Climate change refers to the statistically significant changes in the mean and dispersion values of meteorological factors. Characterizing potential evapotranspiration (ET₀) and its climatic causes will contribute to the estimation of the atmospheric water cycle under climate change. In this study, based on daily meteorological data from 26 meteorological stations in Heilongjiang Province from 1960 to 2019, ET₀ was calculated by the Penman–Monteith formula, linear regression method and the Mann–Kendall trend test were used to reveal the seasonal and inter-annual changing trend of ET₀. The sensitivity-contribution rate method was used to clarify the climatic factors affecting ET₀. The results showed that: (1) From 1960 to 2019, the maximum temperature (T_max), minimum temperature (T_min) and average temperature (T_mean) showed an increasing trend, with climate tendency rate of 0.22 °C per decade (10a), 0.49 °C/(10a), 0.36 °C/(10a), respectively. The relative humidity (RH), wind speed (U) and net radiation (Rn) showed a decreasing trend, with a climate tendency rate of −0.42%/(10a), −0.18 m/s/(10a), −0.08 MJ/m²/(10a), respectively. (2) ET₀ showed a decreasing trend on seasonal and inter-annual scales. Inter-annually, the average climate tendency rate of ET₀ was −8.69 mm/(10a). seasonally, the lowest climate tendency rate was −6.33 mm/(10a) in spring. (3) ET₀ was negatively sensitive to T_min, and RH, while positively sensitive to T_max, T_meanU and Rn, its sensitivity coefficient of U was the highest, which was 1.22. (4) The contribution rate of U to ET₀ was the highest on an inter-annual scale as well as in spring and autumn, which were −8.96%, −9.79% and −13.14%, respectively, and the highest contribution rate to ET₀ were Rn and T_min in summer and winter, whose contribution rates were −4.37% and −11.46%, respectively. This study provides an understanding on the response of evapotranspiration to climatic change and further provides support on the optimal allocation of regional water resource and agricultural water management under climate change.

Predicting the impact of future climate change on food security has important implications for sustainable food production. The 26 meteorological stations’ future climate data in the study area are assembled from four global climate models under two representative concentration pathways (RCP4.5 and RCP8.5). The future maize yield, actual crop evapotranspiration (ET_a), and water use efficiency (WUE) were predicted by calibrated AquaCrop model under two deficit irrigation (the regulated deficit irrigation (RDI) at jointing stage(W1), filling stage(W2)), and full irrigation (W3) during the three periods (2021–2040, 2041–2060, and 2061–2080). The result showed that the maize yields under W1, W2, and W3 of RCP4.5 were 2.8%, 2.9%, and 2.5% lower than those in RCP8.5, respectively. In RCP8.5, the yield of W3 was 1.9% and 1.4% higher than W1 and W2, respectively. Under the RCP4.5, the ET_a of W1, W2, and W3 was 481.32 mm, 484.94 mm, and 489.12 mm, respectively. Moreover, the ET_a of W1 was significantly lower than W2 under the RCP4.5 and RCP8.5 (p > 0.05). In conclusion, regulated deficit irrigation at the maize jointing stage is recommended in the study area when considering WUE.

Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.

Climate change will have a significant effect on crop water requirement (ET_c). The spatial and temporal variations of water requirement of maize under climate change are essential elements when conducting a global water security assessment. In this paper, annual reference crop evapotranspiration (ET₀) and the crop water requirement of maize were calculated by the single crop coefficient method. The crop water surplus deficit index (CWSDI) and coupling degree of ET_c and effective precipitation (P_e) were calculated to analyze the relationship between ET_c, ET₀, and P_e. The result shows that maize average annual ET₀, ET_c, and precipitation were 552.97, 383.05, and 264.97 mm, respectively. Moreover, ET₀, ET_c, and P_e decreased by 3.28, 2.56, and 6.25 mm every decade from 1960 to 2015. The ET_c decreased less than P_e did, which led to the decreasing of both CWSDI and the coupling degree of ET_c and P_e. The tendency of ET₀, ET_c decreased first and then increased, while P_e and CWSDI increased first and then decreased, from west to east of the Heilongjiang Province. In addition, the highest ET₀, ET_c, and lowest CWSDI and P_e were found in the western part of Heilongjiang Province. This study indicated that even though the water deficit in the western region was alleviated and the water deficit in the eastern region grew gradually serious from 1960 to 2015, the drought situation in western Heilongjiang Province should still be taken seriously.

The Ashe River Basin (ARB), long known as the “Golden Waterway” in Manchu, has become one of China’s most polluted rivers. The basin area of the Ashe River is 3545 km² and the total length of the river is 257 km. There have not been specific studies on land use change and non-point pollution in the ARB region. This paper uses the ARB watershed as the study area, simulates the watershed using the SWAT (Soil and Water Assessment Tool) model, and analyzes the hydrological processes and the temporal and spatial changes of total nitrogen and total phosphorus in the watershed with hydrology and water quality as the objectives under different periods of land use to reduce pollution in the watershed and protect the environment. The results show that the simulation of runoff, and even the R² and NS (both the coefficient of determination and the Nash–Sutcliffe efficiency coefficient are simulated by SWAT-CUP, which is generally used to validate the simulation results of the hydrological model, where the closer the result is to 1, the better the effect) of total nitrogen and total phosphorus in the watershed, are also all above 0.75 and have good applicability during regular and validation periods. Since 2000, the simulated monthly average total nitrogen and total phosphorus levels have progressively grown. The most polluted areas are concentrated in the middle and lower reaches of the watershed near the main streams owing to the rise in load per unit area caused by the collection of pollutants from the upper watershed to the watershed outlet, and even an increase in fertilizer application due to the larger area of cultivated land.

Agricultural irrigation depends heavily on freshwater resources. Under the context of increasingly severe water shortages, studying the relationship among crop water requirements (ET_c), actual crop evapotranspiration (ET_a), irrigation water requirements (I_r), yield, and water use efficiency (WUE) would be beneficial to improve the agricultural application of irrigation water. Based on the daily data of 26 meteorological stations in Heilongjiang Province from 1960 to 2015, this study used the calibrated AquaCrop model to calculate the ET_c, ET_a, I_r, and yield of maize (Zea mays L.) in different hydrological years (extremely dry years, dry years, normal years, and wet years) along with WUE to evaluate the mass of yield produced per unit mass of crop evapotranspiration (ET) under rainfed and irrigated scenarios. The results showed that ET_c and ET_a decreased first and then increased from the west to the east during the four types of hydrological years. I_r exhibited a decreasing trend from the west to the east. Compared with the irrigation scenario, the rainfed scenario’s average yield only decreased by 2.18, 0.55, 0.03, and 0.05 ton/ha, while the WUE increased by 0.32, 0.4, 0.33, and 0.21 kg/m³ in the extremely dry years, dry years, normal years, and wet years, respectively. The results indicated that in the normal and wet years, the WUE was high in the central regions, and irrigation did not significantly increase yield; further, we determined that irrigation should not be considered in these two hydrological years in Heilongjiang Province. In the extremely dry and dry years, irrigation was necessary because it increased the yield, even though the WUE decreased. This study provides a theoretical basis for studying the regional irrigation schedule in Heilongjiang Province.