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

doi:10.11821/dlxb202005011

Abstract

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

SHI Haiyang, LUO Geping, ZHENG Hongwei, CHEN Chunbo, BAI Jie, LIU Tie. Water use analysis of Syr Darya river basin: Based on "Water-Energy-Food-Ecology" nexus and Bayesian network[J].Acta Geographica Sinica, 2020, 75(5): 1036-1052.

Figures/Tables 12

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References 43

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数据种类	时间	数据说明
径流量	1970—2015年	水文站点数据, 日尺度和年尺度	https://www.bafg.de/GRDC (1970—1989年) http://www.cawater-info.net/water_quality_in_ca/syr_e.htm (1911—2016年)
降水	1970—2015年	气象站点数据, 日尺度	https://www.ncdc.noaa.gov
干旱指数	1979—2015年	遥感模型反演数据产品, 日尺度, 分辨率0.04°×0.04°	https://developers.google.com/earth-engine/datasets/catalog/IDAHO_ EPSCOR_PDSI
水库运行	1974—2015年	月尺度	Siegfried T ^[26] (1974—2006年) http://www.icwc-aral.uz(2007—2015年)
用水能源	1970—2015年 1991—2015年	年尺度和州尺度年尺度和国家尺度	https://www.cawater-info.net https://www.ceicdata.com
社会经济	1970—2015年	统计公报年尺度和州尺度	http://stat.gov.kz (2003—2015年), http://www.stat.kg (2012—2015年) https://stat.uz/uz(2000—2015年), https://data.worldbank.org.cnhttp://www.fao.org/statistics, 苏联国民经济统计年鉴(1960—1987年), 独联体统计委员会数据库(1992—2010年)
咸海水情	1970—2015年		https://www.cawater-info.net
生态指标	1980—2010年		https://www.cawater-info.net, Micklin P ^{[1, 25]}

变量	解释	状态分级	单位
年径流量	乌奇库尔干等站	280~360, 360~440, 440~650	10⁸ m³
下游降水量	克孜勒奥尔达、费尔干纳、吉扎克等地区气象站均值	170~190, 190~210, 210~230	mm
下游干旱指数PDSI		-8~-4, -4~0, 0~6
上游水库蓄水量	托克托古尔水库	0~6, 6~12, 12~20	km³
水库夏季放水量		1800~2800, 2800~3800, 3800~4800	10⁶ m³
水库冬季放水量		3500~3800, 3800~4200, 4200~4500	10⁶ m³
上游水力发电量		0.3~0.8, 0.8~1.2, 1.2~1.5	10¹⁰ kW·h
上游进口天然气量	从下游进口	0~1, 1~2, 2~3	10⁹ m³
下游棉花种植面积		700~750, 750~800, 800~850	10³ ha
下游棉花产量		1100~2200, 2200~3300, 3300~4400	10³ t
下游粮食作物种植面积		1000~1100, 1100~1200, 1200~1300	10³ hm²
下游粮食产量		1500~2500, 2500~3500, 3500~4500	10³ t
下游大牲畜量	牛、羊数量	7000~10000, 10000~13000, 13000~16000	10³头
下游灌溉定额		9500~10000, 10000~10500, 10500~11000	m³/hm²
下游总用水量		33~35, 35~37, 37~40	km³
流入洼地水量	流入Arnasay洼地水量	1.5~4.5, 4.5~6.5, 6.5~8.5	km³
下游农业产值		2~4, 4~6, 6~8	10⁹ US$
下游总产值		10~30, 30~50, 50~70	10⁹ US$
下游人口		14~16, 16~18, 18~20	10⁶人
咸海入湖水量	卡扎林斯克站	2.5~4, 4~5, 5~6.5	km³
咸海水量		10~100, 100~200, 200~300	km³
下游荒漠面积	包括Aralkum沙漠	14~16, 16~18, 18~20	10⁴ km²
下游土壤盐分含量	克孜勒奥尔达地区	0~3, 3~8, 8~30	g/kg
下游沙、盐尘暴频率		0~30, 30~60, 60~100	d/a
下游饮用水矿化度	克孜勒奥尔达地区	0~0.5, 0.5~1, 1~3	g/L
下游人均预期寿命		64~66, 66~68, 68~70, 70~72	a

节点	80%可信度的贝叶斯可信区间	单位
咸海入湖水量	(2.95, 6.22)	km³
上游总用水量	(4.37, 9.38)	km³
下游总用水量	(33.71, 39.18)	km³
下游农业产值	(2.49, 7.31)	10⁹ US$
下游沙、盐尘暴频率	(13.27, 89.44)	d/a
下游饮用水矿化度	(0.55, 2.72)	g/L

实际下游总用水量(km³)	预测下游总用水量(km³)
实际下游总用水量(km³)	33~35	35~37	37~40	行总和
33~35	2	0	1	3
35~37	0	3	1	4
37~40	0	1	2	3
列总和总体精度(%)	2	4	4	10
列总和总体精度(%)	70

目标变量	情景变量
	RF	DR	EI	UR	WR	IQ	DG	DC	DA	UA	DI	WD
下游总用水量(低)	-1.2			+0.2		+1.2	+1.7		-1.6		-1.8	+0.3
上游总用水量(低)	-4.3			+2.5						-0.9
下游人均产值(高)							+0.6		+0.5		+4.7
上游人均产值(高)			+0.3							+1.3
下游畜牧业产值(高)									+5.1
下游粮食产量(高)		+0.3				-0.3	+13.6
上游能源总量(高)	+2.0		+5.9	-2.7	+2.6
咸海入湖水量(高)	+2.4			+3.6	+1.3	+2.3	+0.5	+2.6				+23.5
咸海水量(高)	+1.5			+0.6								+3.5
荒漠面积(低)		+9.6
土壤盐分含量(低)		+5.5
沙、盐尘暴频率(低)	+0.1	+3.7		+0.8								+1.1
用水矿化度(低)		+1.3
下游人均寿命(高)		+0.2

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

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