Evaluating net irrigation requirements could support water resources managements, especially in arid regions. Shiyanghe watershed was in the northwest of China, and its dominant grain crops were spring wheat and spring maize. Their net irrigation requirements rate would be assessed by subtracting effective rainfall from crop evapotranspiration rate at four meteorological stations in the study area in this study. Among which effective rainfall would be calculated by an empirical effective rainfall formula of Northern China and crop evapotranspiration rate would be estimated by FAO Penman–Monteith equation. Then, the estimated results of accumulated net irrigation requirements rate at different stations would be expanded to the entire watershed by using inverse distance weighted interpolation method. Results showed that the accumulated net irrigation requirements rate tended to be the biggest at Minqin station and smallest at Wushaoling station for both crops during different stages, which accord with the geography distribution in the watershed. And mid-season stage had the biggest net irrigation requirements rate, then development stage, last season stage, and initial stage. The zonal averaged accumulated net irrigation requirement rates of spring wheat and spring maize in the entire watershed except for Jingtai county were 353.20mm/whole season and 432.62mm/whole season, respectively. And comparison with local irrigation norms and irrigative efficiencies indicated insufficient local agricultural water supply. This study can help water resources and weather modification departments organizing arrangements and operations to mitigate with water shortage problems. The detailed data selection and processing methods are also referenceable for relevant researches. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province, China, owing to the influences of spatial attributes on the irrigation efficiency. The sample set of this study comprised 165 agricultural test sites. A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables, including the actual irrigation area, the percentage of farmland using water-saving irrigation, the type of irrigation area, the net water consumption per mu, the water intake method, the terrain slope, and the soil field capacity. Results revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang Province. The geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression model. The obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang, and offer a new approach on a regional scale.

The excessive application of agricultural irrigation water and chemical fertilizer has increased crop yields to help meet the demand for food, but it has also led to major water environment problem, i.e. non-point source (NPS) pollution, which needs to be addressed to achieve sustainable development targets. Although numerous studies have focused on the control and reduction of agricultural NPS pollution from the perspective of irrigation and fertilizer, the effects of different cropping systems on NPS pollution (ammonia nitrogen (NH3-N)) in the Dongjiang River Basin (DRB) were seldom assessed. Specifically, variation in the NH3-N load was simulated and analyzed at the annual and semi-annual scales under ten different cropping systems using the Soil and Water Assessment Tool (SWAT) model, which was calibrated and validated with satisfactory statistical index values in the DRB. The results indicated that the NH3-N load decreased, distinctly increased, slightly decreased when sweet potato, peanut, and rice were planted, respectively. Compared with mono-cropping, crop rotation could reduce the NH3-N load, and the planting sequence of crops could affect the NH3-N load to a certain extent. Planting peanuts in spring would dramatically increase NH3-N load. To evaluate NH3-N pollution, a critical threshold of NH3-N emission (5.1 kg.ha(-1).year(-1)) was proposed. Meeting the NH3-N emission threshold cannot be achieved by altering the cropping system alone; additional measures are needed to reduce agricultural NPS pollution. This study facilitates the development of cropping systems and provides relevant information to aid the sustainable development of agriculture in the DRB.

Alpine basins are typically poorly gauged and inaccessible owing to the harsh prevailing environment and complex terrain. In this study, two representative satellite precipitation products (Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RTV7 and Integrated Multi-Satellite Retrievals for GPM (IMERG) Final Run Version 06) and two reanalysis precipitation products (China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)) in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed (GO) data. Results show that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG exhibits the best performance, followed by the CMADS, CFSR, and 3B42RTV7. Models that use the GO as input yielded satisfactory performance during 2008-2013, and precipitation products have poor simulation results. Although the model using the IMERG as input yielded unsatisfactory performance during 2014-2016, this did not affect the use of the IMERG as a potential data source for the YRSR. The model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R (2)=0.77, Nash-Sutcliffe efficiency (NSE)=0.72 at the Tangnaihai station; R (2)=0.53, NSE=0.48 at the Jimai station).

研究汉江流域水文干旱风险及驱动因素，为区域干旱预警和水资源管理提供参考。选用1964—2016年汉江流域白河、黄家港和沙洋3个水文站逐月实测径流资料，采用标准化径流指数SRI进行水文干旱分析，基于游程理论进行干旱特征变量识别，运用Copula函数计算干旱特征变量之间的联合累积概率，分析了联合重现期和同现重现期。结果表明：（1）在1964—2016年，白河站共发生水文干旱事件54次，干旱发生频繁但旱情较轻；黄家港和沙洋站分别发生干旱事件34,32次，发生频次相对较少但旱情较重，旱情总体呈现从上游向下游加重的趋势；（2）随着干旱特征变量值增大，联合累积概率值也增大，但增大趋势变缓；（3）两变量重现期随着单变量取值增大而增大，但在相同增幅情况下，同现重现期的增幅要明显高于联合重现期；（4）当白河站干旱历时和烈度达到最大时，水文干旱事件的联合重现期为50 a左右，同现重现期为1 600 a左右，黄家港与沙洋站呈现出与白河站大致相同的趋势；（5）人类活动是1991年以来汉江流域径流减少的主要原因，对白河和沙洋站径流变化的贡献率分别为59.6%和69.2%,是汉江流域水文干旱加重的主导因素。研究显示汉江流域下游旱情较上游重，且持续受人类活动影响，应增强变化环境下区域干旱风险应对能力。

