Articles | Volume 28, issue 12
https://doi.org/10.5194/hess-28-2617-2024
© Author(s) 2024. 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-28-2617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2024
Research article |
| 20 Jun 2024
| 20 Jun 2024
Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
Marian Schönauer
CORRESPONDING AUTHOR
Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany
Anneli M. Ågren
Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden
Klaus Katzensteiner
Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Austria
Florian Hartsch
Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany
Paul Arp
Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada
Simon Drollinger
Department of Physical Geography, University of Göttingen, Göttingen, Germany
Dirk Jaeger
Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany
Related authors
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Anneli M. Ågren, Eliza Maher Hasselquist, Johan Stendahl, Mats B. Nilsson, and Siddhartho S. Paul
SOIL, 8, 733–749, https://doi.org/10.5194/soil-8-733-2022, https://doi.org/10.5194/soil-8-733-2022, 2022
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Historically, many peatlands in the boreal region have been drained for timber production. Given the prospects of a drier future due to climate change, wetland restorations are now increasing. Better maps hold the key to insights into restoration targets and land-use management policies, and maps are often the number one decision-support tool. We use an AI-developed soil moisture map based on laser scanning data to illustrate how the mapping of peatlands can be improved across an entire nation.
Johannes Larson, William Lidberg, Anneli M. Ågren, and Hjalmar Laudon
Hydrol. Earth Syst. Sci., 26, 4837–4851, https://doi.org/10.5194/hess-26-4837-2022, https://doi.org/10.5194/hess-26-4837-2022, 2022
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Terrain indices constitute a good candidate for modelling the spatial variation of soil moisture conditions in many landscapes. In this study, we evaluate nine terrain indices on varying DEM resolution and user-defined thresholds with validation using an extensive field soil moisture class inventory. We demonstrate the importance of field validation for selecting the appropriate DEM resolution and user-defined thresholds and that failing to do so can result in ambiguous and incorrect results.
Wolfgang Knierzinger, Ruth Drescher-Schneider, Klaus-Holger Knorr, Simon Drollinger, Andreas Limbeck, Lukas Brunnbauer, Felix Horak, Daniela Festi, and Michael Wagreich
E&G Quaternary Sci. J., 69, 121–137, https://doi.org/10.5194/egqsj-69-121-2020, https://doi.org/10.5194/egqsj-69-121-2020, 2020
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We present multi-proxy analyses of a 14C-dated peat core covering the past ⁓5000 years from the ombrotrophic Pürgschachen Moor. Pronounced increases in cultural indicators suggest significant human activity in the Bronze Age and in the period of the late La Tène culture. We found strong, climate-controlled interrelations between the pollen record, the humification degree and the ash content. Human activity is reflected in the pollen record and by heavy metals.
Anneli M. Ågren and William Lidberg
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-34, https://doi.org/10.5194/hess-2019-34, 2019
Publication in HESS not foreseen
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Headwaters make up the majority of any given stream network, yet, they are poorly mapped. A solution to this is to model the stream networks from high resolution digital elevation models. Matthews Correlation Coefficient (MCC) for a modelled stream network was 0.463 while the best topographical maps of today, had an MCC of 0.387. A residual analysis showed that 15 % of the errors could be explained by variability in runoff, quaternary deposits, local topography and location.
M. Mayer, B. Matthews, A. Schindlbacher, and K. Katzensteiner
Biogeosciences, 11, 6081–6093, https://doi.org/10.5194/bg-11-6081-2014, https://doi.org/10.5194/bg-11-6081-2014, 2014
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In this study soil CO2 efflux was measured at a montane mixed-forest site and a subalpine spruce forest site. Each site consisted of an undisturbed forest and two adjacent windthrow areas, differing regarding the time since disturbance. The combination of chronosequence and direct time-series approaches enabled us to investigate soil CO2 efflux dynamics over 12 years post-disturbance. According to our estimates, ~ 500 to 700 g C m-2 yr-1 are released via soil CO2 efflux from younger windthrows.
A. M. Ågren, W. Lidberg, M. Strömgren, J. Ogilvie, and P. A. Arp
Hydrol. Earth Syst. Sci., 18, 3623–3634, https://doi.org/10.5194/hess-18-3623-2014, https://doi.org/10.5194/hess-18-3623-2014, 2014
A. M. Ågren, I. Buffam, D. M. Cooper, T. Tiwari, C. D. Evans, and H. Laudon
Biogeosciences, 11, 1199–1213, https://doi.org/10.5194/bg-11-1199-2014, https://doi.org/10.5194/bg-11-1199-2014, 2014
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Modelling approaches
A systematic review of climate change science relevant to Australian design flood estimation
Technical Note: Resolution enhancement of flood inundation grids
Floods and droughts: a multivariate perspective
Technical note: Statistical generation of climate-perturbed flow duration curves
Deep learning methods for flood mapping: a review of existing applications and future research directions
Extreme floods in Europe: going beyond observations using reforecast ensemble pooling
Hydroinformatics education – the Water Informatics in Science and Engineering (WISE) Centre for Doctoral Training
Wetropolis extreme rainfall and flood demonstrator: from mathematical design to outreach
Technical note: The beneficial role of a natural permeable layer in slope stabilization by drainage trenches
Assessing the impacts of reservoirs on downstream flood frequency by coupling the effect of scheduling-related multivariate rainfall with an indicator of reservoir effects
Observation operators for assimilation of satellite observations in fluvial inundation forecasting
Contribution of potential evaporation forecasts to 10-day streamflow forecast skill for the Rhine River
Inundation mapping based on reach-scale effective geometry
Effects of variability in probable maximum precipitation patterns on flood losses
The challenge of forecasting impacts of flash floods: test of a simplified hydraulic approach and validation based on insurance claim data
A comparison of the discrete cosine and wavelet transforms for hydrologic model input data reduction
Hydrological modeling of the Peruvian–Ecuadorian Amazon Basin using GPM-IMERG satellite-based precipitation dataset
