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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 × 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. Plain Language Summary 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. Key Points 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

Characterizing and understanding the changes in the flow regimes of rivers have been challenging. Existing global river network data sets are not updated and can only identify rivers wider than 30 m. We propose a novel automated method to map river networks on a monthly basin scale for the first time at 10‐m resolution using Sentinel‐1 Synthetic Aperture Radar, Sentinel‐2 multispectral images, and the AW3D30 Digital Surface Model. This method achieved an overall accuracy of 95.8%. The total length of the Yellow River network produced is 40,280 km, approximately 3.2 times that of the Global River Widths from Landsat (GRWL) database, more effectively covering small and medium rivers. The monthly river geometry revealed a positive correlation between river network area and precipitation. This study is expected to provide a cost‐effective alternative to accurately mapping global river networks and advance our understanding of the changes and drivers of river systems. Plain Language Summary Understanding the impacts of climate change and human activities on water resources across different regions greatly depends on the knowledge of river networks with high spatial and temporal resolution. Small tributaries are important components in river network evolution and water transmission. To date, several studies have mapped interannual variations in rivers with widths >30 m; however, the distribution and variations in small rivers remain unclear. By integrating multispectral and radar satellite remote sensing images as well as topographic data, we created continuous monthly river network maps at the basin scale, allowing us to capture the details of dynamic changes in river networks with higher spatiotemporal resolution. As a result, the method used in this study provides detailed information on small and medium rivers, with the length of the connected rivers being thrice that of the existing data sets. We demonstrate the possibility of mapping global river networks monthly at a resolution of 10 m, providing valuable information for global surface water resource planning and management and improving our understanding of spatial links between land and water interfaces. Key Points We proposed a new method for mapping 10 m resolution continuous river networks on a monthly basin scale using satellite images and DEMs This method provided detailed information on small‐ and medium‐sized rivers with an overall accuracy of 95.8% There is a strong positive correlation between monthly river network area and precipitation

Satellite-based Earth observations (EO) are an accurate and reliable data source for atmospheric and environmental science. Their increasing spatial and temporal resolutions, as well as the seamless availability over ungauged regions, make them appealing for hydrological modeling. This work shows recent advances in the use of high-resolution satellite-based EO data in hydrological modeling. In a set of six experiments, the distributed hydrological model Continuum is set up for the Po River basin (Italy) and forced, in turn, by satellite precipitation and evaporation, while satellite-derived soil moisture (SM) and snow depths are ingested into the model structure through a data-assimilation scheme. Further, satellite-based estimates of precipitation, evaporation, and river discharge are used for hydrological model calibration, and results are compared with those based on ground observations. Despite the high density of conventional ground measurements and the strong human influence in the focus region, all satellite products show strong potential for operational hydrological applications, with skillful estimates of river discharge throughout the model domain. Satellite-based evaporation and snow depths marginally improve (by 2 % and 4 %) the mean Kling–Gupta efficiency (KGE) at 27 river gauges, compared to a baseline simulation (KGEmean= 0.51) forced by high-quality conventional data. Precipitation has the largest impact on the model output, though the satellite data on average shows poorer skills compared to conventional data. Interestingly, a model calibration heavily relying on satellite data, as opposed to conventional data, provides a skillful reconstruction of river discharges, paving the way to fully satellite-driven hydrological applications.

Land use/cover (LCLU) is considered as one of the most serious environmental challenges that threatens developed and less developed countries. LCLU changes’ monitoring using the integration of remote sensing (RS) and geographical information systems (GIS) and their predicting using an artificial neural network (ANN) in the western part of the Tarim River Basin (Aksu), north-western Xinjiang-China, from 1990 to 2030 have been investigated first time through satellite imageries available. The imageries of 1990, 2000, 2005, 2010, and 2015 were downloaded from GLCF and USGS websites. After digital image processing, the object-oriented image classification approach was applied. The ANN method with MOLUSCE Plugin was used to simulate the LCLU changes in 2020, 2025, and 2030. GIS has also been used to calculate the distance from the road and water and etc. The simulation results of 2010 and 2015 were validated using classification data with Kappa coefficient. The results showed high accuracy of the classification and prediction as the validation of simulated 2010 and 2015 maps to the referenced maps have high accuracy of Kappa 84 and 88%, respectively. The results revealed that the land cover classes forest-, grass-, wet-, and barren land have been decreased from 50.01, 13.06, 8.24, and 1.06% in 1990 to 32.03, 3.06, 6.26, and 0.97% in 2015, respectively, while the land use classes, crop or farm land, and urban land have been increased almost double from 25.5 and 2.13% in 1990 to 53.71 and 3.86% from the total area in 2015, respectively. For the prediction, forest- and wetlands will loss more than half of their areas by 2030, the grass land will be cleared completely to be only 1.3% from the total study area, while the urban land will be increased to be 4.4% or the double of 1990. These results are attributed to population growth and expanding of agriculture land on the grass land, but the effect of climate was weak as the rainfall increased during the study period. Causes and effects of the LCLU changes were briefly discussed. The output of the study serves as useful tools for policy and decision makers combatting natural resources misused in arid lands.

