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\"Since the earliest development of states, groups of people escaped or were exiled. As capitalism developed, people tried to escape capitalist constraints connected with state control. This powerful book gives voice to three communities living at the edges of capitalism: Cossacks on the Don River in Russia; Zapatistas in Chiapas, Mexico; and prisoners in long-term isolation since the 1970s. Inspired by their experiences visiting Cossacks, living with the Zapatistas, and developing connections and relationships with prisoners and ex-prisoners, Andrej Grubacic and Denis O'Hearn present a uniquely sweeping, historical, and systematic study of exilic communities engaged in mutual aid. Following the tradition of Peter Kropotkin, Pierre Clastres, James Scott, Fernand Braudel and Imanuel Wallerstein, this study examines the full historical and contemporary possibilities for establishing self-governing communities at the edges of the capitalist world-system, considering the historical forces that often militate against those who try to practice mutual aid in the face of state power and capitalist incursion\"--Provided by publisher.

The H‐shaped feature, characterized by a single connecting channel (CC) linking two inflows, is a common geomorphological unit in delta river networks. This structure plays a critical role in redistributing upstream flows, affecting the hydrological connectivity of the network. Despite previous studies on geometric structures and flow distribution, the mechanisms influencing hydrological connectivity remain poorly understood due to the complex structure of deltas and the interactions among various controlling factors. This study investigates the flow distribution and hydrological connectivity of H‐shaped structures using numerical simulations and graph theory. Results indicate that, in H‐shaped structures, the gravitational pressure caused by variations in Upstream Discharge Ratios (UDR) and CC topography generates uneven spatial velocity fields, resulting in different levels of diversion capacity in the CC. The hydrological connectivity indexes of the distributary subnetwork increase with the growing diversion capacity of the CC, whereas the confluence subnetwork exhibits the opposite trend. The CC's influence on downstream flow regulation and hydrological connectivity is influenced by the structure itself. Enhanced CC diversion capacity in H‐shaped structures balances flow distribution and strengthens system resilience. The study emphasizes that simpler river network topologies concentrate flows in fewer channels while maintaining strong subnetwork exchanges, while complex networks distribute flows broadly but reduce inter‐subnetwork connectivity. Therefore, we recommend that deltaic river network management consider their topological characteristics and implement strategies such as constructing additional CC or modifying existing channel topography to enhance flow exchange capacity. These findings offer valuable insights for global management and conservation of deltaic river networks. Key Points The H‐shaped structure, functioning as a flow‐adaptive regulation system, alleviates downstream outlet flux asymmetry H‐shaped structures in deltaic river networks enhance hydrological connectivity and improve network disturbance resistance The impact of variations in upstream inflow and the topography of the CC on the bifurcation characteristics of the river network

In nature, rivers are always connected in various forms to constitute a specific type of river network. The identification and classification of river network types in watersheds is the premise of hydrological research. In this study, the Yellow River Basin, Huaihe River Basin, Haihe River Basin and Yangtze River Basin are divided into 71 sub-basins. According to the definition of river network types, the sub-basin river networks are qualitatively divided into 7 types. By comparing and analysing three river network characteristic parameters, which are river network density, river flow direction and river sinuosity, this study found that the types of river networks can be preliminarily determined according to the statistical data distribution of river sinuosity. The Cauchy distribution is used to fit the distribution characteristics of river sinuosity to further accurately determine the types of river networks. Except for the average R2 of the rectangular river network, which is 0.66, the R2 values of the fitting curves of the other river network types all range from 0.86 to 0.97. This method is applied to the four major watersheds, and the results are consistent with the hierarchical clustering analysis, with an accuracy of 82.86%. The method proposed in this study has application potential and can be applied to the automatic classification of river network types with high accuracy and efficiency.

