|
|
As multicentury records of natural hydrologic variability, tree ring reconstructions of streamflow have proven valuable in water resources planning and management. All previous reconstructions have used parametric methods, most often regression, to develop a model relating a set of tree ring data to a target hydrology. In this paper, we present the first development and application of a K nearest neighbor (KNN) nonparametric method to reconstruct naturalized annual streamflow ensembles from tree ring chronology data in the Upper Colorado River Basin region. The method is developed using tree ring chronologies from the period 1400?2005 and naturalized streamflow from the period 1906?2005 at the important Lees Ferry,...
|
Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. We compare and contrast the performance of regression-based statistical methods for the spatial estimation of precipitation in two hydrologically different basins and confirmed that widely used regression-based estimation schemes fail to describe the realistic spatial variability of daily precipitation field. The methods assessed are: (1) inverse distance weighted average; (2) multiple linear regression (MLR); (3) climatological MLR; and (4) locally weighted polynomial regression (LWP). In order to improve the performance of the interpolations, the...
Categories: Publication;
Types: Citation;
Tags: Chemistry and Earth Sciences,
Computational Intelligence,
Computer Science,
Daily precipitation,
Earth Sciences, All tags...
Hydrologic,
Interpolation,
Local polynomial,
Math. Appl. in Environmental Science,
Modeling,
Physics,
Probability Theory and Stochastic Processes,
Statistics for Engineering,
Waste Water Technology / Water Pollution Control /,
general,
regression, Fewer tags
|
To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues....
|
We present a technique for providing seasonal ensemble streamflow forecasts at several locations simultaneously on a river network. The framework is an integration of two recent approaches: the nonparametric multimodel ensemble forecast technique and the nonparametric space-time disaggregation technique. The four main components of the proposed framework are as follows: (1) an index gauge streamflow is constructed as the sum of flows at all the desired spatial locations; (2) potential predictors of the spring season (April?July) streamflow at this index gauge are identified from the large-scale ocean-atmosphere-land system, including snow water equivalent; (3) the multimodel ensemble forecast approach is used to...
|
This paper describes a data assimilation method that uses observations of snow covered area (SCA) to update hydrologic model states in a mountainous catchment in Colorado. The assimilation method uses SCA information as part of an ensemble Kalman filter to alter the sub-basin distribution of snow as well as the basin water balance. This method permits an optimal combination of model simulations and observations, as well as propagation of information across model states. Sensitivity experiments are conducted with a fairly simple snowpack/water-balance model to evaluate effects of the data assimilation scheme on simulations of streamflow. The assimilation of SCA information results in minor improvements in the accuracy...
|
View more...
|
