Sensitivity analysis of agricultural hydrological processes to climate change in the North China Plain

Abstract

Introduction:

Climate change is profoundly altering hydrological cycles, posing serious challenges to agricultural sustainability and water resource security.

Methods:

Quantifying the sensitivity of agro-hydrological processes to climate variability is therefore essential for developing effective adaptation strategies. In this study, a calibrated and improved Soil and Water Assessment Tool (SWAT) model, combined with sensitivity analysis was employed to evaluate the sensitivity and contributions of key climatic factors to major agro-hydrological variables in the North China Plain (NCP) during 1966–2016.

Results:

The region exhibited a pronounced warming and drying trend, with minimum temperature rising more rapidly (0.56 °C 10 yr⁻¹) than maximum temperature (0.12 °C 10 yr⁻¹), while precipitation and solar radiation decreased 3.98 mm 10 yr⁻¹ and 0.51 MJ m⁻² 10 yr⁻¹, respectively. Actual evapotranspiration (ET_a) was more sensitive to air temperature during the winter wheat growing season but more responsive to solar radiation during the summer maize season. Irrigation demand exhibited strong positive sensitivity to air temperature and solar radiation and negative sensitivity to precipitation; whereas water yield demonstrated exceptionally high sensitivity to precipitation, particularly at the main watershed outlet. Contribution analysis further identified solar radiation as the dominant factor governing actual evapotranspiration variation, whereas precipitation was the primary driver controlling changes in water yield.

Discussion:

These findings provide a quantitative foundation for crafting targeted water management strategies, such as optimizing irrigation scheduling and reinforcing flood control infrastructure, to enhance climate resilience in this vital agricultural zone.

1 Introduction

Current climate change, characterized by rising temperatures and erratic precipitation patterns, fundamentally threatens global crop productivity by intensifying heat and water stress (Lobell and Di Tommaso, 2025; IPCC, 2023). Simultaneously, it disrupts key agricultural hydrological processes, leading to increased crop evapotranspiration and amplified irrigation demands, thereby exacerbating water scarcity pressures on agricultural systems (Jägermeyr et al., 2021; IPCC, 2023; Lakhiar et al., 2024a). This global challenge has been documented in key agricultural zones worldwide. Rising heat and water stress are curbing yields in the U.S. Midwest Corn Belt (Lobell et al., 2013; Zhou et al., 2024; Lakhiar et al., 2024b), climate variability is compounding water scarcity for the Indo-Gangetic Plains’ rice-wheat systems (Singh et al., 2024), and similar pressures are reshaping agriculture in South America (Heinemann et al., 2017). As a vital grain-producing region in China, the North China Plain (NCP) faces equally severe threats. Ongoing climate change may seriously jeopardize the region’s water resources and agricultural production security (Kang and Eltahir, 2018; Wang Y. et al., 2025).

In recent years, the NCP has faced challenges such as rising temperatures, increased precipitation variability, and intensified droughts (Wang et al., 2019; Yu et al., 2025). Numerous researchers have conducted a series of studies focusing on climate change in this region. These primarily include the impacts of climate change on hydrothermal dynamics, its effects on crop water use and yield, and sensitivity analyses of climate factor variations on reference evapotranspiration (ET₀) (Song et al., 2011; Wang D. L. et al., 2023; Lakhiar et al., 2026). Multiple studies have indicated that from the 1950s to the first decade of the 21st century, the annual precipitation in the NCP generally exhibited a declining trend, with significant reductions in precipitation during summer and autumn (Zhang and Feng, 2010; Ma et al., 2010; Wang et al., 2022). Additionally, both the reference crop evapotranspiration and sunshine duration also showed downward trends (Ma et al., 2010; Song et al., 2011). However, precipitation in the NCP has shown an increasing trend since 2010, primarily due to the frequent occurrence of extreme precipitation events (Wang Q. et al., 2025). Furthermore, several studies focusing on evapotranspiration in the NCP region, employing sensitivity analysis, have revealed important trends and drivers. Liu et al. (2019) investigated different evapotranspiration types in the NCP and found that while ET₀ declined from 1998 to 2005, actual evapotranspiration (ET_a) increased. Their sensitivity analysis identified relative humidity as the factor to which ET₀ was most sensitive. Bi et al. (2020) examined the spatiotemporal evolution of ET₀ specifically in the Beijing-Tianjin-Hebei region. Their work also confirmed a declining trend in ET₀ and similarly found its highest sensitivity to relative humidity. In addition, some research has also focused on the impacts of climate change on water surplus and deficit. From 1961 to 2022, the annual water surplus and deficit in the NCP showed a slight overall increasing trend, primarily attributable to a significant decline in potential evapotranspiration (Zhang J. et al., 2024). Currently, research on the impacts of climate change on agro-hydrological elements predominantly focuses on evapotranspiration and often examines this primarily through the lens of changes in climatic factors (Han et al., 2018; Sun et al., 2017). However, agro-hydrological elements encompass not only evapotranspiration but also factors such as irrigation water use and soil moisture. Serving as a critical link between climate change and agricultural production, changes in these elements directly influence crop water consumption, irrigation demands, and regional water resource security. Therefore, an in-depth examination of the sensitivity of agro-hydrological elements to climate change is of paramount importance.