Technical note: Design flood under hydrological uncertainty
Topography- and nightlight-based national flood risk assessment in Canada
Regime shifts in annual maximum rainfall across Australia – implications for intensity–frequency–duration (IFD) relationships
Performance evaluation of groundwater model hydrostratigraphy from airborne electromagnetic data and lithological borehole logs
A continuous rainfall model based on vine copulas
Estimates of global dew collection potential on artificial surfaces
Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes
An assessment of the ability of Bartlett–Lewis type of rainfall models to reproduce drought statistics
Modeling root reinforcement using a root-failure Weibull survival function
Socio-hydrology: conceptualising human-flood interactions
Application of a model-based rainfall-runoff database as efficient tool for flood risk management
Estimating actual, potential, reference crop and pan evaporation using standard meteorological data: a pragmatic synthesis
HydroViz: design and evaluation of a Web-based tool for improving hydrology education
Web 2.0 collaboration tool to support student research in hydrology – an opinion
SCS-CN parameter determination using rainfall-runoff data in heterogeneous watersheds – the two-CN system approach
Discharge estimation combining flow routing and occasional measurements of velocity
Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application
Comment on "A praxis-oriented perspective of streamflow inference from stage observations – the method of Dottori et al. (2009) and the alternative of the Jones Formula, with the kinematic wave celerity computed on the looped rating curve" by Koussis (2009)
An evaluation of the Canadian global meteorological ensemble prediction system for short-term hydrological forecasting
Conrad Wasko, Seth Westra, Rory Nathan, Acacia Pepler, Timothy H. Raupach, Andrew Dowdy, Fiona Johnson, Michelle Ho, Kathleen L. McInnes, Doerte Jakob, Jason Evans, Gabriele Villarini, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 28, 1251–1285, https://doi.org/10.5194/hess-28-1251-2024, https://doi.org/10.5194/hess-28-1251-2024, 2024
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In response to flood risk, design flood estimation is a cornerstone of infrastructure design and emergency response planning, but design flood estimation guidance under climate change is still in its infancy. We perform the first published systematic review of the impact of climate change on design flood estimation and conduct a meta-analysis to provide quantitative estimates of possible future changes in extreme rainfall.
Seth Bryant, Guy Schumann, Heiko Apel, Heidi Kreibich, and Bruno Merz
Hydrol. Earth Syst. Sci., 28, 575–588, https://doi.org/10.5194/hess-28-575-2024, https://doi.org/10.5194/hess-28-575-2024, 2024
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A new algorithm has been developed to quickly produce high-resolution flood maps. It is faster and more accurate than current methods and is available as open-source scripts. This can help communities better prepare for and mitigate flood damages without expensive modelling.
Manuela Irene Brunner
Hydrol. Earth Syst. Sci., 27, 2479–2497, https://doi.org/10.5194/hess-27-2479-2023, https://doi.org/10.5194/hess-27-2479-2023, 2023
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I discuss different types of multivariate hydrological extremes and their dependencies, including regional extremes affecting multiple locations, such as spatially connected flood events; consecutive extremes occurring in close temporal succession, such as successive droughts; extremes characterized by multiple characteristics, such as floods with jointly high peak discharge and flood volume; and transitions between different types of extremes, such as drought-to-flood transitions.
Veysel Yildiz, Robert Milton, Solomon Brown, and Charles Rougé
Hydrol. Earth Syst. Sci., 27, 2499–2507, https://doi.org/10.5194/hess-27-2499-2023, https://doi.org/10.5194/hess-27-2499-2023, 2023
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The proposed approach is based on the parameterisation of flow duration curves (FDCs) to generate hypothetical streamflow futures. (1) We sample a broad range of future climates with modified values of three key streamflow statistics. (2) We generate an FDC for each hydro-climate future. (3) The resulting ensemble is ready to support robustness assessments in a changing climate. Our approach seamlessly represents a large range of futures with increased frequencies of both high and low flows.
Roberto Bentivoglio, Elvin Isufi, Sebastian Nicolaas Jonkman, and Riccardo Taormina
Hydrol. Earth Syst. Sci., 26, 4345–4378, https://doi.org/10.5194/hess-26-4345-2022, https://doi.org/10.5194/hess-26-4345-2022, 2022
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Deep learning methods have been increasingly used in flood management to improve traditional techniques. While promising results have been obtained, our review shows significant challenges in building deep learning models that can (i) generalize across multiple scenarios, (ii) account for complex interactions, and (iii) perform probabilistic predictions. We argue that these shortcomings could be addressed by transferring recent fundamental advancements in deep learning to flood mapping.
Manuela I. Brunner and Louise J. Slater
Hydrol. Earth Syst. Sci., 26, 469–482, https://doi.org/10.5194/hess-26-469-2022, https://doi.org/10.5194/hess-26-469-2022, 2022
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Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System to increase the sample size available to estimate the frequency of extreme flood events. We demonstrate that such ensemble pooling produces more robust estimates than observation-based estimates.
Thorsten Wagener, Dragan Savic, David Butler, Reza Ahmadian, Tom Arnot, Jonathan Dawes, Slobodan Djordjevic, Roger Falconer, Raziyeh Farmani, Debbie Ford, Jan Hofman, Zoran Kapelan, Shunqi Pan, and Ross Woods
Hydrol. Earth Syst. Sci., 25, 2721–2738, https://doi.org/10.5194/hess-25-2721-2021, https://doi.org/10.5194/hess-25-2721-2021, 2021
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How can we effectively train PhD candidates both (i) across different knowledge domains in water science and engineering and (ii) in computer science? To address this issue, the Water Informatics in Science and Engineering Centre for Doctoral Training (WISE CDT) offers a postgraduate programme that fosters enhanced levels of innovation and collaboration by training a cohort of engineers and scientists at the boundary of water informatics, science and engineering.
