the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A Framework for Parameter Estimation, Sensitivity Analysis, and Uncertainty Analysis for Holistic Hydrologic Modeling Using SWAT+
Salam A. Abbas
Ryan T. Bailey
Jeremy T. White
Jeffrey G. Arnold
Michael J. White
Natalja Čerkasova
Jungang Gao
Abstract. Parameter Sensitivity analysis plays a critical role in efficiently determining main parameters, enhancing the effectiveness of estimation of parameters, and uncertainty quantification in hydrologic modeling. In this paper, we demonstrate uncertainty and sensitivity analysis technique for the holistic SWAT+ model, coupled with new gwflow module, spatially distributed, physically based groundwater flow modeling. Main calculated groundwater inflows and outflows include boundary exchange, pumping, saturation excess flow, groundwater–surface water exchange, recharge, groundwater–lake exchange, and tile drainage outflow. We present the method for four watersheds located in different areas of the United States for 16 years (2000–2015), emphasizing regions of extensive tile drainage (Winnebago River, Minnesota, Iowa), intensive surface–groundwater interaction (Nanticoke River, Delaware, Maryland), groundwater pumping for irrigation (Cache River, Missouri, Arkansas), and mountain snowmelt (Arkansas Headwaters, Colorado).
The main parameters of coupled SWAT+gwflow model are estimated utilizing the parameter estimation software (PEST). The monthly streamflow of holistic SWAT+gwflow is evaluated based Nash–Sutcliffe efficiency index (NSE), percentage bias (PBIAS), determination coefficient (R2), and Kling–Gupta efficiency coefficient (KGE), whereas groundwater head is evaluated using mean absolute error (MAE). The Morris method is employed to identify the key parameters influencing hydrological fluxes. Furthermore, the iterative ensemble smoother (iES) is utilized as a technique for Uncertainty Quantification (UQ) and Parameter Estimation (PE) and to decrease the computational cost owing to the large number of parameters.
Depending on the watershed, key identified selected parameters include aquifer specific yield, aquifer hydraulic conductivity, recharge delay, streambed thickness, streambed hydraulic conductivity, area of groundwater inflow to tile, depth of tiles below ground surface, hydraulic conductivity of the drain perimeter, river depth (for groundwater flow processes); runoff curve number (for surface runoff processes); plant uptake compensation factor, soil evaporation compensation factor (for Potential and actual evapotranspiration processes); soil available water capacity, percolation coefficient (for Soil water processes). The presence of gwflow parameters permits for the recognition of all key parameters in the surface/subsurface flow processes, with results substantially differing if the base SWAT+ models are utilized.
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Salam A. Abbas et al.
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RC1: 'Comment on hess-2023-127', Anonymous Referee #1, 21 Jul 2023
The authors present a study in which they couple SWAT+ with gwflow model and examine the parameters, sensitivity, and uncertainties for four study areas in the United States. The areas differ not only in terms of climatic conditions, but also in terms of hydro(geo)logic processes such as water withdrawal, GW-SW interactions, etc.
They demonstrate how a systematic workflow using relatively recently developed tools such as iES can be used within the PEST framework to take full advantage of PEST and its utilities in calibrating groundwater and surface water models and in analyzing the uncertainties in the predictions made by these models before and after calibration.
Although I believe that more studies of this type are needed, and that uncertainties and sensitivities are still too infrequently determined systematically, I do have comments that should be included.
In general, I find that the current manuscript does not discuss enough the results in a broader context (a discussion section would be extremely useful). For example, it could be discussed why bed_thickness is an important parameter in almost all study areas. Here a stronger link to existing literature should be made. Also, a comparison/discussion to integrated models could be made (such as, HGS, Parflow, etc.) and the assumptions of the coupling SWAT+&gwflow for the groundwater-surface water interaction could be discussed.
Model simplifications can be extremely important (even if difficult to quantify) and should be discussed more. Otherwise, it seems that with the presented approach all uncertainties are known very precisely, which is not the case.
Simplifications and limitations in the calibration should also be discussed more intensively. Calibration against GW annual average values are certainly not optimal. Especially since it has often been shown that a calibration against groundwater levels alone is often not sufficient (see for example Schilling et al 2019).
Also, I am not sure how the calibration has dealt with parameters that are correlated. Is this corrected by the ensemble approach or do the dependencies still exist? Some more information would be useful . Also I don't quite understand how the flux from adjacent aquifer was determined. Is this flux also calibrated? From Table 4 it does not seem so but since I am not sure of all the abbreviations, it might be good to at least include in the SI the full name with a small description of the parameters from Table 4.
Other minor comments.
Line 60ff: How is this different from the Null-Space Monte Carlo approach (e.g. Alberti et al.2018, Herckenrath et al., 2011, Moeck et al., 2020) One-two sentences would certainly be useful.
Line 95ff: The calibration/validation selection is relatively classic (which is ok) but perhaps more information could be gleaned from the data when "extremes" are considered. This is certainly something that could be addressed in a discussion.
