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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Bringing Statistical Learning Machines Together For Hydro-Climatological Predictions - Case Study For Sacramento San Joaquin River Basin, California, Balbhadra Thakur, Ajay Kalra, Sajjad Ahmad, Kenneth W. Lamb, Venkat Lakshmi
Bringing Statistical Learning Machines Together For Hydro-Climatological Predictions - Case Study For Sacramento San Joaquin River Basin, California, Balbhadra Thakur, Ajay Kalra, Sajjad Ahmad, Kenneth W. Lamb, Venkat Lakshmi
Civil and Environmental Engineering and Construction Faculty Research
Study region: Sacramento San Joaquin River Basin, California Study focus: The study forecasts the streamflow at a regional scale within SSJ river basin with largescale climate variables. The proposed approach eliminates the bias resulting from predefined indices at regional scale. The study was performed for eight unimpaired streamflow stations from 1962–2016. First, the Singular Valued Decomposition (SVD) teleconnections of the streamflow corresponding to 500 mbar geopotential height, sea surface temperature, 500 mbar specific humidity (SHUM500), and 500 mbar U-wind (U500) were obtained. Second, the skillful SVD teleconnections were screened non-parametrically. Finally, the screened teleconnections were used as the streamflow predictors …
Incorporating Antecedent Soil Moisture Into Streamflow Forecasting, Abdoul Oubeidillah, Glenn Tootle, Thomas Piechota
Incorporating Antecedent Soil Moisture Into Streamflow Forecasting, Abdoul Oubeidillah, Glenn Tootle, Thomas Piechota
Mathematics, Physics, and Computer Science Faculty Articles and Research
This study incorporates antecedent (preceding) soil moisture into forecasting streamflow volumes within the North Platte River Basin, Colorado/Wyoming (USA). The incorporation of antecedent soil moisture accounts for infiltration and can improve streamflow predictions. Current Natural Resource Conservation Service (NRCS) forecasting methods are replicated, and a comparison is drawn between current NRCS forecasts and proposed forecasting methods using antecedent soil moisture. Current predictors used by the NRCS in regression-based streamflow forecasting include precipitation, streamflow persistence (previous season streamflow volume) and snow water equivalent (SWE) from SNOTEL (snow telemetry) sites. Proposed methods utilize antecedent soil moisture as a predictor variable in addition …
Hydrologic Trends And Spatial Relationships Of Stream Temperature And Discharge In Urbanizing Watersheds In The Portland Metropolitan Area Of The Pacific Northwest, Emma Lee Brenneman
Hydrologic Trends And Spatial Relationships Of Stream Temperature And Discharge In Urbanizing Watersheds In The Portland Metropolitan Area Of The Pacific Northwest, Emma Lee Brenneman
Dissertations and Theses
This study explores various relationships of streamflow and stream temperature over the Portland Metropolitan area in two urbanizing watersheds. Four stream temperature and discharge metrics were derived from USGS stream gauges in the Tualatin River and Johnson Creek watersheds and were analyzed for monotonic trends. Additionally, this study explored the sensitivity of stream temperature to air temperature and streamflow to assess where locations throughout the watershed may be more sensitive to these changes. Relationships among stream temperature, air temperature, and streamflow were assessed using linear and nonlinear bivariate regression for yearly values and summer months. Additionally, this study seeks to …
Using Satellite-Based Hydro-Climate Variables And Machine Learning For Streamflow Modeling At Various Scales In The Upper Mississippi River Basin, Dongjae Kwon
Theses and Dissertations
Streamflow data are essential to study the hydrologic cycle and to attain appropriate water resource management policies. However, the availability of gauge data is limited due to various reasons such as economic, political, instrumental malfunctioning, and poor spatial distribution. Although streamflow can be simulated by process-based and machine learning approaches, applicability is limited due to intensive modeling effort, or its black-box nature, respectively. Here, we introduce a machine learning (Boosted Regression Tree (BRT)) approach based on remote sensing data to simulate monthly streamflow for three of varying sizes watersheds in the Upper Mississippi River Basin (UMRB). By integrating spatial land …