Numerous methods exist to gain insight on a model performance. Sensitivity analysis (SA) tools provide information on how a model output depends on model parameters. It is widely argued that SA is an essential tool for assessing model uncertainty. Here, I review global SA using Variogram Analysis of Response Surfaces (VARS), variance-based methods (Sobol' indices) and polynomial chaos expansion. For the comparison, we use a set of denitrification models, which are needed to assess the fate of nitrate, a global challenge. For each of the models, we assess the uncertainty and reliability of predictions, and the use of SA tools in designing experiments to reduce model uncertainty.

How to cite: Carrera, J. and Petchamé, J.: A comparison of sensitivity analysis methods and their value for comparing denitrification models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13589, https://doi.org/10.5194/egusphere-egu23-13589, 2023.

Temporal sensitivity analyses can be used to detect dominant model parameters at different time steps (e.g. daily or monthly) providing insights on their temporal patterns and reflecting the temporal variability in dominant hydrological processes. However, hydrological processes do not only vary in time under different hydrometeorological conditions, but also the time scales of implemented processes are different. Here, the impact of different time scales (e.g. daily vs. monthly) on sensitivity patterns is investigated.

A temporal parameter sensitivity analysis is applied to three hydrological models (HBV, mHM and SWAT) for nine catchments in Germany. These catchments represent the variability of landscapes in Germany and are dominated by different runoff generation processes. In addition to discharge, further model fluxes and states such as evapotranspiration or soil moisture are used as target variables for the sensitivity analysis.

To analyse the impact of different time scales, two approaches are compared. In a first approach, daily simulated time series are used for the sensitivity analysis and aggregated then to monthly averaged sensitivities (Post-Agg). In a second approach, the simulated time series is first aggregated to a monthly time series and than used as input for the sensitivity analysis (Pre-Agg).

Our analysis shows that monthly averaged sensitivity patterns of different model outputs vary between Post- and Pre-Aggregation approach. Model parameters that are related to fast-reacting runoff processes, e.g. surface runoff or fast subsurface flow, are more sensitive when using daily time series for the sensitivity analysis (Post-Agg). In contrast, model parameters related processes with longer time scales such as snowmelt or evapotranspiration are more emphasized in monthly time series (Pre-Agg). These differences in the sensitivity results between Post-Agg and Pre-Agg are in particularly pronounced when using the integrated value of discharge as the target variable. Instead, the differences are smaller when applying the sensitivity analysis directly to represent model fluxes.

Moreover, our analysis shows changes in dominant parameters along a north-south gradient which can be explained by the physiographic characteristics of the catchments. The differences in the sensitivity results between the models can be related to the different model structures.

Based on our analysis, we recommend to either using model outputs of the major hydrological variables or different time scales for the sensitivity analysis to derive the maximum information from the diagnostic model analysis and to understand how model parameters describe hydrological systems.

How to cite: Guse, B., Herzog, A., Thober, S., Spieler, D., Melsen, L., Kiesel, J., Staudinger, M., Wagner, P., Loritz, R., Müller, S., Stölzle, M., Scholz, L., Berg, J., Pilz, T., Ehret, U., Düthmann, D., Houska, T., Pool, S., and Tarasova, L. and the other members of the DFG Scientific network IMPRO: Time-varying sensitivity analysis across different hydrological model structures, variables and time scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8423, https://doi.org/10.5194/egusphere-egu23-8423, 2023.

Understanding how land surface models (LSMs) partition precipitation into evapotranspiration and runoff under changing climate is key to improved future hydrologic predictions. This sensitivity is rarely tuned in land models, as evidenced by prevalent biases in the sensitivity of simulated runoff to precipitation and temperature change compared to observational estimates. Here, using the Community Land Model (CLM5) over the Colorado River basin (CRB), we investigate what the informative model parameters for runoff sensitivities are and how their choices affect the sensitivities under changing temperature and precipitation. We focus on the headwater region of the CRB, motivated by inconsistent model estimates of runoff sensitivities in the region and the critical need to better understand runoff changes to address the ongoing water crises in the CRB. In each headwater basin, a set of informative parameters were identified through parameter perturbations using “one at a time” method within an adaptive surrogate-based model optimization scheme (ASMO). Results of perturbations highlight that different parameter sets with similar performance (with respect to water-year discharge) provide very different runoff sensitivities to temperature and precipitation during the 1951-2010 period. Additionally, both precipitation and temperature sensitivities of runoff show sensitivity to similar parameters across the region. The most sensitive parameters control the conductance-photosynthesis relationship, soil surface resistance for direct evaporation, the partitioning of runoff into the surface and the subsurface component, and soil hydraulic properties. We show how the importance of each parameter varies through the parameter space and derive parameter estimates by maximizing the “fit to observed sensitivities” within the ASMO scheme. Our results provide key insights regarding parameters optimization to improve long-term hydrologic sensitivities in LSMs.

How to cite: Pokhrel, Y., Elkouk, A., Luo, L., Payton, L., Livneh, B., and Cheng, Y.: Impact of Model Parameters on Runoff Sensitivities in the Community Land Model: A Study on the Upper Colorado River Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10644, https://doi.org/10.5194/egusphere-egu23-10644, 2023.

As part of the Great-Lakes Runoff Inter-comparison Project (GRIP-GL; Mai et al., 2022), which aims at comparing the performances of different hydrologic models over the Great-Lakes when calibrating them using the same meteorological inputs and geophysical databases, the GEM-Hydro hydrologic model used at Environment and Climate Change Canada (ECCC) to perform operational hydrologic forecasts was calibrated using different strategies. Following the calibration work related to GRIP-GL, progress has been achieved with regard to improving the calibration of the GEM-Hydro model.

The work presented here focuses on improvements achieved with regard to calibrating the GEM-Hydro model, compared to the default version of the model and to the performances obtained during the GRIP-GL project. For various reasons explained, the GEM-Hydro calibration performed as part of GRIP-GL was suboptimal. The general calibration framework remains the same as in GRIP-GL, for example by using the MESH-SVS-Raven model to speed-up simulation times and transferring the calibrated parameters into GEM-Hydro afterwards, by relying on global calibrations for each of the 6 Great-Lakes subdomains, etc. However, several important changes have been made compared to the work performed in GRIP-GL, like a new approach to represent the effect of Tile Drains, changing the set of flow stations used for calibration, revising the objective function, etc.

The proposed calibration methodology updates significantly improve GEM-Hydro streamflow performance across the Great-Lakes domain and in addition also improve or maintain similar performance levels as the default version of the model, with respect to auxiliary variables and surface fluxes: snow, soil moisture, evapotranspiration, 2m air temperature and dew point. Indeed, the model relies on 40m atmospheric forcings for wind speed, temperature and humidity, and simulates its own 2m atmospheric variables. To achieve this, it was necessary to constrain some parameter interval values during calibration, in order to prevent the calibration algorithm to choose physically-irrelevant parameter values that could allow to improve streamflow performances while degrading other hydrologic variables, due to equifinality.

Reference:

Mai, J., Shen, H., Tolson, B. A., Gaborit, E., Arsenault, R., Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D., Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy, T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet, V., and Waddell, J. W. (2022). The Great Lakes Runoff Intercomparison Project Phase 4: The Great Lakes (GRIP-GL). Hydrol. Earth Syst. Sci., 26, 3537–3572. Highlight paper. Accepted Jun 10, 2022.  https://doi.org/10.5194/hess-26-3537-2022

How to cite: Gaborit, E., Princz, D., Mai, J., Shen, H., Tolson, B., and Fortin, V.: On the elaboration of a robust calibration strategy for the large-scale GEM-Hydro model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6986, https://doi.org/10.5194/egusphere-egu23-6986, 2023.

Due to the lack of accurate representation of hydrological processes and parameter measurements, physically-based hydrological models consist of many parameters requiring calibration to historical observations so that reliable hydrological inference can be obtained. With the increasing data availability from various sources (e.g., satellite remote sensing, climate model reanalysis), additional information on different water balance components (e.g., soil moisture, groundwater storage, etc.) are used to constrain and validate hydrological models, resulting in better model performance and parameter identifiability. However, given the emergence of multiple datasets for various water budget components, and their differences in temporal and spatial resolutions, the uncertainties in these datasets, when used together in driving and evaluating hydrological models, could introduce potential inconsistencies in water balance estimation and lead to a non-closure problem, which could result in potentially biased parameter and water balance component estimates in hydrological modelling.

This study addresses this issue by examining the impact of inconsistent water balance component data on model performance and exploring the importance of hydrologically consistent data for robust hydrological inference. The assessment is done using a Canadian Hydrologic-Land Surface Models named MESH in the Saskatchewan River basin, Canada over the period of 2002 to 2016. Seven precipitation datasets, seven evapotranspiration products, one source of water storage data – GRACE from three different centers using spherical harmonic and mass concentration approaches – and observed discharge data from hydrometric stations are selected as the input and evaluation data. A reference water balance dataset is developed to optimally combine all available data sources for each water balance component and to obtain water balance closure though a constrained Kalman filter data assimilation technique. The MESH model is rerun with this reference dataset and results are assessed and compared to different combinations of input and evaluation data. Preliminary results reveal great variations of model performance in the water balance components when using different combinations of input and evaluation data and results of using the reference dataset is expected to have less biased water balance component estimates. This study aims to highlight the necessity of using a set of hydrologically consistent data before any model runs and model evaluation.

How to cite: Wong, J. S., Yassin, F., and Famiglietti, J. S.: Does hydrologically consistent data improve model performance? The importance of closing the water balance of input and evaluation data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16924, https://doi.org/10.5194/egusphere-egu23-16924, 2023.

Groundwater resources are heavily exploited to supply domestic, industrial and agricultural water consumption. Climate and societal changes and associated higher abstraction will alter the subsurface storage in terms of quantity and quality in currently unpredictable ways. In order to ensure sustainable groundwater management, we must evaluate the intrinsic and spatially variable vulnerability of aquifers in terms of water quality issues and the resilience of groundwater volumes to external perturbations such as severe droughts in connection with intensive irrigation. For this purpose, physically based numerical groundwater models are of great importance, especially on the regional scale. The equations applied in these models must be fed with the hydrogeological parameters: The transmissivity T and the storativity S.

Both parameters are typically obtained through time consuming and cost intensive hydrogeological in-situ tests or by laboratory analysis of core samples from point information (drillings and wells), resulting in parameters with limited transferability to regional settings. Instead, we propose to determine the parameters by spectral analysis of groundwater level fluctuations using (semi-)analytical solutions for the frequency domain. We developed a fully automatized workflow, taking groundwater level and recharge time series together with little information about the geometry of the aquifer to derive T and S as well as t_c (the characteristic response time). While the first two will be used for hydrogeological modelling, the latter can serve as an indication to assess the resilience of the groundwater system directly without additional modelling attempts. The methodology was tested with great success in simplified numerical environments and was applied to real groundwater time series in southern Germany. The response times and the storativities could be robustly estimated while the transmissivities inherit quantifiable uncertainties. Depending on the hydrogeological regime, the parameters represented effective and regional estimates.

How to cite: Houben, T., Di-Dato, M., Siebert, C., Kalbacher, T., Fischer, T., and Attinger, S.: Spectral analysis of groundwater level time series reveals hydrogeological parameters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5635, https://doi.org/10.5194/egusphere-egu23-5635, 2023.

The applications of statistical learning theory (SLT) in hydrology have been either in the form of Support Vector Machines and other complexity regularized machine learning algorithms that learn and predict input-output patterns such as rainfall-runoff time series or of identifying optimal complexity of low order models such as k nearest neighbour models to predict hydrological time series such as streamflow. The regularization of model complexity offers a way to identify minimal complexity of a model to accurately predict a time series of interest. However such applications often assume that the modelled residual are independent of each other. This limits its application to conceptual hydrological models where residuals are often auto-correlated. This paper applies recent results of risk bounds for time series forecasting and SLT approaches to dynamical system identification to conceptual hydrological models, offering a means to identify optimal complexity of conceptual models and complexity regularised streamflow predictions based on it.

Basins from CAMELS data set are used to demonstrate the effect of regularizing the problem of hydrological model calibration on streamflow prediction over unseen data. SAC-SMA and SIXPAR (a lower order version of SACSMA) are used as model examples. Preliminary results show that prediction uncertainty bounds are narrower if regularization does not improve the performance of a calibrated model over unseen data. This effect is stronger in drier basins than in humid ones. Also, as expected, this effect is stronger when training data size is small and holds for both SACSMA and SIXPAR. 

How to cite: Pande, S. and Moayeri, M.: Complexity-based robust hydrologic prediction: extension of statistical learning theory to conceptual hydrological models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16039, https://doi.org/10.5194/egusphere-egu23-16039, 2023.

How to cite: Kavianihamedani, H., Quinn, J., and Smith, J.: New Diagnostic Assessment of MCMC Algorithms Effectiveness, Efficiency, Reliability, and Controllability in Calibrating Hydrological Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10326, https://doi.org/10.5194/egusphere-egu23-10326, 2023.

Bayesian inference has become the most popular approach to uncertainty assessment in vadose zone hydrological modeling. By combining prior information with observations and model predictions, it became popular among hydrologists as it enables them to infer parameter posterior distributions, verify model adequacy, and assess the model's predictive uncertainty. In particular, the posterior distribution is frequently the variable of interest for modelers as it describes the epistemic uncertainty of model parameters conditioned on measurements. Gradient-free Markov-Chain Monte Carlo (MCMC) ensemble samplers based on Differential Evolution (DE) or Affine Invariant (AI) strategies have been used to approximate the posterior distribution, which is frequently anisotropic and correlated in vadose zone-related problems. However, a rigorous benchmark of different MCMC algorithms to provide guidelines for their application in vadose zone hydrological model calibration is still missing. In this study, we elucidate the behavior of MCMC ensemble samplers by performing an in-depth comparison of four samplers that use AI moves or DE-based strategies to approximate the target density. Two Rosenbrock distributions, and one synthetic and one actual case study focusing on the inverse estimation of soil hydraulic parameters using HYDRUS-1D, are used to compare algorithms in different dimensions. The analysis reveals that AI-based samplers are immune to affine transformations of the target density, which instead double the autocorrelation time for DE-based samplers. This behavior is reiterated in the synthetic scenario, for which AI-based algorithms outperform DE-based strategies. However, this performance gain disappears when the number of soil parameters increases from 7 to 16, with both samplers exhibiting poor acceptance rates, which are not improved by increasing the number of chains from 50 to 200 or by mixing different strategies.

How to cite: Brunetti, G., Simunek, J., Wöhling, T., and Stumpp, C.: Pitfalls and Opportunities in the Use of Markov-Chain Monte Carlo Ensemble Samplers for Vadose Zone Model Calibration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11129, https://doi.org/10.5194/egusphere-egu23-11129, 2023.

Understanding the origin of errors in model predictions is a critical element in hydrologic model calibration and uncertainty estimation. While there exist a variety of plausible error sources, only one measure of the total residual error can be ascertained when the observed response is known. Here we show that collecting extra information a priori to characterise the data error before calibration can assist in improved model calibration and uncertainty estimation. A new model calibration strategy using the satellite metadata information is proposed as a means to inform the model prior, and subsequently to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that 1) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; 2) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance ; 3) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

How to cite: Zou, H., Marshall, L., and Sharma, A.: Characterising errors using satellite metadata for eco-hydrological modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5100, https://doi.org/10.5194/egusphere-egu23-5100, 2023.