Onno Bokhove, Tiffany Hicks, Wout Zweers, and Thomas Kent
Hydrol. Earth Syst. Sci., 24, 2483–2503, https://doi.org/10.5194/hess-24-2483-2020, https://doi.org/10.5194/hess-24-2483-2020, 2020
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Wetropolis is a
table-topdemonstration model with extreme rainfall and flooding, including random rainfall, river flow, flood plains, an upland reservoir, a porous moor, and a city which can flood. It lets the viewer experience extreme rainfall and flood events in a physical model on reduced spatial and temporal scales with an event return period of 6.06 min rather than, say, 200 years. We disseminate its mathematical design and how it has been shown most prominently to over 500 flood victims.
Gianfranco Urciuoli, Luca Comegna, Marianna Pirone, and Luciano Picarelli
Hydrol. Earth Syst. Sci., 24, 1669–1676, https://doi.org/10.5194/hess-24-1669-2020, https://doi.org/10.5194/hess-24-1669-2020, 2020
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The aim of this paper is to demonstrate, through a numerical approach, that the presence of soil layers of higher permeability, a not unlikely condition in some deep landslides in clay, may be exploited to improve the efficiency of systems of drainage trenches for slope stabilization. The problem has been examined for the case that a unique pervious layer, parallel to the ground surface, is present at an elevation higher than the bottom of the trenches.
Bin Xiong, Lihua Xiong, Jun Xia, Chong-Yu Xu, Cong Jiang, and Tao Du
Hydrol. Earth Syst. Sci., 23, 4453–4470, https://doi.org/10.5194/hess-23-4453-2019, https://doi.org/10.5194/hess-23-4453-2019, 2019
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We develop a new indicator of reservoir effects, called the rainfall–reservoir composite index (RRCI). RRCI, coupled with the effects of static reservoir capacity and scheduling-related multivariate rainfall, has a better performance than the previous indicator in terms of explaining the variation in the downstream floods affected by reservoir operation. A covariate-based flood frequency analysis using RRCI can provide more reliable downstream flood risk estimation.
Elizabeth S. Cooper, Sarah L. Dance, Javier García-Pintado, Nancy K. Nichols, and Polly J. Smith
Hydrol. Earth Syst. Sci., 23, 2541–2559, https://doi.org/10.5194/hess-23-2541-2019, https://doi.org/10.5194/hess-23-2541-2019, 2019
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Flooding from rivers is a huge and costly problem worldwide. Computer simulations can help to warn people if and when they are likely to be affected by river floodwater, but such predictions are not always accurate or reliable. Information about flood extent from satellites can help to keep these forecasts on track. Here we investigate different ways of using information from satellite images and look at the effect on computer predictions. This will help to develop flood warning systems.
Bart van Osnabrugge, Remko Uijlenhoet, and Albrecht Weerts
Hydrol. Earth Syst. Sci., 23, 1453–1467, https://doi.org/10.5194/hess-23-1453-2019, https://doi.org/10.5194/hess-23-1453-2019, 2019
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A correct estimate of the amount of future precipitation is the most important factor in making a good streamflow forecast, but evaporation is also an important component that determines the discharge of a river. However, in this study for the Rhine River we found that evaporation forecasts only give an almost negligible improvement compared to methods that use statistical information on climatology for a 10-day streamflow forecast. This is important to guide research on low flow forecasts.
Cédric Rebolho, Vazken Andréassian, and Nicolas Le Moine
Hydrol. Earth Syst. Sci., 22, 5967–5985, https://doi.org/10.5194/hess-22-5967-2018, https://doi.org/10.5194/hess-22-5967-2018, 2018
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Inundation models are useful for hazard management and prevention. They are traditionally based on hydraulic partial differential equations (with satisfying results but large data and computational requirements). This study presents a simplified approach combining reach-scale geometric properties with steady uniform flow equations. The model shows promising results overall, although difficulties persist in the most complex urbanised reaches.
Andreas Paul Zischg, Guido Felder, Rolf Weingartner, Niall Quinn, Gemma Coxon, Jeffrey Neal, Jim Freer, and Paul Bates
Hydrol. Earth Syst. Sci., 22, 2759–2773, https://doi.org/10.5194/hess-22-2759-2018, https://doi.org/10.5194/hess-22-2759-2018, 2018
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We developed a model experiment and distributed different rainfall patterns over a mountain river basin. For each rainfall scenario, we computed the flood losses with a model chain. The experiment shows that flood losses vary considerably within the river basin and depend on the timing of the flood peaks from the basin's sub-catchments. Basin-specific characteristics such as the location of the main settlements within the floodplains play an additional important role in determining flood losses.
Guillaume Le Bihan, Olivier Payrastre, Eric Gaume, David Moncoulon, and Frédéric Pons
Hydrol. Earth Syst. Sci., 21, 5911–5928, https://doi.org/10.5194/hess-21-5911-2017, https://doi.org/10.5194/hess-21-5911-2017, 2017
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This paper illustrates how an integrated flash flood monitoring (or forecasting) system may be designed to directly provide information on possibly flooded areas and associated impacts on a very detailed river network and over large territories. The approach is extensively tested in the regions of Alès and Draguignan, located in south-eastern France. Validation results are presented in terms of accuracy of the estimated flood extents and related impacts (based on insurance claim data).
Ashley Wright, Jeffrey P. Walker, David E. Robertson, and Valentijn R. N. Pauwels
Hydrol. Earth Syst. Sci., 21, 3827–3838, https://doi.org/10.5194/hess-21-3827-2017, https://doi.org/10.5194/hess-21-3827-2017, 2017
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The accurate reduction of hydrologic model input data is an impediment towards understanding input uncertainty and model structural errors. This paper compares the ability of two transforms to reduce rainfall input data. The resultant transforms are compressed to varying extents and reconstructed before being evaluated with standard simulation performance summary metrics and descriptive statistics. It is concluded the discrete wavelet transform is most capable of preserving rainfall time series.
