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{"Abstract":["Most large rivers in densely populated areas split flow into multiple\n channels, forming interconnected pathways called threads. Multithread\n rivers are sensitive to hydroclimatic changes, yet understanding their\n dynamics is challenging due to the lack of robust metrics to characterize\n their evolution. To investigate the drivers of river evolution, we analyze\n 38 years of Landsat imagery alongside discharge records for 97\n multithread  reaches worldwide spanning diverse climates and both\n wandering and braided morphologies. We quantify the number of active\n threads and their allocated discharge through space and time using the\n entropic Braiding Index (eBI), coupled with metrics for bank migration\n rate, floodplain reworking, and channel-belt size. Data reveal that\n multithread river dynamics are strongly controlled by flow\n intermittency—expressed as the dimensionless ratio of long-term mean\n discharge to bankfull discharge. Rivers with lower flow intermittency\n (i.e., higher discharge relative to bankfull conditions) exhibit more\n active threads, decelerated thread migration, prolonged floodplain\n reworking timescales, and smaller channel-belt area normalized by\n channelized area. Lower flow intermittency also results in preferential\n flow routing among threads (lower eBI relative to thread count).\n Channel-belt area relative to channelized area exponentially declines with\n thread count, potentially reflecting a greater propensity for\n reconfiguration over lateral migration in braided rivers. Furthermore,\n multithread rivers in cold climates exhibit slower evolution rates across\n scales, likely due to permafrost influence. Together, results suggest that\n future increases in discharge variability could cause multithread rivers\n to split into more active threads and accelerate movement within channel\n belts, potentially impacting livelihoods and ecosystems along river\n corridors. "],"TechnicalInfo":["This repository contains the code and data used in the publication: \n **Global Hydroclimatic Controls on Multithread River Dynamics** Authors:\n Feifei Zhao, Vamsi Ganti, Austin Chadwick, Evan Greenberg, Jonah McLeod,\n Yinxue Liu, Louise Slater, and Efi Foufoula-Georgiou Corresponding author:\n Feifei Zhao: [xiafeizhao@ucsb.edu](mailto:xiafeizhao@ucsb.edu) ##\n Repository Structure The archive consists of the following main\n components: ├── CODE.zip/ │ ├── eBIcalculator/ │ ├── misc/ │ ├── figures/\n ├── DATA.zip/ │ ├── [Data folders described below] ├──\n ebi_combined_statistics_clean.csv CODE.zip/eBIcalculator/ Contains scripts\n and notebooks for computing the cross-sectional entropic Braiding Index\n (eBI) and Braiding Index (BI) from a time series of river masks. *\n mesh_maker.ipynb Generates evenly spaced cross sections perpendicular to a\n river centerline using rivgraph. These cross sections are used to\n calculate a reach-averaged entropic braiding index. * preprocess_images.py\n Preprocesses binary river masks derived from Landsat imagery. This\n includes removing empty (blank) masks from the time series, cropping\n images to remove edge artifacts, and using rivgraph functionalities to\n remove speckle. * rivgraph_eBI.py Main script to compute a reach-averaged\n entropic Braiding Index (eBI) using topological network outputted from\n mesh_maker.ipynb. Outputs eBI, BI, and wetted area. CODE.zip/misc/ Utility\n scripts for summarizing outputs and generating bulk metrics. *\n generate_hist_csv.py Aggregates eBI histogram statistics across sites into\n a single CSV for comparative analysis. * generate_stat_csv.py Generates\n summary statistics (e.g., average eBI, migration rate, mobility metrics)\n per site from processed time series data. * RF_model.py Trains and\n evaluates a random forest regression model to evaluate discharge controls\n on channel planform and kinematics. * \\[Other scripts may exist in this\n folder for plotting, testing, or exploratory analysis.] CODE.zip/figures/\n Contains scripts used to generate the figures included in the published\n manuscript.