River networks play a central role in regulating biogeochemical transformations at the catchment scale, yet a general scaling theory linking network structure to integrated metabolic function is still lacking. By positing the fundamental differences between blue (fluvial) and green (terrestrial) metabolisms in river basins, we focus on the former and, inspired by allometric scaling in biology, we treat river networks as model organisms whose effective body size is given by the volume of water stored within the channelized system. Using finite‐size scaling theory for space‐filling dendritic networks we derive exact relationships linking universal geomorphic exponents, such as Hack's law, the Hurst exponent, and the probability distribution of the total contributing area to novel scaling laws for river network volume and for a broader class of capacity measures that represent geometric controls on material processing. The theoretical predictions are validated using both optimal channel networks and digital elevation models of real river basins, which display exponent convergence, collapse of rescaled distributions across nested subbasins, and robustness under coarse graining. By linking river network geometry to integrated metabolic capacity, our framework provides a direct, scale‐invariant bridge between geomorphic organization and catchment‐scale blue metabolism. The resulting allometric relations suggest Kleiber‐like scaling between metabolic interfaces and the network equivalent of body size, offering a unifying basis for predicting riverine biogeochemical function across scales and setting the stage for integrated blue‐green metabolic theories.

The fragile ecological environment near mines provide advantageous conditions for the development of landslides. Mine landslide susceptibility mapping is of great importance for mine geo-environment control and restoration planning. In this paper, a total of 493 landslides in Shangli County, China were collected through historical landslide inventory. 16 spectral, geomorphic and hydrological predictive factors, mainly derived from Landsat 8 imagery and Global Digital Elevation Model (ASTER GDEM), were prepared initially for landslide susceptibility assessment. Predictive capability of these factors was evaluated by using the value of variance inflation factor and information gain ratio. Three models, namely artificial neural network (ANN), support vector machine (SVM) and information value model (IVM), were applied to assess the mine landslide sensitivity. The receiver operating characteristic curve (ROC) and rank probability score were used to validate and compare the comprehensive predictive capabilities of three models involving uncertainty. Results showed that ANN model achieved higher prediction capability, proving its advantage of solve nonlinear and complex problems. Comparing the estimated landslide susceptibility map with the ground-truth one, the high-prone area tends to be located in the middle area with multiple fault distributions and the steeply sloped hill.

Digital city planning increasingly relies on data-driven approaches to address complex urban sustainability challenges through innovative network analysis methodologies. This study introduces a comprehensive spatiotemporal network framework to examine digital innovation networks as fundamental infrastructure for urban sustainable development, focusing on the Yellow River Basin as a representative case study. Utilizing digital patent data as innovation indicators across 57 urban centers, we employ advanced network analysis techniques including Social Network Analysis (SNA) and the Quadratic Assignment Procedure (QAP) to investigate the spatiotemporal evolution patterns and underlying driving mechanisms of regional digital innovation networks. The methodology integrates big data analytics with urban planning applications to provide evidence-based insights for digital city planning strategies. Our empirical findings reveal three critical dimensions of urban sustainable development through digital innovation networks: First, the region demonstrated significant enhancement in digital innovation capacity from 2012 to 2022, with accelerated growth patterns post 2020, indicating robust urban resilience and adaptive capacity for sustainable transformation. Second, the spatial network configuration exhibited increasing interconnectivity characterized by strengthened urban–rural linkages and enhanced cross-regional innovation flows, forming a hierarchical centrality pattern where major metropolitan centers (Xi’an, Zhengzhou, Jinan, and Lanzhou) serve as innovation hubs driving coordinated regional development. Third, analysis of network formation mechanisms indicates that spatial proximity, market dynamics, and industrial foundations negatively correlate with network density, suggesting that regional heterogeneity in these characteristics promotes innovation diffusion and strengthens inter-urban connections, while technical human capital and governmental interventions show limited influence on network evolution. This research contributes to the digital city planning literature by demonstrating how data-driven network analysis can inform sustainable urban development strategies, providing valuable insights for policymakers and urban planners implementing AI technologies and big data applications in regional development planning.

