Jianhua Wang

Under the dual pressures of water scarcity and rising food demand, dynamic irrigation identification is essential for optimizing water allocation and improving agricultural water-use efficiency. However, constrained by the spatiotemporal resolution trade-off of single-source remote sensing data, limited capability in characterizing irrigation dynamics, and the difficulty of generating dynamic samples, current approaches remain insufficient for continuous and automated detection of irrigation events at fine scales. To address these limitations, this study proposes a dynamic irrigation identification method integrating spatiotemporal fusion and dynamic sample generation. First, daily 10 m re-modified perpendicular drought index (RPDI) time series are reconstructed using the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and combined with in situ soil moisture observations to characterize RPDI responses to irrigation events. Second, canal water release records and RPDI response features are integrated to generate dynamic irrigation samples with per-time-step labels. Finally, a multi-head attention-enhanced Bi-LSTM model is developed for automated identification of irrigation signals. Application in the Yongji Irrigation District showed that the proposed model outperformed LSTM, Bi-LSTM, and single-head attention-enhanced Bi-LSTM in both per-time-step irrigation response state identification and actual irrigation event identification, with average test-set OA and F1 values of 0.965 and 0.945. Independent validation showed no false-positive or false-negative actual irrigation events and accurate irrigation frequency retrieval. By contrast, although the baseline models achieved relatively high per-time-step accuracy, with OA values ranging from 0.887 to 0.960, they failed to accurately retrieve irrigation frequency, with OAfreq ranging from 0.211 to 0.868. These results demonstrate that the proposed method is capable of dynamically identifying irrigation events and effectively retrieving irrigation frequency information.

构建水网是解决水资源时空分布不均问题、提升水安全保障能力的重要手段。国内外水网建设主要受减灾兴利的治水实践驱动，自然与人工水网的研究融合度不高，水网基础认知及其形成与演变机制不清，制约水网规划与建设成效。本文系统解析了水网的科学内涵，提出了基于三层次健康水平衡的水网构建目标准则与“平衡点”“平衡度”确定方法。在此基础上，揭示了从年至千万年等不同尺度上水网形成和演变的主导路径，并从驱动水流角度引入“势”“阻”理论，分别针对自然与人工水网阐释了相应的势、阻含义与构成，提出了水网形成和演变的“势差驱动与阻力约束”机制，明确了势阻关系对自然与人工水网作用的相同本质与联动效应。研究将自然与人工水网纳入到统一的机制框架，力图为水网科学规划布局提供基础支撑。

Ecological restoration projects in the Loess Plateau have significantly altered the underlying surface, which has profoundly affected the regional water cycle. In the context of the ongoing climate change, quantitatively identifying the factors influencing runoff changes and simulating runoff responses to various land management policies are essential for achieving sustainable development in arid/semi-arid regions. Daily hydrological and meteorological data from 1981 to 2020 along with the SWAT model were employed to analyze the attribution of runoff changes in the Yanhe River basin and simulate runoff responses under different climate and land-use scenarios. The results show the following: (1) the improvement of the underlying surface conditions appeared to be the leading factor of runoff retention, with a contribution of 81.21%, while the influence of climate change on runoff was minimal; (2) woodland generally exhibited superior performance in retaining runoff compared to grassland under diverse climate conditions; (3) converting farmland on slopes between 15 and 25 degrees into woodland and farmland on slopes exceeding 25 degrees into grassland demonstrated to be a more effective approach to controlling soil erosion; (4) it is recommended that a balance between water resource utilization and the extent of afforestation should be considered concurrently in future ecological restoration.

Western Route of South-to-North Water Diversion Project; water source areas; adjustable water volume; ecological water demand; inter-basin water transfer

Climate warming leads to frequent extreme precipitation events, which is a prominent manifestation of the variation of the global water cycle. In this study, data from 1842 meteorological stations in the Huang–Huai–Hai–Yangtze River Basin and 7 climate models of CMIP6 were used to obtain the historical and future precipitation data using the Anusplin interpolation, BMA method, and a non-stationary deviation correction technique. The temporal and spatial variations of extreme precipitation in the four basins were analysed from 1960 to 2100. The correlation between extreme precipitation indices and their relationship with geographical factors was also analysed. The result of the study indicates that: (1) in the historical period, CDD and R99pTOT showed an upward trend, with growth rates of 14.14% and 4.78%, respectively. PRCPTOT showed a downward trend, with a decreasing rate of 9.72%. Other indices showed minimal change. (2) Based on SSP1-2.6, the intensity, frequency, and duration of extreme precipitation changed by approximately 5% at SSP3-7.0 and 10% at SSP5-8.5. The sensitivity to climate change was found to be highest in spring and autumn. The drought risk decreased, while the flood risk increased in spring. The drought risk increased in autumn and winter, and the flood risk increased in the alpine climate area of the plateau in summer. (3) Extreme precipitation index is significantly correlated with PRCPTOT in the future period. Different atmospheric circulation factors significantly affected different extreme precipitation indices of FMB. (4) CDD, CWD, R95pD, R99pD, and PRCPTOT are affected by latitude. On the other hand, RX1day and RX5day are affected by longitude. The extreme precipitation index is significantly correlated with geographical factors, and areas above 3000 m above sea level are more sensitive to climate change.

