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Article

Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions

by
Yaoyu Li
1,2,
Kaixuan Li
1,
Xifeng Liu
1,
Zhimin Zhang
1,
Zihao Gao
1,
Qiang Wang
1,
Guofang Wang
3 and
Wuping Zhang
1,*
1
College of Software, Shanxi Agricultural University, Jinzhong 030801, China
2
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
3
College of Resources and Environmental Sciences, Shanxi Agricultural University, Jinzhong 030801, China
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(13), 1442; https://doi.org/10.3390/agriculture15131442
Submission received: 4 June 2025 / Revised: 30 June 2025 / Accepted: 2 July 2025 / Published: 4 July 2025
(This article belongs to the Special Issue Enhancing Soil Health and Water Use Efficiency in Sustainable Agriculture)
Browse Figures
Versions Notes

Abstract

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges.

1. Introduction

Soil water resources are a fundamental component of terrestrial water systems and a key determinant of agricultural productivity, particularly in dryland regions [1,2]. Functioning alongside surface and groundwater, soil moisture participates in the entire agricultural water cycle [3], and its precise evaluation is essential for guiding agricultural practices and advancing water-saving technologies [4,5]. In water-scarce areas such as Shanxi Province, where precipitation is unevenly distributed both spatially and temporally, understanding the dynamics of soil water availability and crop water demand is critical for improving water use efficiency and ensuring sustainable agricultural development.
In recent years, the study of the spatial distribution characteristics of precipitation has emerged as a critical focus in ecology and environmental sciences, with widespread practical applications [6,7]. The kriging method, due to its superior interpolation accuracy, has been demonstrated to outperform inverse distance weighting and the basic Thiessen polygon method in precipitation estimation [8,9]. Based on long-term meteorological data from Zhengzhou, Henan Province (1951–2015), Zai [10] reported substantial variability in precipitation across years, months, and ten-day periods, even under consistent irrigation design reliability. This observed variability highlights the complexity and necessity of accurate precipitation assessment in agricultural water resource management. Current challenges in Shanxi include significant intra- and inter-annual variation in precipitation, declining rainfall trends, and an increasing mismatch between crop water needs and natural water availability. These problems are exacerbated by climate change and limited irrigation infrastructure. Recent studies have highlighted that elevation and climate warming can amplify precipitation variability across spatial and temporal scales, further complicating accurate water resource assessment and management in mountainous and semi-arid regions [11,12]. Adjusting the irrigation system and cropping structure from the perspective of the water cycle [13,14] offers a pathway to improve water resource allocation and mitigate drought impacts. Therefore, an urgent scientific question arises: How can we quantitatively assess soil water resources and develop adaptive irrigation strategies under variable hydrological conditions?
While previous studies have explored regional soil moisture patterns, precipitation interpolation methods, and crop water use [15,16,17], relatively few have attempted to integrate these components into a unified framework. For instance, Chen investigated differences in soil water storage and use efficiency across typical vegetation types in the Loess Plateau [18], and Shao analyzed long-term changes in water storage across the region [19]. Additionally, Pani assessed crop-specific water requirements and actual water use under pivot irrigation systems in Inner Mongolia [20]. However, comprehensive studies that systematically link all three aspects remain limited, particularly under variable hydrological conditions. Spatial variability due to topography, vegetation, and soil properties, and temporal variability driven by monsoonal climate patterns, contribute to the complexity of soil water dynamics in Shanxi [21,22,23]. Xue [24] and Du [25] identified significant changes in precipitation and runoff using mutation detection and the Mann-Kendall method in the Xiangjiang River Basin, highlighting the dual influence of climate change and anthropogenic activities on hydrological variability. In the Longdong region of the Loess Plateau, Guo found that soil moisture in winter wheat fields was highly sensitive to precipitation and evapotranspiration, directly affecting crop yield—underscoring the agricultural significance of soil water variability [26]. The concept of the “soil reservoir” in the Loess Plateau underscores the importance of deep soil water in sustaining crop growth during dry seasons [27,28].
Recent advancements in methods, such as soil–plant–atmosphere continuum (SPAC)-based modeling [29], unmanned aerial vehicle (UAV)-based estimation [30], evapotranspiration modeling [31], and water demand calculations using the Penman-Monteith method [32,33], provide tools for more accurate water resource evaluation. However, there remains a gap in integrating these tools with long-term hydrological analysis to support irrigation planning under climate uncertainty.
In this context, this study aims to:
(1)
Classify hydrological years (dry, normal, wet) in Shanxi Province using daily precipitation data (1963–2023) and theoretical frequency curves.
(2)
Quantify soil water resource availability under rainfed conditions across different hydrological years.
(3)
Analyze the reproductive-stage water demand and deficits of key crops (millet, soybean, sorghum, winter wheat, maize, and potato).
(4)
Propose irrigation optimization strategies tailored to hydrological variability and crop-specific needs.

2. Materials and Methods

2.1. Overview of the Study Area

Shanxi Province, China (34°34′–40°44′ N, 110°14′–114°33′ E), is located on the eastern flank of the Loess Plateau and in the central part of the Yellow River Basin, covering a total area of 156,700 km2 (Figure 1). Bordered by the Taihang Mountains to the east and the Yellow River serving as a natural boundary to the west and south, the province features a high terrain in the northeast that gradually slopes downward toward the southwest, with an average elevation exceeding 1500 m above sea level. Its highest peak, Yedou Peak of Wutaishan Mountain, reaches 3061.1 m, making it the tallest peak in northern China. The region is characterized by complex and diverse landforms, with mountains and hills accounting for over 80% of its area, while rivers are relatively sparse. Shanxi has a temperate continental monsoon climate, marked by four distinct seasons, significant temperature variations between day and night, and notable climatic differences between the north and south. It exhibits pronounced arid and semi-arid characteristics, with annual sunshine hours ranging from 2000 to 3000 h, evapotranspiration between 1500 and 2300 mm, and a frost-free period lasting 120 to 220 days. Annual precipitation varies between 358 and 621 mm across different regions, making water resources scarce, with precipitation as the primary water source. Seasonal climatic patterns include dry and windy springs with minimal precipitation, hot and rainy summers with high evaporation rates, dry and windy autumns, and cold, dry winters. These conditions contribute to periodic droughts of varying severity, posing challenges to agricultural production.

2.2. Data Collection

Day-by-day meteorological data from 1963–2023 were obtained from the China Meteorological Data Network (http://data.cma.cn, accessed on 15 April 2024), including latitude and longitude, altitude (m), precipitation (mm), average air temperature (°C), daily maximum temperature (°C), daily minimum temperature (°C), average relative humidity (%), minimum relative humidity (%), hours of sunshine (h), wind speed (m/s), etc. According to the boundary of Shanxi Province, 27 meteorological stations were selected for calculation and analysis. The growth parameters of winter wheat, summer maize, sorghum, cereal grains, potato, and soybean during the whole life cycle were obtained from the relevant parameters recommended in the database of the Food and Agriculture Organization of the United Nations (FAO) [34] as well as the data accumulated over the years in the Plant Phenology Laboratory of the School of Software, Shanxi Agricultural University, including the time of each fertility stage, crop coefficients, and crop leaf area index (Table 1).

2.3. Evaluation of Coupling

In this study, based on the water balance principle and the single crop coefficient model, we conducted daily-scale calculations of actual evapotranspiration, canopy interception, and changes in soil water storage. These values were used to estimate the soil water resources for representative years. Furthermore, the spatial and temporal distribution patterns of soil water resources in Shanxi Province, as well as their availability during the reproductive stages of major crops, were analyzed to comprehensively evaluate the region’s soil water resource status.
The water balance method represents the quantitative relationship between the links in the hydrological cycle, and the difference between the recharge and consumption in the water cycle of soil water resources in the calculated time period is equal to the change in soil water resources. In this study, soil water resources (Equation (1)) were calculated under conditions without irrigation or surface cover, assuming a specified initial soil water content and using a hydrological year as the cycle to represent soil water dynamics [35,36].
W = W 0 + P − R r − R s − E T − E V + E g + Q
where W is soil water storage; W 0 is initial water content; P is precipitation; R r is precipitation recharge to groundwater; R s is areal runoff; E T is actual evaporation (excluding submersible evaporation); E g is submersible evaporation (groundwater recharge to soil water); E V is plant retention; and Q is condensate recharge to soil water.
Based on the analysis of hydrological cycle process of soil water resources [35,37], Equation (1) was modified to obtain Equation (2).
W S R = P − E V
where W S R is the amount of soil water resources, P is the amount of precipitation, and E V is the amount of plant retention.
The Thiessen polygons method [8] was used to calculate the average areal precipitation (Equation (3)) in Shanxi Province.
P ¯ = P 1 f 1 + P 2 f 2 + … + P n f n F = ∑ i = 1 n P i f i F
where P ¯ is the average areal precipitation in the study area (mm); P is the precipitation in the same period at each meteorological station (mm); f i is the area occupied by the first precipitation station in the polygon (km2); F is the area of the watershed (km2); and f i / F is the weighting coefficient of the polygon area at each meteorological station.
A generalized canopy interception model [38] was employed to estimate crop plant interception (Equation (4)).
E V = 0.092 L A I ∗ P 0.53 − 0.0085 P − 0.5 P ≤ 17   mm / d 0.225 L A I P > 17   mm / d
where E V is the plant retention of daily precipitation (mm); L A I is the crop leaf area index; and P is the daily precipitation (mm).
The reference crop evapotranspiration was calculated using the Penman–Monteith formula recommended by FAO [39], and the reference crop water requirement was calculated using Equation (5).
E T c = K c · E T 0
where E T c is the crop water requirement (mm/d); E T 0 is the reference crop evapotranspiration (mm/d); K c is the crop coefficient.
Crop-specific water deficits were calculated as the difference between crop water requirements (as determined by Equation (5)) and the available soil water resources (as defined in Equation (2)).
Soil water resources evaluation coefficient C (Equation (6)) refers to the ratio of soil water resources to plant evapotranspiration, the greater the ratio, the greater the contribution of soil water resources, i.e., the better the soil water resources can meet plant evapotranspiration [40].
C = W S R / E T c
where W S R is the amount of soil water resources; and E T c is the amount of plant transpiration.
The ratio of the amount of soil water resources to the amount of precipitation is known as the soil water resources coefficient (Equation (7)), which means how much precipitation enters the soil and is converted into soil water resources [41].
K = W S R / P
where K is the soil water resource coefficient; W S R is the amount of soil water resources; and P is the amount of precipitation.

3. Results

3.1. Trends in Precipitation in Shanxi Province

Based on the precipitation data of Shanxi Province from 1963 to 2023, the areal precipitation values in the study area were calculated according to the Thiessen polygons method, the average areal precipitation in Shanxi Province was analyzed in terms of frequency, and the trend of precipitation resources was analyzed after selecting typical representative years of areal precipitation in different frequency years from the theoretical frequency curves, which provided the basis for the calculation and analysis of soil water resources in different typical hydrological years in Shanxi Province.

3.1.1. Frequency Analysis of Precipitation in Shanxi Province

Based on the distribution of meteorological stations and the completeness of hydrological series in Shanxi Province, China, precipitation data from 27 meteorological stations in Shanxi Province were selected in this study, and Thiessen polygons were constructed by using ArcGIS (Figure 2) to calculate the average areal precipitation in Shanxi Province as a reference for precipitation (Table 2).
The Pearson Ⅲ distribution curve (P-III) was selected to derive the empirical frequencies of theoretical areal precipitation corresponding to different frequencies in Shanxi Province from 1963 to 2023 (Figure 3), and the theoretical area precipitation of P = 25%, P = 50% and P = 75% was calculated (Table 3). Based on the theoretical frequencies, the precipitation frequencies are selected as wet year, as normal year, and as dry year to determine the study year precipitation.
To analyze the distribution patterns of annual areal precipitation under different hydrologic frequencies, this study identified representative years corresponding to wet, normal, and dry conditions. The selection was based on the “actual representative year” method, whereby the year with observed precipitation closest to the calculated theoretical precipitation for each frequency (within a ±15 mm range) was selected. Based on this criterion, the wet year was determined to be 1969 (697.31 mm), the normal year was 1981 (613.46 mm), and the dry year was 2009 (545.49 mm), as their observed values closely matched the theoretical values calculated in Table 3.

