Articles | Volume 11, issue 7
https://doi.org/10.5194/gmd-11-2841-2018
© 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/gmd-11-2841-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
13 Jul 2018
Development and technical paper |
| 13 Jul 2018
| 13 Jul 2018
EDDA 2.0: integrated simulation of debris flow initiation and dynamics considering two initiation mechanisms
Ping Shen
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Clear Water Bay, Hong Kong
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Clear Water Bay, Hong Kong
Hongxin Chen
Key Laboratory of Geotechnical and Underground Engineering of
Ministry of Education, Department of Geotechnical Engineering, Tongji
University, China
Ruilin Fan
Department of Civil and Environmental Engineering, The Hong Kong
University of Science and Technology, Clear Water Bay, Hong Kong
Related authors
No articles found.
Kaiheng Hu, Hao Li, Shuang Liu, Li Wei, Xiaopeng Zhang, Limin Zhang, Bo Zhang, and Manish Raj Gouli
EGUsphere, https://doi.org/10.5194/egusphere-2024-312, https://doi.org/10.5194/egusphere-2024-312, 2024
Short summary
Short summary
This paper shows how glacier-related sediment supply changes in response to earthquakes and climate warming at a catchment in the eastern Himalayas using several decades of aerial imagery and high-resolution UAV data. The results highlight the importance of debris-flow-driven extreme sediment delivery on landscape change in High Mountain Asia that have undergone substantial climate warming. This study is helpful for a better understanding of future risk of periglacial debris flows.
Meng Lu, Jie Zhang, Lulu Zhang, and Limin Zhang
Nat. Hazards Earth Syst. Sci., 20, 1833–1846, https://doi.org/10.5194/nhess-20-1833-2020, https://doi.org/10.5194/nhess-20-1833-2020, 2020
Short summary
Short summary
When analyzing the risk of landslides hitting moving vehicles, the spacing between vehicles and the vehicle types on the highway can be highly uncertain. Using a highway slope case study in Hong Kong, this paper presents a method to assess the risk of moving vehicles being hit by a rainfall-induced landslide; the method allows for the investigation of the possible number of different types of vehicles hit by the landslide and provides a new guideline for highway slope design.
Liang Gao, Limin Zhang, and Mengqian Lu
Hydrol. Earth Syst. Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, https://doi.org/10.5194/hess-21-4573-2017, 2017
Short summary
Short summary
Rainfall is the primary trigger of landslides. However, the rainfall intensity is not uniform in space, which causes more landslides in the area of intense rainfall. The primary objective of this paper is to quantify spatial correlation characteristics of three landslide-triggering large storms in Hong Kong. The spatial maximum rolling rainfall is represented by a trend surface and a random field of residuals. The scales of fluctuation of the residuals are found between 5 km and 30 km.
L. Gao and L. M. Zhang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-12-6981-2015, https://doi.org/10.5194/hessd-12-6981-2015, 2015
Manuscript not accepted for further review
Short summary
Short summary
A storm may cause serious damage to infrastructures and public safety. The objective of this paper is to quantify the spatial characteristics of three severe storms in Hong Kong. The spatial distribution of the maximum rolling rainfall is represented by a rotated ellipsoid trend surface and a random field of residuals. The principal directions of the surface trend are between 25° and 45°. The scales of fluctuation of the residuals along eight directions are found between 5 km and 25 km.
H. X. Chen and L. M. Zhang
Geosci. Model Dev., 8, 829–844, https://doi.org/10.5194/gmd-8-829-2015, https://doi.org/10.5194/gmd-8-829-2015, 2015
Short summary
Short summary
A new numerical model, EDDA, is developed for simulating debris-flow erosion, deposition, and associated changes in debris mass, properties, and topography. An adaptive time stepping algorithm is adopted to assure both numerical accuracy and computational efficiency. The performance of the model has been verified through four numerical tests and a large-scale case study. EDDA can be a powerful tool for debris-flow risk assessment in a large area and real-time landslide warning.