\\ These scripts rely on precomputed summary statistics and\n regression results (ebi_combined_statistics_clean.csv) ## Requirements *\n Python ≥ 3.8 * Required packages: * numpy, pandas, rasterio, matplotlib,\n geopandas * rivgraph\n ([https://github.com/VeinsOfTheEarth/rivgraph](https://github.com/VeinsOfTheEarth/rivgraph)) RivGraph is not redistributed in this archive. Users wishing to reproduce the analysis should obtain RivGraph directly from the original repository and comply with its license. DATA.zip/ Contains all input and output data referenced in the study (excluding Landsat images due to file size constraints). Each subfolder within DATA.zip corresponds to one river reach, and all river folders follow the same internal file structure. [Site_Name] corresponds to the "River" field in ebi_combined_statistics_clean.csv. Below is the file structure for each reach: \\[Site_Name]/ ├── [Site_Name]_cropped.tif │ - Preprocessed DSWE-derived binary channel mask used to extract the river centerline and generate cross sections. ├── [Site_Name]_dem.tif │ - DEM raster used to delineate channel-belt extent. ├── [Site_Name]_cb.gpkg │ - Vector polygon defining the mapped channel-belt extent, derived from NASA DEM products. ├── output_annual/ │ └── masks/ │ - Binary water masks generated from Landsat median annual composites. ├── preparedimagery_annual/ │ - RGB versions of annual images prepared for visualization and presentation purposes. ├── rivgraph/ │ ├── nodes/ │ ├── links/ │ └── centerline.shp │ - River network topology outputs generated using the RivGraph Python package │ ([https://github.com/VeinsOfTheEarth/RivGraph](https://github.com/VeinsOfTheEarth/RivGraph)). │ - Includes annual reach-averaged eBI, BI, and wetted area metrics used in this study. For a subset of river reaches, the files `[Site_Name]_cb.gpkg` and `[Site_Name]_dem.tif` are not included in this archive. These reaches overlap with a previously published channel-belt dataset produced by our research group. For those sites, the corresponding channel-belt polygons and DEM-derived products are archived separately and are publicly available at: [https://datadryad.org/dataset/doi:10.5061/dryad.wm37pvmvf](https://datadryad.org/dataset/doi:10.5061/dryad.wm37pvmvf) All other files within each river reach folder follow the same structure and naming convention described above. ebi_combined_statistics_clean.csv Summary csv file contains compiled site-level statistics used in the analysis. Each row represents a river reach analyzed in the study. Column descriptions: * River: Name of the river reach, matches with [site_name] of each river folder in DATA/ * Width(m): Average channel-belt width in meters. * Stream Power (W/m): Stream power per unit channel length, calculated from discharge and slope. * Classification: Channel pattern classification — either Braided (B), HSW (High Sinuosity Wandering), or LSW (Low Sinuosity Wandering). * Sinuosity: Centerline sinuosity of the reach. * Slope (cm/km): Reach-scale slope of the river, in centimeters per kilometer. * Qm: Mean discharge (in m³/s). * Qmax(m3/s): Maximum discharge across the time series. * Qmin(m3/s): Minimum discharge across the time series. * Climate Zone: Köppen–Geiger climate classification for the region. * CB/Aw: Normalized channel-belt area. * T_R: Floodplain reworking timescale (in years). * average_discharge_annual: Annual average discharge. * median_discharge_annual: Annual median discharge. * cov_discharge_site: Coefficient of variation in discharge over time. * wetted_area_avg_subannual: Average wetted area. * mean_ebi_site: Time- and reach-averaged eBI * median_ebi_site: Median eBI across time and reach. * std_ebi_site: Standard deviation of reach-averaged eBI. * cov_ebi_site: Coefficient of variation of reach-averaged eBI * 95_ebi_site: 95th percentile of eBI. * 5_ebi_site: 5th percentile of eBI. * mean_bi_site: Time- and reach-averaged BI * median_bi_site: Median BI across time and reach. * std_bi_site: Standard deviation of reach-averaged BI. * cov_bi_site: Coefficient of variation of reach-averaged BI * 95_bi_site: 95th percentile of BI. * 5_bi_site: 5th percentile of BI. * eBI_BI_ratio_site: Ratio of eBI to BI * Qbf point: bankful discharge derived from a machine learning model. * Iw: Intermittency factor. * dim_Q: Dimensionless discharge metric. * mean_migration_rate: Mean riverbank migration rate (in m/year). * std_migration: Standard deviation of migration rate. * norm_migration_rate: Normalized riverbank migration rate. * norm_error: Error in normalized migration rates."]} 