The Soil and Water Assessment Tool (SWAT) model is a semi-distributed, continuous-time hydrological model renowned for its strong physical basis (Neitsch et al., 2011). It is used extensively to simulate water quantity, soil erosion, and the impacts of land management practices and climate change on water resources within complex watersheds (Tan et al., 2020; Ahmadzadeh et al., 2022; Chakilu et al., 2022; Wang Y. et al., 2023; Jiang et al., 2020). Tan L. L. et al. (2022) utilized an enhanced SWAT model and a CMIP6 (Coupled Model Intercomparison Project Phase 6) ensemble to study climate change impacts on the Haihe River Basin’s double-cropping systems, showing that irrigated agriculture under future climate is projected to achieve higher crop yields and water productivity alongside lower irrigation water requirements during growing seasons. Zhang X. L. et al. (2024) employed an improved SWAT model with CMIP6 projections to optimize sprinkler irrigation in the NCP. Under future climate change, they found that increased precipitation and CO₂ levels could enhance wheat yields and groundwater recharge. Beyond its widespread use in North China, the SWAT model has also been widely applied in other basins across China. Wang et al. (2024) combined an enhanced SWAT model with CMIP6 data, a Deep Belief Network (DBN), and a Modified Sparrow Search Optimizer (MSSO) to assess climate change impacts on runoff in the Upper Yangtze River basin. Their projections revealed a future decrease in runoff, underscoring the need for adaptive water management. An et al. (2024) modified the SWAT model to study eco-hydrological processes in the Wei River Basin. This enhancement significantly improved the simulation of leaf area index and demonstrated that dynamic vegetation growth elevates evapotranspiration, consequently reducing streamflow and water yield at the watershed scale. These successful applications across multiple watersheds indicate that the SWAT model can reliably simulate and predict the impacts of climate change on complex hydrological and agricultural systems.

The present study builds upon that previous work (Tan L. L. et al., 2022) and employs an integrated approach combining the improved SWAT model with sensitivity analysis to quantify the sensitivity of agro-hydrological elements to climate change at the regional scale in the NCP. Therefore, the objectives of this research are: (1) to analyze the spatiotemporal variation characteristics of meteorological factors in the NCP over the past 50 years (1966–2016); (2) to quantitatively assess the sensitivity of agro-hydrological elements to changes in climatic factors; and (3) to explore the contribution of changes in various climatic factors to agro-hydrological elements. This study provides important implications for the rational allocation of regional water resources, the development of agricultural management practices such as irrigation, and the formulation of regional climate adaptation strategies.