3.1.2. Frequency Analysis of Annual Areal Precipitation in Shanxi Province

Using a 3-year sliding average window and linear regression analysis, we analyzed trends in annual areal precipitation from 1963 to 2023. The sliding average method was applied to smooth short-term fluctuations and reveal long-term variability. A simple linear regression model was fitted to the annual precipitation data (Y = aX + b), assuming a linear trend and independent, homoscedastic residuals. Results showed that the areal precipitation exhibited a fluctuating trend with three phases—rising, falling, and rising again (Figure 4)—while the overall linear trend was significantly negative (p < 0.05), indicating a gradual decline in precipitation over the past 60 years.
Based on the Mann–Kendall (hereinafter referred to as the M-K test) non-parametric statistical test [42], the trend of precipitation changes in Shanxi Province from 1963 to 2023 and the mutation points were analyzed (Figure 5). In the Mann–Kendall test, UF (forward sequence statistic) and UB (backward sequence statistic) are used to detect monotonic trends and identify potential abrupt change points in a time series. Specifically, a positive UF value indicates an upward (increasing) trend in the data, while a negative UF value reflects a downward (decreasing) trend. The intersection points of the UF and UB curves within the confidence bounds are interpreted as the likely points of abrupt change, suggesting structural shifts in the precipitation pattern. The UF statistic was greater than 0 in 1964, while it was less than 0 in all the other years, and it fluctuated and declined from 1965 to 1980, then rose briefly. The UF curve exceeds the upper limit of the 0.05 significance level from 2006 to 2023, indicating a significant downward trend of areal precipitation in this period. Although the 3-year moving average suggests a temporary rise in precipitation from 2006 to 2023, the Mann–Kendall test reveals a statistically significant long-term decreasing trend during this period. This contrast highlights the difference between short-term fluctuations and long-term statistical trends. This indicates that the annual areal precipitation in Shanxi Province has an overall decreasing trend. This is consistent with the analysis of the linear trend in Figure 3. The three intersections of the UF and UB curves at the 0.01 significance level are the sudden change points of annual areal precipitation, indicating that the annual precipitation produced sudden changes in 1986, 1989, and 1991, respectively, which may be affected by extreme weather events or climate change.
In this study, distance average refers to the deviation of monthly precipitation from the long-term mean, serving as an indicator of interannual variability. Cumulative distance denotes the sequential aggregation of these deviations over time, characterizing the integrated fluctuation patterns of precipitation across distinct hydrological periods. According to the cumulative distance plot of areal precipitation in each month (Figure 6), it can be seen that the distance averages in each month alternated between positive and negative, and the gap between the maximum and minimum values was large, indicating that the fluctuation between years was large. At the same time, the peaks of each month show asynchronous characteristics, indicating that in different years, the peaks of the changes in different months do not completely overlap, reflecting the unevenness of the monthly areal precipitation in Shanxi Province in terms of temporal distribution. In the pre-flood period (January to May), the overall fluctuation of the distance mean is relatively small, but there are still obvious differences between months. For example, individual years will show more prominent positive or negative distance flattening in February or May, indicating that there will still be some extreme variations in the pre-flood period. The cumulative distance averages, on the other hand, show a trend of less fluctuation, and the overall cumulative trend is relatively smooth despite the large variations in some individual months. Distance averages during the flood season (June to September) tend to be higher than in other months, especially in July and August, which is consistent with seasonal hydroclimatic characteristics. Interannual variations were large, with significant peaks in 2003, 2007, and 2010, indicating that the flood season in these years had a significant impact on the study population. At the same time, the mean cumulative distances show significant high values in some years, indicating that the cumulative effect of the flood season contributes more to the total variation of the year. The late flood period (October to December) usually has lower precipitation, and the corresponding distance averages are also low overall, especially in November and December. However, in some years (2011 or 2015), relatively high values are also found in October, indicating that the late flood period is not completely free of fluctuations. Overall, the cumulative distance averages in the late flood period show a tendency to decrease or fluctuate little from year to year, in contrast to the dominant role of the flood season in the annual accumulation.
Taken together, on the one hand, the flood season (June to September) has the largest variation, which determines the cumulative value of the whole year; on the other hand, there are fluctuations in the pre-flood period and the post-flood period, but the influence on the whole year is relatively limited. The asynchrony of the peaks between months indicates that the monthly areal precipitation in Shanxi Province does not change uniformly in different years and months and has strong temporal distribution variability.

