Determining the precise placement of hydrological stations on a simulated digital river network is crucial for constructing hydrological models applicable to process simulation, water resource management, and flood forecasting endeavors. To solve this problem, we categorized and scrutinized deviations between the simulated and their actual station locations, and proposed a novel automatic hydrological station relocation algorithm (ASRA). The algorithm was first validated in the Amazon Basin using Global Runoff Data Centre (GRDC) hydrological stations and 90 m x 90 m Shuttle Radar Topography Mission (SRTM) data, successfully correcting the spatial position and corresponding catchment area (CCA) of each station. Findings revealed that CCA inaccuracies were notably decreased, transitioning from an initial 7.62% when employing a conventional 5-km search radius to 5.43% after adopting an iteratively optimized, objective, and rational 8-km search radius. The ASRA method was subsequently applied to GRDC stations within the HDMA and HydroSHEDS data sets, successfully repositioning 8,339 and 8,026 stations respectively, all with catchment area deviations of less than 5%, thus either exceeding or at least equaling the precision of prior research efforts. A Python program was developed and incorporated into an ArcGIS toolbox that features user-friendly attributes, enabling swift computation and accurate rectification, as a result of building upon our method. In short, our study presents a fresh approach and a robust tool for tackling the inconsistencies of hydrological station locations. The updated global GRDC hydrological station locations specifically tailored for both HDMA and HydroSHEDS data sets, together with the toolbox developed, were accessible for download on the figshare platform. As a result of the inherent limitations posed by DEM resolution, hydrological stations' actual positions frequently diverge from their corresponding simulated locations on the digital river network derived from DEM data. These discrepancies can vary across different DEM data sets. While traditional manual methods have been reliable in terms of accuracy, they suffer from inefficiency, while automatic approaches, conversely, have offered speed but at the cost of precision. Hence, this study introduces an automated method and ArcGIS-based toolbox that combines both high accuracy and efficiency, exemplifying its application in the Amazon basin as well as globally. The deviation of hydrological station locations on a digital river network were categorized into three types according to the catchment area and location A new ASRA was developed to deal with all deviation types with the advantages of convenience, efficiency, and easy integration with ArcGIS This method provides an objective and reasonable search radius to decrease the catchment area deviation based on an empirical search radius

The flood produced by short duration heavy rainfall events in cities will still exist after raining and continues to cause harm and impact. To accurately predict the depth and duration of the flood, a coupled model of the extreme gradient boosting and long short-term memory algorithms was proposed. A practical application of three representative flooded points in the Zhengzhou city, China, the results showed the coupled model could fit and forecast the flood. The average of Mean relative error, Nash–Sutcliffe efficiency coefficient and Qualified rate of validation data were 9.13

Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.

This study used the Innovative trend analysis method, Mann-Kendall, and Sen’s slope estimator test to investigate the mean annual precipitation, annual mean air temperature, and river discharge at selected stations of the sub-basin. The results showed that the trend of annual precipitation trend was significantly increased in Nizhneangarsk station (ф = 0.88 and Z = –0.64) and Bolshoe Goloustnoe (ф = 0.24 and Z = 1.14*) whereas, the other stations showed a decreasing trend. The most decreasing trend was observed in the Ust Barguzin station (ф = –0.68, Z = –2.38**, and β = –0.95). The average air temperature has increased by 1.7°C from 1970 to 2019. All stations showed an increasing trend of annual air temperature during the study period. The most increasing condition was observed in the Ulan Ude station (ф = 9.20***, Z = 7.78***, and β = 0.04). The river discharge showed an increasing trend in Utulik River, Upper Angara River, and Barguzin rivers. The most decreasing is in the Selenga river gauge station (ф = –2.25**, Z = –2.16**, and β = –2.96**). The warming rates in the Selenga river sub-basin of the study area were higher than those in the other four sub-basins and the global climate change more affects the semi-arid region.

Effective precipitation plays an important role in crop growth, and subsoiling may have an impact on the effective precipitation of farmland. The question how subsoiling influences effective precipitations has prompted this research. The major objective of this study was to quantify the effect of subsoiling on effective precipitation of farmland. The main soil type in the study area is loam. Six scenarios were set with three factors, namely, the thickness of the soil ploughing layer, porosity, and soil permeability. The hydrological process from 2000 to 2015 was simulated with a distributed hydrological model. The results showed that a 10-cm increase in the soil thickness of the plough layer had little effect on the effective precipitation. When soil porosity increased by 0.1, the effective precipitation increased by approximately 19%. When the soil permeability coefficient increased by 0.5 times, the farmland and watershed surface runoff decreased by 24% and 13%, respectively, and the effective precipitation increased by 1.7%. This study proves that subsoiling has a positive effect on the local effective precipitation and confirms previous hypotheses.