2 Materials and methods

2.1 Study area

The study area is geographically located between N 36°05′ ~ 42°40′ and E113°27′ ~ 119°50′, bounded by the Bohai Sea to the east and the Taihang Mountains to the west, covering a total area of 42,017 km² (Figure 1). It is divided into the Daqing River Plain (DQP) in the north and the Ziya River Plain (ZYP) in the south (Ren et al., 2007). The region experiences a semi-arid to semi-humid continental monsoon climate, with an average annual precipitation of 500 ~ 600 mm. Precipitation is unevenly distributed throughout the year, with over 70% occurring between July and September. The mean annual temperature ranges from 8 to 15 °C. The average surface water resources capable of annual renewalare 0.8 billion m³ in the DQP and 0.08 billion m³ in the ZYP (1991–2000) (Ren et al., 2007). The area is characterized by flat terrain, with cropland being the dominant land use type, accounting for over 90% of the total area. The flat topography and favorable climate conditions make this region one of China’s major production areas for high-quality wheat. The primary cropping system is winter wheat-summer maize rotation. During the winter wheat growing season, natural precipitation is insufficient to meet the total crop water requirement (ET_c), necessitating supplemental irrigation. In this region, the average annual irrigation depth required to offset this deficit is approximately 400 mm (Zhang X. L. et al., 2024).

Figure 1

2.2 SWAT and enhanced SWAT

The SWAT model has become a representative and widely used tool in various research fields, including watershed hydrological modeling, climate change simulation, and optimization of best management practices (BMPs) (Tan et al., 2020; Disasa et al., 2026). In previous work, researchers reported making multiple improvements to the SWAT model based on their specific study directions (Wu et al., 2019; Tan et al., 2020). The improved SWAT model developed by Chen et al. (2018) was used to simulate agro-hydrological processes in this study. The improvement of the automatic module is based on management allowed depletion (MAD). MAD-based irrigation management uses the following expression Equation 1:

where sol_FC is the one-dimensional amount of water held in the soil profile at field capacity (mm); sol_SW is the amount of water stored in soil profile on any given day (mm); PAW is plant available water, determined by both soil specific properties and plant specific maximum rooting depth (mm); and MAD is the management allowed depletion, expressed as a decimal value ranging from 0 to 1.

In SWAT, the water yield is calculated using Equation 2 (Neitsch et al., 2011):

where WYLD is water yield (mm), which means total amount of water leaving the HRU (Hydrologic Response Unit) and entering main channel during the time step; SURQ is the surface runoff (mm); LATQ is the lateral flow (mm); GWQ is the groundwater flow, or base flow (mm); TLOSS is transmission losses (mm); and pond abstractions is the pumping of water from a pond (mm).

The hydrological cycle simulated by SWAT is based on the water balance Equation 3:

where SW_t is the final soil water content (mm); SW₀ is the initial soil water content on day i (mm); t is the time (days); R_day is the amount of precipitation on day i (mm); Q_surf is the amount of surface runoff on day i (mm); E_a is the amount of evapotranspiration on day i (mm); w_seep is the amount of water entering the vadose zone from the soil profile on day i (mm); and Q_gw is the amount of return flow on day i (mm).

In a previous work, Tan L. L. et al. (2022) established the SWAT-MAD model for this study area using multi-source and multi-scale datasets, and conducted rigorous model calibration, validation, and applicability evaluation, achieving reliable simulation performance. During model establishment, considering the close correlation between ET_a and crop parameters across the NCP, Tan L. L. et al. (2022) performed spatial zoning of crop sowing and harvesting dates, irrigation water sources, and irrigation schedules to improve the simulation accuracy of regional ET_a and irrigation amount. Meanwhile, the MAD thresholds were determined as 0.3 for winter wheat and 0.4 for summer maize, respectively. More detailed information was provided in Tan L. L. et al. (2022) and Supplementary materials.

2.3 Climate tendency rate

Climate tendency rate refers to the trend or rate of change of climatic factors in a given region over a specific time period, and is used to quantify the magnitude and direction of climate change. Let Y_i denote a certain climatic factor with a sample size of n, and let t represent the time corresponding to Y_i. A simple linear regression equation between Y_i and t is established as Equation 4:

where a is the regression constant; b is the regression coefficient. The regression coefficient (b) and the regression constant (a) of a linear equation are estimated using the least squares method. Set the climate trend rate for the climate factor to 10 times b (Yang et al., 2011). The calculation formulas for b and a are given by Equations 5, 6, respectively:

where x_i is the value of the independent variable for the i-th observation; y_i is the value of the dependent variable for the i-th observation; n is the number of observations in the sample; is the sum of all independent variable observations; is the sum of all dependent variable observations; is the sum of the products of corresponding x_i and y_i values.