Related subject area
Hydrology
Modelling rainfall with a Bartlett–Lewis process: pyBL (v1.0.0), a Python software package and an application with short records
Virtual Joint Field Campaign: a framework of synthetic landscapes to assess multiscale measurement methods of water storage
SERGHEI v2.0: introducing a performance-portable, high-performance, three-dimensional variably saturated subsurface flow solver (SERGHEI-RE)
The global water resources and use model WaterGAP v2.2e: description and evaluation of modifications and new features
Generalised drought index: a novel multi-scale daily approach for drought assessment
Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake
Deep dive into hydrologic simulations at global scale: harnessing the power of deep learning and physics-informed differentiable models (δHBV-globe1.0-hydroDL)
PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration
Prediction of hysteretic matric potential dynamics using artificial intelligence: application of autoencoder neural networks
LM4-SHARC v1.0: Resolving the Catchment-scale Soil-Hillslope Aquifer-River Continuum for the GFDL Earth System Modeling Framework
Regionalization in global hydrological models and its impact on runoff simulations: a case study using WaterGAP3 (v 1.0.0)
Wastewater matters: Incorporating wastewater reclamation into a process-based hydrological model (CWatM v1.08)
The Water Table Model (WTM) v2.0.1: Coupled groundwater and dynamic lake modelling
STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds
Fluvial flood inundation and socio-economic impact model based on open data
RoGeR v3.0.5 – a process-based hydrological toolbox model in Python
Coupling a large-scale glacier and hydrological model (OGGM v1.5.3 and CWatM V1.08) – towards an improved representation of mountain water resources in global assessments
An open-source refactoring of the Canadian Small Lakes Model for estimates of evaporation from medium-sized reservoirs
EvalHyd v0.1.2: a polyglot tool for the evaluation of deterministic and probabilistic streamflow predictions
Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software
HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model
Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications
Reservoir Assessment Tool version 3.0: a scalable and user-friendly software platform to mobilize the global water management community
HydroFATE (v1): a high-resolution contaminant fate model for the global river system
Validation of a new global irrigation scheme in the land surface model ORCHIDEE v2.2
GPEP v1.0: the Geospatial Probabilistic Estimation Package to support Earth science applications
GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers
mesas.py v1.0: a flexible Python package for modeling solute transport and transit times using StorAge Selection functions
rSHUD v2.0: advancing the Simulator for Hydrologic Unstructured Domains and unstructured hydrological modeling in the R environment
GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model
Development of inter-grid-cell lateral unsaturated and saturated flow model in the E3SM Land Model (v2.0)
Selecting a conceptual hydrological model using Bayes' factors computed with Replica Exchange Hamiltonian Monte Carlo and Thermodynamic Integration
pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information
Representing the impact of Rhizophora mangroves on flow in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Dynamically weighted ensemble of geoscientific models via automated machine-learning-based classification
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Chi-Ling Wei, Pei-Chun Chen, Chien-Yu Tseng, Ting-Yu Dai, Yun-Ting Ho, Ching-Chun Chou, Christian Onof, and Li-Pen Wang
Geosci. Model Dev., 18, 1357–1373, https://doi.org/10.5194/gmd-18-1357-2025, https://doi.org/10.5194/gmd-18-1357-2025, 2025
Short summary
Short summary
pyBL is an open-source package for generating realistic rainfall time series based on the Bartlett–Lewis (BL) model. It can preserve not only standard but also extreme rainfall statistics across various timescales. Notably, compared to traditional frequency analysis methods, the BL model requires only half the record length (or even shorter) to achieve similar consistency in estimating sub-hourly rainfall extremes. This makes it a valuable tool for modelling rainfall extremes with short records.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Zhi Li, Gregor Rickert, Na Zheng, Zhibo Zhang, Ilhan Özgen-Xian, and Daniel Caviedes-Voullième
Geosci. Model Dev., 18, 547–562, https://doi.org/10.5194/gmd-18-547-2025, https://doi.org/10.5194/gmd-18-547-2025, 2025
Short summary
Short summary
We introduce SERGHEI-RE, a 3D subsurface flow simulator with performance-portable parallel computing capabilities. SERGHEI-RE performs effectively on various computational devices: from personal computers to advanced clusters. It allows users to solve flow equations with multiple numerical schemes, making it adaptable to various hydrological scenarios. Testing results show its accuracy and performance, confirming that SERGHEI-RE is a powerful tool for hydrological research.
Hannes Müller Schmied, Tim Trautmann, Sebastian Ackermann, Denise Cáceres, Martina Flörke, Helena Gerdener, Ellen Kynast, Thedini Asali Peiris, Leonie Schiebener, Maike Schumacher, and Petra Döll
Geosci. Model Dev., 17, 8817–8852, https://doi.org/10.5194/gmd-17-8817-2024, https://doi.org/10.5194/gmd-17-8817-2024, 2024
Short summary
Short summary
Assessing water availability and water use at the global scale is challenging but essential for a range of purposes. We describe the newest version of the global hydrological model WaterGAP, which has been used for numerous water resource assessments since 1996. We show the effects of new model features, as well as model evaluations, against water abstraction statistics and observed streamflow and water storage anomalies. The publicly available model output for several variants is described.
