Articles | Volume 10, issue 8
https://doi.org/10.5194/gmd-10-3001-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-3001-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper
| 
10 Aug 2017
Model description paper |
| 10 Aug 2017
lumpR 2.0.0: an R package facilitating landscape discretisation for hillslope-based hydrological models
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
Till Francke
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
Axel Bronstert
Institute of Earth and Environmental Science, University of Potsdam, Potsdam, Germany
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Elodie Marret, Peter M. Grosse, Lena Scheiffele, Katya Dimitrova Petrova, Till Francke, Daniel Altdorff, Maik Heistermann, Merlin Schiel, Carsten Neumann, Daniel Scheffler, Mehdi Saberioon, Matthias Kunz, Miroslav Zboril, Jonas Marach, Marcel Reginatto, Anna Balenzano, Daniel Rasche, Christine Stumpp, Benjamin Trost, and Sascha E. Oswald
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-546, https://doi.org/10.5194/essd-2025-546, 2025
Preprint under review for ESSD
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This data paper describes a comprehensive collection of soil moisture and related data from an extensive cosmic-ray neutron sensing (CRNS) network at an agricultural research site in north-east Germany. The data set comprises not only soil moisture observations at different spatio-temporal scales, but also a wealth of accompanying data that provide the context to interpret soil moisture dynamics within a broader hydrological and environmental framework.
Till Francke and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 25, 2783–2802, https://doi.org/10.5194/nhess-25-2783-2025, https://doi.org/10.5194/nhess-25-2783-2025, 2025
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Brandenburg is among the driest federal states in Germany. The low groundwater recharge (GWR) is fundamental to both water supply and the support of natural ecosystems. In this study, we show that the decline of observed discharge and groundwater tables since 1980 can be explained by climate change in combination with an increasing leaf area index. Still, simulated GWR rates remain highly uncertain due to the uncertainty in precipitation trends.
Marie-Therese Schmehl, Yojana Adhikari, Cathrina Balthasar, Anja Binder, Danica Clerc, Sophia Dobkowitz, Werner Gerwin, Kristin Günther, Heinrich Hartong, Thilo Heinken, Carsten Hess, Pierre L. Ibisch, Florent Jouy, Loretta Leinen, Thomas Raab, Frank Repmann, Susanne Rönnefarth, Lilly Rohlfs, Marina Schirrmacher, Jens Schröder, Maren Schüle, Andrea Vieth-Hillebrand, and Till Francke
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-313, https://doi.org/10.5194/essd-2025-313, 2025
Revised manuscript accepted for ESSD
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We present data recorded by eight institutions within the PYROPHOB project, running from 2020 to 2024 at two forest research sites in northeastern Germany. The aim of the project was to monitor abiotic and biotic parameters of forest regrowth under different management regimes on former wildfire sites. The multitude of collected data allows for detailed analyses of the observables separately, as well as their interaction for a more multidisciplinary view on forest recovery after a wildfire.
Nazaré Suziane Soares, Carlos Alexandre Gomes Costa, Till Francke, Christian Mohr, Wolfgang Schwanghart, and Pedro Henrique Augusto Medeiros
EGUsphere, https://doi.org/10.5194/egusphere-2025-884, https://doi.org/10.5194/egusphere-2025-884, 2025
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We use drone surveys to map river intermittency in reaches and classify them into "Wet", "Transition", "Dry" or "Not Determined". We train Random Forest models with 40 candidate predictors, and select altitude, drainage area, distance from dams and dynamic predictors. We separate different models based on dynamic predictors: satellite indices (a) and (b); or (c) accumulated precipitation (30 days). Model (a) is the most successful in simulating intermittency both temporally and spatially.