2.4 Sensitivity coefficient approach

The sensitivity analysis method was proposed by McCuen (1974) and Beven (1979) to evaluate the sensitivity of ET₀ to perturbations in different climate factors. The method involves plotting the relative change of the dependent variable against the relative change of the independent variable to form a curve. On the graph, the sensitivity coefficient is represented by the slope of the tangent line to the sensitivity curve at the origin, which indicates the magnitude of change in the target variable relative to changes in climatic factors. The specific method involves varying a single climatic factor within a range of −20 to 20% at 5% intervals, while keeping all other climatic factors constant (Gong et al., 2006). In this study, the method was used to assess the sensitivity of agro-hydrological elements to changes in climatic factors. The formula for calculating the sensitivity coefficient is given by Equation 7:

Where is the sensitivity coefficient, V_i is the value of the i-th climatic factor, is the change in value of the i-th climatic factor, Agro-Hydro_f is the original value of a specific agro-hydrological element, and ΔAgro-Hydro_f is the change in agro-hydrological element induced by the variation in climatic factor.

A positive value of the sensitivity coefficient indicates that the value of the agro-hydrological element increases with the increase in the value of the climatic factor, while a negative value of the coefficient indicates that the value of the agro-hydrological element decreases as the value of the climatic factor increases. The magnitude of the sensitivity coefficient value represents the extent of the impact of changes in the climatic factor on the agro-hydrological element.

2.5 Contribution estimation

In this study, the method proposed by Yin et al. (2010) was adopted to quantitatively evaluate the contribution of climatic factors to changes in agro-hydrological elements. Specifically, the contribution rate of each climate factor is calculated as the product of its sensitivity coefficient and its multi-year relative change. A positive value indicates a positive contribution, while a negative value denotes a negative contribution. The contribution rate can be calculated according to Equations 8, 9:

Where v_i,C and v_i,S represent the contribution rate and sensitivity coefficient of climatic factor v_i to changes in agro-hydrological elements, respectively; v_i,RC, v_i,T, and denote the multi-year relative change rate, multi-year linear tendency rate, and multi-year mean value of climatic factor v_i, respectively; and n is the number of years.

2.6 Climate perturbation scenarios design

Based on the sensitivity coefficient approach, 25 perturbation scenarios of climatic factors were simulated, which were detailed in Table 1. Meteorological data were obtained from the China Meteorological Data Network.¹ Daily observational data from 1966 to 2016 were collected from 17 national meteorological stations within and around the study area. The dataset includes maximum air temperature, minimum air temperature, precipitation, relative humidity, and sunshine duration. Daily solar radiation was estimated based on sunshine duration using the Angstrom equation (Allen et al., 1998), and the calculation formula is provided in Supplementary materials. The data at each station were generally complete, and any occasional missing values were filled using linear interpolation. According to these perturbation scenarios, 25 meteorological databases were compiled and individually input into the calibrated and improved SWAT model, resulting in a total of 25 project files. Each SWAT model was run separately to output monthly and annual-scale data for each subbasin, including precipitation, maximum air temperature, minimum air temperature, solar radiation, actual evapotranspiration (ET_a), irrigation amount, soil water content, and water yield. The processing of meteorological data was conducted using the R software (version 4.5.2; R Core Team, 2025). Spatial distribution maps were generated using the spatial interpolation method in ArcGIS 10.6. Figure 2 illustrates the framework of this study.

Figure 2

3 Results

3.1 Improved SWAT evaluation

In a previous work, the establishment, calibration, and validation of a SWAT model for the study area were documented in detail (Supplementary Tables S1–S4; Supplementary Figures S1–S4). The calibration and validation results demonstrated strong model performance. For monthly streamflow, the values of R² > 0.87, and the NSE were > 0.75 (Supplementary Table S5 and Supplementary Figure S5). For ET_a, R² values ranged from 0.68 to 0.77, while the PBIAS varied between 8.0 and 17.7% (Supplementary Table S6). Furthermore, the enhanced SWAT model demonstrated improved performance in simulating both irrigation and ET_a (Supplementary Figure S6). Specifically, the RMSE for irrigation and ET_a during the winter wheat growing season were 41.11 mm and 28.24 mm, respectively. During the summer maize growing season, the RMSE values for irrigation and ET_a were 44.79 mm and 18.11 mm, respectively. The enhanced SWAT models with sufficient calibration and validation can simulate agricultural hydrological processes well.

