43 research outputs found
From maps to movies:high-resolution time-varying sensitivity analysis for spatially distributed watershed models
Distributed watershed models are now widely used in practice to simulate
runoff responses at high spatial and temporal resolutions. Counter to this
purpose, diagnostic analyses of distributed models currently aggregate
performance measures in space and/or time and are thus disconnected from the
models' operational and scientific goals. To address this disconnect, this
study contributes a novel approach for computing and visualizing time-varying
global sensitivity indices for spatially distributed model parameters. The
high-resolution model diagnostics employ the method of Morris to identify
evolving patterns in dominant model processes at sub-daily timescales over a
six-month period. The method is demonstrated on the United States National
Weather Service's Hydrology Laboratory Research Distributed Hydrologic Model
(HL-RDHM) in the Blue River watershed, Oklahoma, USA. Three hydrologic events
are selected from within the six-month period to investigate the patterns in
spatiotemporal sensitivities that emerge as a function of forcing patterns as
well as wet-to-dry transitions. Events with similar magnitudes and durations
exhibit significantly different performance controls in space and time,
indicating that the diagnostic inferences drawn from representative events
will be heavily biased by the a priori selection of those events. By
contrast, this study demonstrates high-resolution time-varying sensitivity
analysis, requiring no assumptions regarding representative events and
allowing modelers to identify transitions between sets of dominant parameters
or processes a posteriori. The proposed approach details the dynamics
of parameter sensitivity in nearly continuous time, providing critical
diagnostic insights into the underlying model processes driving predictions.
Furthermore, the approach offers the potential to identify transition points
between dominant parameters and processes in the absence of observations,
such as under nonstationarity
Technical Note:Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models
The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models
Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models
The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models