Articles | Volume 22, issue 8
https://doi.org/10.5194/hess-22-4251-2018
© 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-22-4251-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Aug 2018
Research article |
| 13 Aug 2018
| 13 Aug 2018
Multi-source data assimilation for physically based hydrological modeling of an experimental hillslope
Anna Botto
CORRESPONDING AUTHOR
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
Enrica Belluco
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
Matteo Camporese
Department of Civil, Environmental and Architectural Engineering,
University of Padua, Padua, Italy
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Chen Wang, Lennert Schepers, Matthew L. Kirwan, Enrica Belluco, Andrea D'Alpaos, Qiao Wang, Shoujing Yin, and Stijn Temmerman
Earth Surf. Dynam., 9, 71–88, https://doi.org/10.5194/esurf-9-71-2021, https://doi.org/10.5194/esurf-9-71-2021, 2021
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Coastal marshes are valuable natural habitats with normally dense vegetation. The presence of bare patches is a symptom of habitat degradation. We found that the occurrence of bare patches and regrowth of vegetation is related to spatial variations in soil surface elevation and to the distance and connectivity to tidal creeks. These relations are similar in three marshes at very different geographical locations. Our results may help nature managers to conserve and restore coastal marshes.
Elena Crestani, Matteo Camporese, and Paolo Salandin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-127, https://doi.org/10.5194/hess-2019-127, 2019
Preprint withdrawn
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This work presents a laboratory facility designed to simulate saltwater intrusion in coastal aquifers, with the goal of providing benchmarks for numerical models. The experiment studies the evolution of the seawater wedge into a homogeneous aquifer also in case of a freshwater pumping. The agreement between the numerical and the experimental (photos and electrical resistivity tomography) results proves that the proposed facility can provide the aforementioned benchmark, even in complex cases.
Related subject area
Subject: Hillslope hydrology | Techniques and Approaches: Modelling approaches
Elucidating the role of soil hydraulic properties on aspect-dependent landslide initiation
Recession discharge from compartmentalized bedrock hillslopes
Frozen soil hydrological modeling for a mountainous catchment northeast of the Qinghai–Tibet Plateau
On the similarity of hillslope hydrologic function: a clustering approach based on groundwater changes
Spatiotemporal changes in flow hydraulic characteristics and soil loss during gully headcut erosion under controlled conditions
Estimation of rainfall erosivity based on WRF-derived raindrop size distributions
Physically based model for gully simulation: application to the Brazilian semiarid region
Assessing the perturbations of the hydrogeological regime in sloping fens due to roads
A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates
Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting
A new method, with application, for analysis of the impacts on flood risk of widely distributed enhanced hillslope storage
Towards improved parameterization of a macroscale hydrologic model in a discontinuous permafrost boreal forest ecosystem
Reconstructing long-term gully dynamics in Mediterranean agricultural areas
Evaluating performance of simplified physically based models for shallow landslide susceptibility
Multiresponse modeling of variably saturated flow and isotope tracer transport for a hillslope experiment at the Landscape Evolution Observatory
Determinants of modelling choices for 1-D free-surface flow and morphodynamics in hydrology and hydraulics: a review
Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale
Quantification of the influence of preferential flow on slope stability using a numerical modelling approach
Hydrological hysteresis and its value for assessing process consistency in catchment conceptual models
Derivation and evaluation of landslide-triggering thresholds by a Monte Carlo approach
Stable water isotope tracing through hydrological models for disentangling runoff generation processes at the hillslope scale
Analysis of landslide triggering conditions in the Sarno area using a physically based model
The influence of grid resolution on the prediction of natural and road-related shallow landslides
Incipient subsurface heterogeneity and its effect on overland flow generation – insight from a modeling study of the first experiment at the Biosphere 2 Landscape Evolution Observatory
Coupled prediction of flood response and debris flow initiation during warm- and cold-season events in the Southern Appalachians, USA
Predicting subsurface stormflow response of a forested hillslope – the role of connected flow paths
Interplay of riparian forest and groundwater in the hillslope hydrology of Sudanian West Africa (northern Benin)
A model-based assessment of the potential use of compound-specific stable isotope analysis in river monitoring of diffuse pesticide pollution
A paradigm shift in stormflow predictions for active tectonic regions with large-magnitude storms: generalisation of catchment observations by hydraulic sensitivity analysis and insight into soil-layer evolution
Derivation of critical rainfall thresholds for shallow landslides as a tool for debris flow early warning systems
Statistical analysis and modelling of surface runoff from arable fields in central Europe
Hydrological modelling of a slope covered with shallow pyroclastic deposits from field monitoring data
Physically based modeling of rainfall-triggered landslides: a case study in the Luquillo forest, Puerto Rico
Characterization of groundwater dynamics in landslides in varved clays
A critical assessment of simple recharge models: application to the UK Chalk
The effect of spatial throughfall patterns on soil moisture patterns at the hillslope scale
Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems
Suspended sediment concentration–discharge relationships in the (sub-) humid Ethiopian highlands
A model of hydrological and mechanical feedbacks of preferential fissure flow in a slow-moving landslide
Scale effect on overland flow connectivity at the plot scale
Physical models for classroom teaching in hydrology
Coupling the modified SCS-CN and RUSLE models to simulate hydrological effects of restoring vegetation in the Loess Plateau of China
Effects of peatland drainage management on peak flows
A conceptual model of the hydrological influence of fissures on landslide activity
A structure generator for modelling the initial sediment distribution of an artificial hydrologic catchment
A novel explicit approach to model bromide and pesticide transport in connected soil structures
Quantifying spatial and temporal discharge dynamics of an event in a first order stream, using distributed temperature sensing
Effect of high-resolution spatial soil moisture variability on simulated runoff response using a distributed hydrologic model
A steady-state saturation model to determine the subsurface travel time (STT) in complex hillslopes
Comparison of algorithms and parameterisations for infiltration into organic-covered permafrost soils
Yanglin Guo and Chao Ma
Hydrol. Earth Syst. Sci., 27, 1667–1682, https://doi.org/10.5194/hess-27-1667-2023, https://doi.org/10.5194/hess-27-1667-2023, 2023
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In a localized area with the same vegetation, an overwhelming propensity of shallow landslides on the south-facing slope over the north-facing slope could not be attributed to plant roots. We provide new evidence from the pore water pressure of failing mass, unsaturated hydraulic conductivity, water storage, and drainage and the hillslope stability fluctuation to prove that the infinite slope model may be suitable for elucidating the aspect-dependent landslide distribution in the study area.