João António Martins Careto, Rita Margarida Cardoso, Ana Russo, Daniela Catarina André Lima, and Pedro Miguel Matos Soares
Geosci. Model Dev., 17, 8115–8139, https://doi.org/10.5194/gmd-17-8115-2024, https://doi.org/10.5194/gmd-17-8115-2024, 2024
Short summary
Short summary
This study proposes a new daily drought index, the generalised drought index (GDI). The GDI not only identifies the same events as established indices but is also capable of improving their results. The index is empirically based and easy to compute, not requiring fitting the data to a probability distribution. The GDI can detect flash droughts and longer-term events, making it a versatile tool for drought monitoring.
Laura L. Swatridge, Ryan P. Mulligan, Leon Boegman, and Shiliang Shan
Geosci. Model Dev., 17, 7751–7766, https://doi.org/10.5194/gmd-17-7751-2024, https://doi.org/10.5194/gmd-17-7751-2024, 2024
Short summary
Short summary
We develop an operational forecast system, Coastlines-LO, that can simulate water levels and surface waves in Lake Ontario driven by forecasts of wind speeds and pressure fields from an atmospheric model. The model has relatively low computational requirements, and results compare well with near-real-time observations, as well as with results from other existing forecast systems. Results show that with shorter forecast lengths, storm surge and wave predictions can improve in accuracy.
Dapeng Feng, Hylke Beck, Jens de Bruijn, Reetik Kumar Sahu, Yusuke Satoh, Yoshihide Wada, Jiangtao Liu, Ming Pan, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 17, 7181–7198, https://doi.org/10.5194/gmd-17-7181-2024, https://doi.org/10.5194/gmd-17-7181-2024, 2024
Short summary
Short summary
Accurate hydrologic modeling is vital to characterizing water cycle responses to climate change. For the first time at this scale, we use differentiable physics-informed machine learning hydrologic models to simulate rainfall–runoff processes for 3753 basins around the world and compare them with purely data-driven and traditional modeling approaches. This sets a benchmark for hydrologic estimates around the world and builds foundations for improving global hydrologic simulations.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Geosci. Model Dev., 17, 7083–7103, https://doi.org/10.5194/gmd-17-7083-2024, https://doi.org/10.5194/gmd-17-7083-2024, 2024
Short summary
Short summary
Geoscientists commonly use various potential evapotranpiration (PET) formulas for environmental studies, which can be prone to errors and sensitive to climate change. PyEt, a tested and open-source Python package, simplifies the application of 20 PET methods for both time series and gridded data, ensuring accurate and consistent PET estimations suitable for a wide range of environmental applications.
Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
Short summary
Short summary
The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Minki Hong, Nathaniel Chaney, Sergey Malyshev, Enrico Zorzetto, Anthony Preucil, and Elena Shevliakova
EGUsphere, https://doi.org/10.5194/egusphere-2024-2005, https://doi.org/10.5194/egusphere-2024-2005, 2024
Short summary
Short summary
This study aims to understand the significance of groundwater in resolving water-energy budgets in the context of Earth system processes. LM4-SHARC describes the hillslope groundwater using its emergent properties derived from streamflow observations and yields noticeable improvements in soil moisture/temperature and groundwater discharge predictions. The implication of the groundwater-mediated hydrologic interactions between hillslope and stream needs further exploration in the ESM community.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
Short summary
Short summary
Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
Kerry L. Callaghan, Andrew D. Wickert, Richard Barnes, and Jacqueline Austermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-131, https://doi.org/10.5194/gmd-2024-131, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
We present the Water Table Model (WTM), which simulates groundwater and lake levels at continental scales over millennia. Our simulations show that North America held more ground- and lake-water at the Last Glacial Maximum than in the present day – enough to lower sea level by 6 cm. We also simulate the changing water table from 21,000 to 16,000 years ago, finding that groundwater storage decreased following reduced precipitation in the model inputs. Open-source WTM code is available on Github.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
Short summary
Short summary
River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
Short summary
Short summary
The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
Short summary
Short summary
This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
Short summary
Short summary
This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
Short summary
Short summary
The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
Short summary
Short summary
We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
Short summary
