Utilizing Hydrology and Geomorphology Relationships to Estimate Streamflow Conditions on Maui and O‘Ahu, Hawai‘I

As the population on the island of Maui drastically increases, water resource demands continue to rise. In order to match water demands and to manage water resources, it is important to understand streamflow and drainage basin interactions. If relationships between a drainage basin’s hydrologic and geomorphologic characteristics can be quantified, then streamflow conditions of ungaged streams can potentially be estimated. The baseflow recession constant is an important variable to analyze for water management, yet until this study, recession constants were not calculated for the island of Maui, or Hawai‘i as a whole. Recession constants of currently gaged streams on Maui correlated to the permeability and flow conditions of the watersheds. Streams with recession constants \u3e0.95 were generally placed in areas of the island with dike-impounded groundwater and streams with recession constant

Stream-Channel and Watershed Delineations and Basin-Characteristic Measurements using Lidar Elevation Data for Small Drainage Basins within the Des Moines Lobe Landform Region in Iowa, TR-692

Basin-characteristic measurements related to stream length, stream slope, stream density, and stream order have been identified as significant variables for estimation of flood, flow-duration, and low-flow discharges in Iowa. The placement of channel initiation points, however, has always been a matter of individual interpretation, leading to differences in stream definitions between analysts. This study investigated five different methods to define stream initiation using 3-meter light detection and ranging (lidar) digital elevation models (DEMs) data for 17 stream gages with drainage areas less than 50 square miles within the Des Moines Lobe landform region in north-central Iowa. Each DEM was hydrologically enforced and the five stream initiation methods were used to define channel initiation points and the downstream flow paths. The five different methods to define stream initiation were tested side-by-side for three watershed delineations: (1) the total drainage-area delineation, (2) an effective drainage-area delineation of basins based on a 2-percent annual exceedance probability (AEP) 12-hour rainfall, and (3) an effective drainage-area delineation based on a 20-percent AEP 12-hour rainfall. Generalized least squares regression analysis was used to develop a set of equations for sites in the Des Moines Lobe landform region for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent AEPs. A total of 17 streamgages were included in the development of the regression equations. In addition, geographic information system software was used to measure 58 selected basin-characteristics for each streamgage. Results of the regression analyses of the 15 lidar datasets indicate that the datasets that produce regional regression equations (RREs) with the best overall predictive accuracy are the National Hydrographic Dataset, Iowa Department of Natural Resources, and profile curvature of 0.5 stream initiation methods combined with the 20-percent AEP 12-hour rainfall watershed delineation method. These RREs have a mean average standard error of prediction (SEP) for 4-, 2-, and 1-percent AEP discharges of 53.9 percent and a mean SEP for all eight AEPs of 55.5 percent. Compared to the RREs developed in this study using the basin characteristics from the U.S. Geological Survey StreamStats application, the lidar basin characteristics provide better overall predictive accuracy

Methods for Estimating Annual Exceedance-Probability Discharges for Streams in Iowa, Based on Data through Water Year 2010, TR-519, 2013