Ricardo Zubieta, Augusto Getirana, Jhan Carlo Espinoza, Waldo Lavado-Casimiro, and Luis Aragon
Hydrol. Earth Syst. Sci., 21, 3543–3555, https://doi.org/10.5194/hess-21-3543-2017, https://doi.org/10.5194/hess-21-3543-2017, 2017
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This paper indicates that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, in comparison to observed rainfall (by 11.1 % and 15.7 %, respectively). Statistical analysis indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets for estimating observed streamflows in Andean–Amazonian regions (Ucayali Basin, southern regions of the Amazon Basin of Peru and Ecuador).
Anna Botto, Daniele Ganora, Pierluigi Claps, and Francesco Laio
Hydrol. Earth Syst. Sci., 21, 3353–3358, https://doi.org/10.5194/hess-21-3353-2017, https://doi.org/10.5194/hess-21-3353-2017, 2017
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The paper provides an easy-to-use implementation of the UNCODE framework, which allows one to estimate the design flood value by directly accounting for sample uncertainty. Other than a design tool, this methodology is also a practical way to quantify the value of data in the design process.
Amin Elshorbagy, Raja Bharath, Anchit Lakhanpal, Serena Ceola, Alberto Montanari, and Karl-Erich Lindenschmidt
Hydrol. Earth Syst. Sci., 21, 2219–2232, https://doi.org/10.5194/hess-21-2219-2017, https://doi.org/10.5194/hess-21-2219-2017, 2017
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Flood mapping is one of Canada's major national interests. This work presents a simple and effective method for large-scale flood hazard and risk mapping, applied in this study to Canada. Readily available data, such as remote sensing night-light data, topography, and stream network were used to create the maps.
D. C. Verdon-Kidd and A. S. Kiem
Hydrol. Earth Syst. Sci., 19, 4735–4746, https://doi.org/10.5194/hess-19-4735-2015, https://doi.org/10.5194/hess-19-4735-2015, 2015
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Rainfall intensity-frequency-duration (IFD) relationships are required for the design and planning of water supply and management systems around the world. Currently IFD information is based on the "stationary climate assumption". However, this paper provides evidence of regime shifts in annual maxima rainfall time series using 96 daily rainfall stations and 66 sub-daily rainfall stations across Australia. Importantly, current IFD relationships may under- or overestimate the design rainfall.
P. A. Marker, N. Foged, X. He, A. V. Christiansen, J. C. Refsgaard, E. Auken, and P. Bauer-Gottwein
Hydrol. Earth Syst. Sci., 19, 3875–3890, https://doi.org/10.5194/hess-19-3875-2015, https://doi.org/10.5194/hess-19-3875-2015, 2015
H. Vernieuwe, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 19, 2685–2699, https://doi.org/10.5194/hess-19-2685-2015, https://doi.org/10.5194/hess-19-2685-2015, 2015
H. Vuollekoski, M. Vogt, V. A. Sinclair, J. Duplissy, H. Järvinen, E.-M. Kyrö, R. Makkonen, T. Petäjä, N. L. Prisle, P. Räisänen, M. Sipilä, J. Ylhäisi, and M. Kulmala
Hydrol. Earth Syst. Sci., 19, 601–613, https://doi.org/10.5194/hess-19-601-2015, https://doi.org/10.5194/hess-19-601-2015, 2015
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The global potential for collecting usable water from dew on an
artificial collector sheet was investigated by utilising 34 years of
meteorological reanalysis data as input to a dew formation model. Continental dew formation was found to be frequent and common, but daily yields were
mostly below 0.1mm.
D. E. Mora, L. Campozano, F. Cisneros, G. Wyseure, and P. Willems
Hydrol. Earth Syst. Sci., 18, 631–648, https://doi.org/10.5194/hess-18-631-2014, https://doi.org/10.5194/hess-18-631-2014, 2014
M. T. Pham, W. J. Vanhaute, S. Vandenberghe, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 5167–5183, https://doi.org/10.5194/hess-17-5167-2013, https://doi.org/10.5194/hess-17-5167-2013, 2013
M. Schwarz, F. Giadrossich, and D. Cohen
Hydrol. Earth Syst. Sci., 17, 4367–4377, https://doi.org/10.5194/hess-17-4367-2013, https://doi.org/10.5194/hess-17-4367-2013, 2013
G. Di Baldassarre, A. Viglione, G. Carr, L. Kuil, J. L. Salinas, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, https://doi.org/10.5194/hess-17-3295-2013, 2013
L. Brocca, S. Liersch, F. Melone, T. Moramarco, and M. Volk
Hydrol. Earth Syst. Sci., 17, 3159–3169, https://doi.org/10.5194/hess-17-3159-2013, https://doi.org/10.5194/hess-17-3159-2013, 2013
T. A. McMahon, M. C. Peel, L. Lowe, R. Srikanthan, and T. R. McVicar
Hydrol. Earth Syst. Sci., 17, 1331–1363, https://doi.org/10.5194/hess-17-1331-2013, https://doi.org/10.5194/hess-17-1331-2013, 2013
E. Habib, Y. Ma, D. Williams, H. O. Sharif, and F. Hossain
Hydrol. Earth Syst. Sci., 16, 3767–3781, https://doi.org/10.5194/hess-16-3767-2012, https://doi.org/10.5194/hess-16-3767-2012, 2012
A. Pathirana, B. Gersonius, and M. Radhakrishnan
Hydrol. Earth Syst. Sci., 16, 2499–2509, https://doi.org/10.5194/hess-16-2499-2012, https://doi.org/10.5194/hess-16-2499-2012, 2012
K. X. Soulis and J. D. Valiantzas
Hydrol. Earth Syst. Sci., 16, 1001–1015, https://doi.org/10.5194/hess-16-1001-2012, https://doi.org/10.5194/hess-16-1001-2012, 2012
G. Corato, T. Moramarco, and T. Tucciarelli
Hydrol. Earth Syst. Sci., 15, 2979–2994, https://doi.org/10.5194/hess-15-2979-2011, https://doi.org/10.5194/hess-15-2979-2011, 2011
A. Elshorbagy, G. Corzo, S. Srinivasulu, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, https://doi.org/10.5194/hess-14-1943-2010, 2010
A. D. Koussis
Hydrol. Earth Syst. Sci., 14, 1093–1097, https://doi.org/10.5194/hess-14-1093-2010, https://doi.org/10.5194/hess-14-1093-2010, 2010
J. A. Velázquez, T. Petit, A. Lavoie, M.-A. Boucher, R. Turcotte, V. Fortin, and F. Anctil
Hydrol. Earth Syst. Sci., 13, 2221–2231, https://doi.org/10.5194/hess-13-2221-2009, https://doi.org/10.5194/hess-13-2221-2009, 2009
Cited articles
Ågren, A., Lidberg, W., and Ring, E.: Mapping Temporal Dynamics in a Forest Stream Network – Implications for Riparian Forest Management, Forests, 6, 2982–3001, https://doi.org/10.3390/f6092982, 2015.
Ågren, A., Larson, J., Paul, S. S., Laudon, H., and Lidberg, W.: Use of multiple LIDAR-derived digital terrain indices and machine learning for high-resolution national-scale soil moisture mapping of the Swedish forest landscape, Geoderma, 404, 115280, https://doi.org/10.1016/j.geoderma.2021.115280, 2021.
Ågren, A. M., Lidberg, W., Strömgren, M., Ogilvie, J., and Arp, P. A.: Evaluating digital terrain indices for soil wetness mapping – a Swedish case study, Hydrol. Earth Syst. Sci., 18, 3623–3634, https://doi.org/10.5194/hess-18-3623-2014, 2014.
Ala-Ilomäki, J., Lindeman, H., Mola-Yudego, B., Prinz, R., Väätäinen, K., Talbot, B., and Routa, J.: The effect of bogie track and forwarder design on rut formation in a peatland, International Journal of Forest Engineering, 45, 1–8, https://doi.org/10.1080/14942119.2021.1935167, 2021.
Allman, M., Jankovský, M., Messingerová, V., and Allmanová, Z.: Soil moisture content as a predictor of soil disturbance caused by wheeled forest harvesting machines on soils of the Western Carpathians, J. Forestry Res., 28, 283–289, https://doi.org/10.1007/s11676-016-0326-y, 2017.
Ampoorter, E., van Nevel, L., de Vos, B., Hermy, M., and Verheyen, K.: Assessing the effects of initial soil characteristics, machine mass and traffic intensity on forest soil compaction, Forest Ecol. Manag., 260, 1664–1676, https://doi.org/10.1016/j.foreco.2010.08.002, 2010.
Awaida, A. and Westervelt, J.: Geographic Resources Analysis Support System (GRASS GIS), Geographic Resources Analysis Support System (GRASS GIS) Software, USA, https://grass.osgeo.org (last access: 22 August 2023), 2020.
Beylich, A., Oberholzer, H.-R., Schrader, S., Höper, H., and Wilke, B.-M.: Evaluation of soil compaction effects on soil biota and soil biological processes in soils, Soil and Tillage Research, 109, 133–143, https://doi.org/10.1016/j.still.2010.05.010, 2010.
Bezirksregierung Köln: Digitales Geländemodell DGM1 [Digital elevation model], https://www.bezreg-koeln.nrw.de/geobasis-nrw/produkte-und-dienste/hoehenmodelle/digitale-gelaendemodelle/digitales-gelaendemodell (last access: 8 November 2021), 2020.
Bezirksregierung Köln: Landbedeckung NRW, https://www.bezreg-koeln.nrw.de/geobasis-nrw/produkte-und-dienste/luftbild-und-satellitenbildinformationen/aktuelle-luftbild-und-3 (last access: 16 November 2023), 2023.
Bivand, R., Krug, R., Lovelace, R., Neteler, M., Jeworutzki, S., and Vanderhaeghe, F.: rgrass: Interface Between “GRASS” Geographical Information System and “R”: version 0.3.9, 10 September, https://CRAN.R-project.org/package=rgrass (last access: 31 May 2024), 2023.
Breiman, L.: Random forests, Machine Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Cambi, M., Certini, G., Neri, F., and Marchi, E.: The impact of heavy traffic on forest soils: A review, Forest Ecol. Manag., 338, 124–138, https://doi.org/10.1016/j.foreco.2014.11.022, 2015.
Campbell, D. M. H., White, B., and Arp, P.: Modeling and mapping soil resistance to penetration and rutting using LiDAR-derived digital elevation data, J. Soil Water Conserv., 68, 460–473, https://doi.org/10.2489/jswc.68.6.460, 2013.
Carranza, C., Nolet, C., Pezij, M., and van der Ploeg, M.: Root zone soil moisture estimation with Random Forest, J. Hydrol., 593, 125840, https://doi.org/10.1016/j.jhydrol.2020.125840, 2021.
Cavalli, A., Francini, S., McRoberts, R. E., Falanga, V., Congedo, L., Fioravante, P. de, Maesano, M., Munafò, M., Chirici, G., and Scarascia Mugnozza, G.: Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets, Remote Sens., 15, 923, https://doi.org/10.3390/rs15040923, 2023.
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., and Li, Y.: xgboost: Extreme Gradient Boosting, https://CRAN.R-project.org/package=xgboost (last access: 9 November 2021), 2021.
Copernicus Climate Change Service: ERA5-Land hourly data from 2001 to present, ECMWF, https://doi.org/10.24381/cds.e2161bac, 2019.
Crawford, L. J., Heinse, R., Kimsey, M. J., and Page-Dumroese, D. S.: Soil Sustainability and Harvest Operations, General Technical Report RMRS, https://doi.org/10.2737/RMRS-GTR-421, 2021.
Curzon, M. T., Slesak, R. A., Palik, B. J., and Schwager, J. K.: Harvest impacts to stand development and soil properties across soil textures: 25-year response of the aspen Lake States LTSP installations, Forest Ecol. Manag., 504, 119809, https://doi.org/10.1016/j.foreco.2021.119809, 2022.
D'Acqui, L. P., Certini, G., Cambi, M., and Marchi, E.: Machinery's impact on forest soil porosity, J. Terramechanics, 91, 65–71, https://doi.org/10.1016/j.jterra.2020.05.002, 2020.
DeArmond, D., Ferraz, J., Higuchi, N., and Ferraz, J. B. S.: Natural Recovery of Skid Trails. A Review, Can. J. Forest Res., 51, 948–961, https://doi.org/10.1139/cjfr-2020-0419, 2021.
Eijkelkamp Agrisearch Equipment: User Manual for the Moisture Meter type HH2, https://www.royaleijkelkamp.com/media/nrwjyah3/m-0615sae-penetrologger.pdf (last access: 7 August 2020), 2013.
Eliasson, L.: Effects of forwarder tyre pressure on rut formation and soil compaction, Silva Fenn., 39, 549–557, https://doi.org/10.14214/sf.366, 2005.
Finnish Meteorological Institute: Harvester Seasons, https://harvesterseasons.com/HarvesterSeasons_Description2pager_v2.pdf (last access: 8 November 2023), 2023.
Fjeld, D., Persson, M., Fransson, J. E. S., Bjerketvedt, J., and Bråthen, M.: Modelling forest road trafficability with satellite-based soil moisture variables, International Journal of Forest Engineering, 35, 93–104, https://doi.org/10.1080/14942119.2023.2276628, 2024.
Francesca, V., Osvaldo, F., Stefano, P., and Paola, R. P.: Soil Moisture Measurements: Comparison of Instrumentation Performances, J. Irrig. Drain E., 136, 81–89, https://doi.org/10.1061/(ASCE)0733-9437(2010)136:2(81), 2010.
Grüll, M.: Den Waldboden schonen – Vorsorgender Bodenschutz beim Einsatz von Holzerntetechnik [Soil protection in forest operations], Eberswalder Forstliche Schriftenreihe, 47, 37–44, https://www.waldwissen.net/assets/technik/holzernte/boden/lfe_bodenschutz/download/lfe_bodenschutz_originalbeitrag.pdf.pdf (last access: 31 May 2024), 2011.
Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L.: A Review of Wetland Remote Sensing, Sensors, 17, 777, https://doi.org/10.3390/s17040777, 2017.
Hamner, B. and Frasco, M.: Metrics: Evaluation Metrics for Machine Learning, https://CRAN.R-project.org/package=Metrics, 2018.
Hansson, L., Šimůnek, J., Ring, E., Bishop, K., and Gärdenäs, A. I.: Soil Compaction Effects on Root-Zone Hydrology and Vegetation in Boreal Forest Clearcuts, Soil Sci. Soc. Am. J., 83, 239, https://doi.org/10.2136/sssaj2018.08.0302, 2019.
Hauglin, M., Rahlf, J., Schumacher, J., Astrup, R., and Breidenbach, J.: Large scale mapping of forest attributes using heterogeneous sets of airborne laser scanning and National Forest Inventory data, Forest Ecosystems, 8, 65, https://doi.org/10.1186/s40663-021-00338-4, 2021.
Heppelmann, J. B., Talbot, B., Antón Fernández, C., and Astrup, R.: Depth-to-water maps as predictors of rut severity in fully mechanized harvesting operations, International Journal of Forest Engineering, 33, 108–118, https://doi.org/10.1080/14942119.2022.2044724, 2022.
Heubaum, F.: Bodenschutz im Staatsbetrieb Sachsenforst [Soil protection]: Projekte zur Technologieerprobung, Staatsbetrieb Sachsenforest, https://www.sbs.sachsen.de/download/Bodenschutz_Projekte_2015_09_30.pdf (last access: 5 November 2021), 2015.
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, https://CRAN.R-project.org/package=raster (last access: 31 May 2024), 2020.
Hillel, D.: Environmental soil physics: Fundamentals, applications, and environmental considerations, Elsevier, San Diego, California, ISBN 0080544150, 1998.
Hoffmann, S., Schönauer, M., Heppelmann, J., Asikainen, A., Cacot, E., Eberhard, B., Hasenauer, H., Ivanovs, J., Jaeger, D., Lazdins, A., Mohtashami, S., Moskalik, T., Nordfjell, T., Stereńczak, K., Talbot, B., Uusitalo, J., Vuillermoz, M., and Astrup, R.: Trafficability Prediction Using Depth-to-Water Maps: the Status of Application in Northern and Central European Forestry, Curr. Forestry Rep., 338, 124, https://doi.org/10.1007/s40725-021-00153-8, 2022.
Horn, R., Vossbrink, J., Peth, S., and Becker, S.: Impact of modern forest vehicles on soil physical properties, Forest Ecol. Manag., 248, 56–63, https://doi.org/10.1016/j.foreco.2007.02.037, 2007.
James, S. E., Pärtel, M., Wilson, S. D., and Peltzer, D. A.: Temporal heterogeneity of soil moisture in grassland and forest, J. Ecol., 91, 234–239, 2003.
Jones, M.-F. and Arp, P.: Relating Cone Penetration and Rutting Resistance to Variations in Forest Soil Properties and Daily Moisture Fluctuations, Open Journal of Soil Science, 07, 149–171, https://doi.org/10.4236/ojss.2017.77012, 2017.
Jones, M.-F. and Arp, P.: Soil Trafficability Forecasting, Open Journal of Forestry, 9, 296–322, https://doi.org/10.4236/ojf.2019.94017, 2019.
Kelliher, F. M., Leuning, R., and Schulze, E. D.: Evaporation and canopy characteristics of coniferous forests and grasslands, Oecologia, 95, 153–163, https://doi.org/10.1007/BF00323485, 1993.
Kemppinen, J., Niittynen, P., Riihimäki, H., and Luoto, M.: Modelling soil moisture in a high-latitude landscape using LiDAR and soil data, Earth Surf. Proc. Land., 43, 1019–1031, https://doi.org/10.1002/esp.4301, 2018.
Hufkens, K., Stauffer, R., and Campitelli, E.: khufkens/ecmwfr: ecmwfr, Zenodo [code], https://doi.org/10.5281/zenodo.2647541, 2019.
Kristensen, J. A., Balstrøm, T., Jones, R. J. A., Jones, A., Montanarella, L., Panagos, P., and Breuning-Madsen, H.: Development of a harmonised soil profile analytical database for Europe: a resource for supporting regional soil management, SOIL, 5, 289–301, https://doi.org/10.5194/soil-5-289-2019, 2019.
Kuglerová, L., Hasselquist, E. M., Richardson, J. S., Sponseller, R. A., Kreutzweiser, D. P., and Laudon, H.: Management perspectives on Aqua incognita Connectivity and cumulative effects of small natural and artificial streams in boreal forests, Hydrol. Process., 31, 4238–4244, https://doi.org/10.1002/hyp.11281, 2017.
Kursa, M. B. and Rudnicki, W. R.: Feature Selection with the Boruta Package, J. Stat. Softw., 36, 11, https://doi.org/10.18637/jss.v036.i11, 2010.
Labelle, E. R. and Jaeger, D.: Management Implications of Using Brush Mats for Soil Protection on Machine Operating Trails during Mechanized Cut-to-Length Forest Operations, Forests, 10, 19, https://doi.org/10.3390/f10010019, 2018.
Labelle, E. R., Poltorak, B. J., and Jaeger, D.: The role of brush mats in mitigating machine-induced soil disturbances: An assessment using absolute and relative soil bulk density and penetration resistance, Can. J. Forest Res., 49, 164–178, https://doi.org/10.1139/cjfr-2018-0324, 2019.
Lal, P., Singh, G., Das, N. N., Colliander, A., and Entekhabi, D.: Assessment of ERA5-Land Volumetric Soil Water Layer Product Using In Situ and SMAP Soil Moisture Observations, IEEE Geosci. Remote S., 19, 1–5, https://doi.org/10.1109/LGRS.2022.3223985, 2022.
Larson, J., Lidberg, W., Ågren, A. M., and Laudon, H.: Predicting soil moisture conditions across a heterogeneous boreal catchment using terrain indices, Hydrol. Earth Syst. Sci., 26, 4837–4851, https://doi.org/10.5194/hess-26-4837-2022, 2022.
Leach, J. A., Lidberg, W., Kuglerová, L., Peralta-Tapia, A., Ågren, A., and Laudon, H.: Evaluating topography-based predictions of shallow lateral groundwater discharge zones for a boreal lake-stream system, Water Resour. Res., 53, 5420–5437, https://doi.org/10.1002/2016WR019804, 2017.
Lidberg, W., Nilsson, M., and Ågren, A.: Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape, Ambio, 49, 475–486, https://doi.org/10.1007/s13280-019-01196-9, 2020.
Mattila, U. and Tokola, T.: Terrain mobility estimation using TWI and airborne gamma-ray data, J. Environ. Manage., 232, 531–536, https://doi.org/10.1016/j.jenvman.2018.11.081, 2019.
McNabb, D. H., Startsev, A. D., and Nguyen, H.: Soil Wetness and Traffic Level Effects on Bulk Density and Air-Filled Porosity of Compacted Boreal Forest Soils, Soil Sci. Soc. Am. J., 65, 1238–1247, https://doi.org/10.2136/sssaj2001.6541238x, 2001.
Mohtashami, S., Eliasson, L., Jansson, G., and Sonesson, J.: Influence of soil type, cartographic depth-to-water, road reinforcement and traffic intensity on rut formation in logging operations: a survey study in Sweden, Silva Fenn., 51, 5, https://doi.org/10.14214/sf.2018, 2017.
Moore, I. D., Grayson, R. B., and Ladson, A. R.: Digital terrain modelling: A review of hydrological, geomorphological, and biological applications, Hydrol. Process., 5, 3–30, https://doi.org/10.1002/hyp.3360050103, 1991.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Murphy, P. N. C., Ogilvie, J., Connor, K., and Arp, P.: Mapping wetlands: A comparison of two different approaches for New Brunswick, Canada, Wetlands, 27, 846–854, https://doi.org/10.1672/0277-5212(2007)27[846:MWACOT]2.0.CO;2, 2007.
Murphy, P. N. C., Ogilvie, J., and Arp, P.: Topographic modelling of soil moisture conditions: A comparison and verification of two models, Eur. J. Soil Sci., 60, 94–109, https://doi.org/10.1111/j.1365-2389.2008.01094.x, 2009.
Murphy, P. N. C., Ogilvie, J., Meng, F.-R., White, B., Bhatti, J. S., and Arp, P.: Modelling and mapping topographic variations in forest soils at high resolution: A case study, Ecol. Model., 222, 2314–2332, https://doi.org/10.1016/j.ecolmodel.2011.01.003, 2011.
Oliveira, V. A., Rodrigues, A. F., Morais, M. A. V., Terra, M. D. C. N. S., Guo, L., and Mello, C. R.: Spatiotemporal modelling of soil moisture in an A tlantic forest through machine learning algorithms, Eur. J. Soil Sci., 72, 1969–1987, https://doi.org/10.1111/ejss.13123, 2021.
Peng, J., Albergel, C., Balenzano, A., Brocca, L., Cartus, O., Cosh, M. H., Crow, W. T., Dabrowska-Zielinska, K., Dadson, S., Davidson, M. W. J., de Rosnay, P., Dorigo, W., Gruber, A., Hagemann, S., Hirschi, M., Kerr, Y. H., Lovergine, F., Mahecha, M. D., Marzahn, P., Mattia, F., Musial, J. P., Preuschmann, S., Reichle, R. H., Satalino, G., Silgram, M., van Bodegom, P. M., Verhoest, N. E. C., Wagner, W., Walker, J. P., Wegmüller, U., and Loew, A.: A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements, Remote Sens. Environ., 252, 112162, https://doi.org/10.1016/j.rse.2020.112162, 2021.