Short summary
We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
Short summary
Short summary
The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
Short summary
Short summary
Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
Short summary
Short summary
We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
Short summary
Short summary
Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
Short summary
Short summary
We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
Short summary
Short summary
Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
Short summary
Short summary
Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
Short summary
Short summary
This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
Short summary
Short summary
We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Damian N. Mingo, Remko Nijzink, Christophe Ley, and Jack S. Hale
EGUsphere, https://doi.org/10.5194/egusphere-2023-2865, https://doi.org/10.5194/egusphere-2023-2865, 2024
Short summary
Short summary
Hydrologists are often faced with selecting amongst a set of competing models with different numbers of parameters and ability to fit available data. The Bayes’ factor is a tool that can be used to compare models, however it is very difficult to compute the Bayes’ factor numerically. In our paper we explore and develop highly efficient algorithms for computing the Bayes’ factor of hydrological systems, which will bring this useful tool for selecting models to everyday hydrological practice.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
Short summary
Short summary
We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
Short summary
Short summary
Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
Short summary
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
Short summary
Short summary
Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
Short summary
Short summary
NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
Short summary
Short summary
We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
Short summary
Short summary
Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
Short summary
Short summary
DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
Short summary
Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
Short summary
Short summary
Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
Short summary
Short summary
Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
Short summary
Short summary
In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
Short summary
Short summary
We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
Short summary
Short summary
It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Cited articles
Archfield, S. A., Steeves, P. A., Guthrie, J. D., and Ries III, K. G.:
Towards a publicly available, map-based regional software tool to estimate
unregulated daily streamflow at ungauged rivers, Geosci. Model Dev., 6,
101-115, https://doi.org/10.5194/gmd-6-101-2013, 2013.
Baum, R. L. and Godt, J. W.: Early warning of rainfall-induced shallow
landslides and debris flows in the USA, Landslides, 7, 259–272,
https://doi.org/10.1007/s10346-009-0177-0, 2010.
Bartelt, P., Buehler, Y., Christen, M., Deubelbeiss, Y., Graf, C., McArdell,
B., Salz, M., and Schneider, M.: A numerical model for debris flow in
research and practice, User Manual v1.5 Debris Flow, WSL Institute for Snow
and Avalanche Research SLF, Switzerland, 2013.
Beguería, S., Van Asch, Th. W. J., Malet, J.-P., and Gröndahl, S.: A
GIS-based numerical model for simulating the kinematics of mud and debris
flows over complex terrain, Nat. Hazards Earth Syst. Sci., 9, 1897–1909,
https://doi.org/10.5194/nhess-9-1897-2009, 2009.
Berti, M. and Simoni, A.: Experimental evidences and numerical modelling of
debris flow initiated by channel runoff, Landslides, 2, 171–182,
https://doi.org/10.1007/s10346-005-0062-4, 2005.
Berti, M., Martina, M. L. V., Franceschini, S., Pignone, S., Simoni, A., and
Pizziolo, M.: Probabilistic rainfall thresholds for landslide occurrence
using a Bayesian approach, J. Geophys. Res.-Earth, 117, F04006,
https://doi.org/10.1029/2012JF002367, 2012.
Boss Corporation: DAMBRK-User's manual, Boss International Inc., Madison,
Wisconsin, USA, 1989.
Caine, N.: The rainfall intensity: duration control of shallow landslides and
debris flows, Geogr. Ann. A, 62, 23–27, https://doi.org/10.2307/520449, 1980.
Cannon, S. H., Kirkham, R. M., and Parise, M.: Wildfire-related debris-flow
initiation processes, Storm King Mountain, Colorado, Geomorphology, 39,
171–188, https://doi.org/10.1016/S0169-555X(00)00108-2, 2001.
Cannon, S. H., Gartner, J. E., Wilson, R., Bowers, J., and Laber, J.: Storm
rainfall conditions for floods and debris flows from recently burned areas in
southwestern Colorado and southern California, Geomorphology, 96, 250–269,
https://doi.org/10.1016/j.geomorph.2007.03.019, 2008.
Chang, D. S., Zhang, L. M., Xu, Y., and Huang, R. Q.: Field testing of
erodibility of two landslide dams triggered by the 12 May Wenchuan
earthquake, Landslides, 8, 321–332, https://doi.org/10.1007/s10346-011-0256-x, 2011.