The analysis of climatic drivers is of utmost importance for defining new hydrological parameters. The territory of Serbia covers the area of 88,361 km2 and all its rivers belong to three big basins, the Aegean, the Black sea and the Adriatic. Extreme weather conditions have been determining the properties of drainage basins within the last sixty year (1953–2023). This research, conducted for the first time in the territory of Serbia, has shown comparative data on the changes of drainage basins and the changes in the type of river networks for the period of last sixty years. The research also includes the changes of the topography, the terrain and the surface of the basins. The main methodology is based on GIS methods, geostatistical method and remote sensing. Adjusted Strahler’s order was also used for the purpose of this research. The obtained results on the topology of river networks were classified into six different types, them being parallel, dendritic, mixed, radial, fan-shaped and rectangular. There has been an increase by 8% in parallel, mixed and dendritic type. The most significant changes in the last sixty years were observed within the Aegean and Adriatic sea basin. The surface of the Black sea basin increased by 1.2%, with the addition of a higher number of tributaries, whereas the basins of Adriatic and the Aegean sea had a decrease by 0.3% and 0.9% respectively. The number of tributaries decreased by 5.5% within the Adriatic sea basin, and by 8.8% within the Aegean sea basin.

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

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics—every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adaptative evolution and on the yet-unexplored range of applications of the OCN concept.

A major goal in ecology is to understand mechanisms that influence patterns of biodiversity and community assembly at various spatial and temporal scales. Understanding how community composition is created and maintained also is critical for natural resource management and biological conservation. In this study, we investigated environmental and spatial factors influencing beta diversity of local fish assemblages along the longitudinal gradient of a nearly pristine Neotropical river in the Colombian Llanos. Standardized surveys were conducted during the low-water season at 34 sites within the Bita River Basin. Physical, chemical, and landscape parameters were recorded at each site, and asymmetric eigenvector maps were used as spatial variables. To examine the relative influence of dispersal and environmental variables on beta diversity and its components, distance-based redundancy analysis (db-RDA) and variation partitioning analysis were conducted. We proposed that spatial scale of analysis and position within the river network would constrain patterns of beta diversity in different ways. However, results indicated that in this system, high beta diversity was consistent among species assemblages no matter the scale of analysis or position within the river network. Species replacement (turnover) dominated beta diversity, an indication of the importance of species sorting. These findings suggested that conservation of fish diversity in tropical rivers requires maintenance of both habitat heterogeneity (spatial variation in habitat conditions) and connectivity at the scale of entire river basins.

The unprecedented hydraulic visibility of rivers surfaces deformation with SWOT satellite offers tremendous information for improving hydrological‐hydraulic models and discharge estimations for rivers worldwide. However, estimating the uncertain or unknown parameters of hydraulic models, such as inflow discharges, bathymetry, and friction parameters, poses a high‐dimensional inverse problem, which is ill‐posed if based solely on altimetry observations. To address this, we couple the hydraulic model with a semi‐distributed hydrological model, to constrain the ill‐posed inverse problem with sufficiently accurate initial estimates of inflows at the network upstreams. A robust variational data assimilation of water surface elevation (WSE) data into a 1D Saint‐Venant river network model, enables the inference of inflow hydrographs, effective bathymetry, and spatially distributed friction at network scale. The method is demonstrated on the large, complex, and poorly gauged Maroni basin in French Guiana. The pre‐processing chain enables (a) building an effective hydraulic model geometry from drifting ICESat‐2 WSE altimetry and Sentinel‐1 width; (b) filtering noisy SWOT Level 2 WSE data before assimilation. A systematic improvement is achieved in fitting the assimilated WSE (85% cost improvement), and in validating discharge at 5 gauges within the network. For assimilation of SWOT data alone, 70% of data‐model fit is in [−0.25;0.25m]$[-0.25;\\,0.25\\,\\mathrm{m}]$and the discharge NRMSE ranges between 0.05 and 0.18 (18%–71% improvement from prior). The high density of SWOT WSE enables the inferrence of detailed spatial variability in channel bottom elevation and friction, and inflows timeseries. The approach is transferable to other rivers networks worldwide.

