Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.
[1] A fundamental tradeoff exists in watershed modeling between a model's flexibility for representing watersheds with different characteristics versus its potential for overparameterization. This study uses global sensitivity analysis to investigate how a commonly used intermediate-complexity model, the Sacramento Soil Moisture Accounting Model (SAC-SMA), represents a wide range of watersheds with diverse physical and hydroclimatic characteristics. The analysis aims to establish a detailed understanding of model behavior across watersheds and time periods with the ultimate objective to guide model calibration and evaluation studies. Sobol's sensitivity analysis is used to evaluate the SAC-SMA in 12 Model Parameter Estimation Experiment (MOPEX) watersheds in the US. The watersheds span a wide hydroclimatic gradient from arid to humid systems. Four evaluation metrics reflecting base flows, midrange flows, peak flows, and long-term water balance were used to comprehensively characterize trends in sensitivity and model behavior. Results show significant variation in parameter sensitivities that are correlated with the hydroclimatic characteristics of the watersheds and time periods analyzed. The sensitivity patterns are consistent with the expected dominant processes and demonstrate the need for moderate model complexity to represent different hydroclimatic regimes. The analysis reveals that the primary model controls for some aspects of the simulated hydrograph are different from those typically assumed for the SAC-SMA. Results also show that between 6 and 10 parameters are regularly identifiable from daily hydrologic data, which is about twice the range that is often assumed (i.e., 3 to 5). Synthesized results provide comprehensive SAC-SMA calibration guidance, demonstrate the flexibility of the model for representing multiple hydroclimatic regimes, and highlight the great difficulty in generalizing model behavior across watersheds.
[1] This study provides a step-wise analysis of a conceptual grid-based distributed rainfall-runoff model, the United States National Weather Service (US NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). It evaluates model parameter sensitivities for annual, monthly, and event time periods with the intent of elucidating the key parameters impacting the distributed model's forecasts. This study demonstrates a methodology that balances the computational constraints posed by global sensitivity analysis with the need to fully characterize the HL-RDHM's sensitivities. The HL-RDHM's sensitivities were assessed for annual and monthly periods using distributed forcing and identical model parameters for all grid cells at 24-hour and 1-hour model time steps respectively for two case study watersheds within the Juniata River Basin in central Pennsylvania. This study also provides detailed spatial analysis of the HL-RDHM's sensitivities for two flood events based on 1-hour model time steps selected to demonstrate how strongly the spatial heterogeneity of forcing influences the model's spatial sensitivities. Our verification analysis of the sensitivity analysis method demonstrates that the method provides robust sensitivity rankings and that these rankings could be used to significantly reduce the number of parameters that should be considered when calibrating the HL-RDHM. Overall, the sensitivity analysis results reveal that storage variation, spatial trends in forcing, and cell proximity to the gauged watershed outlet are the three primary factors that control the HL-RDHM's behavior.Citation: Tang, Y., P. Reed, K. van Werkhoven, and T. Wagener (2007), Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis, Water Resour. Res., 43, W06415,
Abstract. Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration period is reflected in the resulting parameter estimates and model performance can be negatively impacted if the climate for which projections are made is significantly different from that during calibration. So how can we calibrate a hydrologic model for historically unobserved climatic conditions? To address this issue, we propose a new trading-space-for-time framework that utilizes the similarity between the predictions under change (PUC) and predictions in ungauged basins (PUB) problems. In this new framework we first regionalize climate dependent streamflow characteristics using 394 US watersheds. We then assume that this spatial relationship between climate and streamflow characteristics is similar to the one we would observe between climate and streamflow over long time periods at a single location. This assumption is what we refer to as trading-space-for-time. Therefore, we change the limits for extrapolation to future climatic situations from the restricted locally observed historical variability to the variability observed across all watersheds used to derive the regression relationships. A typical watershed model is subsequently calibrated (conditioned) on the predicted signatures Correspondence to: R. Singh (rus197@psu.edu) for any future climate scenario to account for the impact of climate on model parameters within a Bayesian framework. As a result, we can obtain ensemble predictions of continuous streamflow at both gauged and ungauged locations. The new method is tested in five US watersheds located in historically different climates using synthetic climate scenarios generated by increasing mean temperature by up to 8 • C and changing mean precipitation by −30 % to +40 % from their historical values. Depending on the aridity of the watershed, streamflow projections using adjusted parameters became significantly different from those using historically calibrated parameters if precipitation change exceeded −10 % or +20 %. In general, the trading-space-for-time approach resulted in a stronger watershed response to climate change for both high and low flow conditions.
Abstract. This study tested four sensitivity analysis methods: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.
[1] We present evidence that the characteristics of rainfall events strongly control the value of streamflow observations for the identification of distributed watershed models. A series of synthetic rainfall events with different spatiotemporal extents and dynamics are used to investigate spatially-distributed global parameter sensitivities for a typical watershed model. The model's parametric sensitivities vary greatly with rainfall distribution characteristics, location of the model cell in relation to the watershed's gauged outlet, and, to a lesser degree, the initial model states. This study demonstrates that the information content of streamflow is a dynamic property and that distributed model identification methodologies should consider the impact of spatio-temporal rainfall dynamics.Citation: van Werkhoven, K., T. Wagener, P. Reed, and Y. Tang (2008), Rainfall characteristics define the value of streamflow observations for distributed watershed model identification,
[1] In a previous paper, van Werkhoven et al. (2008b) demonstrated that the information content of streamflow observations at a watershed outlet is a dynamic entity and is dependent on the spatiotemporal dynamics of the causal precipitation event. This result has important consequences for distributed hydrological model calibration strategies and for the design of observation networks. However, the conclusions drawn were based only on the analysis of the model parameter sensitivities to the hydrograph peak fit because of the use of the root-mean-square error objective function. An unanswered question is how will the previous result change if alternative objective functions are used? Here we extend the earlier analysis by adding low-flow and water balance objective functions. We study their impact on how much information can be extracted during calibration overall and for specific model components (parameters) using a synthetic rainfall-runoff event. Results suggest that both vertical (within a model cell) and spatial (across cells) sensitivities vary greatly with the objective function used. Timing-related objective functions show sensitivity largely focused on the area close to the outlet, while a volume-based objective function shows sensitivity distributed more evenly across the watershed. These results demonstrate the importance of using multiple evaluation metrics when assessing distributed model predictions. The resultant multiobjective sensitivity maps provide helpful tools for assessing the actual information provided by gauges in observation networks and motivate the need for a new generation of dynamic calibration strategies that would consider how the spatial parameter controls on the model response of interest vary in time.Citation: Wagener, T., K. van Werkhoven, P. Reed, and Y. Tang (2009), Multiobjective sensitivity analysis to understand the information content in streamflow observations for distributed watershed modeling, Water Resour. Res.,
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