Chen, C. Y., Chen, T. C., Yu, F. C., Yu, W. H., and Tseng, C. C.: Rainfall
duration and debris-flow initiated studies for real-time monitoring, Environ.
Geol., 47, 715–724, https://doi.org/10.1007/s00254-004-1203-0, 2005.
Chen, H. X. and Zhang, L. M.: A physically-based distributed cell model for
predicting regional rainfall-induced shallow slope failures, Eng. Geol., 176,
79–92, https://doi.org/10.1016/j.enggeo.2014.04.011, 2014.
Chen, H. X. and Zhang, L. M.: EDDA 1.0: integrated simulation of debris flow
erosion, deposition and property changes, Geosci. Model Dev., 8, 829–844,
https://doi.org/10.5194/gmd-8-829-2015, 2015.
Chen, H. X., Zhang, L. M., Chang, D. S., and Zhang, S.: Mechanisms and runout
characteristics of the rainfall-triggered debris flow in Xiaojiagou in
Sichuan Province, China, Nat. Hazards, 62, 1037–1057,
https://doi.org/10.1007/s11069-012-0133-5, 2012.
Chen, H. X., Zhang, L. M., Zhang, S., Xiang, B., and Wang, X. F.: Hybrid
simulation of the initiation and runout characteristics of a catastrophic
debris flow, J. Mt. Sci., 10, 219–232, https://doi.org/10.1007/s11629-013-2505-z, 2013.
Chen, H. X., Zhang, L. M., Gao, L., Zhu, H., and Zhang, S.: Presenting
regional shallow landslide movement on three-dimensional digital terrain,
Eng. Geol., 195, 122–134, https://doi.org/10.1016/j.enggeo.2015.05.027, 2015.
Chen, N. S., Zhou, W., Yang, C. L., Hu, G. S., Gao, Y. C., and Han, D.: The
processes and mechanism of failure and debris flow initiation for gravel soil
with different clay content, Geomorphology, 121, 222–230,
https://doi.org/10.1016/j.geomorph.2010.04.017, 2010.
Chen, Z., Ma, L., Yu, S., Chen, S., Zhou, X., Sun, P., and Li, X.: Back
analysis of the draining process of the Tangjiashan barrier lake, J. Hydraul.
Eng., 141, 05014011, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000965, 2015.
Coe, J. A., Kinner, D. A., and Godt, J. W.: Initiation conditions for debris
flows generated by runoff at Chalk Cliffs, central Colorado, Geomorphology,
96, 270–297, https://doi.org/10.1016/j.geomorph.2007.03.017, 2008.
Cui, P.: Study on condition and mechanisms of debris flow initiation by means
of experiment, Chinese Sci. Bull., 37, 759–763, 1992.
De Luca, D. L. and Versace, P. A.: Comprehensive framework for empirical
modeling of landslides induced by rainfall: the Generalized FLaIR Model
(GFM), Landslides, 14, 1009–1030, https://doi.org/10.1007/s10346-016-0768-5, 2017a.
De Luca, D. L. and Versace, P.: Diversity of Rainfall Thresholds for early
warning of hydro-geological disasters, Adv. Geosci., 44, 53–60,
https://doi.org/10.5194/adgeo-44-53-2017, 2017b.
Fletcher, C. A. J.: Computational Techniques for Fluid Dynamics, vol. I, 2nd
edn., Springer-Velag, New York, 1990.
FLO-2D Software Inc.: FLO-2D reference manual, Nutrioso, Arizona, USA, 2009.
Formetta, G., Mantilla, R., Franceschi, S., Antonello, A., and Rigon, R.: The
JGrass-NewAge system for forecasting and managing the hydrological budgets at
the basin scale: models of flow generation and propagation/routing, Geosci.
Model Dev., 4, 943–955, https://doi.org/10.5194/gmd-4-943-2011, 2011.
Gao, L., Zhang, L. M., Chen, H. X., and Shen, P.: Simulating debris flow
mobility in urban settings, Eng. Geol., 214, 67–78,
https://doi.org/10.1016/j.enggeo.2016.10.001, 2016.
Gao, L., Zhang, L. M., and Cheung, R. W. M.: Relationships between natural
terrain landslide magnitudes and triggering rainfall based on a large
landslide inventory in Hong Kong, Landslides, 15, 727–740,
https://doi.org/10.1007/s10346-017-0904-x, 2017a.
Gao, L., Zhang, L., and Lu, M.: Characterizing the spatial variations and
correlations of large rainstorms for landslide study, Hydrol. Earth Syst.