Line 191ff: Monthly flow data is used, but the time series can be decomposed to get different components such as monthly or event quantities; base and fast flow, flow duration statistics, etc. to use in a multi-component objective function. If done properly, the processing behind each component should distill information of a different type from the measurement data set. Each of these components should therefore inform different parameters or groups of parameters. This can be done for surface water but also for groundwater data (Heudorfer et al., 2019).
Figure 2: How are the data be used between the different weather stations (simple interpolation)?
Figure 14: Even if the general trend is clear and also described how the calculation takes place, I have my difficulties with the values on the x and y axes. For example, the values of mu vary from 0 to 1250 or 30000 depending on the study area. Does this say anything about the significance/sensitivity? Some more context would be extremely useful in the text.
Figure 16: Seasonality in some parameters should be discussed in the text and also why there is a trend in bed_thick for Winnebago and bed_k for Cache.
Figure 17: Even with the posterior ensemble some observed streamflow data cannot reproduced. Does that mean the model is too simple for some events?
Table 7: why is stream seepage negative? Moreover, the recharge values seems to be very small compared to a visual (and only simple) comparison to the Reitz et al. (2017) data. It does not mean that the Reitz data are better (or the method) but please check if the data you simulated are in a plausible range.
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Schilling, Oliver S., Peter G. Cook, and Philip Brunner. "Beyond classical observations in hydrogeology: The advantages of including exchange flux, temperature, tracer concentration, residence time, and soil moisture observations in groundwater model calibration." Reviews of Geophysics 57.1 (2019): 146-182.
Moeck, C., Molson, J., & Schirmer, M. (2020). Pathline density distributions in a null‐space Monte Carlo approach to assess groundwater pathways. Groundwater, 58(2), 189-207.
Alberti, L., L. Colombo, and G. Formentin. 2018. Null-space Monte Carlo particle tracking to assess groundwater PCE (Tetrachloroethene) diffuse pollution in north-eastern Milan functional urban area. Science of the Total Environment 621: 326– 339.
Herckenrath, D., C.D. Langevin, and J. Doherty. 2011. Predictive uncertainty analysis of a saltwater intrusion model using null-space Monte Carlo. Water Resources Research 47.
Heudorfer, B., Haaf, E., Stahl, K., & Barthel, R. (2019). Index‐based characterization and quantification of groundwater dynamics. Water Resources Research, 55(7), 5575-5592.
Reitz, M., Sanford, W. E., Senay, G. B., & Cazenas, J. (2017). Annual estimates of recharge, quick‐flow runoff, and evapotranspiration for the contiguous US using empirical regression equations. JAWRA Journal of the American Water Resources Association, 53(4), 961-983.
Citation: https://doi.org/10.5194/hess-2023-127-RC1 -
AC1: 'Reply on RC1', Salam Abbas, 04 Aug 2023
Dear Reviewer,
We appreciate you and your precious time in reviewing our paper and providing valuable comments. It was your valuable and insightful comments that led to possible improvements in the current version. The authors have carefully considered the comments and tried our best to address every one of them. We hope the manuscript after careful revisions meet your high standards. Please, have a look at the attached file to response to your comments. The authors welcome further constructive comments if any.
Sincerely,
Salam A. Abbas, PhD
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AC1: 'Reply on RC1', Salam Abbas, 04 Aug 2023
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RC2: 'Comment on hess-2023-127', Anonymous Referee #2, 19 Sep 2023
This paper focuses on uncertainty and sensitivity analysis techniques applied to the holistic SWAT+ model, which is coupled with a new gwflow module for physically based groundwater flow modeling. The study evaluates these techniques in four different watersheds across the United States, each with distinct hydrologic characteristics. The main parameters of the coupled SWAT+gwflow model are estimated using parameter estimation software (PEST), and model performance is assessed based on various hydrological metrics. The results are intriguing.
- Please make sure the abbreviation is defined at the very beginning, GLM, NHD, SWAT, etc.
- For table 1, what is the reason of using 1000 m instead of 500 m resolution for Arkansas Headwaters?
- Figure 1, please add unit of DEM?
- Figure 7, it looks like the predicted values are smaller than observed value in most cases, what is the reason? It needs more discussion to evaluate the model performance.
- Figure 9, why there is only 2 points in the mean absolute error plot?
- Could you add more detail how you calibrate the model. Until how many iterations you consider good calibration?
- More studies with similar hydrology parameters should be tested to see if the method could have consistent performance.
Citation: https://doi.org/10.5194/hess-2023-127-RC2 -
AC2: 'Reply on RC2', Salam Abbas, 22 Sep 2023
Dear Reviewer,
We extend our gratitude to you for dedicating your valuable time to review our paper and share your insightful comments. Your input has been helpful in enhancing the current version of the manuscript. The authors have meticulously considered each of your suggestions and strives to address them comprehensively. We trust that the manuscript, following these careful revisions, now aligns with your exacting standards. Please find attached the response file addressing your comments. We warmly welcome any additional constructive feedback you may have.
Sincerely,
Salam A. Abbas, PhD
Salam A. Abbas et al.
Salam A. Abbas et al.
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