Abstract. More and more high-resolution data sets are simulated worldwide and used in various research. However, in addition to the improvement in accuracy, the practical significance of the spatial distribution of data must also be considered. Considering that the most accurate water withdrawal data are mainly provided by the state, water is mainly concentrated on artificial surface and cultivated land. Whenever possible, using data published by regional or national governments and interpolating and extending them to specific land uses will maximize data accuracy. Based on this, we provide a set of water withdrawal intensity products from 1960 to 2017 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set of the accurate intensity of water withdrawal. The datasets described in this article are publicly and freely available through the FigShare. The DOI for the data is https://doi.org/10.6084/m9.figshare.10012559.v1 (Yanetal.,2019).

The Ugii Nuur Lake is not only one of the small hydrologically closed lakes located in the Orkhon River Basin in Central Mongolia but also the most vulnerable area for global climate change. Therefore, this study aims to investigate the impacts of recent global climate change on the water surface area. The data we analyzed were various measured hydro-meteorological variables of the lake basin and the lake surface area, which was estimated from Landsat series satellite data from 1986 to 2018. The methods we used were Mann-Kendall (MK), Innovative trend analysis method (ITAM), Sen’s slope estimator test, correlation, and regression analysis. The variation of lake water surface area has a strong positive correlation with the change of the lake water level (r = 0.95). The Mann-Kendall trend analysis has indicated that under a significant decrease in total annual precipitation ( Z = −0.902) and inflow river discharge ( Z = −5.392) and a considerable increase in total annual evaporation ( Z = 4.385) and annual average air temperature ( Z = 4.595), the surface area of the Ugii Nuur Lake has decreased sharply ( Z = −6.021). The total annual evaporation (r = −0.64) and inflow river discharge (r = 0.67) were the essential hydro-meteorological factors affecting the surface area of the Ugii Nuur Lake. The lake surface area decreased by 13.5% in 2018 compared with 1986. In the near future, it is vital to conduct scientific studies considering the volume of lake water, groundwater, and the anthropogenic impact.

Understanding the response of runoff composition in the Qinghai-Tibet Plateau (QTP) is essential for the ecology and environment. However, precise measurements of runoff composition in the QTP, especially in remote areas with limited climatic records, are a challenge. In rivers and lakes, some plankton is highly sensitive to changes in runoff composition. Here, we demonstrated how plankton indicators could be used as an indicator of runoff composition in the QTP. The stable isotopes of runoff, precipitation, groundwater and snowmelt in 9 sampling sites were studies of the Nagqu River in the QTP. The multi-source mass balance model (IsoSource model) was used to calculate the range of runoff composition at each sampling site. We found that the change of plankton biodiversity was associated to the runoff composition, and groundwater ratio was negatively correlated with biodiversity, while precipitation ratio was positively correlated with biodiversity, but not with snowmelt. Therefore, plankton diversity is expected to be a simple indicator of runoff composition, which can be easily quantified in remote plateau areas.

River network coding is important for hydrological model on the aspects of water cycles simulation, water resources evaluation and extreme hydrological events forecast. However, the existing river network coding methods had several disadvantages, such as, the river network coding structure is too complex to understand, the river network topology is difficult to identify, the river network coding capacity is limited. To overcome the above-mentioned shortcomings, this paper proposed a new topological and hierarchical river coding method based on the river network created by the DEM (Digital Elevation Method) data and ArcGIS software. This method systematically defines the river levels based on the hydrology structure from outlet to source. The river directly flows into the watershed outlet is river L1, the branch of river L1 with a certain catchment area is river L2, the branch of L2 with a certain catchment area is river L3, and so on. Secondly, we proposed a flexible coding scheme that encode the river segments with pure digital numbers hierarchically. Taking the two-digits scheme for example, the L1 river segments are coded with a sequence number, such as [01], [02], [03], [...]. The L2 river segments inherit the river code from the L1 stem river segment, add a serial of self-order numbers (01, 02, 03, ......) from the junction to the river source, and become [0101], [0102], [0103], [...]. The process of coding the river segments at subsequent level is similar. Finally, this new coding method was programmed in Matlab software, and successfully examined in the Amazon River basin, the world's largest watershed, as a case study. The results show that the new method is competent for easily controlling the river level and river segment density, flexibly adjusting the code digital numbers to fit different basins with abundant or little rivers, easily plotting the topological relationship, automatically encoding by computer program and easily understood. In a word, this study is a useful attempt to create new river network coding method, and it also has positive significance to promote the hydrological simulation research.

