Dry–wet variation is one of the most important indicators to reflect the surface water supply–demand balance under various climates, and stongly influences hydrological processes, agricultural stability, and ecosystem security. Particularly, Northeast China, as the grain production base and ecological barrier, plays a crucial role in national food security and sustainable development. Therefore, it is often required to explore the driving mechanisms of dry–wet variations in this region. This study aims to determine the spatiotemporal variations in evapotranspiration and dry–wet conditions in Northeast China from 2001 to 2024. A systematic analysis was also used to further explore the driving factors using multiple influencing variables. MOD16A2GF evapotranspiration dataset was constructed for the regional dry–wet variations. Evapotranspiration observations were collected from two stations, and meteorological data were from 116 stations. A linear regression method was applied to correct actual evapotranspiration (ET) and potential evapotranspiration (PET). A leave-one-year-out cross-validation was used to evaluate the accuracy and reliability of the parameters. Furthermore, the crop water stress index (CWSI) was calculated for surface dry-wet conditions using ET and PET after correction. The results showed that: 1) MOD16A2GF data showed good better agreement with observed values, in terms of variation trends. Compared with the original data, the root mean square error (RMSE) and Bias were reduced, while the Nash–Sutcliffe efficiency (NSE) was improved, indicating the high data accuracy and reliability. 2) ET values showed a significantly increasing trend from 2001 to 2024, with a growth rate of 3.11 mm/a. Meanwhile, PET and CWSI values showed a significantly decreasing trend, with an annual decline rate of 3.23 mm/a and 0.01, respectively, indicating an overall wetting trend and a gradual alleviation of water stress. 3) In terms of spatial patterns, ET decreased from 800 to 100 mm from east to west. PET and CWS decreased from 1 200 to 500 mm and from 0.9 to 0.1, respectively, from southwest to northeast. The maximum CWSI values, representing the most severe water stress, were observed in the southeastern part of the Eastern Four Leagues in Inner Mongolia. From the perspective of spatial trends, most regions showed increasing ET and decreasing PET, indicating the widespread wetting trend in the study area. Moreover, there were largely overlapped areas with a significant decrease in CWSI, where ET significantly increased, while PET decreased, further confirming the spatial consistency and robustness of the wetting trend. 4) Precipitation was positively correlated with ET and negatively correlated with PET and CWSI. The increase in precipitation significantly promoted actual evapotranspiration and suppressed potential evapotranspiration demand, thus playing a dominant role in the dry-wet variations in Northeast China. In addition, agricultural irrigation increased surface water supply, while vegetation growth regulated the water exchange process between the land surface and the atmosphere. Both driving factors enhanced actual evapotranspiration and alleviated water stress. The regional water resource can be allocated to prevent drought risks in sustainable agriculture under climatic conditions.

Spring maize is suffering from the ever-increasing drought under global warming in Northeast China in recent years. Most previous studies focused on the disaster level threshold of segmented agrometeorological drought, according to the crop development stage. However, it cannot fully meet the practical application under complex conditions at present. This study aims to construct the drought dynamic threshold of spring maize in Northeast China from 2000 to 2020. Time series solar-induced chlorophyll fluorescence (SIF) index with the actual data of drought disaster was utilized to construct the drought and drought-free sample sets. Binomial, trinomial and Gaussian fitting were selected to determine the different drought level curves, according to the curve change of SIF value in the study period. The optimal fitting model was obtained using the coefficient of determination (R²), Akaike information criterion (AIC) and Bayesian information criterion (BIC). The average of the fitting curves for the adjacent drought levels was then taken as the dynamic critical threshold of each drought level during the whole growth period of maize. The optimal dynamic critical threshold was finally verified by independent samples and typical drought events. The results showed that the Gaussian fitting model was more effective in representing the SIF values of different drought levels, compared with the binomial and trinomial fitting. The dynamic thresholds of different drought levels better represented the actual drought situation of spring maize. The drought level identification was fully consistent with the actual disaster level in 82.76% of cases, and basically consistent with the actual disaster level in 91.03% of cases, indicating the high verification accuracy. Taking the typical drought event in Liaoning Province as an example, the drought process was verified by the threshold value. The times of drought occurrence and end were completely consistent with the actual disaster records. Spatially, the drought-affected areas were also consistent in the actual disaster records. Taking Chifeng City as a typical drought site, the drought events with threshold values were verified to be consistent in both the time and level of drought occurrence. There was basically 100% matching between the actual disaster records and threshold verification in the whole drought, indicating a high verification accuracy. Therefore, the dynamic drought threshold can be expected to better reflect the spatiotemporal evolution characteristics of spring maize drought, including the occurrence and development dynamics of drought disasters. Better recognition of drought level was achieved in the dynamic drought threshold, compared with the segmented threshold in the development stage. But some challenges also remained. The SIF data makes it difficult to capture the phenomenon of sudden drought, indicating the drought duration less than 8 days. In addition, there is a certain impact on the identification, because the phenology of maize varies in the climate change, leading to the advanced or postponed seedling stage.

As the world’s largest potato producer, China plays a crucial role in global food security. However, the impacts of climate change on both the potential planting regions and climatic suitability of potato cultivation in China remain poorly quantified. In this study, potato planting zones were delineated based on the thermal requirements of potato, utilizing the temperature data from 2177 meteorological sites during 1961‒2020. A comprehensive climatic suitability index (CCSI) was developed by integrating temperature, light, and precipitation suitability indices, weighted through Agricultural Production Systems Simulator (APSIM)-Potato model simulation. During 1991‒2020, compared to 1961‒1990, the unsuitable and single-season planting regions decreased by 18% and 8%, while the multi-season and winter planting regions expanded by 93% and 6%, respectively. During 1961‒2020, the CCSI was highest in single-season planting regions (e.g., Northeast China and the north agro-pastoral ecotone), followed by multi-season and winter planting regions. During 1991‒2020, compared to 1961‒1990, CCSI of potato planting in the single-season planting region showed a slight decrease, but it increased by 1%‒2% in the multi-season and winter planting regions. These findings demonstrate that the increase in potato climate suitability supports the expansion of potato planting area and implementation of the “Potato as Staple Food” policy. Increased precipitation and temperature identify Northwest and Southwest China as the potential expansion regions for potato planting.