3.2 Spatial and temporal changes of various climatic factors

From 1966 to 2016, the study area exhibited an annual mean air temperature of 13.6 °C, an average annual precipitation of 506.7 mm, and an annual average solar radiation of 13.9 MJ m⁻². Specifically, the maximum air temperature increased by 0.12 °C 10 yr⁻¹, the minimum air temperature rose by 0.56 °C 10 yr⁻¹, the precipitation decreased by 3.98 mm 10 yr⁻¹, and the solar radiation decreased by 0.51 MJ m⁻² 10 yr⁻¹ (Figure 3). These indicated that over the past 50 years, the increase in minimum temperature in the study area had been greater than that in maximum temperature, implying more pronounced nocturnal warming.

Figure 3

The spatial distribution map of the climate tendency rate showed that precipitation across the entire study area generally exhibited an increasing trend from north to south (Figure 4a). In detail, the precipitation in the DQP decreased by 9.34 mm 10 yr⁻¹, while that in the ZYP increased by 1.84 mm 10 yr⁻¹ (Figure 4a). The maximum air temperature in general increased regionally (Figure 4b). The increase rate of the maximum temperature in the DQP is slightly higher than that in the ZYP, rising by 0.16 °C 10 yr⁻¹ and 0.09 °C 10 yr⁻¹, respectively. The minimum air temperature across the entire study area also increased, with a rise of 0.60 °C 10 yr⁻¹ (Figure 4c). An overall decreasing trend was observed in solar radiation, and the reduction rate was slightly greater in the ZYP (0.51 MJ m⁻² 10 yr⁻¹) compared to that in the DQP (0.46 MJ m⁻² 10 yr⁻¹) (Figure 4d).

Figure 4

3.3 Spatiotemporal changes of sensitivity coefficients

The monthly sensitivity coefficients of ET_a to precipitation, air temperature, and solar radiation were broadly similar, with mean values of 0.18, 0.38, and 0.26, respectively (Figure 5a). In comparison, ET_a demonstrated greater sensitivity to air temperature and solar radiation, and the temporal trends of the sensitivity coefficients were generally consistent (Figure 5a).

Figure 5

The sensitivity coefficients of irrigation amount to air temperature, precipitation, and solar radiation exhibited considerable seasonal variability, with the most pronounced responses occurring during the summer months (Figure 5b). The irrigation amount was positively sensitive to air temperature and solar radiation, with mean monthly coefficients of 0.60 and 0.63, respectively, but negatively sensitive to precipitation (mean monthly coefficient: −0.63) (Figure 5b). This indicated that irrigation demand decreased with increasing precipitation but increased with rising air temperature and solar radiation.

In contrast to irrigation amount, soil water content exhibited negative sensitivity to air temperature and solar radiation, with mean monthly coefficients of −0.36 and −0.34, respectively. Conversely, it showed a positive response to precipitation, with a mean monthly coefficient of 0.48 (Figure 5c).

Similar to the sensitivity of soil water content to climatic factors, water yield increased with precipitation but decreased with rising air temperature and solar radiation. Moreover, its sensitivity to precipitation was higher compared to the other two climate factors. Specifically, the monthly mean sensitivity coefficients of water yield to precipitation, air temperature, and solar radiation were 3.30, −0.80, and −1.07, respectively (Figure 5d).

During crop growing seasons (Table 2), ET_a was more sensitive to temperature in winter wheat season but to solar radiation in summer maize season. Irrigation amount showed greater sensitivity to temperature and solar radiation in summer maize than in winter wheat. For soil water content, precipitation had the highest sensitivity coefficient, while rising temperature and solar radiation reduced it. Water yield was more sensitive to precipitation in summer maize (3.62) than in winter wheat (3.13).