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
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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.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
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The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 28, 989–1000, https://doi.org/10.5194/hess-28-989-2024, https://doi.org/10.5194/hess-28-989-2024, 2024
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Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
Stefano Gianessi, Matteo Polo, Luca Stevanato, Marcello Lunardon, Till Francke, Sascha E. Oswald, Hami Said Ahmed, Arsenio Toloza, Georg Weltin, Gerd Dercon, Emil Fulajtar, Lee Heng, and Gabriele Baroni
Geosci. Instrum. Method. Data Syst., 13, 9–25, https://doi.org/10.5194/gi-13-9-2024, https://doi.org/10.5194/gi-13-9-2024, 2024
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Soil moisture monitoring is important for many applications, from improving weather prediction to supporting agriculture practices. Our capability to measure this variable is still, however, limited. In this study, we show the tests conducted on a new soil moisture sensor at several locations. The results show that the new sensor is a valid and compact alternative to more conventional, non-invasive soil moisture sensors that can pave the way for a wide range of applications.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, and Axel Bronstert
Hydrol. Earth Syst. Sci., 28, 139–161, https://doi.org/10.5194/hess-28-139-2024, https://doi.org/10.5194/hess-28-139-2024, 2024
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How suspended sediment export from glacierized high-alpine areas responds to future climate change is hardly assessable as many interacting processes are involved, and appropriate physical models are lacking. We present the first study, to our knowledge, exploring machine learning to project sediment export until 2100 in two high-alpine catchments. We find that uncertainties due to methodological limitations are small until 2070. Negative trends imply that peak sediment may have already passed.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
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Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863, https://doi.org/10.5194/hess-27-1841-2023, https://doi.org/10.5194/hess-27-1841-2023, 2023
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We present a suitable method to reconstruct sediment export from decadal records of hydroclimatic predictors (discharge, precipitation, temperature) and shorter suspended sediment measurements. This lets us fill the knowledge gap on how sediment export from glacierized high-alpine areas has responded to climate change. We find positive trends in sediment export from the two investigated nested catchments with step-like increases around 1981 which are linked to crucial changes in glacier melt.
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
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Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Lena Katharina Schmidt, Till Francke, Erwin Rottler, Theresa Blume, Johannes Schöber, and Axel Bronstert
Earth Surf. Dynam., 10, 653–669, https://doi.org/10.5194/esurf-10-653-2022, https://doi.org/10.5194/esurf-10-653-2022, 2022
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Climate change fundamentally alters glaciated high-alpine areas, but it is unclear how this affects riverine sediment transport. As a first step, we aimed to identify the most important processes and source areas in three nested catchments in the Ötztal, Austria, in the past 15 years. We found that areas above 2500 m were crucial and that summer rainstorms were less influential than glacier melt. These findings provide a baseline for studies on future changes in high-alpine sediment dynamics.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
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This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
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Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
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Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
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Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Erwin Rottler, Axel Bronstert, Gerd Bürger, and Oldrich Rakovec
Hydrol. Earth Syst. Sci., 25, 2353–2371, https://doi.org/10.5194/hess-25-2353-2021, https://doi.org/10.5194/hess-25-2353-2021, 2021
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The mesoscale hydrological model (mHM) forced with an ensemble of climate projection scenarios was used to assess potential future changes in flood seasonality in the Rhine River basin. Results indicate that future changes in flood characteristics are controlled by increases in precipitation sums and diminishing snowpacks. The decreases in snowmelt can counterbalance increasing precipitation, resulting in only small and transient changes in streamflow maxima.
Cited articles
Ajami, H., Khan, U., Tuteja, N. K., and Sharma, A.: Development of a computationally efficient semi-distributed hydrologic modeling application for soil moisture, lateral flow and runoff simulation, Environ. Modell. Softw., 85, 319–331, https://doi.org/10.1016/j.envsoft.2016.09.002, 2016.
Band, L. E., Tague, C. L., Brun, S. E., Tenenbaum, D. E., and Fernandes, R. A.: Modelling Watersheds as Spatial Object Hierarchies: Structure and Dynamics, Trans. GIS, 4, 181–196, https://doi.org/10.1111/1467-9671.00048, 2000.
Beven, K.: Linking parameters across scales: Subgrid parameterizations and scale dependent hydrological models, Hydrol. Process., 9, 507–525, https://doi.org/10.1002/hyp.3360090504, 1995.
Beven, K.: Searching for the Holy Grail of scientific hydrology: Qt = (S, R, Δt)A as closure, Hydrol. Earth Syst. Sci., 10, 609–618, https://doi.org/10.5194/hess-10-609-2006, 2006.
Beven, K., Calver, A., and Morris, E. M.: The Institute of Hydrology distributed model, IH Report 98, Institute of Hydrology, Wallingford, UK, 1987.
Bogaart, P. W. and Troch, P. A.: Curvature distribution within hillslopes and catchments and its effect on the hydrological response, Hydrol. Earth Syst. Sci., 10, 925–936, https://doi.org/10.5194/hess-10-925-2006, 2006.