3.1.3. Characteristics of Typical Annual Areal Precipitation Distribution

Figure 7 shows the distribution of monthly precipitation and the proportion of monthly precipitation to annual precipitation for a typical year. The total amount of precipitation in the flood season of the dry year is 353.36 mm, and the highest amount of precipitation in July is 123.34 mm, accounting for 22.61% of the annual precipitation, which is about 1.1 times that of August, and the precipitation varies a lot during the year; the total amount of precipitation in the flood season of a normal year is 476.36 mm, and the highest amount of precipitation in July is 179.47 mm, which is 29.26% of the annual precipitation, and about 1.1 times that of August; the largest month of areal precipitation in a wet year is July, with the areal precipitation of 176.47 mm, and about 1.1 times that of August. 1.1 times; in the year of wet, the month with the highest areal precipitation is July, with an areal precipitation of 176.10 mm, accounting for 25.25% of the annual areal precipitation, and compared with the year of normal, the peak and flood season precipitation totals are close to each other, but the annual precipitation total in the year of wet is much higher than that in the year of dry and the year of normal, which is 697.31 mm.
Figure 8 illustrates the daily distribution of areal precipitation during representative wet, normal, and dry years. In hydrology, precipitation intensity greater than 50 mm/d is defined as heavy precipitation. In the three typical years, the daily precipitation is not greater than 50 mm, and the daily precipitation is less than 5 mm in a larger proportion. The peak daily precipitation is 27.75 mm in the dry year, 30.19 mm in the normal year, and 25.04 mm in the year of wet, and the distribution of the number of days of precipitation shows that there are 282 days of precipitation in the dry year, accounting for 77.26% of the whole year; 310 days of precipitation in the normal year, accounting for 84.93% of the whole year; and 320 days of precipitation in the year of wet, accounting for 87.67% of the whole year.
According to the actual precipitation statistics of Shanxi Province, on the basis of hydrological regulations, the precipitation intensity is classified into less than 5 mm/d, 5–10 mm/d, 10–25 mm/d, and more than 25 mm/d, and the proportions of precipitation of different precipitation classes to the annual precipitation are calculated for different months in Shanxi Province in the years 2009 (dry year), 1981 (normal year), and 1969 (wet year) (Table 4).
In the dry year pattern, July and August are the months with the highest number of precipitation days. Precipitation intensity of less than 5 mm/d is mainly concentrated in January, February, April, and December with 100%; the highest percentage of this type of precipitation intensity is found in March, June, and October. Precipitation intensity of 10 to 25 mm/d is highest in September, with 64.77%. Precipitation intensity of 5 to 10 mm/d is highest in March, with 42.07%. Precipitation with an intensity greater than 25 mm/d occurs only in July. In the whole year, there are 251 days of precipitation less than 5 mm/d, accounting for 89.01% of the total number of precipitation days, and the total amount of precipitation of this kind of intensity accounts for a large proportion of the total annual precipitation amount, which reaches 35.90%.
In the normal year pattern, July and August are the months with the highest number of precipitation days, accounting for 20% of the total number of precipitation days. Precipitation intensity of less than 5 mm/d was mainly concentrated in January, February, and December, with a share of 100%; the highest shares of this type of precipitation intensity were found in the months of April, May, September, October, and November, with 57.53%, 64.67%, 69.18%, 64.48%, and 64.27%, respectively. Precipitation intensity of 10 to 25 mm/d was highest in March and June, with 53.57% and 43.89%, respectively. Precipitation intensity of 5 to 10 mm/d was 36.26% in August, respectively. Precipitation greater than 25 mm/d occurs in July and August, with 16.82% and 18.09%, respectively. There were 276 days with precipitation less than 5 mm/d, accounting for 89.03% of the total number of days with precipitation; the total amount of this type of precipitation accounted for 33.81% of the total annual precipitation. Unlike the dry years, precipitation greater than 25 mm/d accounted for a larger proportion of the annual precipitation, which was 9.62%.
In the wet year pattern, July, August, and September are the months with the highest number of precipitation days, accounting for 28.84% of the total number of precipitation days. Precipitation with an intensity of less than 5 mm/d is mainly concentrated in January, February, November, and December, with a proportion of 100%; the highest proportion of this type of precipitation intensity is found in March, May, June, and October. Precipitation intensity of 5 to 10 mm/d was highest in June, with 46.33%. The highest percentage of precipitation intensity of 10 to 25 mm/d was recorded in September with 74.48%. Precipitation greater than 25 mm/d occurs in April with 36.57%. In the whole year, there were 276 days with precipitation less than 5 mm/d, accounting for 86.25% of the total number of days with precipitation, and the proportions of precipitation from 5 to 10 mm/d, from 10 to 25 mm/d, and greater than 25 mm/d were 7.19%, 6.25%, and 0.31%, respectively.
Therefore, as far as precipitation intensity is concerned, the number of days with daily precipitation reaching the criterion of heavy precipitation (greater than 25 mm/d) is relatively small in Shanxi Province, and even during the flood season from June to September, the precipitation accounts for no more than 30% of the annual precipitation. Daily precipitation in Shanxi Province is mainly dominated by less than 5 mm/d precipitation. In the actual agricultural production, this precipitation intensity cannot meet the growth demand of plants, which is one of the main reasons for water shortage in Shanxi Province.

3.2. Spatial and Temporal Distribution of Soil Water Resources

Temporal Distribution of Soil Water Resources

As can be seen from Figure 9, before March and after October, the daily precipitation is less, but due to the lower temperature, the actual evaporation and plant retention are at a relatively stable low level, so most of the change in soil water storage is negative, and the fluctuation is relatively small, indicating that the soil water deficit is enough to maintain a relatively stable soil moisture. At the beginning of March, the temperature rises, the soil began to thaw, the actual evaporation gradually increased, the increase in precipitation makes the plant retention also increased, and the daily change in soil water content is larger. This is the critical period of crop growth, so this period, one should pay attention to irrigation to replenish crops. The increase in precipitation makes the plant retention also increase, the soil water content daily change is larger, and this stage is the critical period of crop growth and development, so in this period should pay attention to the crop irrigation water replenishment. From June onward, rising air temperatures lead to increased crop water demand and higher evapotranspiration rates. As precipitation intensifies in July, daily soil water storage begins to rise, with positive changes sustained through September. This period marks the distribution of precipitation, the critical replenishment phase for soil moisture, supporting crop growth during the reproductive stage.
No matter what kind of hydrological year, the law of change in soil water storage change value and the law of change in precipitation basically coincide with the law of change; the bigger the change in the year, the bigger the precipitation, the bigger the change value of soil water storage, and the same day to reach the peak value.
Based on the area of cultivated land in the statistical yearbook of each city in Shanxi Province, the amount of soil water resources (mm) was multiplied by the area of cultivated land to obtain the corresponding total soil water resources (billion m3), and the soil water resources coefficients were calculated simultaneously for each typical year (Table 5).
The precipitation in the south of Shanxi Province is more than that in the north in all types of typical years (Figure 10). As can be seen from Figure 11, soil water resources in Shanxi province differed greatly from east to west, with the north significantly lower than the south. Especially Taiyuan and Yangquan had the least amount of soil water resources, and Yuncheng City had the most, followed by Xinzhou City, Linfen City, and Luliang City in the west. The amount of soil water resources is positively proportional to the area of arable land; the larger the area of arable land, the more soil water is available to plants, and therefore the greater the amount of soil water resources. It is also related to the distribution of precipitation, which is higher in Linfen, Lvliang, Yuncheng, Jincheng, Yangquan, and Xinzhou than in other regions. In terms of the soil water resource coefficient (Figure 12), the coefficients are higher in Jincheng, Yangquan, and Taiyuan in the north and center, i.e., the proportion of precipitation converted into soil water resources is higher. However, in the whole Shanxi province, the soil water resource coefficient is higher than 80 percent, which shows that precipitation resources are effectively used in agricultural production to alleviate the water shortage problem in Shanxi Province.