Clément Roques, David E. Rupp, Jean-Raynald de Dreuzy, Laurent Longuevergne, Elizabeth R. Jachens, Gordon Grant, Luc Aquilina, and John S. Selker
Hydrol. Earth Syst. Sci., 26, 4391–4405, https://doi.org/10.5194/hess-26-4391-2022, https://doi.org/10.5194/hess-26-4391-2022, 2022
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Streamflow dynamics are directly dependent on contributions from groundwater, with hillslope heterogeneity being a major driver in controlling both spatial and temporal variabilities in recession discharge behaviors. By analysing new model results, this paper identifies the major structural features of aquifers driving streamflow dynamics. It provides important guidance to inform catchment-to-regional-scale models, with key geological knowledge influencing groundwater–surface water interactions.
Hongkai Gao, Chuntan Han, Rensheng Chen, Zijing Feng, Kang Wang, Fabrizio Fenicia, and Hubert Savenije
Hydrol. Earth Syst. Sci., 26, 4187–4208, https://doi.org/10.5194/hess-26-4187-2022, https://doi.org/10.5194/hess-26-4187-2022, 2022
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Frozen soil hydrology is one of the 23 unsolved problems in hydrology (UPH). In this study, we developed a novel conceptual frozen soil hydrological model, FLEX-Topo-FS. The model successfully reproduced the soil freeze–thaw process, and its impacts on hydrologic connectivity, runoff generation, and groundwater. We believe this study is a breakthrough for the 23 UPH, giving us new insights on frozen soil hydrology, with broad implications for predicting cold region hydrology in future.
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
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We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Mingming Guo, Zhuoxin Chen, Wenlong Wang, Tianchao Wang, Qianhua Shi, Hongliang Kang, Man Zhao, and Lanqian Feng
Hydrol. Earth Syst. Sci., 25, 4473–4494, https://doi.org/10.5194/hess-25-4473-2021, https://doi.org/10.5194/hess-25-4473-2021, 2021
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Gully headcut erosion is always a difficult issue in soil erosion, which hinders the revelation of gully erosion mechanisms and the establishment of a gully erosion model. This study clarified the spatiotemporal changes in flow properties, energy consumption, and soil loss, confirming that gully head consumed the most of flow energy (78 %) and can contribute 89 % of total soil loss. Critical energy consumption initiating soil erosion of the upstream area, gully head, and gully bed is confirmed.
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422, https://doi.org/10.5194/hess-24-5407-2020, https://doi.org/10.5194/hess-24-5407-2020, 2020
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Rainfall is a driving force that accounts for a large proportion of soil loss around the world. Most previous studies used a fixed rainfall–energy relationship to estimate rainfall energy, ignoring the spatial and temporal changes of raindrop microphysical processes. This study proposes a novel method for large-scale and long-term rainfall energy and rainfall erosivity investigations based on rainfall microphysical parameterization schemes in the Weather Research and Forecasting (WRF) model.
Pedro Henrique Lima Alencar, José Carlos de Araújo, and Adunias dos Santos Teixeira
Hydrol. Earth Syst. Sci., 24, 4239–4255, https://doi.org/10.5194/hess-24-4239-2020, https://doi.org/10.5194/hess-24-4239-2020, 2020
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Soil erosion by water has been emphasized as a key problem to be faced in the 21st century. Thus, it is critical to understand land degradation and to answer fundamental questions regarding how and why such processes occur. Here, we present a model for gully erosion (channels carved by rainwater) based on existing equations, and we identify some major variables that influence the initiation and evolution of this process. The successful model can help in planning soil conservation practices.
Fabien Cochand, Daniel Käser, Philippe Grosvernier, Daniel Hunkeler, and Philip Brunner
Hydrol. Earth Syst. Sci., 24, 213–226, https://doi.org/10.5194/hess-24-213-2020, https://doi.org/10.5194/hess-24-213-2020, 2020
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Roads in sloping fens constitute a hydraulic barrier for surface and subsurface flow. This can lead to the drying out of downslope areas of the fen as well as gully erosion. By combining fieldwork and numerical models, this study presents an assessment of the hydrogeological impact of three road structures especially designed to limit their impact. The study shows that the impact of roads on the hydrological regime in fens can be significantly reduced by using appropriate engineering measures.