Bronstert, A.: Modellierung der Abflussbildung und der Bodenwasserdynamik von Hängen, in: Mitteilungen des Instituts für Hydrologie und Wasserwirtschaft, 46, Universität Karlsruhe, 1994.
Bronstert, A.: Capabilities and limitations of detailed hillslope hydrological modelling, Hydrol. Process., 13, 21–48, https://doi.org/10.1002/(SICI)1099-1085(199901)13:1<21::AID-HYP702>3.0.CO;2-4, 1999.
Bronstert, A. and Bárdossy, A.: The role of spatial variability of soil moisture for modelling surface runoff generation at the small catchment scale, Hydrol. Earth Syst. Sci., 3, 505–516, https://doi.org/10.5194/hess-3-505-1999, 1999.
Bronstert, A., de Araújo, J.-C., Batalla, R. J., Costa, A. C., Delgado, J. M., Francke, T., Foerster, S., Guentner, A., López-Tarazón, J. A., Mamede, G. L., Medeiros, P. H., Mueller, E., and Vericat, D.: Process-based modelling of erosion, sediment transport and reservoir siltation in mesoscale semi-arid catchments, J. Soils Sediments, 14, 2001–2018, https://doi.org/10.1007/s11368-014-0994-1, 2014.
Cochrane, T. and Flanagan, D.: Representative hillslope methods for applying the WEPP model with DEMs and GIS, T. ASAE, 46, 1041–1049, 2003.
Costa-Cabral, M. C. and Burges, S. J.: Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas, Water Resour. Res., 30, 1681–1692, https://doi.org/10.1029/93WR03512, 1994.
Dawes, W. R. and Short, D.: The significance of topology for modeling the surface hydrology of fluvial landscapes, Water Resour. Res., 30, 1045–1055, https://doi.org/10.1029/93WR02479, 1994.
de Araújo, J. C. and Medeiros, P. H. A.: Impact of Dense Reservoir Networks on Water Resources in Semiarid Environments, Aust. J. Water Resour., 17, 87–100, 2013.
de Figueiredo, J. V., de Araújo, J. C., Medeiros, P. H. A., and Costa, A. C.: Runoff initiation in a preserved semiarid Caatinga small watershed, Northeastern Brazil, Hydrol. Process., 30, 2390–2400, https://doi.org/10.1002/hyp.10801, 2016.
DeVantier, B. A. and Feldman, A. D.: Review of GIS Applications in Hydrologic Modeling, J. Water Res. Pl.-ASCE, 119, 246–261, https://doi.org/10.1061/(ASCE)0733-9496(1993)119:2(246), 1993.
Di Luzio, M., Srinivasan, R., and Arnold, J. G.: A GIS-Coupled Hydrological Model System for the Watershed Assessment of Agricultural Nonpoint and Point Sources of Pollution, Trans. GIS, 8, 113–136, https://doi.org/10.1111/j.1467-9671.2004.00170.x, 2004.
Euser, T., Hrachowitz, M., Winsemius, H. C., and Savenije, H. H.: The effect of forcing and landscape distribution on performance and consistency of model structures, Hydrol. Process., 29, 3727–3743, https://doi.org/10.1002/hyp.10445, 2015.
Fenicia, F., Kavetski, D., Savenije, H. H. G., and Pfister, L.: From spatially variable streamflow to distributed hydrological models: Analysis of key modeling decisions, Water Resour. Res., 52, 954–989, https://doi.org/10.1002/2015WR017398, 2016.
Flanagan, D. C. and Nearing, M. A.: USDA Water Erosion Prediction Project – Hillslope Profile and Watershed Model Documentation, NSERL Report 10, USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, Indiana, USA, 1995.
Flanagan, D. C., Frankenberger, J. R., Cochrane, T. A., Renschler, C. S., and Elliot, W. J.: Geospatial Application of the Water Erosion Prediction Project (WEPP) Model, T. ASABE, 56, 591–601, https://doi.org/10.13031/2013.42681, 2013.
Flügel, W.-A.: Delineating hydrological response units by geographical information system analyses for regional hydrological modelling using PRMS/MMS in the drainage basin of the River Brül, Germany, Hydrol. Process., 9, 423–436, https://doi.org/10.1002/hyp.3360090313, 1995.
