability of Shenzhen. Furthermore, three scenarios are designed in terms of water supply security and economic operation, and a comparative analysis is performed to obtain an optimal scenario.

Managing Water Supply and Demand to Achieve Economic and Environmental Objectives: Application of Mathematical Programming and ANFIS Models

Integrated management of water supply and demand has been considered by many policymakers and due to its complexity, the decision makers have faced many challenges. In this study, we proposed an efficient framework for managing water supply and demand in line with the economic and environmental objectives of the basin. To design this framework, a combination of ANFIS and multi-objective augmented ε-constraint programming models and TOPSIS were used. First, using hydrological data from 2001 to 2017, the rate of water release from the dam reservoir was estimated with the ANFIS model; afterwards, its allocation to agricultural areas was performed by combining multi-objective augmented ε-constraint models and TOPSIS. To prove the reliability of the proposed model, the southern Karkheh basin in Khuzestan province, Iran, was considered as a case study. The results showed that this model is able to reduce irrigation water consumption and improve its economic productivity in the basin.