The sensitivity of ET_a to precipitation was considerably higher in the western piedmont plain than in the eastern plain (Figure 6a). In terms of the sensitivity of irrigation amount to precipitation, the ZYP was slightly more sensitive than the DQP. This implied that as precipitation increases, the irrigation water requirement in ZYP decreases more than it did in DQP. The sensitivity of soil water content to precipitation was lower in the central region than in the northern and southern regions (Figure 6c), which meant that soil water content in the central plain did not show a marked increase with higher precipitation. It was noteworthy that the sensitivity coefficients of water yield to precipitation were all relatively high positive values, with a numerical range considerably larger than those of other agro-hydrological elements. High-sensitivity areas were concentrated at the main watershed outlet, contrasting with the low-sensitivity areas of the western piedmont plain (Figure 6d).

Figure 6

Both ET_a and irrigation exhibited their highest positive sensitivity to air temperature in the central part of the study area (Figures 7a,b). This indicated that rising temperatures increase irrigation demand in the central region to maintain normal crop growth, thereby intensifying both soil water evaporation and plant transpiration. In contrast, the area of highest sensitivity for soil moisture to air temperature was located in the southernmost part of the study area (Figure 7c), suggesting a weaker soil water retention capacity in this region. Furthermore, the high-sensitivity areas for water yield to air temperature were primarily distributed in the western part of the study area (Figure 7d), demonstrating that the hydrologic system in this region responds more strongly to temperature changes. The spatial distribution of sensitivity coefficients of agro-hydrological elements to solar radiation was broadly consistent with that to air temperature (Figure 8).

Figure 7

Figure 8

3.4 Contribution analysis

According to the contribution of climatic factors to ET_a (Table 3), precipitation, temperature, and solar radiation contributed −0.85%, 4.54%, and −6.34% to ET_a changes, respectively. Decreased precipitation and solar radiation reduced ET_a, while increased temperature raised it, though precipitation’s impact was limited. In absolute terms, solar radiation dominated ET_a changes, followed by temperature.

Precipitation and temperature had positive contributions to irrigation water usage, while solar radiation had a negative contribution (Table 3). In terms of contribution rate values, the effects of temperature and solar radiation largely offset each other. Moreover, the total contribution rate of precipitation, temperature, and solar radiation to the irrigation volume is only −0.96%. This indicated that the irrigation volume in the NCP was less affected by changes in climatic factors.

The absolute value of the contribution rate of temperature variation to soil water content was higher than that of precipitation variation (Table 3). This indicated that decreased precipitation and increased temperature exerted negative effects on soil water content, and the influence of temperature change was more significant. The contribution rate of solar radiation to soil water content was positive. Considering the relative change rate over the years, the weakening of solar radiation had “promoted” the increase of soil moisture to a certain extent. However, the contributions of temperature and solar radiation offset each other. Overall, the changes in climatic factors had led to a decrease in soil moisture content.

In terms of contribution to water yield (Table 3), precipitation was the dominant factor, followed by temperature. Decreased precipitation and increased temperature negatively affected water yield. Although the absolute value of solar radiation’s contribution rate to water yield was also relatively high, this did not imply that it was a major factor driving changes in water yield. Despite the fact that reduced solar radiation could affect the consumption and loss of water volume by regulating evapotranspiration, it failed to result in an increase in water volume.

4 Discussion

4.1 Irreversible climate change

The globe is undergoing significant warming (Gebrechorkos et al., 2025; Rosa and Sangiorgio, 2025). Major cities in East Asia and Europe have shifted toward warmer-climate conditions (Paik et al., 2024), with cross-continental correlations in climate change (Cai et al., 2024). According to the China Climate Change Blue Book (China Meteorological Administration, 2025), China’s annual average temperature in 2024 exceeded the 1991–2020 mean by 1.0 °C for the first time. In North China, a notable warming trend of 0.27 °C 10 yr⁻¹ (1953–2022) has been observed (Ju et al., 2024). This study showed that from 1966 to 2016, both minimum and maximum air temperatures in the NCP rose (Figure 3), with the greater increase in the minimum indicating a rise in nighttime temperatures. This finding is consistent with the conclusions of He et al. (2023) for the entire territory of China.