Abstract Satellite precipitation retrieval is inherently an underdetermined inverse problem where additional physical constraints could substantially enhance accuracy. While previous studies have explored static (pixel‐based/spatial‐context‐based) environmental variables at discrete satellite observation times, their temporal dynamic information remains underutilized. Building on our earlier finding that retrieval errors depend on storm progression (event stage), we propose a new, physically interpretable mechanism for improving retrievals, namely, leveraging environmental variables' temporal dynamics as proxies for event stages. Using IMERG satellite product and GV‐MRMS as ground‐truth over CONUS (2018–2020), we first demonstrate robust coevolution patterns of environmental variables and satellite errors throughout events, and show that these variables' temporal gradients reliably infer event stages. We then demonstrate that incorporating these variables and their gradients into a machine‐learning post‐processing framework improves retrieval accuracy. This work inspires and guides more thorough utilization of spatiotemporal atmospheric fields encoding rich physical information within advanced machine‐learning frameworks for further algorithm improvement. 

Abstract Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. Significance StatementThe precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041–80 period. As compared to present-day climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future. 

Abstract Extreme floods and landslides in high‐latitude watersheds have been associated with rain‐on‐snow (ROS) events. Yet, the risks of changing precipitation phases on a declining snowpack under a warming climate remain unclear. Normalizing the total annual duration of ROS with that of the seasonal snowpack, the ERA5 data (1941–2023) show that the frequency of high‐runoff ROS events is a characteristic feature of high‐latitude coastal zones, particularly over the coasts of south‐central Alaska and southern Newfoundland. Total rainfall accumulation per seasonal snowpack duration has increased across western mountain ranges, with the Olympic Mountains experiencing more than 40 mm of additional rainfall over the snowpack in the past eight decades, followed by the Sierra Nevada. These trends could drive an 8% increase in rainfall extremes (e.g., more than 10 mm for 6 hr storm with a 15‐year return period), highlighting the need for resilient flood control systems in high‐latitude coastal cities. 

Abstract Determining the key elements of interconnected infrastructure and complex systems is paramount to ensure system functionality and integrity. This work quantifies the dominance of the networks’ nodes in their respective neighborhoods, introducing a centrality metric, DomiRank, that integrates local and global topological information via a tunable parameter. We present an analytical formula and an efficient parallelizable algorithm for DomiRank centrality, making it applicable to massive networks. From the networks’ structure and function perspective, nodes with high values of DomiRank highlight fragile neighborhoods whose integrity and functionality are highly dependent on those dominant nodes. Underscoring this relation between dominance and fragility, we show that DomiRank systematically outperforms other centrality metrics in generating targeted attacks that effectively compromise network structure and disrupt its functionality for synthetic and real-world topologies. Moreover, we show that DomiRank-based attacks inflict more enduring damage in the network, hindering its ability to rebound and, thus, impairing system resilience. DomiRank centrality capitalizes on the competition mechanism embedded in its definition to expose the fragility of networks, paving the way to design strategies to mitigate vulnerability and enhance the resilience of critical infrastructures. 

Abstract The feedback of topsoil moisture (SM) content on convective clouds and precipitation is not well understood and represented in the current generation of weather and climate models. Here, we use functional decomposition of satellite‐derived SM and cloud vertical profiles (CVP) to quantify the relationship between SM and the vertical distribution of cloud water in the central US. High‐dimensional model representation is used to disentangle the contributions of SM and other land‐surface and atmospheric variables to the CVP. Results show that the sign and strength of the SM‐cloud‐precipitation feedback varies with cloud height and time lag and displays a large spatial variability. Positive anomalies in antecedent 7‐hr SM and land‐surface temperature enhance cloud reflectivity up to 4 dBZ in the lower atmosphere about 1–3 km above the surface. Our approach presents new insights into the SM‐cloud‐precipitation feedback and aids in the diagnosis of land‐atmosphere interactions simulated by weather and climate models. 

Abstract Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPMCore Observatorysatellite are used in synergy with cloud tracking information derived from infrared imagery from theGOES-13andMeteosat-7geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds’ life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations. 

Abstract Most large rivers in densely populated areas split flow into multiple channels, forming interconnected pathways called threads. Multithread rivers are sensitive to hydroclimatic changes, yet understanding their dynamics is challenging due to the lack of robust metrics to characterize their evolution. To investigate the drivers of river evolution, we analyze 38 years of Landsat imagery alongside discharge records for 97 multithread reaches worldwide spanning diverse climates and both wandering and braided morphologies. We quantify the number of active threads and their allocated discharge through space and time using the entropic Braiding Index (eBI), coupled with metrics for bank migration rate, floodplain reworking, and channel‐belt size. Data reveal that multithread river dynamics are strongly controlled by flow intermittency—expressed as the dimensionless ratio of long‐term mean discharge to bankfull discharge. Rivers with lower flow intermittency (i.e., higher discharge relative to bankfull conditions) exhibit more active threads, decelerated thread migration, prolonged floodplain reworking timescales, and smaller channel‐belt area normalized by channelized area. Lower flow intermittency also results in preferential flow routing among threads (lowereBIrelative to thread count). Channel‐belt area relative to channelized area exponentially declines with thread count, potentially reflecting a greater propensity for reconfiguration over lateral migration in braided rivers. Furthermore, multithread rivers in cold climates exhibit slower evolution rates across scales likely due to permafrost influence. Together, results suggest that future increases in discharge variability could cause multithread rivers to split into more active threads and accelerate movement within channel belts, potentially impacting livelihoods and ecosystems along river corridors.