Sci., 21, 4573–4589, https://doi.org/10.5194/hess-21-4573-2017, 2017b.
Godt, J. W., Baum, R. L., and Chleborad, A. F.: Rainfall characteristics for
shallow landsliding in Seattle, Washington, USA, Earth Surf. Proc. Land., 31,
97–110, https://doi.org/10.1002/esp.1237, 2006.
Graf, W. H.: Hydraulics of sediment transport, Water Resources Publications,
Colorado, 1984.
Guzzetti, F., Peruccacci, S., Rossi, M., and Stark, C. P.: The rainfall
intensity-duration control of shallow landslides and debris flows: An update,
Landslides, 5, 3–17, https://doi.org/10.1007/s10346-007-0112-1, 2008.
Hanson, G. J. and Simon, A.: Erodibility of cohesive streambeds in the loess
area of the midwestern USA, Hydrol. Process., 15, 23–38,
https://doi.org/10.1002/hyp.149, 2001.
Hungr, O.: A model for the runout analysis of rapid flow slides, debris
flows, and avalanches, Can. Geotech. J., 32, 610–623, https://doi.org/10.1139/t95-063,
1995.
Hungr, O. and McDougall, S.: Two numerical models for landslide dynamic
analysis, Comput. Geosci., 35, 978–992, https://doi.org/10.1016/j.cageo.2007.12.003,
2009.
Iverson, R. M.: The physics of debris flows, Rev. Geophys., 35, 245–296,
https://doi.org/10.1029/97RG00426, 1997.
Iverson, R. M., Reid, M. E., and LaHusen, R. G.: Debris-flow mobilization
from landslides, Annu. Rev. Earth Pl. Sc., 25, 85–138,
https://doi.org/10.1146/annurev.earth.25.1.85, 1997.
Iverson, R. M., Reid, M. E., Logan, M., LaHusen, R. G., Godt, J. W., and
Griswold, J. P.: Positive feedback and momentum growth during debris-flow
entrainment of wet bed sediment, Nat. Geosci., 4, 116–121,
https://doi.org/10.1038/ngeo1040, 2011.
Johnson, K. A. and Sitar, N.: Hydrologic conditions leading to debris-flow
initiation, Can. Geotech. J., 27, 789–801, https://doi.org/10.1139/t90-092, 1990.
Julian, J. P. and Torres, R.: Hydraulic erosion of cohesive riverbanks,
Geomorphology, 76, 193–206, https://doi.org/10.1016/j.geomorph.2005.11.003, 2006.
Kappes, M. S., Keiler, M., von Elverfeldt, K., and Glade, T.: Challenges of
analyzing multi-hazard risk: a review, Nat. Hazards, 64, 1925–1958,
https://doi.org/10.1007/s11069-012-0294-2, 2012.
King, J. P.: Tsing Shan debris flow, Special Project Report SPR 6/96,
Geotechnical Engineering Office, Hong Kong Government, 133 pp., 1996.
Kwan, J. S. and Sun, H.: An improved landslide mobility model, Can. Geotech.
J., 43, 531–539, https://doi.org/10.1139/t06-010, 2006.
Lee, B. Y., Mok, H. Y., and Lee, T. C.: The latest on climate change in Hong
Kong and its implications for the engineering sector, DHKO in the HKIE Conf.
on Climate Change – Hong Kong Engineers' Perspective, Hong Kong Observatory,
Government of Hong Kong SAR, Hong Kong, 2010.
Liu, K. F. and Huang, M. C.: Numerical simulation of debris flow with
application on hazard area mapping, Comput. Geosci., 10, 221–240,
https://doi.org/10.1007/s10596-005-9020-4, 2006.
Liu, N., Zhang, J. X., Lin, W., Cheng, W. Y., and Chen, Z. Y.: Draining
Tangjiashan Barrier Lake after Wenchuan Earthquake and the flood propagation
after the dam break, Sci. China Ser. E., 52, 801–809,
https://doi.org/10.1007/s11431-009-0118-0, 2009.
Marzocchi, W., Garcia-Aristizabal, A., Gasparini, P., Mastellone, M. L., and
Di Ruocco, A.: Basic principles of multi-risk assessment: a case study in
Italy, Nat. Hazards, 62, 551–573, https://doi.org/10.1007/s11069-012-0092-x, 2012.