Precipitation change is also a major component of global climate change. Due to the combined influence of factors such as atmospheric circulation, land-sea configuration, and topography, precipitation exhibits high variability in both time and space (Iliopoulou and Koutsoyiannis, 2025). Since 1979, the spatial distribution of precipitation intensity in the tropics (including both land and ocean) has shown a broadening trend, manifesting as a strengthening pattern of “the wet getting wetter and the dry getting drier (Gu and Adler, 2024).” Wang Q. et al. (2025) indicate that since 1961, both temperature and precipitation in China have shown an overall increasing trend, with precipitation changes exhibiting clear regional patterns. The long-standing “southern floods and northern droughts” pattern in eastern China has, however, begun to shift gradually since 2010, driven by a significant increase in the frequency and intensity of extreme rainfall and drought events in Northeast and North China. However, this study indicates that precipitation in the study area has shown a decreasing trend from 1966 to 2016 (Figure 3), which contradicts the above research conclusion. This discrepancy may be related to differences in the time scales of the studies. According to relevant research, multiple extreme rainfall events occurred in North China between 2017 and 2025 (Tang et al., 2024; Nie et al., 2025), a period not covered by the scope of this study. Although precipitation shows a decreasing trend across the entire region, it has increased in the ZYP (Figures 2, 3). This may be related to the influence of topography, the subtropical high, and typhoons. During the period from July 19 to 20, 2016, an extreme heavy rainfall event occurred in North China. The accumulated precipitation in the cities of Shijiazhuang, Handan, and Xingtai in Hebei Province reached 400 ~ 690 mm. Notably, these three cities are precisely located within the ZYP (Fu et al., 2017).

Solar radiation is the primary source of energy for the Earth’s surface and serves as the energy foundation for crop growth. The results of this study indicate a reduction in solar radiation over the NCP from 1966 to 2016 (Figure 3). The study by Yang et al. (2018) also indicates that the solar radiation during the summer maize growing season in the NCP decreased by 16.2% during the period of 2011–2015 compared to the period of 1961–1980. Feng (2019) reached the same conclusion in North China. The reduction in solar radiation may be associated with increased atmospheric aerosols and changes in cloud cover (Wild et al., 2026).

4.2 Sensitivity and contribution

The NCP serves as the cornerstone for ensuring China’s food security. Investigating the sensitivity of agro-hydrological elements to climatic factors during crop growing seasons is crucial for understanding and addressing the impacts of climate change on agriculture and water resources. This study revealed that ET_a was more sensitive to air temperature during the winter wheat growing season, whereas it was more sensitive to solar radiation during the summer maize growing season (Table 2). This is primarily because the winter wheat growing season typically occurs during the relatively cooler periods of the year (autumn, winter, and early spring), during which air temperature is the key limiting factor for crop physiological activities and ecological processes (Tao et al., 2017). During the summer maize growing season, when air temperatures are already high, solar radiation as the main energy source for photosynthesis necessitates larger stomatal openings and enhanced transpiration for heat dissipation (Sun et al., 2023). Irrigation amount was more sensitive to changes in air temperature and solar radiation during the summer maize growing season (Table 2). In the NCP, although summer maize grows during a season with concurrent heat and rainfall, intensified climate warming and increased regional extreme high temperatures will significantly raise crop water demand. Consequently, it is imperative to enhance irrigation security during the summer maize growing season under ongoing future warming, as also suggested by Xiao et al. (2020).

At the regional scale, with increasing precipitation, the irrigation water requirement in the ZYP is slightly lower than that in the DQP (Figure 5b). According to Kang et al. (2018), a closed area of low summer maize water requirement (below 300 mm) exists in the Shijiazhuang region of Hebei Province (within the Ziya River Plain), representing the region with the lowest summer maize water requirement across the entire NCP. The sensitivity of soil water content to precipitation was lower in the central part of the study area (Figure 5c), which may be related to soil texture (Fan and Schütze, 2024). Notably, the numerical range of the sensitivity coefficient of water yield to precipitation was much larger than that of other hydrological elements, with areas of higher sensitivity primarily concentrated around the basin’s total outlet (Figure 5d). Therefore, the construction of flood control facilities in the basin’s outlet region should be enhanced to defend against flash floods caused by extreme precipitation events induced by climate change (Yin et al., 2016; Li et al., 2026). Additionally, the western part of the study area shows the highest sensitivity of water yield to air temperature (Figure 7d), a pattern that may be linked to the combined effects of topography and human activities. In North China, heatwaves occur most frequently east of the Taihang Mountains (Na et al., 2019). This spatial pattern results from local foehn heating and topographic amplification via downslope warming and latent heat release, combined with circulation anomalies from remote teleconnections (Xiao H. et al., 2024).