Francke, T., Güntner, A., Mamede, G., Müller, E. N., and Bronstert, A.: Automated catena-based discretization of landscapes for the derivation of hydrological modelling units, Int. J. Geogr. Inf. Sci., 22, 111–132, https://doi.org/10.1080/13658810701300873, 2008.
Freitas, H. R. d. A., Freitas, C. d. C., Rosim, S., and Oliveira, J. R. d. F.: Drainage networks and watersheds delineation derived from TIN-based digital elevation models, Comput. Geosci., 92, 21–37, https://doi.org/10.1016/j.cageo.2016.04.003, 2016.
Gallant, J. C. and Wilson, J. P.: TAPES-G: A grid-based terrain analysis program for the environmental sciences, Comput. Geosci., 22, 713–722, 1996.
Garbrecht, J. and Martz, L. W.: An Overview of TOPAZ: An Automated Digital Landscape Analysis Tool for Topographic Evaluation, Drainage Identification, Watershed Segmentation, and Subcatchment Parameterization, Grazinglands Research Laboratory, USDA, Agricultural Research Service, El Reno, Oklahoma, USA, report no. GRL 99-1, 1999.
Gerstengarbe, F.-W. and Werner, P. C.: Estimation of the beginning and end of recurrent events within a climate regime, Clim. Res., 11, 97–107, 1999.
González, V. I., Carkovic, A. B., Lobo, G. P., Flanagan, D. C., and Bonilla, C. A.: Spatial discretization of large watersheds and its influence on the estimation of hillslope sediment yield, Hydrol. Process., 30, 30–39, https://doi.org/10.1002/hyp.10559, 2016.
Grayson, R. B., Moore, I. D., and McMahon, T. A.: Physically based hydrologic modeling: 1. A terrain-based model for investigative purposes, Water Resour. Res., 28, 2639–2658, https://doi.org/10.1029/92WR01258, 1992.
Güntner, A.: Large-scale hydrological modelling in the semi-arid North-East of Brazil, PIK Report 77, Potsdam Institute for Climate Impact Research, Potsdam, Germany, 2002.
Güntner, A. and Bronstert, A.: Representation of landscape variability and lateral redistribution processes for large-scale hydrological modelling in semi-arid areas, J. Hydrol., 297, 136–161, https://doi.org/10.1016/j.jhydrol.2004.04.008, 2004.
Haghnegahdar, A., Tolson, B. A., Craig, J. R., and Paya, K. T.: Assessing the performance of a semi-distributed hydrological model under various watershed discretization schemes, Hydrol. Process., 29, 4018–4031, https://doi.org/10.1002/hyp.10550, 2015.
Han, J.-C., Huang, G.-H., Zhang, H., Li, Z., and Li, Y.-P.: Effects of watershed subdivision level on semi-distributed hydrological simulations: case study of the SLURP model applied to the Xiangxi River watershed, China, Hydrolog. Sci. J., 59, 108–125, https://doi.org/10.1080/02626667.2013.854368, 2014.
Haverkamp, S., Fohrer, N., and Frede, H.-G.: Assessment of the effect of land use patterns on hydrologic landscape functions: a comprehensive GIS-based tool to minimize model uncertainty resulting from spatial aggregation, Hydrol. Process., 19, 715–727, https://doi.org/10.1002/hyp.5626, 2005.
Hazenberg, P., Fang, Y., Broxton, P., Gochis, D., Niu, G.-Y., Pelletier, J. D., Troch, P. A., and Zeng, X.: A hybrid-3D hillslope hydrological model for use in Earth system models, Water Resour. Res., 51, 8218–8239, 2015.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Catchment hydrologic response with a fully distributed triangulated irregular network model, Water Resour. Res., 40, W11102, https://doi.org/10.1029/2004WR003218, 2004.
Jackisch, C., Zehe, E., Samaniego, L., and Singh, A. K.: An experiment to gauge an ungauged catchment: rapid data assessment and eco-hydrological modelling in a data-scarce rural catchment, Hydrolog. Sci. J., 59, 2103–2125, https://doi.org/10.1080/02626667.2013.870662, 2014.