3.3. Soil Water Resources During the Reproductive Period of Crops

Soil water resources are closely related to crop water requirements, and we have analyzed the water requirements of millet, soybeans, sorghum, winter wheat, potatoes, and summer maize at different fertility stages.
From Figure 13a, it can be seen that the millet has the highest water demand at the nodulation stage among the three typical years, with an average modal coefficient of water consumption of 35.29%, which is attributed to the fact that the highest temperature at the nodulation stage is the period of the most vigorous growth of the grain, and the millet has the highest water demand at this stage. Precipitation was more abundant during the emergence period in the wet year; hence, there was a moisture surplus. The water deficit during the seeding period in a normal year was 4.28 mm, which resulted in insufficient recharge of soil water resources due to less precipitation during the seeding period. Therefore, attention should be paid to the recharge of irrigation water during the seedling and sowing periods of cereals and to the rational allocation of water resources during other reproductive stages to avoid water wastage.
Irrespective of the hydrological year, soybean fertility stages had different degrees of water deficit. As can be seen in Figure 13b, the water demand during the pod-bulging stage was the largest in dry, normal, and wet years, with 150.67 mm, 153.99 mm, and 153.37 mm, respectively. The water demand of soybean in Shanxi Province during the whole reproductive stage ranged from 388.06 to 425.12 mm, and the average daily water demand intensity during the whole reproductive stage was 2.95 mm/d. In dry year, the water deficit was 62.06 mm, and the modal coefficient of water consumption was the largest at the pod-bulging stage. 62.06 mm, and the largest modal coefficient of water consumption was observed at the pod-bulging stage. In conclusion, supplemental irrigation at seedling emergence, bud differentiation, flowering, and pod-bulking stages was quantified to achieve efficient use of water resources.
From Figure 13c, it can be seen that the water demand of sorghum in Shanxi Province ranged from 352.12 to 394.42 mm during the whole life cycle, and the average daily water demand intensity during the whole life cycle was 12.75 mm/d. The water deficit was largest during the node stage of the dry, normal, and wet years, which was 119.81 mm, 63.39 mm, and 119.39 mm, respectively, and there were moisture surpluses of 13.77 mm and 94.22 mm in the tassel-blooming and grouting stages in normal year, 12.65 mm in the grouting stage in dry year, and 12.84 mm in the grouting stage in wet year. The water surpluses of 13.77 mm and 94.22 mm in normal year, 12.65 mm in dry year, and 12.84 mm in wet year were found in the grain maturity stage, indicating that the water conditions of sorghum’s reproductive period were better than those of soybean and basically reached a degree of water equilibrium.
As shown in Figure 13d, the water requirement of winter wheat ranged from 454.66 to 484.07 mm over the whole reproductive period, with an average daily water demand intensity of 17.19 mm/d. From the perspective of the different fertility stages, the daily water demand intensity, stage water demand, water consumption modulus, and water deficit were the largest in winter wheat during the filling stage, which was basically consistent with the results of the study on the water demand of winter wheat in Shanxi Province by Wu Yongli et al. [43]. Soil water deficit was more serious. The average water deficit during the reproductive period of winter wheat was 327.43 mm, and the water consumption modal coefficient was the largest during the grouting period, while the soil moisture deficit would affect the overwintering and pre-winter tillering of winter wheat, so it was especially important for the replenishment of water during the overwintering period and the greening period of winter wheat in Shanxi Province, and timely winter irrigation was needed.
From Figure 13e, it can be seen that the water demand of potato in Shanxi Province ranges from 373.34 to 380.35 mm during the whole life cycle, with an average daily water demand intensity of 10.08 mm/d. The water deficit during the whole life cycle in dry year is the largest, 19.66 mm, and the surpluses during the whole life cycle in wet year and in normal year are 55.81 mm and 89.09 mm, respectively. The deficit during seedling emergence was the largest in dry and wet years, 57.74 mm and 52.21 mm, respectively. In order to improve seedling emergence rate, save water resources, promote root development, and reduce disease occurrence, we need to quantitatively irrigate the potato during the seedling emergence period. At the same time, modern agricultural technologies, such as crop growth models and remote sensing, are used to monitor crop water status and optimize irrigation strategies. With these technologies, irrigation schedules can be adjusted in real time in response to actual water demand and weather changes.
From Figure 13f, it can be seen that the water demand of summer maize during the whole reproductive period ranged from 355.71 to 390.60 mm, and the average daily water demand intensity during the whole reproductive period was 15.35 mm/d, which was lower than the daily water demand intensity of winter wheat. The water deficit of summer maize during the reproductive period is less, with the largest deficit of 39.37 mm in the dry year, the largest surplus of 94.62 mm in normal year, and the surplus of 48.31 mm in wet year. The average water consumption modal intensity during the nodulation period is 49.90 mm. In dry year, the irrigation plan is formulated in advance, and the number or amount of irrigations is increased to ensure the water supply of the crop. In a resource-rich water year, consider rational deployment of excess water resources, stored in reservoirs or used for groundwater recharge, in case of emergency.