A statewide study was performed to develop regional regression equations for estimating selected annual exceedance- probability statistics for ungaged stream sites in Iowa. The study area comprises streamgages located within Iowa and 50 miles beyond the State’s borders. Annual exceedanceprobability estimates were computed for 518 streamgages by using the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak discharges for each streamgage using annual peak-discharge data through 2010. The estimation of the selected statistics included a Bayesian weighted least-squares/generalized least-squares regression analysis to update regional skew coefficients for the 518 streamgages. Low-outlier and historic information were incorporated into the annual exceedance-probability analyses, and a generalized Grubbs-Beck test was used to detect multiple potentially influential low flows. Also, geographic information system software was used to measure 59 selected basin characteristics for each streamgage. Regional regression analysis, using generalized leastsquares regression, was used to develop a set of equations for each flood region in Iowa for estimating discharges for ungaged stream sites with 50-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities, which are equivalent to annual flood-frequency recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. A total of 394 streamgages were included in the development of regional regression equations for three flood regions (regions 1, 2, and 3) that were defined for Iowa based on landform regions and soil regions. Average standard errors of prediction range from 31.8 to 45.2 percent for flood region 1, 19.4 to 46.8 percent for flood region 2, and 26.5 to 43.1 percent for flood region 3. The pseudo coefficients of determination for the generalized leastsquares equations range from 90.8 to 96.2 percent for flood region 1, 91.5 to 97.9 percent for flood region 2, and 92.4 to 96.0 percent for flood region 3. The regression equations are applicable only to stream sites in Iowa with flows not significantly affected by regulation, diversion, channelization, backwater, or urbanization and with basin characteristics within the range of those used to develop the equations. These regression equations will be implemented within the U.S. Geological Survey StreamStats Web-based geographic information system tool. StreamStats allows users to click on any ungaged site on a river and compute estimates of the eight selected statistics; in addition, 90-percent prediction intervals and the measured basin characteristics for the ungaged sites also are provided by the Web-based tool. StreamStats also allows users to click on any streamgage in Iowa and estimates computed for these eight selected statistics are provided for the streamgage

Flood hazard hydrology: interdisciplinary geospatial preparedness and policy

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Floods rank as the deadliest and most frequently occurring natural hazard worldwide, and in 2013 floods in the United States ranked second only to wind storms in accounting for loss of life and damage to property. While flood disasters remain difficult to accurately predict, more precise forecasts and better understanding of the frequency, magnitude and timing of floods can help reduce the loss of life and costs associated with the impact of flood events. There is a common perception that 1) local-to-national-level decision makers do not have accurate, reliable and actionable data and knowledge they need in order to make informed flood-related decisions, and 2) because of science--policy disconnects, critical flood and scientific analyses and insights are failing to influence policymakers in national water resource and flood-related decisions that have significant local impact. This dissertation explores these perceived information gaps and disconnects, and seeks to answer the question of whether flood data can be accurately generated, transformed into useful actionable knowledge for local flood event decision makers, and then effectively communicated to influence policy. Utilizing an interdisciplinary mixed-methods research design approach, this thesis develops a methodological framework and interpretative lens for each of three distinct stages of flood-related information interaction: 1) data generation—using machine learning to estimate streamflow flood data for forecasting and response; 2) knowledge development and sharing—creating a geoanalytic visualization decision support system for flood events; and 3) knowledge actualization—using heuristic toolsets for translating scientific knowledge into policy action. Each stage is elaborated on in three distinct research papers, incorporated as chapters in this dissertation, that focus on developing practical data and methodologies that are useful to scientists, local flood event decision makers, and policymakers. Data and analytical results of this research indicate that, if certain conditions are met, it is possible to provide local decision makers and policy makers with the useful actionable knowledge they need to make timely and informed decisions

Distributed Hydrologic Modeling for Streamflow Prediction at Ungauged Basins

Hydrologic modeling and streamflow prediction of ungauged basins is an unsolved scientific problem as well as a policy-relevant science theme emerging as a major challenge to the hydrologic community. One way to address this problem is to improve hydrologic modeling capability through the use of spatial data and spatially distributed physically based models. This dissertation is composed of three papers focused on 1) the use of spatially distributed hydrologic models with spatially distributed precipitation inputs, 2) advanced multi-objective calibration techniques that estimate parameter uncertainty and use stream gauge and temperature data from multiple locations, and 3) an examination of the relationship between high-resolution soils data and streamflow recession for use in a priori parameter estimation in ungauged catchments. This research contributes to the broad quest to reduce uncertainty in predictions at ungauged basins by integrating developments of innovative modeling techniques with analyses that advance our understanding of natural systems

Estimation of Solute Fluxes from Ungaged Headwater Catchments in the Catskill Park of New York State

