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:

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:

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:

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.