4. Discussion

4.1. Analysis of Precipitation Trends in Shanxi Province

The total trend of areal precipitation shows a general decreasing trend in Shanxi Province over 60 years (1963–2023), which is consistent with the findings of Feng Rui Yun and Zhang Lihua et al. [44,45]. Since entering the 21st century, Shanxi Province has experienced a sharp decrease in precipitation and increasing droughts, and the climate has developed in the direction of warm-dry (drying). Precipitation in Shanxi Province is characterized by pronounced seasonal concentration, with over 70% of the annual total occurring during the flood season (June–September). This pattern is consistent with monsoonal climatic influences and aligns with the findings of Zhang Qian [46]. The spatial distribution of the typical mid-year average precipitation in Shanxi Province is very uneven due to the influence of topography, climate, geographical location, and other factors. The same conclusion was reached by Li Zhaoqi [47]. Meanwhile, it is concluded that the precipitation distribution increases from north to south and decreases from west to east.

4.2. Analysis of the Spatial and Temporal Distribution of Soil Water Resources

This study found that no matter what kind of hydrological year, the law of change in soil water storage value and the law of change in precipitation basically coincide with the law of change; the year change is larger, the larger the precipitation, the greater the value of change in soil water storage, and in the same day to reach the peak value. Chen Qiang and other researchers pointed out that the water storage capacity of the Loess Plateau soil is affected by the nature of the soil, and its infiltration depth is generally shallow, and precipitation cannot reach the deeper layers of the soil, which makes the soil water storage capacity in the shallow and deep layers very different [48]. The spatial distribution of typical annual soil water resources in Shanxi Province is uneven, and annual precipitation and soil water resources have obvious meridional and zonal differences. Precipitation interception depends on vegetation properties, precipitation characteristics, and climatic conditions [49,50]. The vegetation interception link will play a storage role in delaying sinks and weakening flood peaks by influencing the amount, timing, and energy of precipitation reaching the ground [51,52]. In the whole Shanxi Province, the soil water resource coefficient is higher than 80 percent, indicating that precipitation is effectively used in agricultural production.

4.3. Soil Water Resources During the Reproductive Period of Crops

Comparing the crop water demand of six major crops and the soil water resource supply at the corresponding fertility stages in Shanxi Province, the deficits at different fertility stages of the six crops can be calculated, and the coupling characteristics of soil water resource quantity and crop water demand can be clarified. In this study, it was found that precipitation was low during the sowing season, and drought protection was still the main problem of spring sowing in Shanxi Province, which was consistent with the results of Feng RuiYun’s study [44]. Most of Shanxi Province has a one-season-a-year planting pattern, with dryland accounting for 75% of the cultivated area, and the increase in yield of crops such as maize, potato, and millet mostly relies on precipitation, while summer and autumn are the critical seasons for crop growth, and the decrease in precipitation will cause a reduction in crop yield and quality. Therefore, further efforts are needed to promote the breeding and adoption of drought-resistant crop varieties, along with the implementation of effective soil water conservation practices. Research and policy should continue to support strategies aimed at optimizing cropping systems, enhancing soil water retention and recharge, and reducing non-productive water losses. These actions are especially critical under the growing challenges posed by climate change and increasing agricultural water demands.

4.4. Limitations and Prospects

This study represents a preliminary exploration of soil water resource evaluation in Shanxi Province, primarily from the perspective of precipitation inputs. However, due to the province’s complex terrain—particularly in mountainous regions—runoff processes are spatially variable and remain difficult to quantify. The current analysis did not incorporate precipitation-induced runoff from these mountainous areas because of data limitations. Nevertheless, such regions are crucial not only for downstream water redistribution in agricultural zones but also for sustaining native ecosystems. Future research should incorporate hydrological modeling that includes these upland dynamics and assess their impact on both agricultural water availability and environmental water needs. Field experiments under different agricultural management practices will also be important to enhance the efficiency of soil water utilization in dryland farming systems across Shanxi.
However, air temperature also plays a critical role in determining crop water deficits, as it directly influences evapotranspiration. Elevated temperatures during key reproductive stages, such as flowering and grain filling, can exacerbate water stress even in years with sufficient rainfall. Therefore, future studies should consider both precipitation and temperature in combination to better capture the variability and drivers of water deficits. In the future, we will carry out soil water resources evaluation for different soil types in Shanxi Province on this basis, analyze the impact of different soil textures on precipitation infiltration, improve the use of precipitation resources, and provide reference for the rational allocation of water resources and sustainable development of agriculture in Shanxi Province.
Moreover, according to regional climate projections, Shanxi Province is expected to experience a gradual increase in average temperature and more variable precipitation under future climate scenarios. These changes may lead to shifts in crop phenology—such as earlier flowering or shorter reproductive periods—along with mismatches between rainfall timing and crop water demand. If not addressed, this could result in reduced yields, especially for heat-sensitive crops like wheat and soybean. Therefore, adapting crop calendars, introducing climate-resilient varieties, and improving water-saving practices will be essential to maintaining agricultural productivity in the face of climate change.

