Modeling reservoir sedimentation is particularly challenging due to the simultaneous simulation of shallow shores, tributary deltas, and deep waters. The shallow upstream parts of reservoirs, where deltaic avulsion and erosion processes occur, compete with the validity of modeling assumptions used to simulate the deposition of fine sediments in deep waters. We investigate how complex numerical models can be calibrated to accurately predict reservoir sedimentation in the presence of competing model simplifications and identify the importance of calibration parameters for prioritization in measurement campaigns. This study applies Bayesian calibration, a supervised learning technique using surrogate-assisted Bayesian inversion with a Gaussian Process Emulator to calibrate a two-dimensional (2d) hydro-morphodynamic model for simulating sedimentation processes in a reservoir in Albania. Four calibration parameters were fitted to obtain the statistically best possible simulation of bed level changes between 2016 and 2019 through two differently constraining data scenarios. One scenario included measurements from the entire upstream half of the reservoir. Another scenario only included measurements in the geospatially valid range of the numerical model. Model accuracy parameters, Bayesian model evidence, and the variability of the four calibration parameters indicate that Bayesian calibration only converges toward physically meaningful parameter combinations when the calibration nodes are in the valid range of the numerical model. The Bayesian approach also allowed for a comparison of multiple parameters and found that the dry bulk density of the deposited sediments is the most important factor for calibration.
<p>In recent years data-driven techniques, specifically LSTMs, have outperformed conceptual hydrological models for rainfall-runoff prediction. However, even though great progress has been made to explain the internal functioning of the model ((Kratzert, et al., 2019); (Lees, et al., 2022)), their interpretation is still not as straightforward as conceptual models. Additionally, latent variables, different from the target quantity, need postprocessing methods to be extracted. One way to combine the flexibility of data-driven techniques with the interpretability of conceptual models is the use of hybrid models. In our contribution, &#160;we will present results from applying a similar technique as (Kraft, Jung, Korner, & Reichstein, 2020) and (Feng, Liu, Lawson, & Shen, 2022), in which an artificial neural network dynamically calculates the parameters of the conceptual model. This approach increases the model flexibility, allows the inclusion of multiple information sources, and compensates for model uncertainty, while maintaining the straightforward interpretability of the conceptual part. In this contribution, we will look at the performance of the hybrid model, analyze the parameter variation over time, and present a technique to avoid parameter cross-compensation.</p> <p>&#160;</p> <p><strong>References</strong></p> <p>Feng, D., Liu, J., Lawson, K., & Shen, C. (2022). Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy. <em>Water Resources Research</em>. doi:https://doi.org/10.1029/2022WR032404</p> <p>Kraft, B., Jung, M., Korner, M., & Reichstein, M. (2020). HYBRID MODELING: FUSION OF A DEEP LEARNING APPROACH AND A PHYSICS-BASED MODEL FOR GLOBAL HYDROLOGICAL MODELING. <em>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</em>, 1537--1544. doi:https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1537-2020</p> <p>Kratzert, F., Herrnegger, M., Klotz, D., Hochreiter, S., & Klambauer, G. (2019). NeuralHydrology--Interpreting LSTMs in Hydrology. In W. Samek, G. Montavon, A. Vedaldi, L. Hansen, & K.-R. M&#252;ller, <em>Explainable AI: Interpreting, Explaining and Visualizing Deep Learning</em> (pp. 347--362). Springer.</p> <p>Lees, T., Reece, S., Kratzert, F., Klotz, D., Gauch, M., De Bruijn, J., . . . Dadson, S. (2022). Hydrological concept formation inside long short-term memory (LSTM) networks. <em>Hydrology and Earth System Sciences</em>, 3079-3101. doi:https://doi.org/10.5194/hess-26-3079-2022</p>
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