Rational allocation of water resources is very essential to cope with water scarcity. The optimal allocation of limited water resources is required for various purposes to achieve sustainable development. The Awash River Basin is currently faced with a scarcity of water due to increasing demands, urbanization, irrigation expansion, and variability of climates. The excessive abstraction of water resources in the basin without proper assessing of the available water resources contributed to water scarcity. This paper aimed to develop a water evaluation and planning (WEAP) model to allocate the water supplies to demanding sectors based on an economic parameter to maximize the economic benefits. The water demands, water shortages, and supply alternatives were analyzed under different scenarios. Three scenarios were developed, namely reference (1981–2016), medium-term development (2017–2030), and long-term development (2031–2050) future scenarios with the baseline period (1980). The results of this study showed that the total quantity of water needed to meet the irrigation demands of all the stations was 306.96 MCM from 1980 to 2016. Seasonally, March, April, May, and June require the maximum irrigation water demand. However, July, August, and September require minimum demand for water because of the rainy season. The seasonal unmet demand is observed in all months, which ranged from 6 × 106 m3 to 35.9 × 106 m3 in August and May respectively. The trend of streamflow in Melka Kuntre was a statistically significant increasing trend after 2008 (Z = 5.33) whereas the trends in other gauge stations showed a relatively decreasing trend. The results also showed that future water consumption would greatly increase in the Awash River Basin. The prevention of future water shortages requires the implementation of water-saving measures and the use of new water supply technologies. The findings of this study will serve as a reference for water resources managers and policy and decision makers.

The 2303 Wetlands of International Importance distribute unevenly in different continents. Europe owns the largest number of sites, while Africa has the largest area of sites. More than half of the sites are affected by three or four impact factors (55%). The most significant impact factors are pollution (54%), biological resources use (53%), natural system modification (53%), and agriculture and aquaculture (42%). The main affected objects are land area and environment of the wetlands, occurred in 75% and 69% of the sites, respectively. The types most affected by land area occupation are river wetlands and lake wetlands, the types with the greatest impact on environment are marine/coastal wetlands and river wetlands, the type with the greatest impact on biodiversity is river wetlands, the types most affected by water resources regulation are marsh wetlands and river wetlands, and the types most affected by climate change are lake wetlands and marine/coastal wetlands. About one-third of the wetland sites have been artificially reconstructed. However, it is found that the proportions of natural wetland sites not affected or affected by only one factor are generally higher than that of wetland sites both containing natural wetlands and human-made wetlands, while the proportions of wetland sites both containing natural wetlands and human-made wetlands affected by three or four factors are generally higher than that of natural wetland sites. Wetland sites in the UK and Ireland are least affected among all countries. Wetland management plans in different regions still have large space for improvement, especially in Africa and Asia. The protection and restoration of global wetlands can be carried out in five aspects, including management and policy, monitoring, restoration, knowledge, and funding.

Machine learning algorithms are becoming more and more popular in natural disaster assessment. Although the technology has been tested in flood susceptibility analysis of several watersheds, research on global flood disaster risk assessment based on machine learning methods is still rare. Considering that the watershed is the basic unit of water management, the purpose of this study was to conduct a risk assessment of floods in the global fourth-level watersheds. Thirteen conditioning factors were selected, including: maximum daily precipitation, precipitation concentration degree, altitude, slope, relief degree of land surface, soil type, Manning coefficient, proportion of forest and shrubland, proportion of artificial surface, proportion of cropland, drainage density, population, and gross domestic product. Four machine learning algorithms were selected in this study: logistic regression, naive Bayes, AdaBoost, and random forest. The global susceptibility assessment model was constructed based on four machine learning algorithms, thirteen conditioning factors, and global flood inventories. The evaluation results of the model show that the random forest performed better in the test, and is an efficient and reliable tool in flood susceptibility assessment. Sensitivity analysis of the conditioning factors showed that precipitation concentration degree and Manning coefficient were the main factors affecting flood risk in the watersheds. The susceptibility map showed that fourth-level watersheds in the global high-risk area accounted for a large proportion of the total watersheds. With the increase of extreme hydrological events caused by climate change, global flood disasters are still one of the most threatening natural disasters. The global flood susceptibility map from this study can provide a reference for global flood management.

Stable hydrogen and oxygen isotopes are important indicators for studying water cycles. The isotopes are not only affected by climate, but are also disturbed by human activities. Urban construction has changed the natural attributes and underlying surface characteristics of river basins, thus affecting the isotopic composition of river water. We collected urban river water isotope data from the Global Network for Isotopes in Rivers (GNIR) database and the literature, and collected river water samples from the Naqu basin and Huangshui River basin on the Tibetan Plateau to measure hydrogen and oxygen isotopes. Based on 13 pairs of urban area and non-urban area water samples from these data, the relationship between the isotopic values of river water and the artificial surface area of cities around rivers was analyzed. The results have shown that the hydrogen and oxygen isotope (δD and δ18O) values of river water in urban areas were significantly higher than those in non-urban areas. The isotopic variability of urban and non-urban water was positively correlated with the artificial surface area around the rivers. In addition, based on the analysis of isotope data from 21 rivers, we found that the cumulative effects of cities on hydrogen and oxygen isotopes have led to differences in surface water line equations for cities with different levels of development. The combined effects of climate and human factors were the important reasons for the variation of isotope characteristics in river water in cities. Stable isotopes can not only be used to study the effects of climate on water cycles, but also serve as an important indicator for studying the degree of river development and utilization.