Rubianca Benavidez, Bethanna Jackson, Deborah Maxwell, and Kevin Norton
Hydrol. Earth Syst. Sci., 22, 6059–6086, https://doi.org/10.5194/hess-22-6059-2018, https://doi.org/10.5194/hess-22-6059-2018, 2018
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Soil erosion is a global problem and models identify vulnerable areas for management. One such model is the Revised Universal Soil Loss Equation. We review its different sub-factors and compile studies and equations that modified it for local conditions. The limitations of RUSLE include its data requirements and exclusion of gullying and landslides. Future directions include accounting for these erosion types. This paper serves as a reference for others working with RUSLE and related approaches.
Felipe Hernández and Xu Liang
Hydrol. Earth Syst. Sci., 22, 5759–5779, https://doi.org/10.5194/hess-22-5759-2018, https://doi.org/10.5194/hess-22-5759-2018, 2018
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Predicting floods requires first knowing the amount of water in the valleys, which is complicated because we cannot know for sure how much water there is in the soil. We created a unique system that combines the best methods to estimate these conditions accurately based on the observed water flow in the rivers and on detailed simulations of the valleys. Comparisons with popular methods show that our system can produce realistic predictions efficiently, even for very detailed river networks.
Peter Metcalfe, Keith Beven, Barry Hankin, and Rob Lamb
Hydrol. Earth Syst. Sci., 22, 2589–2605, https://doi.org/10.5194/hess-22-2589-2018, https://doi.org/10.5194/hess-22-2589-2018, 2018
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Flooding is a significant hazard and extreme events in recent years have focused attention on effective means of reducing its risk. An approach known as natural flood management (NFM) seeks to increase flood resilience by a range of measures that work with natural processes. The paper develops a modelling approach to assess one type NFM of intervention – distributed additional hillslope storage features – and demonstrates that more strategic placement is required than has hitherto been applied.
Abraham Endalamaw, W. Robert Bolton, Jessica M. Young-Robertson, Don Morton, Larry Hinzman, and Bart Nijssen
Hydrol. Earth Syst. Sci., 21, 4663–4680, https://doi.org/10.5194/hess-21-4663-2017, https://doi.org/10.5194/hess-21-4663-2017, 2017
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This study applies plot-scale and hill-slope knowledge to a process-based mesoscale model to improve the skill of distributed hydrological models to simulate the spatially and basin-integrated hydrological processes of complex ecosystems in the sub-arctic boreal forest. We developed a sub-grid parameterization method to parameterize the surface heterogeneity of interior Alaskan discontinuous permafrost watersheds.
Antonio Hayas, Tom Vanwalleghem, Ana Laguna, Adolfo Peña, and Juan V. Giráldez
Hydrol. Earth Syst. Sci., 21, 235–249, https://doi.org/10.5194/hess-21-235-2017, https://doi.org/10.5194/hess-21-235-2017, 2017
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Gully erosion is one of the most important erosion processes. In this study, we provide new data on gully dynamics over long timescales with an unprecedented temporal resolution. We apply a new Monte Carlo based method for calculating gully volumes based on orthophotos and, especially, for constraining uncertainties of these estimations. Our results show that gully erosion rates are highly variable from year to year and significantly higher than other erosion processes.
Giuseppe Formetta, Giovanna Capparelli, and Pasquale Versace
Hydrol. Earth Syst. Sci., 20, 4585–4603, https://doi.org/10.5194/hess-20-4585-2016, https://doi.org/10.5194/hess-20-4585-2016, 2016
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This paper focuses on performance evaluation of simplified, physically based landslide susceptibility models. It presents a new methodology to systemically and objectively calibrate, verify, and compare different models and models performances indicators in order to individuate and select the models whose behavior is more reliable for a certain case study. The procedure was implemented in a package for landslide susceptibility analysis and integrated the open-source hydrological model NewAge.
Carlotta Scudeler, Luke Pangle, Damiano Pasetto, Guo-Yue Niu, Till Volkmann, Claudio Paniconi, Mario Putti, and Peter Troch
Hydrol. Earth Syst. Sci., 20, 4061–4078, https://doi.org/10.5194/hess-20-4061-2016, https://doi.org/10.5194/hess-20-4061-2016, 2016
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Very few studies have applied a physically based hydrological model with integrated and distributed multivariate observation data of both flow and transport phenomena. In this study we address this challenge for a hillslope-scale unsaturated zone isotope tracer experiment. The results show how model complexity evolves as the number and detail of simulated responses increases. Possible gaps in process representation for simulating solute transport phenomena in very dry soils are discussed.
Bruno Cheviron and Roger Moussa
Hydrol. Earth Syst. Sci., 20, 3799–3830, https://doi.org/10.5194/hess-20-3799-2016, https://doi.org/10.5194/hess-20-3799-2016, 2016
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This review paper investigates the determinants of modelling choices for numerous applications of 1-D free-surface flow and morphodynamics in hydrology and hydraulics. Each case study has a signature composed of given contexts (spatiotemporal scales, flow typology, and phenomenology) and chosen concepts (refinement and subscales of the flow model). This review proposes a normative procedure possibly enriched by the community for a larger, comprehensive and updated image of modelling strategies.
