地理学报 ›› 2020, Vol. 75 ›› Issue (5): 1036-1052.doi: 10.11821/dlxb202005011

• 气候变化与地表过程 • 上一篇    下一篇

基于“水—能源—食物—生态”纽带因果关系和贝叶斯网络的锡尔河流域用水分析

施海洋1,2, 罗格平1,2(), 郑宏伟1,2, 陈春波1, 白洁1, 刘铁1,2   

  1. 1.中国科学院新疆生态与地理研究所,乌鲁木齐 830011
    2.中国科学院大学,北京 100049
  • 收稿日期:2019-02-12 修回日期:2020-02-10 出版日期:2020-05-25 发布日期:2020-07-25
  • 通讯作者: 罗格平 E-mail:luogp@ms.xjb.ac.cn
  • 作者简介:施海洋(1994-), 男, 江苏海门人, 博士生, 主要从事土地变化及其生态效应、遥感与GIS应用研究。E-mail: shihaiyang16@mails.ucas.ac.cn
  • 基金资助:
    中国科学院战略性先导科技专项(XDA20060302);国家自然科学基金项目(U1803243)

Water use analysis of Syr Darya river basin: Based on "Water-Energy-Food-Ecology" nexus and Bayesian network

SHI Haiyang1,2, LUO Geping1,2(), ZHENG Hongwei1,2, CHEN Chunbo1, BAI Jie1, LIU Tie1,2   

  1. 1.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2019-02-12 Revised:2020-02-10 Online:2020-05-25 Published:2020-07-25
  • Contact: LUO Geping E-mail:luogp@ms.xjb.ac.cn
  • Supported by:
    Strategic Priority Research Program of Chinese Academy of Sciences(XDA20060302);National Natural Science Foundation of China(U1803243)

摘要:

跨界流域水资源利用系统因受气候变化、人口增长、政治博弈、生态反馈等许多因素的互馈影响,包含复杂的“水—能源—食物—生态”纽带因果关系,形成具有高度不确定性的复杂非线性系统。传统水资源规划方法中缺少对这种复杂纽带关系的约束,而目前研究这类纽带关系多基于耦合多个模型、集成建模的方法,数据需求大、对复杂因果关系的不确定性模拟能力不足。而贝叶斯网络能以概率分布代替参数确定值来模拟系统中因果关系的不确定性、同时减少数据需求。本文利用贝叶斯概率网络,选取锡尔河流域为研究对象,量化这一因不合理用水而导致咸海生态危机的跨界内陆河流域“水—能源—食物—生态”纽带中的因果关系。结果表明贝叶斯网络能有效地模拟纽带中因果关系的强弱与不确定性,分析1970—2015年间不同时期影响咸海入湖水量的主要因素。为用水预测与流域水资源利用管理提供了系统性认知的基础,并展现出在较低模型复杂度和成本下建模的潜力。苏联时期,咸海入湖水量对农业开发增长的灌溉用水、上游水库建设的蓄水过程和径流量等较敏感;苏联解体后,咸海入湖水量对下游国家不合理使用的灌溉用水及上游水库蓄水量等节点高度敏感。短期内,需提高洗盐与灌溉用水效率,改良种植结构、增加粮食作物占比,并预防干旱危害;长期而言,通过普及先进滴灌技术,能大幅节约农业用水,在50%和80%的滴灌普及率下,新增咸海入湖水量将达到6.4 km 3和9.6 km 3以上,有望逐步缓解咸海生态危机。

关键词: 贝叶斯网络, “水—能源—食物—生态”纽带关系, 锡尔河, 咸海生态危机

Abstract:

Due to the influence of climate change, population growth, politics, ecological feedback and related factors, the water use system of transboundary basins are characterized by a complicated Water-Energy-Food-Ecology nexus, which constitutes a complex nonlinear system with high uncertainty. Unfortunately, the traditional optimization of water resources is often constrained by such a complex nexus. Despite that integrated modeling approach is often used in the simulation of complex nexus, it remains unsupportive of big data needs, thereby making uncertainty reduction a difficult task. The Bayesian network (BN), which is considered a more robust tool for analyzing complex relationships, was applied in this study to characterize the Water-Energy-Food-Ecology nexus of Syr Darya river basin, a transboundary inland basin which contributed to the Aral Sea ecological crisis for unreasonable water use. The annual scale data was introduced into BN to model the impact of stochastic annual runoff and compare their differences using periods "before" and "after" the disintegration of the Soviet Union as benchmark. Results show that during the before period, the amount of water inflow into the Aral Sea was sensitive to increased irrigation for agricultural development, increased water storage of the upstream reservoir and stochastic runoff. This situation became reverse after the disintegration of the Soviet Union. The reverse situation resulted from unresolvable disputes between power generation in upstream and irrigation water in downstream countries. Comprehensive scenario analysis shows that it is effective to improve the proportion of food crops, the efficiency of water use for salt leaching and irrigation, and prevent drought damage. Based on the increased use of advanced drip irrigation technology from 50% to 80%, it is anticipated that the annual inflow into the Aral Sea will increase significantly, reaching 6.4 km 3 and 9.6 km 3, respectively; and is capable of ameliorating ecological crisis within the basin. Finally, this study shows that the BN is a cost-efficient approach for predicting systematic water usage as decision support in water management with less complexity, and it is effective in modeling casual relationships in the Water-Energy-Food-Ecology nexus.

Key words: Bayesian network, water-energy-food-ecology nexus, Syr Darya river, Aral Sea ecological crisis