Endorheic rivers as landlocked systems with no hydrological connections to marine environments are suffering from water and ecosystem crisis worldwide, yet little is known about their structural characteristics with complex geomorphic and climatic dependence. Based on the river networks identified from 30 m resolution digital elevation models and surface water dynamic information derived from Landsat images, we investigate the hierarchical characteristics of 60 sub-basins in the Tarim Basin, the largest endorheic river basin in China. In the Tarim River basin, endorheic rivers exhibit a self-similarity only in the range of stream-orders 1–4, compared to the range of stream-orders 1–5 observed in exorheic rivers, owning to the limited stream power to maintain the similar aggregation of rivers in the arid regions. Moreover, the Tarim River networks demonstrate lower bifurcation ratio (2.48), length ratio (2.03), fractal dimension (1.38), and drainage density (0.24 km−1) in representative sub-basins, with a significant decay in median values compared with those derived from exohreic rivers at similar scales, suggesting sparser and imperfect developed branching river networks in endorheic basins. Further analysis on the Tarim reveals that endorheic river structure is more related to glacier extent (r = 0.67~0.84), potential evapotranspiration (r = 0.63~0.81), and groundwater type index (r = 0.64~0.73), which is essentially different from the structure of exorheic river represented by the Yellow River largely controlled by surface runoff, precipitation, and vegetation coverage. This study stresses the differences in intrinsic structural characteristics and extrinsic drivers of endorheic and exorheic rivers and highlights the necessity of differentiated strategies for endorheic river management in fragile ecosystems.

Floods are among the devastating types of disasters in terms of human life, social and financial losses. Authoritative data from flood gauges are scarce in arid regions because of the specific type of dry climate that dysfunctions these measuring devices. Hence, social media data could be a useful tool in this case, where a wealth of information is available online. This study investigates the reliability of flood related data quality collected from social media, particularly for an arid region where the usage of flow gauges is limited. The data (text, images and videos) of social media, related to a flood event, was analyzed using the Machine Learning approach. For this reason, digital data (758 images and 1413 video frames) was converted into numeric values through ResNet50 model using the VGG-16 architecture. Numeric data of images, videos and text was further classified using different Machine Learning algorithms. Receiver operating characteristics (ROC) curve and area under curve (AUC) methods were used to evaluate and compare the performance of the developed machine learning algorithms. This novel approach of studying the quality of social media data could be a reliable alternative in the absence of real-time flow gauges data. A flash flood that occurred in the United Arab Emirates (UAE) from March 7–11, 2016 was selected as the focus of this study. Random forest showed the highest accuracy of 80.18% among the five other classifiers for images and videos. Precipitation/rainfall data were used to validate social media data, which showed a significant relationship between rainfall and the number of posts. The validity of the machine learning models was assessed using the area under the curve, precision-recall curve, root mean square error, and kappa statistics to confirm the validity and accuracy of the model. The data quality of YouTube videos was found to have the highest accuracy followed by Facebook, Flickr, Twitter, and Instagram. These results showed that social media data could be used when gauge data is unavailable.

River channels are fundamental geomorphological and hydrological features that play a critical role in regulating the Earth’s water cycle and ecosystems and influencing human activities. This study utilized Digital Elevation Model (DEM) data and multi-source remote sensing imagery (including GF-1 WFV, Sentinel-1, and Sentinel-2) to determine river channel dimensions. River water masks were obtained from multiple remote sensing imagery sources and processed through triangulation and segmentation to generate river reach results. Based on these segmented river reaches, buffer analysis was conducted. The buffer analysis results were then used to refine and clip the 5 m DEM and 12.5 m DEM datasets. Finally, river channels were extracted from the clipped DEM data using the natural breaks classification method. The classification accuracy was assessed using a confusion matrix. Experimental results demonstrate a high overall classification accuracy, reaching or exceeding 0.985, with classification consistency (Kappa coefficient) ranging from 0.78 to 0.81. The 5 m resolution DEM exhibited superior performance compared to the 12.5 m resolution DEM in river channel extraction, especially regarding the classification consistency (Kappa coefficient), with the 5 m resolution model outperforming the latter. This approach effectively delineates the river channel boundaries, transcends the constraints of a singular data source, enhances the precision and resilience of river extraction, and possesses several practical applications. The extracted data can support analyses of river evolution, facilitate hydrological modeling at the basin scale, improve flood disaster monitoring, and contribute to various other research domains.