Predictions of flow and subsequent solute fluxes from ungaged basins have important implications both for water resources management and ecosystem monitoring studies. The Catskill region of New York State is one such place that requires both water resources management and ecosystem monitoring due to its strategic location as the main water-supplying region for New York City. This study examines the differences in chemical mass flux estimates made in ungaged basins using three different chemistry aggregation methods for solute concentrations determined from monthly grab samples. The efficacy of area ratios for predicting flow at the upstream location of a nested pair of stream gages based on flow at the downstream reference gage is also explored. The benefit of data set partitioning and development of separate prediction models for different flow regimes of the reference gage is analyzed, and a threshold of area ratio for use of such a method is established, with implications for use in ungaged basins. This work is focused on the Catskill region, but is likely to be applicable to other temperate, montane systems. Significant relationships were observed between upstream and downstream flow in all test watersheds. Furthermore, watershed area ratio was the most important basin parameter for estimating flow at the upstream location of a nested pair of stream gages. The area ratio alone explained 93% of the variance in the functional relation slopes that best fit the flow regressions. Data set partitioning was found to be beneficial only for nested pairs with area ratios greater than 0.1, and was determined by analysis of the root mean square error of the different flow prediction models. Five of the fifteen test watershed pairs had a lower root mean square error using the partitioned relationships and these pairs all had area ratios greater than 0.1. The relative difference between the three different chemistry aggregation methods was found to be relatively small on an annual basis (average difference of 7%) and increase with shorter time steps up to daily flux estimates (average difference of 26%). This finding indicates that simple flow estimation methods based on area ratios are justifiable, and perhaps preferred, for estimation of annual chemical mass fluxes, and that for such estimates of flux, the exact solute chemistry aggregation method matters little on an annual basis

Channel geomorphology relationships for the Beaver Creek watershed

The proposed Appalachian Corridor H will be built through some of the most rugged terrain in West Virginia. The Corridor H section from Davis to Bismarck passes through the Beaver Creek watershed, located in Tucker County, West Virginia, which has been heavily affected by historic mining, deteriorating the water quality conditions of the Beaver Creek watershed. Due to the construction of Corridor H, some of the streams will be disturbed from their present courses. Hence, stream restoration work for these streams is inevitable. It is important to develop a family of stream geometry curves that relate the bankfull parameters as functions of drainage area to carry out stream restoration works.;Positive results by the use of numerical and hydrologic models used in this study show the accuracy and state of the art advancement of such models and that they can be used to infer bankfull flows without having to rely on gaged data. (Abstract shortened by UMI.)

Methods for Development of Planning-Level Estimates of Stormflow at Unmonitored Stream Sites in the Conterminous United States

DTFH61-02-Y-30079This report documents methods for data compilation and analysis of statistics for stormflows that meet data-quality objectives for order-of-magnitude planning-level water-quality estimates at unmonitored sites in the conterminous United States. Statistics for prestorm streamflow, precipitation, and runoff coefficients are used to model stormflows for use with the Stochastic Empirical Loading and Dilution Model (SELDM), which is a highway-runoff model. SELDM is designed to better quantify the risk of exceeding water-quality criteria as precipitation, discharge, ambient water quality, and highway-runoff quality vary from storm to storm. Summary statistics also may be used to help estimate annual-average water-quality loads. Streamflow statistics are used to estimate prestorm flows. Streamflow statistics are estimated by analysis of data from 2,873 U.S. Geological Survey streamgages in the conterminous United States with drainage areas ranging from 10 to 500 square miles and at least 24 years of record during the period 1960 122004. Streamflow statistics are regionalized using U.S. Environmental Protection Agency Level III nutrient ecoregions. Storm-event precipitation statistics are estimated by analysis of data from 2,610 National Oceanic and Atmospheric Administration hourly-precipitation data stations in the conterminous United States with at least 25 years of data during the 1965 122006 period. Storm-event precipitation statistics are regionalized using U.S. Environmental Protection Agency rain zones. Statistics to characterize volumetric runoff coefficients are estimated using data from 6,142 storm events at 306 study sites. Runoff coefficient statistics are not regionalized, but are organized by total impervious area. All of the geographic information system files, computer programs, data files, and regression results developed for this study are included on the CD 12ROM accompanying this report