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- North America – Colorado River Watershed (3)
- Ocean-atmosphere interaction (3)
- Long-range weather forecasting (2)
- Oscillations (2)
- Streamflow – Forecasting (2)
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- Artificial neural network (ANN) (1)
- Atlantic Multidecadal Oscillation (AMO) (1)
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- Colorado River Basin (1)
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- KNN (1)
- Neural networks (Computer science) (1)
- North Atlantic Oscillation (NAO) (1)
- Ocean-atmospheric oscillations (1)
- PDO (1)
- Palmer Drought Severity Index (1)
- Precipitation forecasting (1)
- Precipitation – Forecasting (1)
- Runoff – Forecasting (1)
- SVM (1)
- Soil moisture (1)
- Support Vector Machines (SVM) (1)
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Articles 1 - 4 of 4
Full-Text Articles in Fresh Water Studies
Evaluating Changes And Estimating Seasonal Precipitation For Colorado River Basin Using Stochastic Non-Parametric Disaggregation Technique, Ajay Kalra, Sajjad Ahmad
Evaluating Changes And Estimating Seasonal Precipitation For Colorado River Basin Using Stochastic Non-Parametric Disaggregation Technique, Ajay Kalra, Sajjad Ahmad
Civil and Environmental Engineering and Construction Faculty Research
Precipitation estimation is an important and challenging task in hydrology because of high variability and changing climate. This research involves (1) analyzing changes (trend and step) in seasonal precipitation and (2) estimating seasonal precipitation by disaggregating water year precipitation using a k-nearest neighbor (KNN) nonparametric technique for 29 climate divisions encompassing the Colorado River Basin. Water year precipitation data from 1900 to 2008 are subdivided into four seasons (i.e., autumn, winter, spring, and summer). Two statistical tests (Mann-Kendall and Spearman’s rho) are used to evaluate trend changes, and a rank sum test is used to identify the step change in …
Association Of Oceanic-Atmospheric Oscillations And Hydroclimatic Variables In The Colorado River Basin, Ajay Kalra
Association Of Oceanic-Atmospheric Oscillations And Hydroclimatic Variables In The Colorado River Basin, Ajay Kalra
UNLV Theses, Dissertations, Professional Papers, and Capstones
With increasing evidence of climatic variability, there is a need to improve forecast for hydroclimatic variables i.e., precipitation and streamflow preserving their spatial and temporal variability. Climatologists have identified different oceanic-atmospheric oscillations that seem to influence the behavior of these variables and in turn can be used to extend the forecast lead time. In the absence of a good physical understanding of the linkages between oceanic-atmospheric oscillations and hydrological processes, it is difficult to construct a physical model. An attractive alternative to physically based models are the Artificial Intelligence (AI) type models, also referred to as machine learning or data-driven …
Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad
Using Oceanic-Atmospheric Oscillations For Long Lead Time Streamflow Forecasting, Ajay Kalra, Sajjad Ahmad
Civil and Environmental Engineering and Construction Faculty Research
We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the …
Soil Moisture As An Indicator Of Weather Extremes, Venkat Lakshmi, Thomas C. Piechota, Ujjwal Narayan, Chunling Tang
Soil Moisture As An Indicator Of Weather Extremes, Venkat Lakshmi, Thomas C. Piechota, Ujjwal Narayan, Chunling Tang
Civil and Environmental Engineering and Construction Faculty Research
In this paper, we investigate floods and droughts in the Upper Mississippi basin over a 50-year period (1950–1999) using a hydrological model (Variable Infiltration Capacity Model – 3 Layer). Simulations have been carried out between January 1950 and December 1999 at daily time-step and 1/8° spatial resolution for the water budget and at hourly time-step and 1° spatial resolution for the energy balance. This paper will provide valuable insights to the slow response components of the hydrological cycle and its diagnostic/predictive value in the case of floods and droughts. The paper compares the use of the Palmer Drought Severity Index …