中国西北五省区水资源利用的协调性分析

doi:10.12118/j.issn.1000–6060.2021.075

摘要/Abstract

摘要：

水资源协调利用问题是中国西北五省区经济、社会和生态可持续发展的首要问题。采用协同性分析方法和耦合协调度模型,对中国西北陕西、甘肃、青海、宁夏、新疆五省区水资源利用率和生态用水率之间的区域协同性和耦合协调度进行分析。结果表明：（1）西北五省区的水资源利用率和生态用水率方差齐同,任意两省区之间不具有显著性区域差异,而在五省区内均具有区域协同性。（2）西北五省区生态用水率的升降波动主要取决于当地对农业用水的管理政策是否及时和持续有效。（3）西北五省区中,除新疆处于“勉强协调”等级外,其他四省区水资源利用率与生态用水率的耦合协调度都非常低,普遍远离“协调极”而偏向“失调极”。（4）西北五省区耦合协调度级别的升降与该省区生态用水率的高低存在正相关关系。（5） 2018年,陕西“轻度失调”、甘肃“濒临失调”和新疆“勉强协调”的等级状态相对容易调整上升,关键是提高其生态用水率,而青海“严重失调”和宁夏“中度失调”的等级状态必须引起高度重视并综合设法扭转,防止愈演愈烈。

关键词: 水资源利用, 耦合协调性, 优化用水结构, 生态用水, 西北五省区

Abstract:

Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang are the five provinces of northwest China. This area is the starting point of the westward extension of the Silk Road Economic Belt in China. The coordination of water resource utilization is the most important issue here and is also one of the important contents of the construction of modern water ecological civilization. On the basis of the annual data of total water resources, total water consumption, agricultural water consumption, industrial water consumption, domestic water consumption, and ecological water consumption of the five northwest provinces from 2003 to 2018, the utilization ratio of water resources (CP_j) and the ecological water use ratio (EP_j) are calculated. The above j=1, 2, 3, 4, 5 represent Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang, respectively. With the use of the synergy analysis method and the coupling coordination degree model, the regional synergy and coupling coordination degree between CP_j and EP_j are analyzed. The results are as follows: (1) The variances of CP_j and EP_j of the five northwest provinces are the same. No significant regional difference exists between any two provinces. Thus, CP_j and EP_j have regional synergy in the five provinces. (2) The fluctuation of EP_j in the five provinces depends on whether the local agricultural water use management policy is timely and effective. (3) The coupling coordination degree levels of CP_j and EP_j are very low in the five northwest provinces and is at the level of “barely coordinated” in Xinjiang only. These levels are generally far away from the “coordination pole” and inclined to the “imbalance pole” in the other four provinces. (4) A positive correlation exists between the level of coupling coordination degree and EP_j in the five northwest provinces. (5) The results of coupling coordination degree in 2018 show that the three status levels of “mild imbalance” in Shaanxi, “on the verge of imbalance” in Gansu, and “barely coordinated” in Xinjiang are relatively easy to adjust and increase. The key is to improve the EP_j. However, the two status levels of “serious imbalance” in Qinghai and “moderate imbalance” in Ningxia must be given more attention and comprehensively reversed to prevent them from worsening.

Key words: water resources utilization, coupling coordination, optimize water use structure, ecological water use, the five provinces of northwest China

李秀花,吴纯渊. 中国西北五省区水资源利用的协调性分析[J]. 干旱区地理, 2022, 45(1): 9-16.

LI Xiuhua,WU Chunyuan. Coordination of water resources utilization in the five provinces of northwest China[J]. Arid Land Geography, 2022, 45(1): 9-16.

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耦合协调度（D_j）	耦合协调等级	耦合协调度（D_j）	耦合协调等级
[0.89, 1.00]	优质协调	[0.39, 0.49)	濒临失调
[0.79, 0.89)	良好协调	[0.29, 0.39)	轻度失调
[0.69, 0.79)	中级协调	[0.19, 0.29)	中度失调
[0.59, 0.69)	初级协调	[0.09, 0.19)	严重失调
[0.49, 0.59)	勉强协调	[0.00, 0.09)	极度失调

指标	方差齐性检验	组间自由度	组内自由度	显著性概率
水资源利用率	0.270	4	75	0.896
生态用水率	1.603	4	75	0.182

方差分析方法	组别	显著水平为0.05的检验结果
方差分析方法	组别	水资源利用率子集	生态用水率子集
纽曼-科伊尔斯检验和邓肯氏多重范围检验	1	0.476	0.391
	2	0.507	0.539
	3	0.475	0.319
	4	0.545	0.469
	5	0.377	0.520
纽曼-科伊尔斯检验的显著性概率		0.418	0.311
邓肯氏多重范围检验的显著性概率		0.126	0.089

年份	陕西	甘肃	青海	宁夏	新疆
2003	0.140	0.200	0.156	0.088	0.481
2004	0.264	0.218	0.158	0.159	0.467
2005	0.234	0.381	0.142	0.210	0.480
2006	0.282	0.415	0.162	0.182	0.476
2007	0.260	0.405	0.155	0.198	0.471
2008	0.282	0.430	0.204	0.241	0.476
2009	0.324	0.401	0.176	0.281	0.464
2010	0.254	0.415	0.191	0.249	0.466
2011	0.240	0.402	0.192	0.260	0.496
2012	0.308	0.392	0.140	0.221	0.315
2013	0.336	0.347	0.155	0.244	0.340
2014	0.344	0.375	0.163	0.284	0.357
2015	0.361	0.448	0.188	0.297	0.342
2016	0.388	0.476	0.207	0.262	0.338
2017	0.345	0.447	0.190	0.278	0.383
2018	0.388	0.406	0.177	0.215	0.517