A multimodel regression-sampling algorithm for generating rich monthly streamflow scenarios
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Date
2014
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
American Geophysical Union
Abstract
This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow
simulation. MRS is motivated from the acknowledgment that typical nonparametric models tend to
simulate sequences exhibiting too close a resemblance to historical records and parametric models have
limitations in capturing complex distributional and dependence characteristics, such as multimodality
and nonlinear autocorrelation. The aim of MRS is to generate streamflow sequences with rich scenarios
while properly capturing complex distributional and dependence characteristics. The basic assumptions
of MRS include: (1) streamflow of a given month depends on a feature vector consisting of streamflow of
the previous month and the dynamic aggregated flow of the past 12 months and (2) streamflow can be
multiplicatively decomposed into a deterministic expectation term and a random residual term. Given a
current feature vector, MRS first relates the conditional expectation to the feature vector through an
ensemble average of multiple regression models. To infer the conditional distribution of the residual, MRS
adopts the k-nearest neighbor concept. More precisely, the conditional distribution is estimated by a
gamma kernel smoothed density of historical residuals inside the k-neighborhood of the given feature
vector. Rather than obtaining the residuals from the averaged model only, MRS retains all residuals from
all the original regression models. In other words, MRS perceives that the original residuals put together
would better represent the covariance structure between streamflow and the feature vector. By doing so,
the benefit is that a kernel smoothed density of the residual with reliable accuracy can be estimated,
which is hardly possible in a single-model framework. It is the smoothed density that ensures the generation
of sequences with rich scenarios unseen in historical record. We evaluated MRS at selected stream
gauges and compared with several existing models. Results show that (1) compared with typical nonparametric
models, MRS is more apt at generating sequences with richer scenarios and (2) in contrast to parametric
models, MRS can reproduce complex distributional and dependence characteristics. Since MRS is
flexible at incorporating different covariates, it can be tailored for other potential applications, such as
hydrologic forecasting, downscaling, as well as postprocessing deterministic forecasts into probabilistic
ones.
Description
Keywords
Seasonal streamflow, Streamflow simulation
Citation
Li, C., and V. P. Singh (2014), A multimodel regression-sampling algorithm for generating rich monthly streamflow scenarios, Water Resour. Res., 50, 5958–5979, doi:10.1002/ 2013WR013969.