F. Todisco, L. Brocca, L. F. Termite, and W. Wagner
Hydrol. Earth Syst. Sci., 19, 3845–3856, https://doi.org/10.5194/hess-19-3845-2015, https://doi.org/10.5194/hess-19-3845-2015, 2015
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We developed a new formulation of USLE, named Soil Moisture for Erosion (SM4E), that directly incorporates soil moisture information. SM4E is applied here by using modeled data and satellite observations obtained from the Advanced SCATterometer (ASCAT). SM4E is found to outperform USLE and USLE-MM models in silty–clay soil in central Italy. Through satellite data, there is the potential of applying SM4E for large-scale monitoring and quantification of the soil erosion process.
W. Shao, T. A. Bogaard, M. Bakker, and R. Greco
Hydrol. Earth Syst. Sci., 19, 2197–2212, https://doi.org/10.5194/hess-19-2197-2015, https://doi.org/10.5194/hess-19-2197-2015, 2015
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The effect of preferential flow on the stability of landslides is studied through numerical simulation of two types of rainfall events on a hypothetical hillslope. A model is developed that consists of two parts. The first part is a model for combined saturated/unsaturated subsurface flow and is used to compute the spatial and temporal water pressure response to rainfall. Preferential flow is simulated with a dual-permeability continuum model consisting of a matrix/preferential flow domain.
O. Fovet, L. Ruiz, M. Hrachowitz, M. Faucheux, and C. Gascuel-Odoux
Hydrol. Earth Syst. Sci., 19, 105–123, https://doi.org/10.5194/hess-19-105-2015, https://doi.org/10.5194/hess-19-105-2015, 2015
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We studied the annual hysteretic patterns observed between stream flow and water storage in the saturated and unsaturated zones of a hillslope and a riparian zone. We described these signatures using a hysteresis index and then used this to assess conceptual hydrological models. This led us to identify four hydrological periods and a clearly distinct behaviour between riparian and hillslope groundwaters and to provide new information about the model performances.
D. J. Peres and A. Cancelliere
Hydrol. Earth Syst. Sci., 18, 4913–4931, https://doi.org/10.5194/hess-18-4913-2014, https://doi.org/10.5194/hess-18-4913-2014, 2014
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A Monte Carlo approach, combining rainfall-stochastic models and hydrological and slope stability physically based models, is used to derive rainfall thresholds of landslide triggering. The uncertainty in threshold assessment related to variability of rainfall intensity within events and to past rainfall (antecedent rainfall) is analyzed and measured via ROC-based indexes, with a specific focus dedicated to the widely used power-law rainfall intensity-duration (I-D) thresholds.
D. Windhorst, P. Kraft, E. Timbe, H.-G. Frede, and L. Breuer
Hydrol. Earth Syst. Sci., 18, 4113–4127, https://doi.org/10.5194/hess-18-4113-2014, https://doi.org/10.5194/hess-18-4113-2014, 2014
G. Capparelli and P. Versace
Hydrol. Earth Syst. Sci., 18, 3225–3237, https://doi.org/10.5194/hess-18-3225-2014, https://doi.org/10.5194/hess-18-3225-2014, 2014
D. Penna, M. Borga, G. T. Aronica, G. Brigandì, and P. Tarolli
Hydrol. Earth Syst. Sci., 18, 2127–2139, https://doi.org/10.5194/hess-18-2127-2014, https://doi.org/10.5194/hess-18-2127-2014, 2014
G.-Y. Niu, D. Pasetto, C. Scudeler, C. Paniconi, M. Putti, P. A. Troch, S. B. DeLong, K. Dontsova, L. Pangle, D. D. Breshears, J. Chorover, T. E. Huxman, J. Pelletier, S. R. Saleska, and X. Zeng
Hydrol. Earth Syst. Sci., 18, 1873–1883, https://doi.org/10.5194/hess-18-1873-2014, https://doi.org/10.5194/hess-18-1873-2014, 2014
J. Tao and A. P. Barros
Hydrol. Earth Syst. Sci., 18, 367–388, https://doi.org/10.5194/hess-18-367-2014, https://doi.org/10.5194/hess-18-367-2014, 2014
J. Wienhöfer and E. Zehe
Hydrol. Earth Syst. Sci., 18, 121–138, https://doi.org/10.5194/hess-18-121-2014, https://doi.org/10.5194/hess-18-121-2014, 2014
A. Richard, S. Galle, M. Descloitres, J.-M. Cohard, J.-P. Vandervaere, L. Séguis, and C. Peugeot
Hydrol. Earth Syst. Sci., 17, 5079–5096, https://doi.org/10.5194/hess-17-5079-2013, https://doi.org/10.5194/hess-17-5079-2013, 2013
S. R. Lutz, H. J. van Meerveld, M. J. Waterloo, H. P. Broers, and B. M. van Breukelen
Hydrol. Earth Syst. Sci., 17, 4505–4524, https://doi.org/10.5194/hess-17-4505-2013, https://doi.org/10.5194/hess-17-4505-2013, 2013
