The middle reaches of Yellow River basin, with a fragile ecological environment, are vulnerable to serious flood and drought disasters. In recent years, the controls on soil and water loss in the middle reaches of the Yellow River have been steadily improving under the influence of human activities such as large-scale terrace construction and vegetation restoration projects. These soil and water conservation measures play an important role in flood control, drought relief and disaster reduction. However, at the same time, vegetation construction in the middle reaches of the Yellow River has led to the intensification of dry soil layers, a decrease in runoff and sediment yields, and an increased hydrological drought risk. Urban expansion has also led to drastic change in this disaster-prone environment, including an increased rainstorm and flood peaks in urbanized areas, and increased flood and drought risks. In the middle reaches of the Yellow River basin, the hydrology, sediment and vegetation evolution is highly inter-connected and co-developed. With disturbance from human activities, a new hydrology-sediment-vegetation balance will form. At present, however, the co-evolution processes and mechanisms of the hydrology-sediment-vegetation balance in the basin under a changing environment is not clearly understood. The impact of large-scale distributed human activities such as terrace construction, vegetation restoration projects and urban expansion, on flood and drought disaster risks needs to be scientifically assessed. An intelligent eco-hydrological model of the Yellow River middle reaches under a changing environment should be developed to predict the long-term subsequent impact of large-scale human activities on flood and drought disaster risks. These improvements can provide scientific and technological support for the coordinated development of ecological construction and water conservancy projects in the middle reaches of the Yellow River, serving the effective prevention and mitigation of flood and drought disaster risks.

The prediction and estimation of surface runoff are core research topics in hydrology and geography, with important implications for water resource management and planning. Traditional research relies on measured surface runoff for model calibration and parameter transfer to predict runoff in ungauged basins. However, when measured runoff is scarce or disturbed by strong human activities (such as dam regulation), the ability of parameter transfer is limited, resulting in insufficient runoff prediction capacity. Remote sensing data, with its spatial and temporal continuity, and without being restricted by the observation time series of surface runoff, provides a new approach for runoff prediction. This article explores the method of calibrating hydrological models with remote sensing data in 84 basins in China. Different combinations of remote sensing evapotranspiration, water storage, and soil water data were used to evaluate the potential of predicting runoff by calibrating the hydrological models directly. The results showed that the new method of constraining hydrological models based on grid-based evapotranspiration data with bias correction has great potential for application in both arid and humid areas of China. Grid-based model constraint has a more obvious advantage over lumped model constraint, and multiple hydrological models should be used to enhance the application potential of this method in different basins. The runoff prediction capability and application potential of this method have spatial variability and should be tailored to local conditions to effectively leverage the advantages of remote sensing data.

Remote sensing in hydrology is a crossing field of hydrology and remote sensing. It not only includes remote sensing retrieval models for water cycle factors, but also covers remote sensing hydrological models which serve for calculating the process of hydrology. This review focuses on the past, present and future development of remote sensing hydrological model by using the meta-analysis method and collecting related research in the past 40 years. The results show that remote sensing hydrological model has played a key role in remote sensing hydrology since the 1970s in the world. In China, the researchers of remote sensing in hydrology keep pace with global scientists, and prominent achievements include the concept generation about the remote sensing hydrology, promotion of the combination between remote sensing and hydrology and development of remote sensing hydrological models. In the future, by the help of enhanced information technology, the remote sensing hydrological models will pay more attention to the runoff monitoring by using remote sensing, the intelligent web of hydrological sensors, hydrological analysis in the data scarce watersheds, and the precise simulation of the water flow, water quality as well as the water ecology.

Most of Asian major international rivers originate from China. Their abundant transboundary water resources play an important role in regional "water-energy-food-ecology" security. In recent decades, influenced by global change, especially by the construction of large hydraulic and hydroelectric engineering, the changes in hydrological and ecological processes and their transboundary impacts in the international river regions have attracted more and more attention. The research on these issues in China has achieved prominent results in several aspects, including the changes in hydrological and ecological processes and their attributions, the transboundary impacts and risk regulation, the "water-energy-food-ecology" nexus in transboundary watersheds, and the construction of transboundary water resources coordination mechanism to adapt to global changes. In the face of the increasing risks of transboundary water security and ecological security under global change, the hydro-geographical research on international rivers needs to make use of space-air-ground integrated monitoring technology, modern spatial geographic information technology, intelligent technology, and so on. By providing quantifiable, participatory, and public results, these researches can better provide scientific basis and decision support for the rational utilization of international rivers and geopolitical cooperation, health maintenance and risk control, transboundary water diplomacy and environmental diplomacy.

