揭示流域水储量变化对气候变化和人类活动（环境变化）的响应规律，对认识流域水循环规律和提高水资源管理水平具有重要意义。以往研究主要应用重力场数据和大尺度水文模型反演流域水储量变化，较少考虑反映人类活动的流域水文模拟方法。此外，以往研究尽管分析了多种环境变化因素与流域水储量变化的相关性，但未阐明环境变化因素对流域水储量变化的影响特性，也未有效揭示流域水储量变化对环境变化的响应规律。项目以黑河流域为研究对象，利用重力场数据、流域水文模型和水资源管理模型等估算流域水储量变化。采用二元关系传递性、分步相乘和分类相加假设、向量组线性相关性和格兰杰因果检验法阐释不同环境变化因素对流域水储量变化影响的传递性、干扰性和反馈性。通过人工神经网络计算人类活动和气候变化对流域水储量变化的贡献率，结合水量平衡原理、水热耦合平衡理论、曼宁公式和枯水消退规律，揭示流域水储量变化对环境变化的响应规律。

Revealing response laws of water storage variations to climate changes and human activities (environmental changes) is of great significance for understanding hydrologic cycle regularity and water resources management in a river basin. Previous studies focused on recovering water storage variations by gravitational field data and large scale hydrologic models, taking little account of hydrologic simulations of basins which could reflect human activities. Besides, relationships between water storage variations and multiple environmental change factors were analyzed in past, but the influencing features of environmental change factors and response laws of water storage variations to environmental changes were not clearly explained. This project takes Heihe River basin as a case study, employing gravitational field data, a hydrological model and a water resources management model to estimate water storage variations in the basin. The transitivity of binary relation, assumptions on multiplication step by step and on addition of different classifications, linear correlation of vector group and Granger causality test are adopted to interpret transitivity, interference and feedback features of different environmental change factors. Moreover, an artificial neural network is built to calculate contribution rates of human activities and climate changes for water storage variations. More important, the project will reveal the response laws of water storage to environmental changes by combining water balance principle, moist-heat coupling equilibrium theory, Manning formula and the regression regularity of low flows.