Makoto Tani
Hydrol. Earth Syst. Sci., 17, 4453–4470, https://doi.org/10.5194/hess-17-4453-2013, https://doi.org/10.5194/hess-17-4453-2013, 2013
M. N. Papa, V. Medina, F. Ciervo, and A. Bateman
Hydrol. Earth Syst. Sci., 17, 4095–4107, https://doi.org/10.5194/hess-17-4095-2013, https://doi.org/10.5194/hess-17-4095-2013, 2013
P. Fiener, K. Auerswald, F. Winter, and M. Disse
Hydrol. Earth Syst. Sci., 17, 4121–4132, https://doi.org/10.5194/hess-17-4121-2013, https://doi.org/10.5194/hess-17-4121-2013, 2013
R. Greco, L. Comegna, E. Damiano, A. Guida, L. Olivares, and L. Picarelli
Hydrol. Earth Syst. Sci., 17, 4001–4013, https://doi.org/10.5194/hess-17-4001-2013, https://doi.org/10.5194/hess-17-4001-2013, 2013
C. Lepore, E. Arnone, L. V. Noto, G. Sivandran, and R. L. Bras
Hydrol. Earth Syst. Sci., 17, 3371–3387, https://doi.org/10.5194/hess-17-3371-2013, https://doi.org/10.5194/hess-17-3371-2013, 2013
J. E. van der Spek, T. A. Bogaard, and M. Bakker
Hydrol. Earth Syst. Sci., 17, 2171–2183, https://doi.org/10.5194/hess-17-2171-2013, https://doi.org/10.5194/hess-17-2171-2013, 2013
A. M. Ireson and A. P. Butler
Hydrol. Earth Syst. Sci., 17, 2083–2096, https://doi.org/10.5194/hess-17-2083-2013, https://doi.org/10.5194/hess-17-2083-2013, 2013
A. M. J. Coenders-Gerrits, L. Hopp, H. H. G. Savenije, and L. Pfister
Hydrol. Earth Syst. Sci., 17, 1749–1763, https://doi.org/10.5194/hess-17-1749-2013, https://doi.org/10.5194/hess-17-1749-2013, 2013
G. Martelloni, S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani
Hydrol. Earth Syst. Sci., 17, 1229–1240, https://doi.org/10.5194/hess-17-1229-2013, https://doi.org/10.5194/hess-17-1229-2013, 2013
C. D. Guzman, S. A. Tilahun, A. D. Zegeye, and T. S. Steenhuis
Hydrol. Earth Syst. Sci., 17, 1067–1077, https://doi.org/10.5194/hess-17-1067-2013, https://doi.org/10.5194/hess-17-1067-2013, 2013
D. M. Krzeminska, T. A. Bogaard, J.-P. Malet, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 17, 947–959, https://doi.org/10.5194/hess-17-947-2013, https://doi.org/10.5194/hess-17-947-2013, 2013
A. Peñuela, M. Javaux, and C. L. Bielders
Hydrol. Earth Syst. Sci., 17, 87–101, https://doi.org/10.5194/hess-17-87-2013, https://doi.org/10.5194/hess-17-87-2013, 2013
A. Rodhe
Hydrol. Earth Syst. Sci., 16, 3075–3082, https://doi.org/10.5194/hess-16-3075-2012, https://doi.org/10.5194/hess-16-3075-2012, 2012
G. Y. Gao, B. J. Fu, Y. H. Lü, Y. Liu, S. Wang, and J. Zhou
Hydrol. Earth Syst. Sci., 16, 2347–2364, https://doi.org/10.5194/hess-16-2347-2012, https://doi.org/10.5194/hess-16-2347-2012, 2012
C. E. Ballard, N. McIntyre, and H. S. Wheater
Hydrol. Earth Syst. Sci., 16, 2299–2310, https://doi.org/10.5194/hess-16-2299-2012, https://doi.org/10.5194/hess-16-2299-2012, 2012
D. M. Krzeminska, T. A. Bogaard, Th. W. J. van Asch, and L. P. H. van Beek
Hydrol. Earth Syst. Sci., 16, 1561–1576, https://doi.org/10.5194/hess-16-1561-2012, https://doi.org/10.5194/hess-16-1561-2012, 2012
T. Maurer, A. Schneider, and H. H. Gerke
Hydrol. Earth Syst. Sci., 15, 3617–3638, https://doi.org/10.5194/hess-15-3617-2011, https://doi.org/10.5194/hess-15-3617-2011, 2011
J. Klaus and E. Zehe
Hydrol. Earth Syst. Sci., 15, 2127–2144, https://doi.org/10.5194/hess-15-2127-2011, https://doi.org/10.5194/hess-15-2127-2011, 2011
M. C. Westhoff, T. A. Bogaard, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 15, 1945–1957, https://doi.org/10.5194/hess-15-1945-2011, https://doi.org/10.5194/hess-15-1945-2011, 2011
J. Minet, E. Laloy, S. Lambot, and M. Vanclooster
Hydrol. Earth Syst. Sci., 15, 1323–1338, https://doi.org/10.5194/hess-15-1323-2011, https://doi.org/10.5194/hess-15-1323-2011, 2011
T. Sabzevari, A. Talebi, R. Ardakanian, and A. Shamsai
Hydrol. Earth Syst. Sci., 14, 891–900, https://doi.org/10.5194/hess-14-891-2010, https://doi.org/10.5194/hess-14-891-2010, 2010
Y. Zhang, S. K. Carey, W. L. Quinton, J. R. Janowicz, J. W. Pomeroy, and G. N. Flerchinger
Hydrol. Earth Syst. Sci., 14, 729–750, https://doi.org/10.5194/hess-14-729-2010, https://doi.org/10.5194/hess-14-729-2010, 2010
Cited articles
Baatz, D., Kurtz, W., Franssen, H. H., Vereecken, H., and Kollet, S.: Catchment
tomography – An approach for spatial parameter estimation, Adv. Water Resour., 107, 147–159,
https://doi.org/10.1016/j.advwatres.2017.06.006,
2017. a
Bailey, R. and Baù, D.: Ensemble smoother assimilation of hydraulic head