5. Conclusions

This study, grounded in the practical context of dryland agriculture in Shanxi Province, established a quantitative framework to evaluate soil water resources and analyzed their spatial-temporal distribution across different hydrological year types (dry year: 2009; normal year: 1981; wet year: 1969).
(1)
Precipitation Trends: From 1963 to 2023, Shanxi Province experienced a statistically significant decrease in annual precipitation, with over 70% of rainfall occurring between June and September. The dominance of light rainfall events (<5 mm/day) limits effective soil infiltration and increases drought vulnerability.
(2)
Soil Water Resource Patterns: Soil water availability showed strong spatial heterogeneity—higher in the southwest (e.g., Yuncheng) and lower in the central and eastern regions (e.g., Taiyuan, Yangquan). The average utilization rate of precipitation exceeded 80%, indicating efficient conversion of rainfall into plant-available water under current agronomic practices.
(3)
Crop-Specific Water Deficits: Reproductive-stage water deficits were most severe in dry years, especially for millet, soybean, and winter wheat. Even in wet years, elevated temperatures during flowering and grain-filling stages led to evapotranspiration-driven deficits. These findings emphasize the need for stage-specific irrigation scheduling, combining precipitation forecasting with thermal condition monitoring.
In summary, this study provides targeted insights into when, where, and for which crops water shortages are most critical. The results support the development of region-specific water-saving strategies, such as drought-resistant crop deployment, optimized sowing dates, and adaptive irrigation planning. The framework also offers replicable methodology for other semi-arid agricultural zones globally facing similar climate constraints.

Author Contributions

Conceptualization, Y.L. and K.L.; methodology, Y.L. and X.L.; software, Y.L. and Z.Z.; formal analysis, Y.L. and Z.G.; data curation, Y.L. and Q.W.; writing—original draft preparation, Y.L. and K.L.; writing—review and editing, Y.L.; visualization, X.L.; supervision, W.Z. and G.W.; project administration, G.W.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Graduate Student Innovation Project of Shanxi Province, China [2024KY293]; the Science and Technology Major Project of Shanxi Province, China [202202140601021]; the National Key Research and the Development Program of China [2021YFD1901101]; and the Science and Technology Major Project of Shanxi Province, China [202101140601026].