Climate warming leads to frequent extreme precipitation events, which is a prominent manifestation of the variation of the global water cycle. Using data of 1842 meteorological stations in the Huang-Huai-Hai-Yangtze River Basin and 7 climate models of CMIP6,the historical and future precipitation data were obtained using the Anusplin interpolation, BMA method, and a non-stationary deviation correction technique. The temporal and spatial variations of extreme precipitation in the four basins from 1960 to 2100 were analyzed. The correlation between extreme precipitation indices and their relationship with geographical factors was analyzed. The results show that: (1) in the historical period, CDD and R99pTOT showed an upward trend, with growth rates of 14.14% and 4.78%, respectively. PRCPTOT showed a downward trend, with a decreasing rate of 9.72%. Other indices showed minimal change. (2) Based on SSP1-2.6, the intensity, frequency, and duration of extreme precipitation changed by approximately 5% at SSP3-7.0 and 10% at SSP5-8.5. The sensitivity to climate change was the highest in spring and autumn. The drought risk decreased and the flood risk increased in spring. The drought risk increased in autumn and winter, and the flood risk increased in the alpine climate area of the plateau in summer. (3) CDD, CWD, R95pD, R99pD, and PRCPTOT are affected by latitude. RX1day and RX5day are affected by longitude, and other indices are affected by longitude and latitude. The extreme precipitation index is significantly correlated with geographical factors, and areas above 3000 m above sea level are more sensitive to climate change.

Meteorological factors and human activities are important factors affecting vegetation change. The change in the Upper Yellow River Basin’s (UYRB’s) ecological environment greatly impacts the ecological environment in the middle and lower reaches of the Yellow River. The purpose of this study was to evaluate remotely sensed imageries and vegetation indices as tools for accurately quantifying the driving forces of vegetation distribution. To accomplish this, we utilized the normalized difference vegetation index (NDVI) to examine the temporal and spatial variability of the vegetation distribution in the UYRB between 2000 and 2020. Based on the geographic detector method, the spatial differentiation, driving force, interaction, and suitability of the NDVI were detected. From 2000 to 2020, the estimated annual NDVI value of the UYRB was 0.515, with notable geographic variation in the distribution. The NDVI showed an obvious upward trend with a rate of 0.038 per 10 years. The vegetation coverage significantly improved. However, the vegetation coverage at the source area of the Yellow River marginally deteriorated. The primary driving factors affecting the spatial distribution of the NDVI were yearly precipitation, elevation, soil type, vegetation type, and annual average temperature, with a predictive power of 47%, 46%, 44%, 41%, and 40%, respectively. The interplay of the components had a stronger impact on the NDVI, and the interaction between the yearly precipitation and the soil type had the highest predictive power, reaching 61%. Natural factors and human activities influence NDVI change, with natural factors playing a significant role. Therefore, we should continue to implement the project of returning farmland to forest (grass), increase the efficiency of vegetation precipitation use, and promote the growth of vegetation so that ecological restoration continues to be effectively improved.

Carbon peaking and carbon neutrality; social water cycle; water–energy–carbon nexus; carbon accounting; water management

The numerical simulation of lake–groundwater interaction dynamics is very challenging, and, thus, only few model codes are available. The present study investigated the performance of a new method, namely, the Sloping Lakebed Method (SLM), in comparison to the widely used MODFLOW lake package (LAK3). Coupled lake–groundwater models based on LAK3 and SLM were developed for Lake Hampen, Denmark. The results showed that both methods had essentially the same accuracy when simulating the lake water level, the groundwater head and the overall water balance. The SLM-based model had the potential to reproduce the change of the lake surface area in a more natural way. Moreover, the vertical discretization of a lake in the SLM is independent of the groundwater model, and, thus, the model grid at the top layers could be considerably coarsened without a loss of model accuracy. This could lead to savings in computational time of approximately 30%.