Thus far, reanalysis-based meteorological products have drawn little attention to the influence of meteorological elements of products on hydrological modeling. This study aims to evaluate the hydrological application potential of the precipitation, temperature, and solar radiation of the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR) in an alpine basin. The precipitation, temperature, and solar radiation of the gauge-observed meteorological dataset (GD), CFSR, and CMADS were cross-combined, and 20 scenarios were constructed to drive the SWAT model. From the comprehensive comparisons of all scenarios, we drew the following conclusions: (1) among the three meteorological elements, precipitation has the greatest impact on the simulation results, and using GD precipitation from sparse stations yielded better performance than CMADS and CFSR; (2) although the SWAT modeling driven by CMADS and CFSR performed poorly, with CMADS underestimation and CFSR overestimation, the temperature and solar radiation of CMADS and CFSR can be an alternative data source for streamflow simulation; (3) models using GD precipitation, CFSR temperature, and CFSR solar radiation as input yielded the best performance in streamflow simulation, suggesting that these data sources can be applied to this data-scarce alpine region.

The tidal flat is long and narrow area along rivers and coasts with high sediment content, so there is little feature difference between the waterbody and the background, and the boundary of the waterbody is blurry. The existing waterbody extraction methods are mostly used for the extraction of large water bodies like rivers and lakes, whereas less attention has been paid to tidal flat waterbody extraction. Extracting tidal flat waterbody accurately from high-resolution remote sensing imagery is a great challenge. In order to solve the low accuracy problem of tidal flat waterbody extraction, we propose a fine-grained tidal flat waterbody extraction method, named FYOLOv3, which can extract tidal flat water with high accuracy. The FYOLOv3 mainly includes three parts: an improved object detection network based on YOLOv3 (Seattle, WA, USA), a fully convolutional network (FCN) without pooling layers, and a similarity algorithm for water extraction. The improved object detection network uses 13 convolutional layers instead of Darknet-53 as the model backbone network, which guarantees the water detection accuracy while reducing the time cost and alleviating the overfitting phenomenon; secondly, the FCN without pooling layers is proposed to obtain the accurate pixel value of the tidal flat waterbody by learning the semantic information; finally, a similarity algorithm for water extraction is proposed to distinguish the waterbody from non-water pixel by pixel to improve the extraction accuracy of tidal flat water bodies. Compared to the other convolutional neural network (CNN) models, the experiments show that our method has higher accuracy on the waterbody extraction of tidal flats from remote sensing images, and the IoU of our method is 2.43% higher than YOLOv3 and 3.7% higher than U-Net (Freiburg, Germany).

A novel method of determining reservoir characteristic curves based on high-resolution resource satellite data was proposed in this paper, using remote sensing processing and analysis technology. According to the physical characteristics of absorption, radiation and reflection of surface water on ultraviolet, visible, near-infrared bands, etc., the satellite images at different reservoir water level and different periods were processed to analyze the relationship of measured water level corresponding to the water area. Based on the relationship, the relevance among reservoir water level, water surface area, and reservoir capacity was established, so as to determine the reservoir characteristic curve. The method was applied and validated at Jinshuitan Reservoir and Shitang Reservoir in the Ou River Basin. The results show that this method has high accuracy, and the maximum relative error between calculating values and measured values at different water level are -2.33% and -2.11% in Jinshuitan Reservoir and Shitang Reservoir, respectively. The method improves the convenience of determining the reservoir characteristic curve greatly, and the storage capacity of the reservoir can be calculated rapidly by this method.

China has lots of funds, materials, and human resources in cloud seeding operations to help with water shortage problems across the country. But there is little guide about when and where the operations would obtain better benefits. To fill this gap, the modified Water Utilization Level (WUL) map of China mainland was plotted with the acquirable water utilization data on sub-watershed scales in this study. Then the agricultural drought levels map of different counties were integrated with the modified WUL Map under preset rules to generate an integrated water scarcity map. Lastly, the cloud seeding demand maps of different seasons were drawn by integrating the agricultural drought seasons map of different counties with the integrated water scarcity map and the modified WUL map. Results showed that the middle north of China should have the highest priorities for cloud seeding operations during spring and summer, and some regions at the south during winter and spring, a few scattered regions at the northwest during spring and summer and a few regions at the middle of southeast China during summer and autumn should also have high priorities of cloud seeding operations. This research can help rain enhancement operations to obtain better effects on easing water scarcity problems and deepen the cooperation of water resources department and weather modification department in China. The designed intersecting rules of different maps in this paper also might inspire other relevant researches.

利用1964—2015年汉江流域气温、降雨和径流等资料,采用线性倾向估计与滑动平均法分析白河和沙洋站水文气象要素的变化趋势,通过Mann-Kendall检验和Pettitt检验确定2个站径流序列的突变年份,基于Budyko水热耦合平衡理论,计算径流量对各影响因子的弹性系数,并采用互补关系法进行归因分析。结果表明:白河与沙洋站年径流量均呈现下降趋势,变化速率分别为-2.797 mm/a和-1.875 mm/a,并在1991年发生突变;白河与沙洋站变化期（1992—2015年）径流对降雨、潜在蒸散发和下垫面的弹性系数分别为1.79、-0.79、-0.64和2.07、-1.07、-0.71,表明2个站的径流量均对降雨最为敏感,对下垫面最不敏感;下垫面变化是汉江流域径流量减少的主要因素,其对白河、沙洋站径流量变化的贡献量分别为74.67%、76.37%;变化期的NDVI较基准期整体增加,植被变化是导致汉江流域径流量整体下降的重要原因。