Jacomine, P. K. T., Almeida, J. C., and Medeiros, L. A. R.: Levantamento exploratorio – Reconhecimento de solos do Estado do Ceará, vol. 1, DNPEA, DRN-SUDENE, Recife, Brazil, 1973.
Khan, U., Tuteja, N. K., Ajami, H., and Sharma, A.: An equivalent cross-sectional basis for semidistributed hydrological modeling, Water Resour. Res., 50, 4395–4415, https://doi.org/10.1002/2013WR014741, 2014.
Kinner, D., Mitasova, H., Stallard, R., Harmon, R. S., and Toma, L.: GIS-Based Stream Network Analysis for the Upper Río Chagres Basin, Panama, in: The Río Chagres, Panama: A Multidisciplinary Profile of a Tropical Watershed, edited by: Harmon, R. S., chap. 6, Springer Netherlands, 83–95, https://doi.org/10.1007/1-4020-3297-8_6, 2005.
Kite, G. W.: Scaling of Input Data for Macroscale Hydrologic Modeling, Water Resour. Res., 31, 2769–2781, https://doi.org/10.1029/95WR02102, 1995.
Kneis, D.: A lightweight framework for rapid development of object-based hydrological model engines, Environ. Modell. Softw., 68, 110–121, https://doi.org/10.1016/j.envsoft.2015.02.009, 2015.
Kouwen, N.: WATFLOOD / CHARM Canadian Hydrological And Routing Model, Department of Civil Engineering, University of Waterloo, Waterloo, Ontario, Canada, 2016.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R. A.: Grouped Response Units for Distributed Hydrologic Modeling, J. Water Res. Pl.-ASCE, 119, 289–305, https://doi.org/10.1061/(ASCE)0733-9496(1993)119:3(289), 1993.
Krol, M. S., de Vries, M. J., Oel, P. R., and de Araújo, J. C.: Sustainability of Small Reservoirs and Large Scale Water Availability Under Current Conditions and Climate Change, Water Resour. Manage., 25, 3017–3026, https://doi.org/10.1007/s11269-011-9787-0, 2011.
Krysanova, V., Müller-Wohlfeil, D.-I., and Becker, A.: Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds, Ecol. Model., 106, 261–289, https://doi.org/10.1016/S0304-3800(97)00204-4, 1998.
Kumar, R., Samaniego, L., and Attinger, S.: The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics, J. Hydrol., 392, 54–69, https://doi.org/10.1016/j.jhydrol.2010.07.047, 2010.
Lacroix, M. P., Martz, L. W., Kite, G. W., and Garbrecht, J.: Using digital terrain analysis modeling techniques for the parameterization of a hydrologic model, Environ. Modell. Softw., 17, 125–134, https://doi.org/10.1016/S1364-8152(01)00042-1, 2002.
Lagacherie, P., Rabotin, M., Colin, F., Moussa, R., and Voltz, M.: Geo-MHYDAS: A landscape discretization tool for distributed hydrological modeling of cultivated areas, Comput. Geosci., 36, 1021–1032, https://doi.org/10.1016/j.cageo.2009.12.005, 2010.
Leavesley, G. H., Lichty, R. W., Troutman, B. M., and Saindon, L. G.: Precipitation-Runoff Modeling System: User's manual, Water-resources investigations report 83-4238, United States Department of the Interior, Denver, Colorado, USA, 1983.
Manguerra, H. B. and Engel, B. A.: Hydrologic parameterization of watersheds for runoff prediction using SWAT, J. Am. Water Resour. As., 34, 1149–1162, https://doi.org/10.1111/j.1752-1688.1998.tb04161.x, 1998.
Markstrom, S., Niswonger, R., Regan, R., Prudic, D., and Barlow, P.: GSFLOW–Coupled ground-water and surface-water flow model based on the integration of the Precipitation-Runoff Modeling System (PRMS) and the Modular Ground-Water Flow Model (MODFLOW-2005), U.S. Geological Survey Techniques and Methods 6-D1, 240 pp., 2008.
Maunder, C. J.: An automated method for constructing contour-based digital elevation models, Water Resour. Res., 35, 3931–3940, https://doi.org/10.1029/1999WR900166, 1999.
Maurer, T.: Physikalisch begründete, zeitkontinuierliche Modellierung des Wassertransports in kleinen ländlichen Einzugsgebieten, Dissertation, Mitteilungen Inst. f. Hydrologie u. Wasserwirtschaft, 61, Universität Karlsruhe, 1997.