Solar radiation and air temperature are the two most important meteorological factors influencing actual evapotranspiration. In the NCP, ET_a was more sensitive to changes in air temperature and solar radiation, with solar radiation being the dominant factor (Figure 5a and Table 3). It was found in the NCP (Tan Q. H. et al., 2022) that during the growing seasons of winter wheat and summer maize, ET_a was positively sensitive to both temperature and sunshine duration, and that sunshine duration served as an especially significant factor driving ET_a variation. Our results are in agreement with these findings. Irrigation is one of the most critical agricultural measures for ensuring stable grain production in the NCP. This study showed that irrigation amount decreases with increasing precipitation but increases with rising air temperature and solar radiation (Figure 5b). Air temperature and solar radiation are the primary energy sources driving crop evapotranspiration. Their increase substantially elevates crop water consumption and irrigation demand (Becker et al., 2023). However, in terms of total contribution rate, climatic factors were not the dominant drivers affecting irrigation (Table 3). In the NCP, the winter wheat-summer maize rotation is the predominant cropping system, with an annual water consumption of approximately 800 mm (Xiao L. J. et al., 2024). Spatiotemporal unevenness in precipitation leads to about 400 mm of crop water demand being supplemented by irrigation (Si et al., 2020). Therefore, irrigation is indispensable in the NCP. Additionally, in the NCP, water yield is most significantly influenced by natural precipitation (Guo and Shen, 2015; Chang et al., 2024). This study also revealed that water yield exhibits strong sensitivity to precipitation, and precipitation contributes highly to water yield (Table 3).

4.3 Limitations and uncertainties

This study utilized a calibrated, improved SWAT model and sensitivity analysis methods to quantify the sensitivity of agro-hydrological variables to climatic factors and to assess their relative contributions. Although the model was calibrated and climate scenarios were carefully constructed, several limitations and uncertainties should be acknowledged. First, uncertainties inherent to SWAT simulations include input data quality, calibration and validation datasets, and parameter estimation. Second, the selected climatic factors are interdependent (e.g., precipitation influences solar radiation), and their interactions may introduce error. Third, in the perturbation scenarios, maximum and minimum air temperatures were set to change synchronously. Investigating their effects separately by varying their perturbation amplitudes would yield more nuanced insights into their individual sensitivities and contributions. Moreover, sensitivity was evaluated using period-averaged coefficients, which may mask temporal variability and introduce bias in long-term analyses. Furthermore, although the improved SWAT model’s irrigation module responds relatively accurately to precipitation changes, irrigation demand in the NCP is influenced not only by climatic factors but also by human activities, which should be considered. Finally, this study did not take land use change and crop planting structure into account, and attention should be paid to this in future research.

5 Conclusion

This study analyzed the sensitivity and contribution of climatic factors to key agro-hydrological processes in the NCP from 1966 to 2016 using the SWAT model. The results indicated that the NCP had experienced pronounced warming and drying over the past five decades. The sensitivity of agro-hydrological variables to climatic drivers varies spatially, temporally, and across variables. Specifically, actual evapotranspiration (ET_a) was more sensitive to air temperature during the winter wheat season and to solar radiation during the summer maize season. Irrigation demand increased with rising air temperature and solar radiation but decreased with precipitation. In contrast, soil water content and water yield decreased with enhanced temperature and solar radiation and increased with precipitation. Notably, water yield exhibited strong sensitivity to precipitation, particularly at the main watershed outlet, indicating a heightened flood risk in that area. Contribution analysis revealed that solar radiation was the dominant factor controlling the variation in ET_a. Changes in irrigation amount were not predominantly driven by climatic factors, emphasizing the crucial and independent role of agricultural management practices in this water-stressed region. Precipitation was the primary climatic driver for water yield, followed by air temperature. The findings offer critical insights for developing targeted adaptation strategies, which are essential for ensuring the region’s food and water security in the context of ongoing climate change.

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