Evaluation of agricultural water and soil resource matching characteristics considering increased precipitation-derived “green water”: a case study in the Yellow River Basin, China

Abstract

Well-matched pattern of water and soil resources can provide strong support for agricultural development. Most previous studies have considered the total amount of water resources, the available water resources, or the amount of irrigation water; the characteristics of water resources in different districts have been ignored. This study proposes a method to evaluate the matching of agricultural water and soil resources by combining administrative units and subbasins at different spatial scales while accounting for the variability of precipitation “green water” resources within each spatial unit. Taking the Yellow River Basin in China as an example, the generalized water and soil resource matching coefficient was applied to evaluate the spatial match between water and soil resources in nine provinces among the secondary water subregions within the Yellow River Basin. The results show the following: the degree of matching between agricultural water and soil resources in Sichuan Province (above Longyangxia) was relatively good, while that in the Inner Mongolia Autonomous Region (endorheic region) was relatively poor. The temporal variation trends of water and soil resources in Qinghai Province (Longyangxia to Lanzhou), Gansu Province (Longmen to Sanmenxia), Shanxi Province (Longmen to Sanmenxia), and Ningxia Hui Autonomous Region (Lanzhou to Hekou town) were significantly reduced, and the remaining provinces exhibited no significant changes. According to the relationship between the generalized agricultural water and soil resource matching coefficient and the ratio of “blue water” to “green water,” the study area was divided into four zones, and specific policy measures were proposed for each zone, especially those with unsatisfactory or unstable matching characteristics over time. For zones I and II with a relatively high degree of water and soil resource matching, the government should actively build irrigation facilities to ensure that the water conservancy conditions therein can be fully utilized. For zone III, the government should support the construction of water conservancy facilities and improve the utilization rate of water resources. The water shortage problem in zone IV can be alleviated by establishing an interconnected water system project with zones I and II or a cross-basin water transfer project.

Appendix

1.1 Enclosure 1: Variation coefficient

The variation coefficient is a statistic that measures the variability of each observation. The calculation formula is as follows:

$$\begin{array}{cc}C.{V}_{i}=\frac{S{D}_{i}}{{\text{Mea}}{\text{n}}_{i}}\times 100\%& (i=\mathrm{1,2},...,n)\end{array}$$

(5)

where C. V_i is the variation coefficient (%); SD_i is the standard deviation of each province among the secondary subregions; Mean_i is the average value of each province among the secondary subregions; and n is the index denoting the year beginning with 2010 (n = 7).

1.2 Enclosure 2: Spearman rank correlation coefficient

The Spearman rank correlation coefficient is a sorting-based nonparametric trend analysis method that calculates the rank of the original data and does not require a certain distribution of the original data. The calculation formula is as follows:

$$R=1-6\times \frac{\sum_{i=1}^{n}{({x}_{i}-{y}_{i})}^{2}}{n({n}^{2}-1)}$$

(6)

where R is the rank correlation coefficient; x_i is the year; y_i is the serial number of the original data sorted in ascending order; and n is the index denoting the year beginning with 2010 (n = 7). |R|> 0.929 denotes an extremely significant correlation (significance level α = 0.01), 0.786 <|R|< 0.926 denotes a significant correlation (significance level α = 0.05), and |R|< 0.786 means that there is no significant correlation. Positive values represent an increasing trend, while negative values represent a decreasing trend.