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6 results for “geomorphic heterogeneity”
SHIFT: A DEM-Based Spatial Heterogeneity Improved Mapping of Global Geomorphic Floodplains
<h2>Description</h2> <p><strong>SHIFT</strong> (Spatial Heterogeneity Improved Floodplain by Terrain analysis) is a 90-m resolution global geomorphic floodplain map based on terrain analysis. It takes MERIT-Hydro as the terrain input and Floodplain Hydraulic Geometry (FHG) as the thresholding scheme, with the scaling parameters estimated by a stepwise framework that both respects the power law and approximates the spatial extent of hydrodynamic modeling. SHIFT effectively captures the global patterns of the geomorphic floodplains, with better regional details than existing data.</p> <h2>Data Structure</h2> <p>We provide 2 resolutions of data for different needs.</p> <ul> <li><strong>SHIFT_v3_90m</strong>: The original SHIFT data derived from MERIT-Hydro, with lakes and reservoirs removed. The resolution is 0.000833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 90 meters at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> <li><strong>SHIFT_v3_1km</strong>: The resampled SHIFT data with lakes and reservoirs marked. The resolution is 0.00833333333333 degrees under geographic coordinate system (EPSG:4326), approximately 1 km at the equator. Pixels with value 1 are floodplains, 2 are lakes and reservoirs and 0 are non-floodplains, with empty values set as 255 (denoting pixels not within any watersheds under the threshold of 1000 km2).</li> </ul> <p>Also, we provide our derived spatially-varying parameters in all Level-3 basins to support future studies. Parameters are provided in a shapefile, with 'a' denotes the proportional parameter and 'b' denotes the exponent. We aggregated MERIT-Basins based on its spatial relationship with basins from Level-3 HydroBASINS, ensuring that the centroid of a MERIT-Basin falls within the corresponding boundary. This approach accounts for slight differences in boundaries due to the use of different terrain data, preventing confusion in hydrological representation.</p> <p>For more details, please refer to:</p> <ul> <li>Zheng, K., Lin, P., and Yin, Z.: SHIFT: a spatial-heterogeneity improvement in DEM-based mapping of global geomorphic floodplains, Earth Syst. Sci. Data, 16, 3873–3891, <a href="https://doi.org/10.5194/essd-16-3873-2024" rel="noopener">https://doi.org/10.5194/essd-16-3873-2024</a>, 2024.</li> </ul> <h2>Development Log</h2> <ol> <li><strong>Changes in v3 compared to v2:</strong> <ol> <li> <p><strong>Inclusion of Missing Level-3 Basin:</strong> We have added a previously missing Level-3 basin (PFAF ID: 242) that covers an area in Eastern Europe, specifically from Warsaw to Minsk. This omission was due to a technical problem that has now been resolved. Data are now still available in two resolutions: 90-meter and 1-kilometer.</p> </li> <li> <p><strong>Updated Parameters</strong>: Along with the new boundaries, updated parameters are provided in the shapefile.</p> </li> <li> <p><strong>Re-estimated Global Floodplain Area</strong>: Based on the new data, we have re-estimated the global total floodplain area from 9.9 × 10^6 km² to 9.92 × 10^6 km². This area still represents approximately 6.6% of the total land mass.</p> </li> </ol> </li> <li><strong>Changes in v2 compared to v1:</strong> <ol> <li><strong>Parameter 'b' Estimation:</strong> We modified the technical details of parameter 'b' estimation, specifically the binning parameter, adding a constraining mechanism to handle data noise. This resulted in stabler estimates for large basins and a clearer pattern of global residual uncertainty.</li> <li><strong>Target Function for Parameter 'a':</strong> We changed our target function to balance information from both datasets, using Fleiss’s Kappa (FK) and a penalty term to reduce bias.</li> </ol> </li> </ol> <h2>Contacts</h2> <ul> <li>Kaihao Zheng, <a href="mailto:Mostaly@pku.edu.cn" target="_blank" rel="noopener">Mostaly@pku.edu.cn</a></li> <li>Peirong Lin, <a href="mailto:peironglinlin@pku.edu.cn" target="_blank" rel="noopener">peironglinlin@pku.edu.cn</a></li> </ul> <p> </p>
Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
<p>Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two-kilometer river corridor segments of the Swan River in Montana, USA to examine relationships between wood accumulations (wood accumulation distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (density, aggregation, interspersion, and evenness of patches in the river corridor). We hypothesize that i) more dynamic river segments correlate with a greater presence, persistence, and distribution of wood accumulations; ii) years with higher peak discharge correspond with greater channel dynamism and wood accumulations; and iii) all river corridor variables analyzed play a role in explaining river corridor spatial heterogeneity. Our results suggest that decadal-scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of wood accumulations per surface area and greater persistence of these wood accumulations through time. Second, higher peak discharges correspond to greater values of wood distribution density, but not to greater channel dynamism. Third, persistent values of geomorphic heterogeneity, as reflected in the heterogeneity metrics of aggregation, interspersion, patch density, and evenness, are explained by potential predictor variables analyzed here. Our results reflect the complex interactions of water, sediment, and large wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions</p>
Data from: Interactions of wood accumulations, channel dynamics, and geomorphic heterogeneity within a river corridor
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Data from: Drivers of geomorphic heterogeneity in unconfined non‐perennial river corridors
<p>River corridors along non-perennial stream networks are known to provide diverse physical and ecological functions that are thought to be related to geomorphic heterogeneity. Geomorphic heterogeneity refers to the spatial and temporal variability of geomorphic units, which are geomorphic patches within the river corridor formed by a given set of processes. While studies on the magnitude and drivers of geomorphic heterogeneity are emerging in perennial streams, similar studies in ephemeral streams are lacking. Given the ubiquity of non-perennial streams globally, we aim to answer questions regarding the magnitude and drivers of geomorphic heterogeneity in non-perennial river corridors as well as how geomorphic units and heterogeneity reflect processes related to flood disturbance. Geomorphic units were mapped in 30 unconfined river corridors within six non-perennial watersheds in Utah and Arizona, USA. Landscape heterogeneity metrics – Shannon's Diversity Index, Shannon's Evenness Index, and patch density – were used to quantify geomorphic heterogeneity within each reach. Additionally, variables that may potentially constrain or drive heterogeneity were quantified, including floodplain shape, grain size, large wood abundance, and proxies for flood disturbance. While heterogeneity positively correlated with metrics for morphology and disturbance, statistical models suggest that morphologic context, particularly floodplain width, was a more important predictor for estimating geomorphic heterogeneity. Still, geomorphic units reflected aggradation processes indicative of a range of flood energies, suggesting a strong tie between heterogeneity and disturbance. Results suggest that geomorphic heterogeneity may be resilient to changes in flood disturbance frequency or magnitude, but future studies investigating long-term temporal heterogeneity are needed.</p>
Data from: Drivers of geomorphic heterogeneity in unconfined non‐perennial river corridors
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Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"
<p>Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow</p>
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