Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022–2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R² ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7–13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18–22%, 78–82%; 57–68%, and 32–43%, and transpiration accounted for 40–44%, 56–60%, 45–47%, and 53–55% of ETa (333.8 mm–369.2 mm, 375.2 mm–414.2 mm, 415.7 mm–453.7 mm, and 355.0 mm–385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m³ and 8.61 to 10.81 kg/m³, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.

Soil water and salt transport in soil profiles and capillary rise from shallow groundwater are significant seasonal responses that help determine irrigation schedules and agricultural development in arid areas. In this study the Agricultural Water Productivity Model for Shallow Groundwater (AWPM-SG) was modified by adding a soil salinity simulation to precisely describe the soil water and salt cycle, calculating capillary fluxes from shallow groundwater using readily available data, and simulating the effect of soil salinity on crop growth. The model combines an analytical solution of upward flux from groundwater using the Environmental Policy Integrated Climate (EPIC) crop growth model. The modified AWPM-SG was calibrated and validated with a maize field experiment run in 2016 in which predicted soil moisture, soil salinity, groundwater depth, and leaf area index were in agreement with the observations. To investigate the response of the model, various scenarios with varying groundwater depth and groundwater salinity were run. The inhibition of groundwater salinity on crop yield was slightly less than that on crop water use, while the water consumption of maize with a groundwater depth of 1 m is 3% less than that of 2 m, and the yield of maize with groundwater depth of 1 m is only 1% less than that of 2 m, under the groundwater salinity of 2.0 g/L. At the same groundwater depth, the higher the salinity, the greater the corn water productivity, and the smaller the corn irrigation water productivity. Consequently, using modified AWPM-SG in irrigation scheduling will be beneficial to save more water in areas with shallow groundwater.

An accurate assessment of crop water requirements during the crop growth period can help organize irrigation schedules and investigate the hydrological environments in irrigation districts, especially in shallow groundwater districts. In irrigation scheduling, crop coefficients, representing three development stages, play a critical role in modeling evapotranspiration. Therefore, in this study, the crop coefficient (K_c) of sunflowers under drip irrigation in different hydrological years in the Hetao irrigation district with shallow groundwater is determined. Based on the analysis of rainfall frequency, the experimental situations of 2012, 2013, and 2014 are adopted as high, normal, and low flow years. Using the water balance method, groundwater recharge and crop evapotranspiration in different hydrological years were investigated. The results showed that the groundwater recharge in a high-flow year was larger than that in normal and low-flow years with values of 67.47, 66.75, and 42.61 mm, respectively. Crop evapotranspiration is directly related to the irrigation amount at the same growth stage. The K_c of sunflowers in a high-flow year is higher by 0.1 and 0.15 than that in normal and low-flow years. The K_c of sunflowers under drip irrigation is lower than that under surface irrigation by 8.9% in the initial and late stages of sunflower growth, but higher than that by 10.6% in the mid-stage, which is due to differences in small water potential differences and less evapotranspiration in the initial and late stages under drip irrigation. The study is of great significance for formulating a reasonable schedule of drip irrigation with shallow groundwater and improving the field environment.

With the implementation of water-conservation projects in the Hetao Irrigation District (HID), great changes have taken place in the ecohydrological process. A cultivated land–wasteland–lake system in the upper Yellow River Basin (YRB) was chosen to study the soil salt and water transport process with the Hydrus-1D model. The model parameters were calibrated and validated by measuring the soil salt and water data. Measured values were in good agreement with the simulated values. The results showed, in the whole growth period, the deep percolation of cultivated land was 34–40% of the total applied water (rainfall and irrigation). The capillary rise in the cultivated land, wasteland, and lake boundary was 24%, 29–35%, and 62–68% of their own evapotranspiration, respectively. The capillary rise in the lake boundary was about 2 times that of the wasteland and 2.6 times that of the cultivated land. The salt storage in the 1 m soil zone of the lake boundary was more than 10% and 18% greater than that of the wasteland and cultivated land, respectively. The salt of the capillary rise in the lake boundary exceeded that of the wasteland by a factor of three. The salt accumulation in the upper soil zone of the cultivated land, wasteland, and lake boundary was 13%, 37%, and 48%. Soil salinization in the upper soil zone of the wasteland and lake boundary was serious, and some measures should be taken to reduce the salt content to prevent soil salinization. The results act as a theoretical basis for the ecohydrological control of the HID.

Water resources are severely scarce in desert steppes, and precipitation rarely collects in rivers or is transformed into groundwater. Evapotranspiration (ET) is the primary “export” of precipitation conversion and is the main mechanism for water vapor exchange between the underlying surface and atmosphere. ET changes have certain scale effects. This study focused on the natural grasslands in the Xilamuren Desert Steppe and analyzed and estimated the ET patterns at different scales, including micro-, point-, and surface scales, using observational data from instruments such as a photosynthetic meter, Eddy-covariance system (EC), and large-aperture scintillometer (LAS) from the Ecological Hydrology National Field Science Observation Station in the northern foothills of the Yin Mountains, Inner Mongolia. The spatial scale was extended based on this analysis. The results showed that at the microscale, the diurnal variation in the photosynthetic and transpiration rates of Leymus chinensis followed a bimodal curve. In July and August (high-temperature months), photosynthetic and transpiration rates were almost synchronous. In May and October, when the temperature was moderate, the transpiration rate was delayed compared to the photosynthetic rate at the first peak, and the second peak was significantly smaller than the first peak. At the point-scale, the daily average ET during the growing season was 1.37 mm·d⁻¹ and the total cumulative ET was 251 mm. Transpiration levels exhibited significant seasonal variation in the following order: July > August > June > September > May > October. At the surface-scale, the daily average ET during the growing season was 1.60 mm·d⁻¹ and the total cumulative ET was 294 mm, which was 17% higher than that of the point-scale. The surface-scale ET was estimated using the observed values of the EC and the scale relationship formula and was optimized using different spatial scales of crop coefficients. This well reflected the ET patterns at the surface-scale. Therefore, this study proposes a spatial scale expansion method for a homogeneous underlying surface, verifies its value, and provides methodological support for estimating ET in cases of data scarcity.

As the risk of drought increases due to climate change, understanding ecological drought has become increasingly important for ensuring water resource security and carbon balance. However, most current ecological drought assessments rely on meteorological or hydrological indicators, which may not accurately reflect changes in the eco-physiological status of ecosystems. Therefore, this study establishes an ecological drought assessment framework using solar-induced chlorophyll fluorescence (SIF) as an indicator to examine its interpretable responses to climate–hydrology–environmental variables. The framework was tested across China’s nine major river basins and different ecosystems. Results show that SIF increased in 80.0% of China’s areas, with 60.9% showing significant increases (p < 0.05). Forest ecosystems experienced the lowest frequency of ecological drought but showed increasing duration and intensity, while grassland ecosystems had the highest frequency but decreasing duration and intensity. LightGBM machine learning analysis revealed that surface soil moisture (SMs), temperature (Tm), root-zone soil moisture (SMrz), and CO₂ were the main factors influencing ecological drought, with SMs and Tm contributing to over 66.1% of ecological drought. The SMs-Tm interaction alleviated ecological drought under low-temperature and high-humidity conditions but initially intensified then alleviated ecological drought under high-temperature and high-humidity conditions. The SMs-CO₂ interaction promoted ecological drought at high or low CO₂ concentrations but alleviated it at moderate concentrations.