Picchio, R., Latterini, F., Mederski, P. S., Tocci, D., Venanzi, R., Stefanoni, W., and Pari, L.: Applications of GIS-Based Software to Improve the Sustainability of a Forwarding Operation in Central Italy, Sustainability, 12, 5716, https://doi.org/10.3390/su12145716, 2020.
Poltorak, B. J., Labelle, E. R., and Jaeger, D.: Soil displacement during ground-based mechanized forest operations using mixed-wood brush mats, Soil and Tillage Research, 179, 96–104, https://doi.org/10.1016/j.still.2018.02.005, 2018.
Quinn, P., Beven, K., Chevallier, P., and Planchon, O.: The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models, Hydrol. Process., 5, 59–79, https://doi.org/10.1002/hyp.3360050106, 1991.
R Core Team: R: A Language and Environment for Statistical Computing, The R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 31 March 2024), 2023.
Ring, E., Ågren, A., Bergkvist, I., Finér, L., Johansson, F., and Högbom, L.: A guide to using wet area maps in forestry: En guide för hur man kan använda markfuktighetskartor i skogsbruket, Arbetsrapport 1051-2020, Uppsala, Sweden, https://www.skogforsk.se/cd_20200618101746/contentassets/2b 2c62c4a4934838a1460cbb7b327d20/arbetsrapport-1051-2020-2.pdf (last access: 31 May 2024), 2022.
Schönauer, M.: Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts, Zenodo [data set], https://doi.org/10.5281/zenodo.11032138, 2024.
Schönauer, M. and Maack, J.: R-code for calculating depth-to-water (DTW) maps using GRASS GIS (Version v1), Zenodo [code], https://doi.org/10.5281/zenodo.5638518, 2021.
Schönauer, M., Hoffmann, S., Maack, J., Jansen, M., and Jaeger, D.: Comparison of Selected Terramechanical Test Procedures and Cartographic Indices to Predict Rutting Caused by Machine Traffic during a Cut-to-Length Thinning Operation, Forests, 12, 113, https://doi.org/10.3390/f12020113, 2021a.
Schönauer, M., Väätäinen, K., Prinz, R., Lindeman, H., Pszenny, D., Jansen, M., Maack, J., Talbot, B., Astrup, R., and Jaeger, D.: Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps, Int. J. Appl. Earth Obs., 105, 102614, https://doi.org/10.1016/j.jag.2021.102614, 2021b.
Schönauer, M., Prinz, R., Väätäinen, K., Astrup, R., Pszenny, D., Lindeman, H., and Jaeger, D.: Spatio-temporal prediction of soil moisture using soil maps, topographic indices and SMAP retrievals, Int. J. Appl. Earth Obs., 108, 102730, https://doi.org/10.1016/j.jag.2022.102730, 2022.
Sirén, M., Salmivaara, A., Ala-Ilomäki, J., Launiainen, S., Lindeman, H., Uusitalo, J., Sutinen, R., and Hänninen, P.: Predicting forwarder rut formation on fine-grained mineral soils, Scand. J. Forest Res., 34, 145–154, https://doi.org/10.1080/02827581.2018.1562567, 2019.
Sørensen, R. and Seibert, J.: Effects of DEM resolution on the calculation of topographical indices: TWI and its components, J. Hydrol., 347, 79–89, https://doi.org/10.1016/j.jhydrol.2007.09.001, 2007.
Suvinen, A. and Saarilahti, M.: Measuring the mobility parameters of forwarders using GPS and CAN bus techniques, J. Terramechanics, 43, 237–252, https://doi.org/10.1016/j.jterra.2005.12.005, 2006.
Tromp-van Meerveld, H. J. and McDonnell, J. J.: On the interrelations between topography, soil depth, soil moisture, transpiration rates and species distribution at the hillslope scale, Adv. Water Resour., 29, 293–310, https://doi.org/10.1016/j.advwatres.2005.02.016, 2006.
Uusitalo, J., Ala-Ilomäki, J., Lindeman, H., Toivio, J., and Sirén, M.: Modelling soil moisture – soil strength relationship of fine-grained upland forest soils, Silva Fenn., 53, 10050, https://doi.org/10.14214/sf.10050, 2019.
Uusitalo, J., Ala-Ilomäki, J., Lindeman, H., Toivio, J., and Sirén, M.: Predicting rut depth induced by an 8-wheeled forwarder in fine-grained boreal forest soils, Ann. For. Sci., 77, https://doi.org/10.1007/s13595-020-00948-y, 2020.
Vega-Nieva, D. J., Murphy, P. N. C., Castonguay, M., Ogilvie, J., and Arp, P.: A modular terrain model for daily variations in machine-specific forest soil trafficability, Can. J. Soil Sci., 89, 93–109, https://doi.org/10.4141/CJSS06033, 2009.
Walker, J. P., Willgoose, G. R., and Kalma, J. D.: In situ measurement of soil moisture: a comparison of techniques, J. Hydrol., 293, 85–99, https://doi.org/10.1016/j.jhydrol.2004.01.008, 2004.
White, B., Ogilvie, J., Campbell, D. M. H., Hiltz, D., Gauthier, B., Chisholm, H. K., Wen, H. K., Murphy, P. N. C., and Arp, P.: Using the Cartographic Depth-to-Water Index to Locate Small Streams and Associated Wet Areas across Landscapes, Can. Water Resour. J., 37, 333–347, https://doi.org/10.4296/cwrj2011-909, 2012.
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random Forests for High Dimensional Data in C
Short summary
This work employs innovative spatiotemporal modeling to predict soil moisture, with implications for sustainable forest management. By correlating predicted soil moisture with rut depth, it addresses a critical concern of soil damage and ecological impact – and its prevention through adequate planning of forest operations.
This work employs innovative spatiotemporal modeling to predict soil moisture, with implications...