and return flow data to estimate hydraulic conductivity distribution, Water Resour. Res., 46, w12543, https://doi.org/10.1029/2010WR009147,
2010. a
Bauser, H. H., Jaumann, S., Berg, D., and Roth, K.: EnKF with closed-eye
period – towards a consistent aggregation of information in soil hydrology,
Hydrol. Earth Syst. Sci., 20, 4999–5014,
10.5194/hess-20-4999-2016, 2016. a
Bishop, C. H., Etherton, B. J., and Majumdar, S. J.: Adaptive Sampling with the
Ensemble Transform Kalman Filter. Part I: Theoretical Aspects, Mon. Weather Rev.,
129, 420–436, https://doi.org/10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2,
2001. a
Brandhorst, N., Erdal, D., and Neuweiler, I.: Soil moisture prediction with the
ensemble Kalman filter: Handling uncertainty of soil hydraulic parameters,
Adv. Water Resour., 110, 360–370,
https://doi.org/10.1016/j.advwatres.2017.10.022,
2017. a, b
Cammalleri, C. and Ciraolo, G.: State and parameter update in a coupled
energy/hydrologic balance model using ensemble Kalman filtering, J. Hydrol., 416–417, 171–181,
https://doi.org/10.1016/j.jhydrol.2011.11.049,
2012. a
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman
filter data assimilation for a process-based catchment scale model of surface
and subsurface flow, Water Resour. Res., 45, w10421,
https://doi.org/10.1029/2008WR007031,
2009a. a, b
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Comparison of Data
Assimilation Techniques for a Coupled Model of Surface and Subsurface Flow,
Vadose Zone J., 8, 837–845, https://doi.org/10.2136/vzj2009.0018,
2009b. a, b, c
Camporese, M., Paniconi, C., Putti, M., and Orlandini, S.: Surface-subsurface
flow modeling with path-based runoff routing, boundary condition-based
coupling, and assimilation of multisource observation data, Water Resour. Res., 46, w02512, https://doi.org/10.1029/2008WR007536,
2010. a, b, c
Carsel, R. F. and Parrish, R. S.: Developing joint probability distributions of
soil water retention characteristics, Water Resour. Res., 24, 755–769,
https://doi.org/10.1029/WR024i005p00755,
1988. a, b, c
Chen, Y. and Zhang, D.: Data assimilation for transient flow in geologic
formations via ensemble Kalman filter, Adv. Water Resour., 29, 1107–1122, https://doi.org/10.1016/j.advwatres.2005.09.007,
2006. a
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater,
A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with
the ensemble Kalman filter: Use of streamflow observations to update states
in a distributed hydrological model, Adv. Water Resour., 31, 1309–1324, https://doi.org/10.1016/j.advwatres.2008.06.005,
2008. a
Crow, W. T. and Wood, E. F.: The assimilation of remotely sensed soil
brightness temperature imagery into a land surface model using Ensemble
Kalman filtering: a case study based on ESTAR measurements during SGP97,
Adv. Water Resour., 26, 137–149,
https://doi.org/10.1016/S0309-1708(02)00088-X,
2003. a
De Lannoy, G. J., Houser, P. R., Pauwels, V., and Verhoest, N. E.: State and
bias estimation for soil moisture profiles by an ensemble Kalman filter:
Effect of assimilation depth and frequency, Water Resour. Res., 43, 6, https://doi.org/10.1029/2006WR005100, 2007. a, b
Erdal, D., Neuweiler, I., and Wollschläger, U.: Using a bias aware EnKF to
account for unresolved structure in an unsaturated zone model, Water Resour. Res., 50, 132–147, https://doi.org/10.1002/2012WR013443,
2014. a
Erdal, D., Rahman, M., and Neuweiler, I.: The importance of state
transformations when using the ensemble Kalman filter for unsaturated flow
modeling: Dealing with strong nonlinearities, Adv. Water Resour.,
86, 354–365, https://doi.org/10.1016/j.advwatres.2015.09.008,
2015. a
Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, Springer-Verlag, Berlin, Germany,
2009a. a
Flores, A. N., Bras, R. L., and Entekhabi, D.: Hydrologic data assimilation
with a hillslope-scale-resolving model and L band radar observations:
Synthetic experiments with the ensemble Kalman filter, Water Resour. Res.,
48, W08509, https://doi.org/10.1029/2011WR011500, 2012. a
Francois, C., Quesney, A., and Ottlé, C.: Sequential Assimilation of ERS-1
SAR Data into a Coupled Land Surface–Hydrological Model Using an Extended
Kalman Filter, J. Hydrometeorol., 4, 473–487, 2003. a
Hain, C. R., Crow, W. T., Anderson, M. C., and Mecikalski, J. R.: An ensemble
Kalman filter dual assimilation of thermal infrared and microwave satellite
observations of soil moisture into the Noah land surface model, Water Resour. Res., 48, 11, https://doi.org/10.1029/2011WR011268, 2012. a
