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Advances in Calibration, Sensitivity and Uncertainty Analysis of Hydrological Models

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: closed (25 May 2024) | Viewed by 1022

Special Issue Editors


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Guest Editor
Water Security Program, CSIRO Land and Water, Brisbane, QLD 4001, Australia
Interests: groundwater modelling and management; uncertainty analysis; optimization; water resources management
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Water Security Program, CSIRO Environment, Sydney, NSW 2015, Australia
Interests: water resources; machine learning; hydrological engineering

Special Issue Information

Dear Colleagues,

Hydrological models play an important role in developing improved understanding and managing water resources in a changing world. Accurate predictions of changes in run off, river flow, groundwater recharge and levels and other hydrological variables and processes like surface water – groundwater interaction are essential for informed decision-making in water resource management, and climate change impact assessment.

Process-based and data-driven models of different kinds and complexities are used for such purposes these days. Reliability of predictions made by such models depends on how well they honour data and/or simulate the underlying processes that are important for making the predictions of interest. Sensitivity analysis, calibration and uncertainty analysis of models can help model simplification, optimal parameterisation, history matching and quantify predictive uncertainties of the model caused by model structure and parameters. This special issue invites submissions that focus on advances in methods and application of calibration, sensitivity analysis, and uncertainty quantification in hydrological modeling.

Novel approaches and applications of local and global sensitivity analysis for improving model structure and parameters, computationally efficient approaches for model calibration and data assimilation including the use of Machine Learning based techniques are of interest. Contributions investigating quantification and propagation of model uncertainties for risk assessment improved decision making under uncertainty are also welcome.

Dr. Sreekanth Janardhanan
Dr. Stephanie Clark
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hydrological models
  • sensitivity
  • calibration
  • uncertainty

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Published Papers (1 paper)

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Research

19 pages, 3269 KiB  
Article
Comparative Analysis with Statistical and Machine Learning for Modeling Overall and High Salinity along the Scheldt Estuary
by Boli Zhu, Tingli Wang, Joke De Meester and Patrick Willems
Water 2024, 16(15), 2150; https://doi.org/10.3390/w16152150 - 30 Jul 2024
Viewed by 744
Abstract
Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the [...] Read more.
Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. Full article
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