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The agricultural pastoral ecotone (APE) in Northwest China is an ecological transition zone in the arid area with a very fragile ecosystem. In recent years, the ecosystem has deteriorated sharply, and increasing desertification has made the regional ecosystem more vulnerable and sensitive. In this study, we analyzed (using classical statistical methods) spatial and temporal variations in soil water content (SWC) from 14 September 2016 to 22 April 2019 for high and low vegetation in two grassland sites in Yanchi County, Ningxia. The results showed that the largest average seasonal SWC occurred in autumn. The SWC of the first three layers (0 ÷ 15 cm) of the soil profile responded strongly to precipitation, whereas the SWC in deeper soil (30 ÷ 50 cm) could only be recharged markedly after continuous precipitation. Additionally, the growing process of plants proved to be a cause of variability in soil moisture profiles. Vegetation degradation sped up the course of desertification and decreased soil organic carbon content. These changes left the soil increasingly desiccated and enhanced soil variability. Meanwhile, vegetation degradation also prompted changes in soil temperature and shortened the soil’s frozen time in winter. With the acceleration of global warming, if the process of vegetation degeneration continues and soil temperatures keep rising, the ecosystem is likely to undergo irreversible degradation.

Maize plays an important role in the Agro‐pastoral ecotone of Northwestern China (APENC), where highly sensitive to changes in climate conditions. However, little is reported on the impacts of climate change on crops in the region. In this study, we used Decision Support System for Agrotechnology Transfer model driven by future climate data from 20 general circulations models under two representative concentration pathways (RCPs: RCP4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to project the effects of climate change on maize yield and water use efficiency (WUE) in eight future time periods (interval: 10 years; from 2020s to 2100s). The model was first calibrated based on field observation for phenology, leaf area index, maize yield and calibrated and evaluated results were reasonably good. Simulated results showed that without and with consideration of CO2 effects, maize yield at the end of the 21st century will decrease by 11.7% and 10.3% under the RCP4.5 scenario, and by 22.1% and 21.2% under the RCP8.5 scenario, respectively. We found that there is a significant correlation between maize yield reduction and warming. Specifically, when the increment of annual average temperature reaches 1°C, the maize yield begins to decrease by 11.27% and 10.8% per 1°C warming without and with consideration of CO2 effects, respectively. Furthermore, high temperature not only affects maize yield but also has a negative effect on WUE. The WUE would change by −8.1% and −18.8% under RCP4.5 and RCP8.5, respectively. But if we consider the effects of CO2, the WUE will improve 1.5% under RCP4.5 and 2.2% under RCP8.5, in comparison to those without consideration of CO2 effects. Overall, future climate warming will seriously affect maize yield and WUE. Although the increase of CO2 concentration is beneficial to raise maize yield and WUE, it is hard to offset the negative effects of the increase in temperature. Besides, change in the planting date can be beneficial for the adaptation of maize to climate change in the APENC. These results will provide comprehensive information to support local policy and decision‐making in agricultural production and water resources management.

Abstract. Profile soil moisture (SM) in mountainous areas are significant in water resources management and ecohydrological studies of downstream arid watersheds. Satellite products are useful in providing spatially distributed SM information, but only have limited penetration depth (e.g. top 5 cm). In contrast, in situ observations can provide multi-depth measurements, but only with limited spatial coverage. Spatially continuous estimates of subsurface SM can be obtained from surface observations using statistical methods, but this requires sufficient coupling strength among surface and subsurface SM. This study evaluates methods to calculate subsurface SM from surface SM and an application to the satellite SM product based on a SM observation network in the Qilian Mountains (China) established since 2013. First, we used cross-correlation to analyze the coupling strength among surface (0–10 cm) and subsurface (10–20, 20–30, 30–50, 50–70 cm, and profile of 0–70 cm) SM. Our results indicated an overall strong coupling among surface and subsurface SM in this study area. Afterwards, three different methods were tested to estimate subsurface SM from in-situ surface SM: the exponential filter (ExpF), artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods. The results showed that both ANN and ExpF methods were able to provide accurate estimates of subsurface soil moisture at 10–20 cm, 20–30 cm, and for the profile of 0–70 cm using surface (0–10 cm) soil moisture only. Specifically, the ANN method had the lowest estimation error (RSR) of 0.42, 0.62 and 0.49 for depths of 15 and 25 cm and profile SM, respectively, while the ExpF method best captured the temporal variation of subsurface soil moisture. Furthermore, it could be shown that the performance of the profile SM estimation was not significantly lower with using an area-generalized Topt (optimum T) compared to the station-specific Topt. In a final step, the ExpF method was applied to the satellite SM product (Soil Moisture Active Passive Level 3: SMAP_L3) to estimate profile SM, and the resulting profile SM was compared to in situ observations. The results showed that the ExpF method was able to estimate profile SM from SMAP_L3 surface products with reasonable accuracy (median R of 0.718). It was also found that the combination of ExpF method and SMAP_L3 surface product can significantly improve the estimation of profile SM in the mountainous areas in comparison to the SMAP_L4 root zone product. Overall, it was concluded that the ExpF method is able to estimate profile SM using SMAP surface products in the Qilian Mountains.