Water shortage is becoming a key factor of agricultural sustainable development in China, and threatening food security. It is an urgent need to establish an integrated water saving framework, including theory and related countermeasures, to support society for seeking a better and sustainable solution. In the this paper, we proposed an integrated agricultural water saving study framework from the perspective of geographical science, and introduced how it was applied to pursue the pathways to sustainable agricultural water utilization and solve the groundwater depletion issue in the North China Plain (NCP), where groundwater is suffering most severe depletion due to irrigation. We proposed a conceptual model of regional agricultural water consumption and its productive and ecologic effects, enlarged the scope of "SPAC interface regulatory theory for agricultural water saving", and established a combined "water saving" and "water adaptive" agriculture research framework, namely, the integrated water saving agricultural study. We applied this integrated water saving study in the NCP as a typical case. Firstly, the evapotranspiration (ET) from a wheat-maize double cropping field was quantified as 710 mm/a, the net water deficit is 220 mm/a, based on a 13-year field measurement of water fluxes and budget. It is necessary to achieve water balance at a farmland scale so as to change the current double cropping system to the alternatives, such as three harvests in two years or even monoculture system. At regional scale, under the precondition or scenario of groundwater zero depletion, which could be achieved through reducing planting area and optimizing planting structure, the production of wheat can only meet 75% of the demand for grain self-sufficiency in the Beijing-Tianjin-Hebei region, based on the model simulation. With the current agricultural technology, we have to rely on import of water or food from external basins to bridge the deficit if we want to pursue high agricultural productivity without groundwater depletion. We quantified the apparent water-saving potential by analyzing ET partitioning structure, i.e. E and T, through combining observation and modeling, and further interpreted the major soil depths for evaporation loss (top 0-20 cm layer) and crop root uptake (top 0-40 cm layer). Then, we developed the sub-surface drip irrigation technology, which set up the drip line at 20-40 cm depth below the ground surface with intervals of 80-100 cm according to the soil texture. Compared with the surface irrigation, the sub-surface drip irrigation could reduce ET of wheat and maize season as 88 mm and 60 mm, respectively, without reduction in yield. The water saving effect is equivalent to 1480 m³/hm². We believe that this advanced water-saving technology will have a significant positive effect on water budgets of optimizing the planting structure and cropping system, so as to achieve the sustainable goals for both the agricultural production and groundwater conservation. In this study, we also appeal that we should make more efforts on studying water resource yield, transformation, and utilization in dimension of both quantity and quality under a changing environment in future.

Located in Xinjiang Uygur Autonomous Region, at the national gateway to the Asia-Europe Bridge, the northern slope of the Tianshan Mountains (NSTM) has been experiencing rapid economic development. The oasis economy and irrigated agriculture of NSTM are highly dependent on groundwater. Therefore, it is of great significance to reveal the spatiotemporal evolution of groundwater storage for regional sustainable development. However, long-sequence and high-resolution groundwater observations are relatively scarce on regional scale, which makes the analysis extremely challenging. Based on reconstructed terrestrial water storage anomaly data, ERA5-Land reanalysis data and other auxiliary data such as soil texture, elevation, vegetation index and glaciers, a random forest downscaling model was developed to retrieve high-resolution (8 km) groundwater storage anomaly (GWSA) of NSTM from 1990 to 2020, with which the spatiotemporal evolution of groundwater storage was analyzed. The temporal variation of our GRACE (Gravity Recovery and Climate Experiment)-based GWSA was in high agreement with that of in-situ groundwater level data. The correlation coefficient between GWSA and in-situ data reached a maximum of 0.68. Groundwater storage in NSTM exhibited significant intra- and inter-annual variability, which was higher in spring and summer and lower in autumn and winter, with the peak occurring in June and the minimum in October. Groundwater storage in more than 85% of the NSTM declined significantly during 1990-2020, with a rate of -0.38 cm/a. Groundwater storage in the Ebinur Lake and Central Rivers declined most dramatically, while that in the Emin River was almost constant. The declining rate was the greatest in cropland, followed by grassland, and least in bare land. Increasing demand for water in agriculture was the main driver of groundwater depletion over the past three decades. Compared with seasonal and sub-seasonal variability, long-term variability was the dominant pattern of temporal variability of groundwater storage in NSTM. As for the Emin River, it was jointly dominated by long-term, seasonal and sub-seasonal variability. In particular, it is worth noting that one of the challenges in estimating GWSA in glacier-covered areas is the lack of long-sequence and high-resolution glacier mass data, and most previous studies have ignored the influence of glaciers. This paper showed that ignoring the influence of glaciers would lead to an overestimation of the average groundwater storage change rate in NSTM by at least 27.56%, which needs to be taken seriously.