Medina, V., Hürlimann, M., and Bateman, A.: Application of FLATModel, a
2-D finite volume code, to debris flows in the northeastern part of the
Iberian Peninsula, Landslides, 5, 127–142, https://doi.org/10.1007/s10346-007-0102-3,
2008.
O'Brien, J. S. and Julien, P. Y.: Laboratory analysis of mudflow properties,
J. Hydraul. Eng., 114, 877–887, https://doi.org/10.1061/(ASCE)0733-9429(1988)114:8(877),
1988.
O'Brien, J. S., Julien, P. Y., and Fullerton, W. T.: Two-dimensional water
flood and mudflow simulation, J. Hydraul. Eng., 119, 244–261,
https://doi.org/10.1061/(ASCE)0733-9429(1993)119:2(244), 1993.
Ouyang, C., He, S., and Tang, C.: Numerical analysis of dynamics of debris
flow over erodible beds in Wenchuan earthquake-induced area, Eng. Geol., 194,
62–72, https://doi.org/10.1016/j.enggeo.2014.07.012, 2015.
Pastor, M., Haddad, B., Sorbino, G., Cuomo, S., and Drempetic, V.: A
depth-integrated, coupled SPH model for flow-like landslides and related
phenomena, Int. J. Numer. Anal. Met., 33, 143–172, https://doi.org/10.1002/nag.705,
2009.
Peng, M. and Zhang, L.M.: Breaching parameters of landslide dams, Landslides,
9, 13–31, https://doi.org/10.1007/s10346-011-0271-y, 2012.
Pierson, T. C.: Hyperconcentrated flow - transitional process between water
flow and debris flow, in: Debris-flow hazards and related phenomena, edited
by: Jakob, M. and Hungr, O., Springer-Praxis, Chichester, UK, 159–202,
https://doi.org/10.1007/3-540-27129-5_8, 2005.
Quan Luna, B., Blahut, J., van Asch, T., van Westen, C., and Kappes, M.:
ASCHFLOW-A dynamic landslide run-out model for medium scale hazard analysis,
Geoenvironmental Disasters, 3, 29, https://doi.org/10.1186/s40677-016-0064-7, 2016.
Raia, S., Alvioli, M., Rossi, M., Baum, R. L., Godt, J. W., and Guzzetti, F.:
Improving predictive power of physically based rainfall-induced shallow
landslide models: a probabilistic approach, Geosci. Model Dev., 7, 495–514,
https://doi.org/10.5194/gmd-7-495-2014, 2014.
Roberts, J., Jepsen, R., Gotthard, D., and Lick, W.: Effects of particle size
and bulk density on erosion of quartz particles, J. Hydraul Eng., 124,
1261–1267, https://doi.org/10.1061/(ASCE)0733-9429(1998)124:12(1261), 1998.
Shen, P., Zhang, L. M., Chen, H. X., and Gao, L.: Role of vegetation
restoration in mitigating hillslope erosion and debris flows, Eng. Geol.,
216, 122–133, https://doi.org/10.1016/j.enggeo.2016.11.019, 2017.
Srivastava, R. and Yeh, T. C. J.: Analytical solutions for one-dimensional,
transient infiltration toward the water table in homogeneous and layered
soils, Water Resour. Res., 27, 753–762, https://doi.org/10.1029/90WR02772, 1991.
Staley, D. M., Kean, J. W., Cannon, S. H., Schmidt, K. M., and Laber, J. L.:
Objective definition of rainfall intensity–duration thresholds for the
initiation of post-fire debris flows in southern California, Landslides, 10,
547–562, https://doi.org/10.1007/s10346-012-0341-9, 2013.
Takahashi, T.: Debris flow, Annu. Rev. Fluid Mech., 13, 57–77, 1981.
Takahashi, T.: Debris flow: mechanics, prediction and countermeasures, Taylor
& Francis, London, UK, 2007.
Takahashi, T., Nakagawa, H., Harada, T., and Yamashiki, Y.: Routing debris
flows with particle segregation, J. Hydraul. Eng., 118, 1490–1507,
https://doi.org/10.1061/(ASCE)0733-9429(1992)118:11(1490), 1992.
Tang, C., Rengers, N., van Asch, Th. W. J., Yang, Y. H., and Wang, G. F.:
Triggering conditions and depositional characteristics of a disastrous debris
flow event in Zhouqu city, Gansu Province, northwestern China, Nat. Hazards
Earth Syst. Sci., 11, 2903–2912, https://doi.org/10.5194/nhess-11-2903-2011,
2011.
Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten,
G., and Vandekerckhove, L.: Prediction of landslide susceptibility using rare
events logistic regression: a case-study in the Flemish Ardennes (Belgium),
Geomorphology, 76, 392–410, https://doi.org/10.1016/j.geomorph.2005.12.003, 2006.
von Boetticher, A., Turowski, J. M., McArdell, B. W., Rickenmann, D., and
Kirchner, J. W.: DebrisInterMixing-2.3: a finite volume solver for
three-dimensional debris-flow simulations with two calibration parameters –
Part 1: Model description, Geosci. Model Dev., 9, 2909–2923,
https://doi.org/10.5194/gmd-9-2909-2016, 2016.
Wieczorek, G. F.: Effect of rainfall intensity and duration on debris flows
in central Santa Cruz Mountains, California, Rev. Eng. Geol., 7, 93–104,
https://doi.org/10.1130/REG7-p93, 1987.
Wong, H. N.: Rising to the challenges of natural terrain landslides, Natural
Hillsides: Study and Risk Management Measures, Proc., 29th Annual Seminar of
the HKIE Geotechnical Division, Hong Kong Institution of Engineers, Hong
Kong, 15–53, 2009.
Wu, L. Z., Selvadurai, A. P. S., Zhang, L. M., Huang, R. Q., and Huang, J.:
Poro-mechanical coupling influences on potential for rainfall-induced shallow
landslides in unsaturated soils, Adv. Water Resour., 98, 114–121,
https://doi.org/10.1016/j.advwatres.2016.10.020, 2016.
Xiao, T., Li, D. Q., Cao, Z. J., and Tang, X. S.: Full probabilistic design
of slopes in spatially variable soils using simplified reliability analysis
method, Georisk, 11, 146–159, https://doi.org/10.1080/17499518.2016.1250279, 2017.
Zhan, T. L., Jia, G. W., Chen, Y. M., Fredlund, D. G., and Li, H.: An
analytical solution for rainfall infiltration into an unsaturated infinite
slope and its application to slope stability analysis, Int. J. Numer. Anal.
Met., 37, 1737–1760, https://doi.org/10.1002/nag.2106, 2013.
Zhang, L. L., Zhang, J., Zhang, L. M., and Tang, W. H.: Stability analysis of
rainfall-induced slope failure: a review, P. I. Civil Eng.-Geotec., 164,
299–316, 2011.
Zhang, L. M., Zhang, S., and Huang, R. Q.: Multi-hazard scenarios and
consequences in Beichuan, China: The first five years after the 2008 Wenchuan
earthquake, Eng. Geol., 180, 4–20, 2014.
Zhang, S. and Zhang, L. M.: Impact of the 2008 Wenchuan earthquake in China
on subsequent long-term debris flow activities in the epicentral area,
Geomorphology, 276, 86–103, https://doi.org/10.1016/j.geomorph.2016.10.009, 2017.
Zhang, S., Zhang, L. M., Chen, H. X., Yuan, Q., and Pan, H.: Changes in
runout distances of debris flows over time in the Wenchuan Earthquake zone,
J. Mt. Sci., 10, 281–292, https://doi.org/10.1007/s11629-012-2506-y, 2013.
Zhang, S., Zhang, L. M., Lacasse, S., and Nadim, F.: Evolution of mass
movements near epicentre of Wenchuan earthquake, the first eight years, Sci.
Rep-UK., 6, 36154, https://doi.org/10.1038/srep36154, 2016.
Zhou, W. and Tang, C.: Rainfall thresholds for debris flow initiation in the
Wenchuan earthquake-stricken area, southwestern China, Landslides, 11,
877–887, https://doi.org/10.1007/s10346-013-0421-5, 2014.
Zhu, H. and Zhang, L. M.: Field investigation of erosion resistance of common
grass species for soil-bioengineering in Hong Kong, Acta Geotech., 11,
1047–1059, https://doi.org/10.1007/s11440-015-0408-6, 2016.
Short summary
A rainstorm can trigger numerous debris flows. A difficult task in debris flow risk assessment is to identify debris flow initiation locations and volumes. This paper presents a new model to solve this problem by physically simulating the initiation of debris flows by hillslope bed erosion and transformation from slope failures. The sediment from these two initiation mechanisms joins the flow mixture, and the volume of the flow mixture increases along the flow path due to additional bed erosion.
A rainstorm can trigger numerous debris flows. A difficult task in debris flow risk assessment...
