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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...
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...
Categorical forecasts of streamflow are important for effective water resources management. Typically, these are obtained by generating ensemble forecasts of streamflow and counting the proportion of ensembles in the desired category. Here we develop a simple and direct method to produce categorical streamflow forecasts at multiple sites. The method involves predicting the probability of the leading mode (or principal component) of the basin streamflows above a given threshold and subsequently translating the predicted probabilities to all the sites in the basin. The categorical probabilistic forecasts are obtained via logistic regression using a set of large-scale climate predictors. Application to categorical...
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We propose a multimodel ensemble forecast framework for streamflow forecasts at multiple locations that incorporates large-scale climate information. It has four broad steps: (1) Principal component analysis is performed on the spatial streamflows to identify the dominant modes of variability. (2) Potential predictors of the dominant streamflow modes are identified from several large-scale climate features and snow water equivalent information. (3) Objective criterion is used to select a suite of candidate nonlinear regression models each with different predictors. (4) Ensemble forecasts of the dominant streamflow modes are generated from the candidate models and are combined objectively to produce a multimodel...
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...
In the western United States many rivers experience high salinity resulting from natural and anthropogenic sources. This impacts the water quality and hence, is closely monitored. The salinity is closely linked with streamflow quantity in that, a higher flow brings with it more salt but also provides substantial dilution to reduce the salt concentration and vice-versa during low flow regimes. Decision makers typically plan strategies for salinity mitigation and evaluate impacts of water management policy options on salinity in the basin using decision support models. These models require statistically consistent basin wide scenarios of streamflow and salinity. Recognizing this need, we develop a basin wide stochastic...
A method is introduced to generate conditioned daily precipitation and temperature time series at multiple stations. The method resamples data from the historical record “nens” times for the period of interest (nens = number of ensemble members) and reorders the ensemble members to reconstruct the observed spatial (intersite) and temporal correlation statistics. The weather generator model is applied to 2307 stations in the contiguous United States and is shown to reproduce the observed spatial correlation between neighboring stations, the observed correlation between variables (e.g., between precipitation and temperature), and the observed temporal correlation between subsequent days in the generated weather sequence....
    map background search result map search result map A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin