HS3.8

The calibration of hydrological models using bio-inspired meta-heuristic optimization techniques has been extensively tested to find the optimal parameters for hydrological models. Shuffled frog-leaping algorithm (SFLA) is a population-based cooperative search technique containing virtual interactive frogs distributed into multiple memeplexes. The frogs search locally in each memeplex and are periodically shuffled into new memeplexes to ensure global exploration. Though it is developed for discrete optimization, it can be used to solve multi-objective combinatorial optimization problems as well.

In this study, a hydrological catchment model, Hydrological Predictions for the Environment (HYPE) is calibrated for streamflow and nitrate concentration in the catchment using SFLA. HYPE is a semi-distributed watershed model that simulates runoff and other hydrological processes based on physical as well as conceptual laws. SFLA with 200 runtimes and 5 memeplexes containing 10 frogs each is used to calibrate 22 model parameters. It is compared with manual calibration and Differential Evolution Markov Chain (DEMC) method from the HYPE-tool. The preliminary results of the statistical performance measures for streamflow calibration show that SFLA has the fastest convergence speed and higher stability when compared with the DEMC method. NSE of 0.68 and PBIAS of 7.72 are recorded for the best run of SFLA during the calibration of streamflow. In comparison, the HYPE-tool DEMC produced the best NSE of 0.45 and a PBIAS of -3.37 while the manual calibration resulted in NSE of 0.64 and PBIAS of 2.01.

How to cite: Li, X., Kasargodu Anebgailu, P., and Dietrich, J.: Multi-objective parameter optimization of the HYPE model using shuffled frog-leaping algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8244, https://doi.org/10.5194/egusphere-egu21-8244, 2021.

Ensuring future water security in a changing climate is becoming a top priority for Australia, which is already dealing with the ongoing socio-economic and environmental impacts from record-breaking bushfires, infrastructure damage from recent flash flooding events, and the prospect of continuing compromised water sources in both regional towns and large cities into the future. In response to these significant impacts the Australian Bureau of Meteorology is providing a hydrological projections service, using their national operational hydrological model (The Australian Water Resources Assessment model: AWRA-L, www.bom.gov.au/water/landscape), to project future hydrological fluxes and states using downscaled meteorological inputs from an ensemble of curated global climate models and emissions scenarios at a resolution of 5km out to the end of this century.

Continental model calibration using a long record of Australian observational data has been employed across components of the water balance, to tune the model parameters to Australia's varied hydro-climate, thereby reducing uncertainty associated with inputs and hydrological model structure. This approach has been shown to improve the accuracy of simulated hydrological fields, and the skill of short term and seasonal forecasts. However, in order to improve model performance and stability for use in hydrological projections, it is desirable to choose a model parameterization which produces reasonable hydrological responses under conditions of climate variability as well as under historical conditions. To this end we have developed a two-stage approach: Firstly, a variance based sensitivity analysis for water balance components (e.g. ephemeral flow, average to high flow, recharge, soil moisture and evapotranspiration) is performed, to rank the most influential parameters affecting water balance components. Parameters which are insensitive across components are then fixed to a previously optimized value, decreasing the number of calibratable parameters in order to decrease dimensionality and uncertainty in the calibration process. Secondly, a model configured with reduced calibratable parameters is put through a multi-objective evolutionary algorithm (Borg MOEA, www.borgmoea.org), to capture the tradeoffs between the water balance component performance objectives under climate variable conditions (e.g. wet, dry and historical) and across climate regions derived from the natural resource management model (https://nrmregionsaustralia.com.au/).

The decreased dimensionality is shown to improve the stability and robustness of the existing calibration routine (shuffled complex evolution) as well as the multi-objective routine. Upon examination of the tradeoffs between the water balance component objective functions and in-situ validation data under historical, wet and dry periods and across different Australian climate regions, we show there is no one size fits all parameter set continentally, and thus some concessions need to be made in choosing a suitable model parameterization. However, future work could include developing a set of parameters which suit specific regions or climate conditions in Australia. The approach outlined in this study could be employed to improve confidence in any hydrological model used to simulate the future impacts of climate change. 

How to cite: Sharples, W., Frost, A., Bende-Michl, U., Shokri, A., Wilson, L., and Vogel, E.: Improving continental scale hydrological model performance and stability under variable climate conditions in order to improve the assessment of future water resources, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9284, https://doi.org/10.5194/egusphere-egu21-9284, 2021.

The application of inverse modeling approaches has been expanded to the field of hydrology this last decade. Here, the inverse modeling has been used to adjust the input parameters of a new agricultural subsurface drainage model (SIDRA-RU) using observations of the model output. SIDRA-RU is a semi-conceptual and semi-analytical model that transforms the rainfall into a daily drainage discharge. The model is divided into two modules. The first one consists of a conceptual reservoir that converts the net rainfall into recharge; the second module simulates the drainage discharge and the water table level above the mid-drains, based on the resolution of the Boussinesq equation.

The adjoint model of SIDRA-RU has been successfully generated by means of the automatic differentiation tool (TAPENADE). First, this adjoint model is used to explore the local and global adjoint sensitivities of the valuable function defined over the drainage discharge simulations (model output), with respect to the model input parameters. Next, the most influential parameters are estimated using both the classical calibration algorithm (PAP-GR) and the variational data assimilation method (4D-VAR). For the latter method, a simple stochastic procedure has been proposed to avoid trapping the minimization process in the local minimum points.

Our results have shown that the quality of the drainage discharge simulations obtained using the 4DVAR method is better than the ones performed by the PAP-GR calibration algorithm, in terms of the water balance in particular. Indeed, less than 5 mm of the cumulative discrepancy was registered between simulated and observed water volume based on the five-year daily drainage discharge data of the Chantemerle agricultural field. However, some numerical tests, conducted to investigate the convergence of the variational calibration method, indicate the potential presence of the equifinality issues. This could be highlighted by the self-compensation of the physical soil parameters (K_sat and µ) and those managing the conceptual SIDRA-RU reservoir (S_inter and SSDI). The performed sensitivity analysis has shown that the parameters having the most impact on the drainage discharge are those controlling the nervousness and recession of the water level in soils followed by those managing the start of the drainage season.

How to cite: Chelil, S., Oubanas, H., Henine, H., Gejadze, I., Malaterre, P. O., and Tournebize, J.: On the use of inverse modeling to improve subsurface drainage simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14768, https://doi.org/10.5194/egusphere-egu21-14768, 2021.

Municipal wastewater treatment plants (WWTPs) reuse domestic sewage, industrial wastewater, and rainfall runoff to realize sustainable utilization of fresh water resources. In order to guarantee the safety, reliability, and profitability of the WWTP, efficient process monitoring and control is becoming increasingly important. However, due to the economic and technical requirements, it is infeasible to place sensors at every process parameter location. Therefore, it is necessary to design the optimal sensor placement scheme which leads to maximum information gain about the plant conditions. Practical issues present in the WWTP, such as harsh physical conditions, fluctuation of water quantity, and variability in process parameters, make the optimal sensor placement problem an especially complicated one. Furthermore, sensors placement problem contains multiple objectives with complex nonlinear relationship. This study focuses on obtaining the optimal flow sensor placement scheme of the WWTP in terms of cost, information richness and redundancy. First, based on the graph theory and structural observability and redundancy criteria, a WWTP system model is constructed. Next, an industrial condition weighting factor setting strategy is introduced to measure the importance of the variables in different processing units, transforming the optimal flow sensor placement problem in the whole process into a discrete multi-objective optimization problem. Then, a novel metaheuristic method named discrete multi-objective state transition algorithm (DMOSTA) is proposed to obtain optimal trade-off solution set. Finally, an evaluation strategy is applied to select the best flow sensor placement scheme from the solution set. The proposed method is applied to three WWTPs with different dimensions. Comparative results show that the optimal flow sensor placement scheme based on the proposed method has the best comprehensive performance in regard to senor cost, process variable observability, sensor redundancy, and computational cost.

How to cite: Li, W., Yang, C., Han, J., Zhang, F., Lan, L., and Li, Y.: Optimal sensor placement method for wastewater treatment plants based on discrete multi-objective state transition algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15061, https://doi.org/10.5194/egusphere-egu21-15061, 2021.

Crop models are used to evaluate the impact of climate change on food security by simulating plant phenology, yield, biomass and leaf area index. Plant phenology defines the timing of crucial growth stages and physiological processes that influence organ appearance and assimilate partitioning. It is governed by environmental factors such as solar radiation, temperature and water availability. Plant phenology is not only specific for the crop species, but also depends on the cultivar. Additionally, growth of a cultivar could vary depending on the environment. Common crop models cannot fully capture the influence of the environment on phenology, resulting in cultivar-specific parameters that are environment-dependent. These parameter estimates may be unreliable in case of limited data. Moreover, crucial species-specific information is ignored. On the other hand, in large regional-scale models covering multiple cultivars and environments, information about the cultivars grown is generally not available. In this case, a shared set of parameters for the crop species would suppress within-species differences leading to unreliable predictions.

A Bayesian hierarchical framework enables us to alleviate these problems by honouring the multi-level data structure. Additionally, we can reflect the uncertainty from different sources, for example, model inputs and measurements. In this study we implement a Bayesian hierarchical framework to estimate parameters of the Soil-Plant-Atmosphere System Simulation (SPASS) model for simulating phenological development of different cultivars of silage maize grown over all the contrasting climatological regions of Germany.

We used data from the German weather service on the phenological development stages of silage maize grown across Germany between 2009 and 2019. During this period, silage maize was grown in over 3000 unique location-year combinations. Maize crops were differentiated into early, mid-early, mid-late and late ripening groups and were further classified into cultivars within each ripening group. Within the hierarchical framework, we estimate maize species-specific parameters as well as parameters per ripening group and cultivar, through Bayesian model calibration. We analyse the influence of environmental conditions on parameter estimates, to further develop the hierarchical structure. We perform cross-validation to assess the prediction quality of the parameterized model.

With this approach, we show that robust parameter estimates account for differences between cultivars, ripening groups as well as different environmental conditions. The parameterized model can be used for large-scale phenology predictions of silage maize grown across Germany. These parameter estimates may perform better than independent species- or cultivar-specific estimates, in predicting phenology of future cultivars where specific cultivar characteristics are not known.

How to cite: Viswanathan, M., Weber, T. K., Scheidegger, A., and Streck, T.: A Bayesian hierarchical approach to improve model parameter estimates and predictions of silage maize phenology in Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7962, https://doi.org/10.5194/egusphere-egu21-7962, 2021.

A hydrological model incurs three types of uncertainties: measurement, structural and parametric uncertainty. Measurement uncertainty exists due to errors in the measurements of rainfall and streamflow data. Structural uncertainty exists due to errors in the mathematical representation of hydrological processes. Parametric uncertainty is a consequence of limited data available to calibrate the model, and measurement and structural uncertainties.

Recently, separation of structural and measurement uncertainties was identified as one of the twenty-three unsolved problems in hydrology. The information about measurement and structural uncertainties is typically available in the form of residual time-series, that is, the difference between observed and simulated streamflow time-series. The residual time-series, however, provides only an aggregate measure of measurement and structural uncertainties. Thus, the measurement and structural uncertainties are inseparable without additional information. In this study, we used random forest (RF) algorithm to gather additional information about measurement uncertainties using hydrological data across several watersheds. Subsequently, the uncertainty bounds obtained by RF were compared against the uncertainty bounds obtained by two other methods: rating-curve analysis and recently proposed runoff-coefficient method. Rating curve analysis yields uncertainty in streamflow measurements only and the runoff-coefficient yields uncertainty in both rainfall and streamflow measurements. The results of the study are promising in terms of using data across different watersheds for the construction of measurement uncertainty bounds. The preliminary results of this study will be presented in the meeting.

How to cite: Gupta, A., Mallya, G., and Govindaraju, R.: Separation of Structural and Measurement Uncertainties in Watershed Hydrological Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-993, https://doi.org/10.5194/egusphere-egu21-993, 2021.

Proper characterization of uncertainty remains a major research and operational challenge in Earth and Environmental Systems Models (EESMs). In fact, model calibration is often more an art than a science: one must make several discretionary choices, guided more by his own experience and intuition than by the scientific method. In practice, this means that the result of calibration (CA) could be suboptimal. One of the challenges of CA is the large number of parameters involved in EESM, which hence are usually selected with the help of a preliminary sensitivity analysis (SA). Finally, the computational burden of EESMs models and the large volume of the search space make SA and CA very time-consuming processes.

This work applies a modern HPC approach to optimize a complex, over parameterized hydrological model, improving the computational efficiency of SA/CA. We apply the derivative-free optimization algorithms implemented in the Facebook Nevergrad Python library (Rapin and Teytaud, 2018) on a HPC cluster, thanks to the Dask framework (Dask Development Team, 2016).

The approach has been applied to the GEOtop hydrological model (Rigon et al., 2006; Endrizzi et al., 2014) to predict the time evolution of variables as soil water content and evapotranspiration for several mountain agricultural sites in South Tyrol with different elevation, land cover (pasture, meadow, orchard), soil types.

We performed simulations on one-dimensional domains, where the model solves the energy and water budget equations in a column of soil and neglects the lateral water fluxes.  Even neglecting the distribution of parameters across layers of soil, considering a homogeneous column, one has tens of parameters, controlling soil and vegetation properties, where only a few of them are experimentally available. 

Because the interpretation of global SA could be difficult or misleading and the number of model evaluations needed by SA is comparable with CA, we employed the following strategy. We performed CA using a full set of continuous parameters and SA after CA, using the samples collected during CA, to interpret the results. However, given the above-mentioned computational challenges, this strategy is possible only using HPC resources. For this reason, we focused on the computational aspects of calibration from an HPC perspective and examined the scaling of these algorithms and their implementation up to 1024 cores on a cluster. Other issues that we had to address were the complex shape of the search space and robustness of CA and SA against model convergence failure.

HPC  techniques allow to calibrate models with a high number of parameters within a reasonable computing time and  exploring the parameters space properly. This is particularly important with noisy, multimodal objective functions. In our case, HPC was essential to determine the  parameters controlling the water retention curve, which is highly not linear.  The developed  framework, which is published and freely available on GitHub, shows also how libraries and tools used within the machine learning community could be useful and easily adapted to EESMs CA.

How to cite: Bertoldi, G., Campanella, S., Cordano, E., and Sartori, A.: An empirical study on the GEOtop hydrological model optimal estimation and uncertainty reduction using supercomputers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15768, https://doi.org/10.5194/egusphere-egu21-15768, 2021.

Semi-distributed rainfall runoff models are widely used in hydrology, offering a compromise between the computational efficiency of lumped models and the representation of spatial heterogeneity offered by fully distributed models. In semi-distribute models, the catchment is divided into subcatchments, which are used as the basis for aggregating spatial characteristics. During model development, uncertainty is usually estimated from literature, however, subcatchment uncertainty is closely related to subcatchment size and level of spatial heterogeneity. Currently, there is no widely accepted systematic method for determining subcatchment size. Typically, subcatchment discretisation is a function of the spatiotemporal resolution of the available data. In our research, we evaluate the relationship between lumped parameter uncertainty and subcatchment size. Models with small subcatchments are expected to have low spatial uncertainty, as the spatial heterogeneity per subcatchment is also low. As subcatchment size increases, as does spatial uncertainty. Our objectives are to study the trade-off between subcatchment size, parameter uncertainty, and computational expense, to outline a systematic and precise framework for subcatchment discretisation. A proof of concept is presented using the Stormwater Management Model (EPA-SWMM) platform, to study a semi-urban catchment in Southwestern Ontario, Canada. Automated model creation is used to create catchment models with varying subcatchment sizes. For each model variation, uncertainty is estimated using spatial statistical bootstrapping. Applying bootstrapping to the spatial parameters directly provides a model free method for calculating the uncertainty of sample estimates. A Monte Carlo simulation is used to propagate uncertainty through the model and spatial resolution is assessed using performance criteria including the percentage of observations captured by the uncertainty envelope, the mean uncertainty envelope width, and rank histograms. The computational expense of simulations is tracked across the varying spatial resolution, achieved through subcatchment discretisation. Initial results suggest that uncertainty estimates often disagree with typical values listed in literature and vary significantly with respect to subcatchment size; this has significant implications on model calibration.

How to cite: Snieder, E. and Khan, U.: Spatial bootstrapping for model-free estimation of subcatchment parameter uncertainty for a semi-distributed rainfall runoff model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10361, https://doi.org/10.5194/egusphere-egu21-10361, 2021.

Due to their highly nonlinear, non-monotonic or even discontinuous behavior, sensitivity analysis of final repository models can be a demanding task. Most of the output of repository models is typically distributed over several orders of magnitude and highly skewed. Many values of a probabilistic investigation are very low or even zero. Although this is desirable in view of repository safety it can distort the evidence of sensitivity analysis. For the safety assessment of the system, the highest values of outputs are mainly essential and if those are only a few, their dependence on specific parameters may appear insignificant. By applying a transformation, different model output values are differently weighed, according to their magnitude, in sensitivity analysis. Probabilistic methods of higher-order sensitivity analysis, applied on appropriately transformed model output values, provide a possibility for more robust identification of relevant parameters and their interactions. This type of sensitivity analysis is typically done by decomposing the total unconditional variance of the model output into partial variances corresponding to different terms in the ANOVA decomposition. From this, sensitivity indices of increasing order can be computed. The key indices used most often are the first-order index (SI1) and the total-order index (SIT). SI1 refers to the individual impact of one parameter on the model and SIT represents the total effect of one parameter on the output in interactions with all other parameters. The second-order sensitivity indices (SI2) describe the interactions between two model parameters.

In this work global sensitivity analysis has been performed with three different kinds of output transformations (log, shifted and Box-Cox transformation) and two metamodeling approaches, namely the Random-Sampling High Dimensional Model Representation (RS-HDMR) [1] and the Bayesian Sparse PCE (BSPCE) [2] approaches. Both approaches are implemented in the SobolGSA software [3, 4] which was used in this work. We analyzed the time-dependent output with two approaches for sensitivity analysis, i.e., the pointwise and generalized approaches. With the pointwise approach, the output at each time step is analyzed independently. The generalized approach considers averaged output contributions at all previous time steps in the analysis of the current step. Obtained results indicate that robustness can be improved by using appropriate transformations and choice of coefficients for the transformation and the metamodel.

[1] M. Zuniga, S. Kucherenko, N. Shah (2013). Metamodelling with independent and dependent inputs. Computer Physics Communications, 184 (6): 1570-1580.

[2] Q. Shao, A. Younes, M. Fahs, T.A. Mara (2017). Bayesian sparse polynomial chaos expansion for global sensitivity analysis. Computer Methods in Applied Mechanics and Engineering, 318: 474-496.

[3] S. M. Spiessl, S. Kucherenko, D.-A. Becker, O. Zaccheus (2018). Higher-order sensitivity analysis of a final repository model with discontinuous behaviour. Reliability Engineering and System Safety, doi: https://doi.org/10.1016/j.ress.2018.12.004.

[4] SobolGSA software (2021). User manual https://www.imperial.ac.uk/process-systems-engineering/research/free-software/sobolgsa-software/.

How to cite: Spiessl, S. M., Becker, D.-A., and Kucherenko, S.: Comprehensive global sensitivity analysis of a repository model using different types of transformations and metamodeling techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6131, https://doi.org/10.5194/egusphere-egu21-6131, 2021.

Scientific hydrological modellers make multiple decisions during the modelling process, e.g. related to the calibration period and performance metrics. These decisions affect the model results differently. Modelling decisions can refer to several steps in the modelling process. In this project, modelling decisions refer to the decisions made during the whole modelling process, not just the definition of the model structure. Each model output is a hypothesis of the reality; it is an interpretation of the real system underpinned by scientific reasoning and/or expert knowledge. Currently, there is a lack of knowledge and understanding about which modelling decisions are taken and why they are taken. Consequently, the influence of modelling decisions is unknown. Quantifying this influence, which is done in this study, can raise awareness among scientists. This study is based on analysis of interviews with scientific hydrological modellers, thus taking actual practices into account. Different modelling decisions were identified from the interviews, which are subsequently implemented and evaluated in a controlled modelling environment, in our case the modular modelling framework Raven. The variation in the results is analysed to determine which decisions affect the results and how they affect the results. This study pinpoints what aspects are important to consider in studying modelling decisions, and can be an incentive to clarify and improve modelling procedures.

How to cite: Remmers, J., Teuling, R., and Melsen, L.: The impact of modelling decisions in hydrological modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2467, https://doi.org/10.5194/egusphere-egu21-2467, 2021.

Recent investigations have shown it is possible to simultaneously calibrate model structures and model parameters to identify appropriate models for a given task (Spieler et al., 2020). However, this is computationally challenging, as different model structures may use a different number of parameters. While some parameters may be shared between model structures, others might be relevant for only a few structures, which theoretically requires the calibration of conditionally active parameters. Additionally, shared model parameters might cause different effects in different model structures, causing their optimal values to differ across structures. In this study, we tested how two current “of the shelf” mixed-integer optimization algorithms perform when having to handle these peculiarities during the automatic model structure identification (AMSI) process recently introduced by Spieler et al. (2020).

To validate the current performance of the AMSI approach, we conduct a benchmark experiment with a model space consisting of 6912 different model structures.  First, all model structures are independently calibrated and validated for three hydro-climatically differing catchments using the CMA-ES algorithm and KGE as the objective function. This is referred to as standard calibration procedure. We identify the best performing model structure(s) based on validation performance and analyze the range of performance as well as the number of structures performing in a similar range. Secondly, we run AMSI on all three catchments to automatically identify the most feasible model structure based on the KGE performance. Two different mixed-integer optimization algorithms are used – namely DDS and CMA-ES. Afterwards, we compare the results to the best performing models of the standard calibration of all 6912 model structures.

Within this experimental setup, we analyze if the best performing model structure(s) AMSI identifies are identical to the best performing structures of the standard calibration and if there are differences in performance when using different optimization algorithms for AMSI. We also validate if AMSI can identify the best performing model structures for a catchment at a fraction of the computational cost than the standard calibration procedure requires by using “off the shelf” mixed-integer optimization algorithms.

Spieler, D., Mai, J., Craig, J. R., Tolson, B. A., & Schütze, N. (2020). Automatic Model Structure Identification for Conceptual Hydrologic Models. Water Resources Research, 56(9). https://doi.org/10.1029/2019WR027009

How to cite: Spieler, D. and Schütze, N.: How good does Automatic Model Structure Identification work? A Benchmark Study with 6915 Model Structures., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12232, https://doi.org/10.5194/egusphere-egu21-12232, 2021.