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.
Research article
| 
16 May 2018
Research article |
| 16 May 2018
| 16 May 2018
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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- A Differentiable Hydrology Approach for Modeling With Time‐Varying Parameters C. Krapu & M. Borsuk https://doi.org/10.1029/2021WR031377
- Improving Parameter Transferability of GR4J Model under Changing Environments Considering Nonstationarity L. Zeng et al. https://doi.org/10.3390/w11102029
- A hydrologic similarity-based parameters dynamic matching framework: Application to enhance the real-time flood forecasting H. Wu et al. https://doi.org/10.1016/j.scitotenv.2023.167767
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- DeepBase: A Deep Learning-based Daily Baseflow Dataset across the United States P. Ghaneei & H. Moradkhani https://doi.org/10.1038/s41597-025-04389-y
- Improving multi-decadal coastal shoreline change predictions by including model parameter non-stationarity R. Ibaceta et al. https://doi.org/10.3389/fmars.2022.1012041
- Dynamics of hydrological-model parameters: mechanisms, problems and solutions T. Lan et al. https://doi.org/10.5194/hess-24-1347-2020
- Simulating Runoff and Actual Evapotranspiration via Time-Variant Parameter Method: The Effects of Hydrological Model Structures Z. Pan et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0002220
- The temporal variations in runoff-generation parameters of the Xinanjiang model due to human activities: A case study in the upper Yangtze River Basin, China X. Zhang et al. https://doi.org/10.1016/j.ejrh.2021.100910
- Doing hydrology when no in-situ data exists: Surrogate River discharge Model (SRM) H. Yoon et al. https://doi.org/10.1016/j.envsoft.2025.106334
- Data Assimilation and Online Parameter Optimization in Groundwater Modeling Using Nested Particle Filters M. Ramgraber et al. https://doi.org/10.1029/2018WR024408
- Reducing the uncertainty of time-varying hydrological model parameters using spatial coherence within a hierarchical Bayesian framework Z. Pan et al. https://doi.org/10.1016/j.jhydrol.2019.123927
- BiGRU-dXAJ: A hybrid hydrologic model enabling differentiable parameter learning for runoff simulation M. Sun et al. https://doi.org/10.1016/j.envsoft.2026.107136
- Quantifying the flood coincidence likelihood between Huai River and its tributaries considering the nonstationarity Z. Zhang et al. https://doi.org/10.1016/j.ejrh.2024.101887
- Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia H. Ji et al. https://doi.org/10.3390/w11030554
- Optimization of parameters in the surface-layer scheme of the Weather Research and Forecasting (WRF) model for near-surface wind and temperature J. Lee et al. https://doi.org/10.1016/j.atmosres.2026.109034
- Land surface temperature assimilation into a soil moisture-temperature model for retrieving farm-scale root zone soil moisture S. Ahmadi et al. https://doi.org/10.1016/j.geoderma.2022.115923
- Enhancing SWAT’s snow module for multivariate Elevation-dependent snow and streamflow data assimilation M. Bayat et al. https://doi.org/10.1016/j.jhydrol.2025.133153
- Modelling time-variant parameters of a two-parameter monthly water balance model C. Deng et al. https://doi.org/10.1016/j.jhydrol.2019.04.027
- Uncertainty quantification using the particle filter for non-stationary hydrological frequency analysis S. Sen et al. https://doi.org/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. https://doi.org/10.1029/2022WR033318
- An Efficient Estimation of Time‐Varying Parameters of Dynamic Models by Combining Offline Batch Optimization and Online Data Assimilation Y. Sawada https://doi.org/10.1029/2021MS002882
- Generating High-Resolution DEMs from Two Different Resolution Data for 2D Flood Mapping E. Hidayah et al. https://doi.org/10.14796/JWMM.C587
- Physically consistent conceptual rainfall–runoff model for urbanized catchments M. Saadi et al. https://doi.org/10.1016/j.jhydrol.2021.126394
- A framework for seasonal variations of hydrological model parameters: impact on model results and response to dynamic catchment characteristics T. Lan et al. https://doi.org/10.5194/hess-24-5859-2020
- Multiplicative Non‐Gaussian Model Error Estimation in Data Assimilation S. Pathiraja & P. van Leeuwen https://doi.org/10.1029/2021MS002564
- A novel floodwave response model for time-varying streambed conductivity using space-time collocation Trefftz method J. Fang et al. https://doi.org/10.1016/j.jhydrol.2023.129996
- Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. https://doi.org/10.1016/j.jhydrol.2023.130574
- Comparison of data assimilation based approach for daily streamflow simulation under multiple scenarios in Ganjiang River Basin W. Weiguang et al. https://doi.org/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. https://doi.org/10.1016/j.scitotenv.2020.143792
- Effects of Model Spatial Structure and Basin Characteristics on the Performance of Three Hydrologic Models F. Kacar et al. https://doi.org/10.1007/s11269-025-04308-1
- The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework P. Abbaszadeh et al. https://doi.org/10.1029/2018WR023629
- A time-varying parameter estimation approach using split-sample calibration based on dynamic programming X. Zhang & P. Liu https://doi.org/10.5194/hess-25-711-2021
- Assessing the Effects of Urban Landscape Area and Pattern Change on Flood Events in Qinhuai River Basin, China Y. Yuan et al. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001993
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Saved (final revised paper)
Latest update: 17 Oct 2026
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...
