ABSTRACT:

This resource represents the results of a project that: 1) developed a methodology to assess floodplain integrity using geospatial datasets available for large spatial scales; and 2) used the methodology to evaluate spatial patterns of floodplain integrity in the state of Colorado. To accomplish these objectives, the critical floodplain functions of attenuating floods, storing groundwater, regulating sediment, providing habitat, and regulating organics and solutes were evaluated. For each floodplain function, measurable stressors that inhibit the specific function were identified. The density of each stressor variable in the floodplain was quantified using datasets that are publicly available at large spatial scales. The index of integrity for a given floodplain function was then determined using the density of all stressors that inhibit the function. Next, the overall index of floodplain integrity for a given floodplain unit was calculated using a geometric mean of the indices of integrity for each of the five floodplain functions. The index of floodplain integrity methodology was applied in the state of Colorado to analyze the integrity of each of the five floodplain functions and the aggregated overall integrity. This resource contains a table with the resulting numeric index of floodplain integrity for each of the floodplain functions for each floodplain unit segregated by HUC-12 boundaries. It also contains a shapefile of the floodplain-containing HUC-12 units in Colorado with the index of floodplain integrity values as attributes.

ABSTRACT:

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 to 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1 – 6 hours of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

ABSTRACT:

The data in this Hydroshare resource are associated with the manuscript "Experimental observations of floodplain vegetation, bedforms, and sediment transport interactions in a meandering channel".

SedTransport.csv contains the sediment feed and transport rates associated with each flume operation period occurring during each of the 7 runs described in the manuscript. The equilibrium topography used in statistical moving window and patch based analysis are associated with the datasets labeled with cumulative run times 33.7, 75.2, 103.6, 121.5, 135.1, 166.9, and 178.5 hours.

Please contact ryan.morrison@colostate.edu or danny.white@colostate.edu with any questions about this dataset or the manuscript.

ABSTRACT:

The datasets in this repository are associated with “This repository consists of the data associated with the Manuscript "Mass and MomentumFlux Prediction at the Channel-Floodplain Interface Associated with Riparian Vegetation."

The model output folder consists of data for both vegetation-induced and user-assigned roughness conditions for different flow scenarios and vegetation densities. Data also includes the nodes of the left and right bank at the channel-floodplain interface, including the Python script used for post-processing the model outputs for calculating mass and momentum flux analyzed in this manuscript.

Please contact cha.smriti@gmail with any questions related to this dataset.

ABSTRACT:

Floodplains are essential ecosystems that provide a variety of economic, hydrologic, and ecologic services. Within floodplains, surface water-groundwater exchange plays an important role in facilitating biogeochemical processes and can have a strong influence on stream hydrology through infiltration or discharge of water. These functions can be difficult to assess due to the heterogeneity of floodplains and monitoring constraints, so numerical models are useful tools to estimate fluxes, especially at large scales. In this study, we use the SWAT+ (Soil and Water Assessment Tool) ecohydrological model to quantify magnitudes and spatiotemporal patterns of floodplain surface water-groundwater exchange in a mountainous watershed using an updated version of the gwflow module that directly calculates floodplain-aquifer exchange rates during periods of floodplain inundation. The gwflow module is a spatially distributed groundwater modeling subroutine within the SWAT+ code that uses a gridded network and physically based equations to predict groundwater storage, groundwater head, and groundwater fluxes. We used SWAT+ to model the 7,516 km2 Colorado River Headwaters watershed and streamflow data from USGS gages for calibration and testing. Models that included floodplain-groundwater interactions outperformed those without such interactions and provided valuable information about floodplain exchange rates and volumes. Our analyses on the location of floodplain fluxes in the watershed also show that wider areas of floodplains, “beads,” exchanged a higher net and per area volume of water, as well as higher rates of exchange, than narrower areas, “strings.” Study results show that floodplain channel-groundwater exchange is a valuable process to include in hydrologic models, and model outputs could inform land conservation practices by indicating priority locations where substantial hydrologic exchange occurs.

ABSTRACT:

Wide, low-gradient segments within river networks (i.e., beads) play a critical role in absorbing and morphologically adapting to disturbances, including wildfires and debris flows. However, the magnitude and rate of morphological adjustment and subsequent hydraulic conditions provided by beads compared to pre-disturbance conditions are not well understood. This study analyzed trajectories of river morphology, flood attenuation, and fish habitat following the 2020 Cameron Peak Fire and July 2022 debris flow and flood at Little Beaver Creek, Colorado, USA. Using repeat aerial imagery, ground-based surveys, and hydrodynamic modeling, we assessed morphological changes in a 600-m-long bead of Little Beaver Creek. We used remotely sensed imagery for pre- and post-disturbance geomorphic metrics in rates of floodplain destruction and formation, changes in channel width, and channel migration. Metrics of floodplain destruction and formation and channel migration greatly increased in magnitude after the first post-fire runoff season but returned to the historical range of these metrics three years after the fire. The 2022 flood deposited sediment, infilled side channels, reduced pool area, and increased the area of bars and islands. The assessed functions of the system did not show clear improvement or impairment despite more rapid changes in system geometry, geomorphic unit abundance, and geomorphic unit location. The ability of the site to attenuate peak flows changed minimally and inconsistently over the studied floods. Various lotic habitat conditions changed—namely a reduction in floodplain access and deepening of certain pools—but the overall flow-type diversity of the system was not largely impacted. The resilience of the active channel of Little Beaver Creek to the fire and flood disturbances while retaining key services demonstrates the importance of river beads for enhancing river-floodplain resilience to large disturbance events and highlights river beads as key areas for preservation and restoration.