Existing water quality prediction methods often ignore the true flow direction and flow variations of river networks and employ static topological structures. This leads to distorted depictions of pollutant migration processes, resulting in significant bias in prediction results. This in turn leads to misjudgments in ecological risk assessments, hindering scientific decision-making for targeted pollution control and risk early warning. To address this issue, this paper proposes a Temporal Graph Convolutional Network (T-GCN) model that incorporates river network topological constraints to improve the accuracy of watershed water quality predictions and the reliability of ecological risk assessment. The model constructs a directed graph based on the river network structure and introduces a flow-driven dynamic connection mechanism to adaptively reflect the impact of changing hydrological conditions on pollutant transport paths and velocities. It captures water quality evolution through joint spatiotemporal modeling and embeds hydrophysical constraints to ensure the rationality of prediction results. Experiments show that T-GCN outperforms spatiotemporal graph comparative model such as DCRNN, Graph WaveNet, and AGCRN in predicting four water quality indicators: DO (Dissolved Oxygen), Ammonia Nitrogen (NH 3 –N), Phosphorus (TP), and pH (Potential of Hydrogen). Evaluated in original physical units, T-GCN achieved lower prediction errors on the test set, with MSE of DO, NH 3 –N, TP, and pH being 0.940 mg/L, 0.142 mg/L, 0.022 mg/L, and 0.093, respectively, all outperforming the comparative model. The R 2 for the DO indicator reaches 0.884, and the Kappa coefficient for ecological risk discrimination reaches 0.863, 0.826, and 0.763 at low, medium, and high risk levels, respectively, demonstrating superior temporal and spatial stability. This proposed T-GCN model significantly improves the accuracy of watershed water quality prediction and the reliability of ecological risk assessment, providing a highly reliable prediction tool and decision-making support for smart watershed management and ecological risk prevention and control.

Existing global river networks underpin a wide range of hydrological applications but do not represent channels with divergent river flows (bifurcations, multi‐threaded channels, canals), as these features defy the convergent flow assumption that elevation‐derived networks (e.g., HydroSHEDS, MERIT Hydro) are based on. Yet, bifurcations are important features of the global river drainage system, especially on large floodplains and river deltas, and are also often found in densely populated regions. Here we developed the first raster and vector‐based Global RIver Topology that not only represents the tributaries of the global drainage network but also the distributaries, including multi‐threaded rivers, canals and deltas. We achieve this by merging a 30 m Landsat‐based river mask with elevation‐generated streams to ensure a homogeneous drainage density outside of the river mask for rivers narrower than approximately 30 m. Crucially, we employ the new 30 m digital terrain model, FABDEM, based on TanDEM‐X, which shows greater accuracy over the traditionally used SRTM derivatives. After vectorization and pruning, directionality is assigned by a series of elevation, flow angle and continuity approaches. The new global network and its attributes are validated using gauging stations, comparison with existing networks, and randomized manual checks. The new network represents 19.6 million km of streams and rivers with drainage areas greater than 50 km2 and includes 67,495 bifurcations. With the advent of hyper‐resolution modeling and artificial intelligence, GRIT is expected to greatly improve the accuracy of many river‐based applications such as flood forecasting, water availability and quality simulations, or riverine habitat mapping. Plain Language Summary Global river maps often overlook complex features, such as when a single river channel splits into multiple channels. These branching river systems are important because they are often found in densely populated, often flood‐prone regions, and they are crucial for understanding water movement across the Earth's surface. To address this limitation of existing river maps, we developed a new global river network called Global RIver Topology (GRIT), which includes these branching rivers and channels. GRIT was created by combining high‐resolution satellite imagery of rivers with advanced elevation data of the earth's surface. GRIT not only includes the main river channels but also provides information on river flow directions, widths, and points where rivers split. The GRIT river network has a total length of 19.6 million km and includes 67,495 bifurcations. GRIT stands to significantly enhance applications in hydrology, ecology, geomorphology, and flood management. Key Points Existing large‐scale river networks only represent single‐threaded gravity flow paths, rather than observed river centerlines Global RIver Topology (GRIT) was created by merging the 30 m Landsat‐based river mask from Global River Widths from Landsat with elevation streams, using the new 30 m FABDEM for greater accuracy GRIT is the first branching global river network representing bifurcations, multi‐threaded channels, and canals