13 research outputs found
On the Predictive Uncertainty of a Distributed Hydrologic Model
We use models to simulate the real world mainly for prediction purposes. However,
since any model is a simplification of reality, there remains a great deal of
uncertainty even after the calibration of model parameters. The model’s identifiability
of realistic model parameters becomes questionable when the watershed of interest
is small, and its time of concentration is shorter than the computational time step of
the model. To improve the discovery of more reliable and more realistic sets of model
parameters instead of mathematical solutions, a new algorithm is needed. This algorithm
should be able to identify mathematically inferior but more robust solutions as
well as to take samples uniformly from high-dimensional search spaces for the purpose
of uncertainty analysis.
Various watershed configurations were considered to test the Soil and Water Assessment
Tool (SWAT) model’s identifiability of the realistic spatial distribution of
land use, soil type, and precipitation data. The spatial variability in small watersheds
did not significantly affect the hydrographs at the watershed outlet, and the SWAT
model was not able to identify more realistic sets of spatial data. A new populationbased
heuristic called the Isolated Speciation-based Particle Swarm Optimization
(ISPSO) was developed to enhance the explorability and the uniformity of samples in
high-dimensional problems. The algorithm was tested on seven mathematical functions
and outperformed other similar algorithms in terms of computational cost, consistency,
and scalability. One of the test functions was the Griewank function, whose number of minima is not well defined although the function serves as the basis for
evaluating multi-modal optimization algorithms. Numerical and analytical methods
were proposed to count the exact number of minima of the Griewank function within
a hyperrectangle. The ISPSO algorithm was applied to the SWAT model to evaluate
the performance consistency of optimal solutions and perform uncertainty analysis
in the Generalized Likelihood Uncertainty Estimation (GLUE) framework without
assuming a statistical structure of modeling errors. The algorithm successfully found
hundreds of acceptable sets of model parameters, which were used to estimate their
prediction limits. The uncertainty bounds of this approach were comparable to those
of the typical GLUE approach
Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling
Automated Floodway Determination Using Particle Swarm Optimization
The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area because encroachment areas may be permitted for human activities. However, manual determination of the floodway can be time-consuming and subjective depending on the modeler’s knowledge and judgments, and may not necessarily produce a small floodway especially when there are many cross sections because of their correlation. Very little work has been done in terms of floodway optimization. In this study, we propose an optimization method for minimizing the floodway area using the Isolated-Speciation-based Particle Swarm Optimization algorithm and the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). This method optimizes the floodway by defining an objective function that considers the floodway area and hydraulic requirements, and automating operations of HEC-RAS. We used a floodway model provided by HEC-RAS and compared the proposed, manual, and default HEC-RAS methods. The proposed method consistently improved the objective function value by 1–40%. We believe that this method can provide an automated tool for optimizing the floodway model using HEC-RAS
Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”
Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...
Editorial for Special Issue: “Multi-Source Data Assimilation for the Improvement of Hydrological Modeling Predictions”
Physically-based or process-based hydrologic models play a critical role in hydrologic forecasting [...
Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL
We tested four likelihood measures including two limits of acceptability and two absolute model residual methods within the generalized likelihood uncertainty estimation (GLUE) framework using the topography model (TOPMODEL). All these methods take the worst performance of all time steps as the likelihood of a model and none of these methods were successful in finding any behavioral models. We believe that reporting this failure is important because it shifted our attention from which likelihood measure to choose to why these four methods failed and how to improve these methods. We also observed how large parameter samples impact the performance of a hybrid uncertainty estimation method, isolated-speciation-based particle swarm optimization (ISPSO)-GLUE using the Nash–Sutcliffe (NS) coefficient. Unlike GLUE with random sampling, ISPSO-GLUE provides traditional calibrated parameters as well as uncertainty analysis, so over-conditioning the model parameters on the calibration data can affect its uncertainty analysis results. ISPSO-GLUE showed similar performance to GLUE with a lot less model runs, but its uncertainty bounds enclosed less observed flows. However, both methods failed in validation. These findings suggest that ISPSO-GLUE can be affected by over-calibration after a long evolution of samples and imply that there is a need for a likelihood measure that can better explain uncertainties from different sources without making statistical assumptions
Automated Floodway Determination Using Particle Swarm Optimization
The floodway plays an important role in flood modeling. In the United States, the Federal Emergency Management Agency requires the floodway to be determined using an approved computer program for developed communities. It is a local government’s interest to minimize the floodway area because encroachment areas may be permitted for human activities. However, manual determination of the floodway can be time-consuming and subjective depending on the modeler’s knowledge and judgments, and may not necessarily produce a small floodway especially when there are many cross sections because of their correlation. Very little work has been done in terms of floodway optimization. In this study, we propose an optimization method for minimizing the floodway area using the Isolated-Speciation-based Particle Swarm Optimization algorithm and the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). This method optimizes the floodway by defining an objective function that considers the floodway area and hydraulic requirements, and automating operations of HEC-RAS. We used a floodway model provided by HEC-RAS and compared the proposed, manual, and default HEC-RAS methods. The proposed method consistently improved the objective function value by 1–40%. We believe that this method can provide an automated tool for optimizing the floodway model using HEC-RAS
Evaluation of Four GLUE Likelihood Measures and Behavior of Large Parameter Samples in ISPSO-GLUE for TOPMODEL
Water Resources Response to Climate and Land-Cover Changes in a Semi-Arid Watershed, New Mexico, USA
This research evaluates a climate-land cover-water resources interconnected system in a semi-arid watershed with minimal human impact from 1970 - 2009. We found _ increase in temperature and 10.9% decrease in precipitation. The temperature exhibited a lower increase trend and precipitation showed a similar decrease trend compared to previous studies. The dominant land-cover change trend was grass and forest conversion into bush/shrub and developed land and crop land into barren and grass land. These alterations indicate that changes in temperature and precipitation in the study area may be linked to changes in land cover, although human intervention is recognized as the major land-cover change contributor for the short term. These alterations also suggest that decreasing human activity in the study area leads to developed land and crop land conversion into barren and grass land. Hydrological responses to climate and land-cover changes for surface runoff, groundwater discharge, soil water content and evapotranspiration decreased by 10.2, 10.0, 4.1, and 10.5%, respectively. Hydrological parameters generally follow similar trends to that of precipitation in semi-arid watersheds with minimal human development. Soil water content is sensitive to land-cover change and offset relatively by the changes in precipitation
Regionalization of the Modified Bartlett-Lewis Rectangular Pulse Stochastic Rainfall Model
Parameters of the Modified Bartlett-Lewis Rectangular Pulse (MBLRP) stochastic rainfall simulation model were regionalized across the contiguous United States. Three thousand four hundred forty-four National Climate Data Center (NCDC) rain gauges were used to obtain spatial and seasonal patterns of the model parameters. The MBLRP model was calibrated to minimize the discrepancy between the precipitation depth statistics between the observed and MBLRP-generated precipitation time series. These statistics included the mean, variance, probability of zero rainfall and autocorrelation at 1-, 3-, 12- and 24-hour accumulation intervals. The Ordinary Kriging interpolation technique was used to generate maps of the six MBLRP model parameters for each of the 12 months of the year. All parameters had clear to discernible regional tendencies; except for one related to rain cell duration distribution. Parameter seasonality was not obvious and it was more apparent in some locations than in others, depending on the seasonality of the rainfall statistics. Cross-validation was used to assess the validity of the parameter maps. The results indicate that the suggested maps reproduce well the observed rainfall statistics for different accumulation intervals, except for the lag-1 autocorrelation coefficient. The boundaries of the expected residual, with 95% confidence, between the observed rainfall statistics and the simulated rainfall statistics based on the map parameters were approximately ±0.064 mm hr-1, ±1.63 mm2 hr-2, ±0.16, and ±0.030 for the mean, variance, lag-1 autocorrelation and probability of zero rainfall at hourly accumulation levels, respectively. The estimated parameter values were also used to estimate the storm and rain cell characteristics