Medeiros, P. H. A. and de Araújo, J. C.: Temporal variability of rainfall in a semiarid environment in Brazil and its effect on sediment transport processes, J. Soils Sediments, 14, 1216–1223, https://doi.org/10.1007/s11368-013-0809-9, 2014.
Medeiros, P. H. A., Güntner, A., Francke, T., Mamede, G. L., and de Araújo, J. C.: Modelling spatio-temporal patterns of sediment yield and connectivity in a semi-arid catchment with the WASA-SED model, Hydrolog. Sci. J., 55, 636–648, https://doi.org/10.1080/02626661003780409, 2010.
Medeiros, P. H. A., de Araújo, J. C., Mamede, G. L., Creutzfeldt, B., Güntner, A., and Bronstert, A.: Connectivity of sediment transport in a semiarid environment: a synthesis for the Upper Jaguaribe Basin, Brazil, J. Soils Sediments, 14, 1938–1948, https://doi.org/10.1007/s11368-014-0988-z, 2014.
Metz, M., Mitasova, H., and Harmon, R. S.: Efficient extraction of drainage networks from massive, radar-based elevation models with least cost path search, Hydrol. Earth Syst. Sci., 15, 667–678, https://doi.org/10.5194/hess-15-667-2011, 2011.
Miller, S., Semmens, D., Miller, R., Hernandez, M., Goodrich, D., Miller, W., Kepner, W., and Ebert, D.: GIS-based Hydrologic Modeling: The Automated Geospatial Watershed Assessment Tool, in: Second Federal Interagency Hydrologic Modeling Conference, Las Vegas, Nevada, USA, 2002.
Miller, S. N., Semmens, D. J., Goodrich, D. C., Hernandez, M., Miller, R. C., Kepner, W. G., and Guertin, D. P.: The Automated Geospatial Watershed Assessment tool, Environ. Modell. Softw., 22, 365–377, https://doi.org/10.1016/j.envsoft.2005.12.004, 2007.
Molnár, D. K. and Julien, P. Y.: Grid-Size Effects on Surface Runoff Modeling, J. Hydrol. Eng., 5, 8–16, https://doi.org/10.1061/(ASCE)1084-0699(2000)5:1(8), 2000.
Moore, I. D. and Grayson, R. B.: Terrain-based catchment partitioning and runoff prediction using vector elevation data, Water Resour. Res., 27, 1177–1191, https://doi.org/10.1029/91WR00090, 1991.
Moore, I. D., O'Loughlin, E. M., and Burch, G. J.: A contour-based topographic model for hydrological and ecological applications, Earth Surf. Proc. Land., 13, 305–320, https://doi.org/10.1002/esp.3290130404, 1988.
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.
Moretti, G. and Orlandini, S.: Automatic delineation of drainage basins from contour elevation data using skeleton construction techniques, Water Resour. Res., 44, W05403, https://doi.org/10.1029/2007WR006309, 2008.
Moussa, R., Voltz, M., and Andrieux, P.: Effects of the spatial organization of agricultural management on the hydrological behaviour of a farmed catchment during flood events, Hydrol. Process., 16, 393–412, https://doi.org/10.1002/hyp.333, 2002.
Mueller, E. N., Francke, T., Batalla, R. J., and Bronstert, A.: Modelling the effects of land-use change on runoff and sediment yield for a meso-scale catchment in the Southern Pyrenees, CATENA, 79, 288–296, https://doi.org/10.1016/j.catena.2009.06.007, 2009.
Mueller, E. N., Güntner, A., Francke, T., and Mamede, G.: Modelling sediment export, retention and reservoir sedimentation in drylands with the WASA-SED model, Geosci. Model Dev., 3, 275–291, https://doi.org/10.5194/gmd-3-275-2010, 2010.
Neteler, M., Bowman, M. H., Landa, M., and Metz, M.: GRASS GIS: A multi-purpose open source GIS, Environ. Modell. Softw., 31, 124–130, https://doi.org/10.1016/j.envsoft.2011.11.014, 2012.
Nijzink, R. C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H. H. G., and Hrachowitz, M.: The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models, Hydrol. Earth Syst. Sci., 20, 1151–1176, https://doi.org/10.5194/hess-20-1151-2016, 2016.
