Articles | Volume 22, issue 5
https://doi.org/10.5194/hess-22-2903-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-2903-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Time-varying parameter models for catchments with land use change: the importance of model structure
Sahani Pathiraja
CORRESPONDING AUTHOR
Institut für Mathematik,
Universität Potsdam,
Potsdam,
Germany
Water Research Centre,
School of Civil and Environmental Engineering,
University of New South Wales,
Sydney, NSW,
Australia
Daniela Anghileri
Institute of Environmental Engineering,
ETH Zurich,
Zurich,
Switzerland
Paolo Burlando
Institute of Environmental Engineering,
ETH Zurich,
Zurich,
Switzerland
Ashish Sharma
Water Research Centre,
School of Civil and Environmental Engineering,
University of New South Wales,
Sydney, NSW,
Australia
Lucy Marshall
Water Research Centre,
School of Civil and Environmental Engineering,
University of New South Wales,
Sydney, NSW,
Australia
Hamid Moradkhani
Department of Civil, Construction and Environmental Engineering,
University of Alabama,
Tuscaloosa, Alabama,
USA
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- Modelling time-variant parameters of a two-parameter monthly water balance model C. Deng et al. 10.1016/j.jhydrol.2019.04.027
- Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis S. Sen et al. 10.1016/j.jhydrol.2020.124666
- A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy A. Roy et al. 10.1029/2022WR033318
- An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada 10.1029/2021MS002882
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- A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics T. Lan et al. 10.5194/hess-24-5859-2020
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- Comparison of data assimilation based approach for daily streamflow simulation under multiple scenarios in Ganjiang River Basin W. Weiguang et al. 10.18307/2023.0323
- A dynamic land use/land cover input helps in picturing the Sahelian paradox: Assessing variability and attribution of changes in surface runoff in a Sahelian watershed R. Yonaba et al. 10.1016/j.scitotenv.2020.143792
- The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. 10.1029/2018WR023629
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- Development and evaluation of a hydrologic data-assimilation scheme for short-range flow and inflow forecasts in a data-sparse high-latitude region using a distributed model and ensemble Kalman filtering J. Samuel et al. 10.1016/j.advwatres.2019.06.004
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Latest update: 24 Dec 2024
Short summary
Hydrologic modeling methodologies must be developed that are capable of predicting runoff in catchments with changing land cover conditions. This article investigates the efficacy of a recently developed approach that allows for runoff prediction in catchments with unknown land cover changes, through experimentation in a deforested catchment in Vietnam. The importance of key elements of the method in ensuring its success, such as the chosen hydrologic model, is investigated.
Hydrologic modeling methodologies must be developed that are capable of predicting runoff in...