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location and elevation of Shanxi Province, the study area. The left panel shows its position in China; the right panel shows elevation and meteorological stations. Map projection: WGS 1984. Data sources: China Meteorological Administration; National Geomatics Center of China.
Figure 1. Location and elevation of Shanxi Province, the study area. The left panel shows its position in China; the right panel shows elevation and meteorological stations. Map projection: WGS 1984. Data sources: China Meteorological Administration; National Geomatics Center of China.
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Figure 2. Combining ArcGis to construct Thiessen polygons.
Figure 2. Combining ArcGis to construct Thiessen polygons.
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Figure 3. The empirical frequencies of theoretical surface rainfall from 1963 to 2023 were obtained through curve fitting using the Pearson Type III distribution.
Figure 3. The empirical frequencies of theoretical surface rainfall from 1963 to 2023 were obtained through curve fitting using the Pearson Type III distribution.
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Figure 4. Using the sliding average method and linear regression to analyze the areal precipitation, the downward trend of areal precipitation from 1963 to 2023 is shown.
Figure 4. Using the sliding average method and linear regression to analyze the areal precipitation, the downward trend of areal precipitation from 1963 to 2023 is shown.
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Figure 5. The trend of precipitation changes and mutation points from 1963 to 2023 was analyzed using the Mann–Kendall (M-K) test.
Figure 5. The trend of precipitation changes and mutation points from 1963 to 2023 was analyzed using the Mann–Kendall (M-K) test.
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Figure 6. The characteristics of cumulative changes in areal precipitation by month reveal the fluctuation amplitude of the rainfall levels.
Figure 6. The characteristics of cumulative changes in areal precipitation by month reveal the fluctuation amplitude of the rainfall levels.
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Figure 7. The proportion of monthly areal precipitation to annual areal precipitation for a typical year highlights the timing of the flood season.
Figure 7. The proportion of monthly areal precipitation to annual areal precipitation for a typical year highlights the timing of the flood season.
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Figure 8. Distribution of daily areal precipitation during wet, normal, and dry years.
Figure 8. Distribution of daily areal precipitation during wet, normal, and dry years.
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Figure 9. Intra-annual variations in soil water resources for different hydrological years are shown based on the double crop coefficient model and the water balance principle. (A) Normal year; (B) dry year; (C) wet year.
Figure 9. Intra-annual variations in soil water resources for different hydrological years are shown based on the double crop coefficient model and the water balance principle. (A) Normal year; (B) dry year; (C) wet year.
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Figure 10. The spatial distribution of typical annual precipitation illustrates the precipitation amounts across each city. (A) Dry year; (B) normal year; (C) wet year.
Figure 10. The spatial distribution of typical annual precipitation illustrates the precipitation amounts across each city. (A) Dry year; (B) normal year; (C) wet year.
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Figure 11. The spatial distribution of soil water resources for each typical year highlights the amount of soil water resources in each city. (A) Dry year; (B) normal year; (C) wet year.
Figure 11. The spatial distribution of soil water resources for each typical year highlights the amount of soil water resources in each city. (A) Dry year; (B) normal year; (C) wet year.
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Figure 12. The spatial distribution of water resource coefficients for each typical year illustrates the magnitude of these coefficients across each city. (A) Dry year; (B) normal year; (C) wet year.
Figure 12. The spatial distribution of water resource coefficients for each typical year illustrates the magnitude of these coefficients across each city. (A) Dry year; (B) normal year; (C) wet year.
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Figure 13. In each typical year, the water demand, consumption, and surplus or deficit for each crop highlight the differences among various crops. (a) Millet; (b) soybean; (c) sorghum; (d) winter wheat; (e) potato; (f) summer maize.
Figure 13. In each typical year, the water demand, consumption, and surplus or deficit for each crop highlight the differences among various crops. (a) Millet; (b) soybean; (c) sorghum; (d) winter wheat; (e) potato; (f) summer maize.
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Table 1. The crop coefficients, growth period, and leaf area index for the different crops for each period are given in the following table.
Table 1. The crop coefficients, growth period, and leaf area index for the different crops for each period are given in the following table.
CropsParametersSowing StageOverwinteringSeedling StageJointing StageBoot StageCatagenMaturityFertility Cycle
Winter
wheat		Growth period11.29–03.0803.09–04.1604.17–05.0905.10–06.02-06.03–06.1406.15–06.24208
Kc0.480.821.001.16-0.870.50-
LAI3.303.304.603.50-2.502.50-
Summer
corn		Growth period06.25–07.19-07.20–09.0409.05–09.20-09.21–09.3010.1–10.28157
Kc0.85-1.050.95-0.60.86-
LAI2.50-2.504.00-4.003.30-
Growth period05.09–05.23-05.24–06.1706.18–07.12-07.13–08.1608.17–09.18133
SoybeanKc0.40-0.401.15-1.150.50-
LAI0.20-0.601.80-2.853.96-
Growth period05.09–05.15-05.16–06.0906.10–07.0307.04–07.2807.29–08.1408.15–09.04120
MilletKc0.30-0.301.001.001.000.30-
LAI0.10-0.904.104.604.554.98-
Growth period05.09–05.23-05.24–06.2206.23–07.27--07.28–08.19103
SorghumKc0.30-1.051.05--0.55-
LAI0.70-2.104.47--3.93-
Growth period05.09–07.07-07.08–07.22--07.23–08.2108.22–10.05150
PotatoesKc0.50-0.50--1.150.75-
LAI2.00-3.21--4.283.21-
Note: Kc represents the crop coefficient (dimensionless); LAI refers to the leaf area index (m2 leaf area per m2 ground area). All dates are presented in “MM.DD” format. Fertility cycle indicates the total duration from sowing to maturity (days).
Table 2. The areal precipitation from 1963 to 2023 was calculated using precipitation data from 27 meteorological stations across the region.
Table 2. The areal precipitation from 1963 to 2023 was calculated using precipitation data from 27 meteorological stations across the region.
YearAreal
Precipitation (mm)		YearAreal
Precipitation (mm)		YearAreal
Precipitation (mm)		YearAreal
Precipitation (mm)
1963886.05 1978657.76 1994620.26 2009545.49
19641030.39 1979602.21 1995683.70 2010523.71
1965373.89 1980583.00 1996760.41 2011594.48
1966688.82 1981613.46 1997457.67 2012583.87
1967942.65 1982659.30 1998632.68 2013579.60
1968709.04 1983679.91 1999541.88 2014547.58
1969697.31 1984662.92 2000663.15 2015513.78
1970624.38 1985730.06 2001554.14 2016648.66
1971772.12 1986501.22 2002583.46 2017569.93
1972562.48 1987623.41 2003827.44 2018532.75
1973789.40 1988719.06 2004591.75 2019461.44
1974503.18 1989571.52 2005526.00 2020553.80
1975643.26 1990708.52 2006529.76 2021763.88
1976755.29 1991599.82 2007549.86 2022649.46
1977715.19 1992673.23 2008523.02 2023562.36
Table 3. Calculated hydrologic probabilities (25%, 50%, and 75%) of theoretical areal precipitation.
Table 3. Calculated hydrologic probabilities (25%, 50%, and 75%) of theoretical areal precipitation.
YearAverage Areal
Precipitation (mm)		CvCsHydrographic
Frequency (%)		Areal
Precipitation (mm)
1963–2023582.470.186Cv25694.90
50611.18
75546.42
Note: Cv is the coefficient of variation; Cs is the skewness coefficient, calculated based on precipitation data from 1963 to 2023 using the Pearson type III distribution.
Table 4. The proportion of different precipitation classes to the annual areal precipitation for each month of the representational dry, normal and wet years of 2009, 1981, and 1969, respectively.
Table 4. The proportion of different precipitation classes to the annual areal precipitation for each month of the representational dry, normal and wet years of 2009, 1981, and 1969, respectively.
PrecipitationPercentage of Dry Days in 2009 (%)
Sort/mmJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberAnnual
<510010057.910023.865.925.824.435.244.618.410089.01
5–100042.1030.334.116.725.50161804.61
10–25000045.903550.164.839.463.606.03
>2500000022.5000000.35
PrecipitationPercentage of normal days in 1981(%)
Sort/mmJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberAnnual
<510010046.457.564.735.219.714.869.264.564.310088.70
5–10000035.320.925.636.330.835.535.706.78
10–250053.642.5043.937.930.900003.87
>2500000016.81800000.65
PrecipitationPercentage of wet days in 1969(%)
Sort/mmJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberAnnual
<510010072.526.441.453.715.624.817.930.510010086.25
5–100027.520.835.446.339.725.27.614007.19
10–2500016.223.2044.75074.555.5006.25
>2500036.6000000000.31
Table 5. The scale of soil water resources for each typical year.
Table 5. The scale of soil water resources for each typical year.
Typical YearSoil Water Resources (Billion m3)Soil Water Resource Coefficient
Dry year272.642 0.883
Normal year336.6330.892
wet year358.7460.894
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Li, Y.; Li, K.; Liu, X.; Zhang, Z.; Gao, Z.; Wang, Q.; Wang, G.; Zhang, W. Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions. Agriculture 2025, 15, 1442. https://doi.org/10.3390/agriculture15131442

AMA Style

Li Y, Li K, Liu X, Zhang Z, Gao Z, Wang Q, Wang G, Zhang W. Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions. Agriculture. 2025; 15(13):1442. https://doi.org/10.3390/agriculture15131442

Chicago/Turabian Style

Li, Yaoyu, Kaixuan Li, Xifeng Liu, Zhimin Zhang, Zihao Gao, Qiang Wang, Guofang Wang, and Wuping Zhang. 2025. "Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions" Agriculture 15, no. 13: 1442. https://doi.org/10.3390/agriculture15131442

APA Style

Li, Y., Li, K., Liu, X., Zhang, Z., Gao, Z., Wang, Q., Wang, G., & Zhang, W. (2025). Spatiotemporal Evaluation of Soil Water Resources and Coupling of Crop Water Demand Under Dryland Conditions. Agriculture, 15(13), 1442. https://doi.org/10.3390/agriculture15131442

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