Noël, P., Rousseau, A. N., Paniconi, C., and Nadeau, D. F.: Algorithm for Delineating and Extracting Hillslopes and Hillslope Width Functions from Gridded Elevation Data, J. Hydrol. Eng., 19, 366–374, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000783, 2014.
O'Callaghan, J. F. and Mark, D. M.: The extraction of drainage networks from digital elevation data, Lect. Notes Comput. Sc., 28, 323–344, https://doi.org/10.1016/S0734-189X(84)80011-0, 1984.
Passalacqua, P., Do Trung, T., Foufoula-Georgiou, E., Sapiro, G., and Dietrich, W. E.: A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths, J. Geophys. Res.-Earth, 115, F01002, https://doi.org/10.1029/2009JF001254, 2010.
Pianosi, F. and Wagener, T.: A simple and efficient method for global sensitivity analysis based on cumulative distribution functions, Environ. Modell. Softw., 67, 1–11, https://doi.org/10.1016/j.envsoft.2015.01.004, 2015.
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B., and Wagener, T.: Sensitivity analysis of environmental models: A systematic review with practical workflow, Environ. Modell. Softw., 79, 214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: https://www.R-project.org/ (last access: 5 July 2016), 2015.
Refsgaard, J. C.: Parameterisation, calibration and validation of distributed hydrological models, J. Hydrol., 198, 69–97, https://doi.org/10.1016/S0022-1694(96)03329-X, 1997.
Reggiani, P., Sivapalan, M., and Hassanizadeh, S. M.: A unifying framework for watershed thermodynamics: balance equations for mass, momentum, energy and entropy, and the second law of thermodynamics, Adv. Water Resour., 22, 367–398, https://doi.org/10.1016/S0309-1708(98)00012-8, 1998.
Renschler, C. S.: Designing geo-spatial interfaces to scale process models: the GeoWEPP approach, Hydrol. Process., 17, 1005–1017, https://doi.org/10.1002/hyp.1177, 2003.
Samaniego, L., Kumar, R., and Attinger, S.: Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale, Water Resour. Res., 46, W05523, https://doi.org/10.1029/2008WR007327, 2010.
Sangireddy, H., Stark, C. P., Kladzyk, A., and Passalacqua, P.: GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data, Environ. Modell. Softw., 83, 58–73, https://doi.org/10.1016/j.envsoft.2016.04.026, 2016.
Sanzana, P., Jankowfsky, S., Branger, F., Braud, I., Vargas, X., Hitschfeld, N., and Gironás, J.: Computer-assisted mesh generation based on hydrological response units for distributed hydrological modeling, Comput. Geosci., 57, 32–43, https://doi.org/10.1016/j.cageo.2013.02.006, 2013.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Schulla, J. and Jasper, K.: Model Description WaSiM-ETH, Technical report 2007, 181 pp., ETH Zürich, 2007.
Schwartze, C.: Deriving Hydrological Response Units (HRUs) using a Web Processing Service implementation based on GRASS GIS, Geoinformatics FCE CTU, 3, 67–78, 2008.
Smith, T., Marshall, L., McGlynn, B., and Jencso, K.: Using field data to inform and evaluate a new model of catchment hydrologic connectivity, Water Resour. Res., 49, 6834–6846, https://doi.org/10.1002/wrcr.20546, 2013.
Sulis, M., Paniconi, C., and Camporese, M.: Impact of grid resolution on the integrated and distributed response of a coupled surface–subsurface hydrological model for the des Anglais catchment, Quebec, Hydrol. Process., 25, 1853–1865, https://doi.org/10.1002/hyp.7941, 2011.
Tague, C. and Band, L.: RHESSys: Regional Hydro-ecologic simulation system: An object-oriented approach to spatially distributed modeling of carbon, water and nutrient cycling, Earth Interact., 8, 1–42, 2004.
Tarboton, D.: Terrain Analysis Using Digital Elevation Models in Hydrology, in: 23rd ESRI International Users Conference, San Diego, California, USA, 2003.
Tarboton, D. G., Bras, R. L., and Rodriguez-Iturbe, I.: On the extraction of channel networks from digital elevation data, Hydrol. Process., 5, 81–100, https://doi.org/10.1002/hyp.3360050107, 1991.