Han, X. and Li, X.: An evaluation of the nonlinear/non-Gaussian filters for the
sequential data assimilation, Remote Sens. Environ., 112, 1434–1449, 2008. a
Hendricks Franssen, H. J. and Kinzelbach, W.: Real-time groundwater flow
modeling with the Ensemble Kalman Filter: Joint estimation of states and
parameters and the filter inbreeding problem, Water Resour. Res., 44,
w09408, https://doi.org/10.1029/2007WR006505,
2008. a, b
Johnson, S. K.: Distributions in Statistics: Continuous Unvariate Distributions-1, Houghton Mifflin, 1970. a
Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G.,
Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler,
C., Park, Y.-J., Refsgaard, J. C., Stisen, S., and Sudicky, E.: The
integrated hydrologic model intercomparison project, IH-MIP2: A second set of
benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 53, 867–890, https://doi.org/10.1002/2016WR019191,
2017. a
Kurtz, W., He, G., Kollet, S. J., Maxwell, R. M., Vereecken, H., and
Hendricks Franssen, H.-J.: TerrSysMP–PDAF (version 1.0): a modular
high-performance data assimilation framework for an integrated land
surface–subsurface model, Geosci. Model Dev., 9, 1341–1360,
10.5194/gmd-9-1341-2016, 2016. a
Li, L., Zhou, H., Gómez-Hernández, J. J., and Franssen, H.-J. H.:
Jointly mapping hydraulic conductivity and porosity by assimilating
concentration data via ensemble Kalman filter, J. Hydrol., 428–429, 152–169, 2012. a
Lora, M., Camporese, M., and Salandin, P.: Design and performance of a
nozzle-type rainfall simulator for landslide triggering experiments, CATENA,
140, 77–89, https://doi.org/10.1016/j.catena.2016.01.018,
2016a. a, b, c
Lora, M., Camporese, M., Troch, P. A., and Salandin, P.: Rainfall-triggered
shallow landslides: infiltration dynamics in a physical hillslope model,
Hydrol. Process., 30, 3239–3251, https://doi.org/10.1002/hyp.10829,
2016b. a
Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J.-O., Ferguson, I. M.,
Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., Kumar, M., Lopez, S., Niu,
J., Paniconi, C., Park, Y.-J., Phanikumar, M. S., Shen, C., Sudicky, E. A.,
and Sulis, M.: Surface-subsurface model intercomparison: A first set of
benchmark results to diagnose integrated hydrology and feedbacks, Water Resour. Res., 50, 1531–1549, https://doi.org/10.1002/2013WR013725,
2014. a
Monsivais-Huertero, A., Graham, W. D., Judge, J., and Agrawal, D.: Effect of
simultaneous state-parameter estimation and forcing uncertainties on
root-zone soil moisture for dynamic vegetation using EnKF, Adv. Water Resour., 33, 468–484,
https://doi.org/10.1016/j.advwatres.2010.01.011,
2010. a
Montzka, C., Pauwels, V. R. N., Franssen, H.-J. H., Han, X., and Vereecken, H.:
Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A
Review, Sensors, 12, 16291–16333, https://doi.org/10.3390/s121216291,
2012. a
Moradkhani, H.: Hydrologic remote sensing and land surface data assimilation,
Sensors, 8, 2986–3004, 2008., a
Moradkhani, H., Hsu, K.-L., Gupta, H., and Sorooshian, S.: Uncertainty
assessment of hydrologic model states and parameters: Sequential data
assimilation using the particle filter, Water Resour. Res., 41, 5, https://doi.org/10.1029/2004WR003604, 2005. a
Pan, M. and Wood, E. F.: Data Assimilation for Estimating the Terrestrial Water
Budget Using a Constrained Ensemble Kalman Filter, J. Hydrometeorol., 7, 534–547, https://doi.org/10.1175/JHM495.1,
2006. a
Pasetto, D., Camporese, M., and Putti, M.: Ensemble Kalman filter versus
particle filter for a physically-based coupled surface–subsurface model,
Adv. Water Resour., 47, 1–13, 2012. a
Pasetto, D., Niu, G.-Y., Pangle, L., Paniconi, C., Putti, M., and Troch, P. A.:
Impact of sensor failure on the observability of flow dynamics at the
Biosphere 2 LEO hillslopes, Adv. Water Resour., 86, 327–339,
https://doi.org/10.1016/j.advwatres.2015.04.014,
2015. a
Rasmussen, J., Madsen, H., Jensen, K. H., and Refsgaard, J. C.: Data
assimilation in integrated hydrological modeling using ensemble Kalman
filtering: evaluating the effect of ensemble size and localization on filter
performance, Hydrol. Earth Syst. Sci., 19, 2999–3013,
10.5194/hess-19-2999-2015, 2015. a
Rasmussen, J., Madsen, H., Jensen, K. H., and Refsgaard, J. C.: Data
assimilation in integrated hydrological modelling in the presence of
observation bias, Hydrol. Earth Syst. Sci., 20, 2103–2118,
10.5194/hess-20-2103-2016, 2016. a
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130,
103–114, 2002a. a
Reichle, R. H., Walker, J. P., Koster, R. D., and Houser, P. R.: Extended
versus ensemble Kalman filtering for land data assimilation, J. Hydrometeorol., 3, 728–740, 2002b. a
Ridler, M.-E., Madsen, H., Stisen, S., Bircher, S., and Fensholt, R.:
Assimilation of SMOS-derived soil moisture in a fully integrated hydrological
and soil-vegetation-atmosphere transfer model in Western Denmark,
Water Resour. Res., 50, 8962–8981, https://doi.org/10.1002/2014WR015392,
2014. a
Sakov, P., Evensen, G., and Bertino, L.: Asynchronous data assimilation with
the EnKF, Tellus A, 62, 24–29, https://doi.org/10.1111/j.1600-0870.2009.00417.x,
2010. a
Schenato, L., Palmieri, L., Camporese, M., Bersan, S., Cola, S., Pasuto, A.,
Galtarossa, A., Salandin, P., and Simonini, P.: Distributed optical fibre
sensing for early detection of shallow landslides triggering, Sci. Rep., 7, 14686, https://doi.org/10.1038/s41598-017-12610-1, 2017. a
Shi, Y., Davis, K. J., Zhang, F., Duffy, C. J., and Yu, X.: Parameter
estimation of a physically-based land surface hydrologic model using an
ensemble Kalman filter: A multivariate real-data experiment, Adv. Water Resour., 83, 421–427,
https://doi.org/10.1016/j.advwatres.2015.06.009,
2015. a
Tang, Q., Kurtz, W., Schilling, O., Brunner, P., Vereecken, H., and Franssen,
H.-J. H.: The influence of riverbed heterogeneity patterns on river-aquifer
exchange fluxes under different connection regimes, J. Hydrol.,
554, 383–396, https://doi.org/10.1016/j.jhydrol.2017.09.031,
2017. a
Troch, P. A., Paniconi, C., and McLaughlin, D.: Catchment-scale hydrological
modeling and data assimilation, Adv. Water Resour., 26, 131–135,
https://doi.org/10.1016/S0309-1708(02)00087-8,
2003. a
Visser, A., Stuurman, R., and Bierkens, M. F.: Real-time forecasting of water
table depth and soil moisture profiles, Adv. Water Resour., 29, 692–706, 2006. a
Vrugt, J. A., Gupta, H. V., Nualláin, B., and Bouten, W.: Real-Time Data
Assimilation for Operational Ensemble Streamflow Forecasting, J. Hydrometeorol., 7, 548–565, https://doi.org/10.1175/JHM504.1,
2006. a
Warrick, A. W., Wierenga, P. J., Young, M. H., and Musil, S. A.: Diurnal
fluctuations of tensiometric readings due to surface temperature changes,
Water Resour. Res., 34, 2863–2869, https://doi.org/10.1029/98WR02095,
1998. a
Weerts, A. H. and El Serafy, G. Y.: Particle filtering and ensemble Kalman
filtering for state updating with hydrological conceptual rainfall-runoff
models, Water Resour. Res., 42, W09403, https://doi.org/10.1029/2005WR004093, 2006. a
Wösten, J. H. M. and van Genuchten, M. T.: Using Texture and Other Soil
Properties to Predict the Unsaturated Soil Hydraulic Functions, Soil Sci. Soc. Am. J., 52, 1762–1770, 1988. a
Xie, X. and Zhang, D.: Data assimilation for distributed hydrological catchment
modeling via ensemble Kalman filter, Adv. Water Resour., 33, 678–690, https://doi.org/10.1016/j.advwatres.2010.03.012,
2010. a
Zhang, D., Madsen, H., Ridler, M. E., Refsgaard, J. C., and Jensen, K. H.:
Impact of uncertainty description on assimilating hydraulic head in the MIKE
SHE distributed hydrological model, Adv. Water Resour., 86,
400–413, https://doi.org/10.1016/j.advwatres.2015.07.018,
2015.
a
Zhang, H., Kurtz, W., Kollet, S., Vereecken, H., and Franssen, H.-J. H.:
Comparison of different assimilation methodologies of groundwater levels to
improve predictions of root zone soil moisture with an integrated terrestrial
system model, Adv. Water Resour., 111, 224–238,
https://doi.org/10.1016/j.advwatres.2017.11.003,
2018. a, b
Zovi, F., Camporese, M., Franssen, H.-J. H., Huisman, J. A., and Salandin, P.:
Identification of high-permeability subsurface structures with multiple point
geostatistics and normal score ensemble Kalman filter, J. Hydrol.,
548, 208–224, https://doi.org/10.1016/j.jhydrol.2017.02.056,
2017. a
Short summary
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological modeling of a real-world hillslope test case with dominant unsaturated dynamics and strong nonlinearities. Overall, the EnKF is able to correctly update system state and soil parameters. However, multivariate data assimilation may lead to significant tradeoffs between model predictions of different variables, if the observation data are not high quality or representative.
We present a multivariate application of the ensemble Kalman filter (EnKF) in hydrological...