Troch, P. A., Paniconi, C., and Emiel van Loon, E.: Hillslope-storage Boussinesq model for subsurface flow and variable source areas along complex hillslopes: 1. Formulation and characteristic response, Water Resour. Res., 39, 1316, https://doi.org/10.1029/2002WR001728, 2003.
Tucker, G. E., Lancaster, S. T., Gasparini, N. M., Bras, R. L., and Rybarczyk, S. M.: An object-oriented framework for distributed hydrologic and geomorphic modeling using triangulated irregular networks, Comput. Geosci., 27, 959–973, https://doi.org/10.1016/S0098-3004(00)00134-5, 2001.
Uhlenbrook, S. and Leibundgut, C.: Process-oriented catchment modelling and multiple-response validation, Hydrol. Process., 16, 423–440, https://doi.org/10.1002/hyp.330, 2002.
Viviroli, D., Zappa, M., Gurtz, J., and Weingartner, R.: An introduction to the hydrological modelling system PREVAH and its pre- and post-processing-tools, Environ. Modell. Softw., 24, 1209–1222, https://doi.org/10.1016/j.envsoft.2009.04.001, 2009.
Vivoni, E. R., Ivanov, V. Y., Bras, R. L., and Entekhabi, D.: Generation of Triangulated Irregular Networks Based on Hydrological Similarity, J. Hydrol. Eng., 9, 288–302, 2004.
Weiler, M. and Beven, K.: Do we need a Community Hydrological Model?, Water Resour. Res., 51, 7777–7784, https://doi.org/10.1002/2014WR016731, 2015.
Winter, T. C.: The concept of hydrologic landscapes, J. Am. Water Resour. As., 37, 335–349, 2001.
Wood, E. F., Sivapalan, M., Beven, K., and Band, L.: Effects of spatial variability and scale with implications to hydrologic modeling, J. Hydrol., 102, 29–47, https://doi.org/10.1016/0022-1694(88)90090-X, 1988.
Woolhiser, D. A., Smith, R. E., and Goodrich, D. C.: KINEROS, A Kinematic Runoff and Erosion Model: Documentation and User Manual, USDA, Agricultural Research Service, ARS-77, 130 pp., 1990.
Xavier, A. C., King, C. W., and Scanlon, B. R.: Daily gridded meteorological variables in Brazil (1980–2013), Int. J. Climatol., 36, 2644–2659, https://doi.org/10.1002/joc.4518, 2016.
Yang, D., Herath, S., and Musiake, K.: Comparison of different distributed hydrological models for characterization of catchment spatial variability, Hydrol. Process., 14, 403–416, https://doi.org/10.1002/(SICI)1099-1085(20000228)14:3<403::AID-HYP945>3.0.CO;2-3, 2000.
Yang, D., Herath, S., and Musiake, K.: A hillslope-based hydrological model using catchment area and width functions, Hydrol. Sci. J., 47, 49–65, https://doi.org/10.1080/02626660209492907, 2002.
Zehe, E., Ehret, U., Pfister, L., Blume, T., Schröder, B., Westhoff, M., Jackisch, C., Schymanski, S. J., Weiler, M., Schulz, K., Allroggen, N., Tronicke, J., van Schaik, L., Dietrich, P., Scherer, U., Eccard, J., Wulfmeyer, V., and Kleidon, A.: HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments, Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, 2014.
Zhang, L., Dawes, W. R., Hatton, T. J., Reece, P. H., Beale, G. T. H., and Packer, I.: Estimation of soil moisture and groundwater recharge using the TOPOG_IRM Model, Water Resour. Res., 35, 149–161, https://doi.org/10.1029/98WR01616, 1999.
Zhang, W. and Montgomery, D. R.: Digital elevation model grid size, landscape representation, and hydrologic simulations, Water Resour. Res., 30, 1019–1028, https://doi.org/10.1029/93WR03553, 1994.
Short summary
To discretise and transfer a landscape into a hydrological model, many different algorithms and software implementations exist. These are, however, often model specific, commercial, and allow for only a limited workflow automation. Overcoming these limitations, the software package lumpR was developed. It employs an hillslope-based discretisation algorithm directed at large-scale application. The software is demonstrated in a case study and crucial discretisation parameters are investigated.
To discretise and transfer a landscape into a hydrological model, many different algorithms and...
