Articles | Volume 19, issue 1
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-19-485-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
26 Jan 2015
Research article |
| 26 Jan 2015
Precipitation variability within an urban monitoring network via microcanonical cascade generators
P. Licznar
Faculty of Environmental Engineering, Wroclaw University of Technology, Wrocław, Poland
C. De Michele
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy
W. Adamowski
Institute of Environmental Engineering, John Paul II Catholic University of Lublin, Stalowa Wola, Poland
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Carmelo Cammalleri, Carlo De Michele, and Andrea Toreti
Hydrol. Earth Syst. Sci., 28, 103–115, https://doi.org/10.5194/hess-28-103-2024, https://doi.org/10.5194/hess-28-103-2024, 2024
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Precipitation and soil moisture have the potential to be jointly used for the modeling of drought conditions. In this research, we analysed how their statistical inter-relationship varies across Europe. We found some clear spatial patterns, especially in the so-called tail dependence (which measures the strength of the relationship for the extreme values). The results suggest that the tail dependence needs to be accounted for to correctly assess the value of joint modeling for drought.
Fabiola Banfi, Emanuele Bevacqua, Pauline Rivoire, Sérgio C. Oliveira, Joaquim G. Pinto, Alexandre M. Ramos, and Carlo De Michele
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-212, https://doi.org/10.5194/nhess-2023-212, 2023
Preprint under review for NHESS
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Landslides are complex phenomena, causing important impacts in vulnerable areas and they are often triggered by rainfall. Here, we develop a new approach that uses information on the temporal clustering of rainfall, i.e. multiple events close in time, to detect landslide events and compare it with the use of classical empirical rainfall thresholds, considering as a case study the region of Lisbon, Portugal. The results could help to improve the prediction of rainfall-triggered landslides.
F. Ioli, E. Bruno, D. Calzolari, M. Galbiati, A. Mannocchi, P. Manzoni, M. Martini, A. Bianchi, A. Cina, C. De Michele, and L. Pinto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-137-2023, 2023
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
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Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Fabiola Banfi and Carlo De Michele
The Cryosphere, 16, 1031–1056, https://doi.org/10.5194/tc-16-1031-2022, https://doi.org/10.5194/tc-16-1031-2022, 2022
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Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modelling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between April and September since higher temperatures protect snow from erosion. We also compared modelled and observed firn density, obtaining a satisfying agreement.
Roberto Villalobos-Herrera, Emanuele Bevacqua, Andreia F. S. Ribeiro, Graeme Auld, Laura Crocetti, Bilyana Mircheva, Minh Ha, Jakob Zscheischler, and Carlo De Michele
Nat. Hazards Earth Syst. Sci., 21, 1867–1885, https://doi.org/10.5194/nhess-21-1867-2021, https://doi.org/10.5194/nhess-21-1867-2021, 2021
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Climate hazards may be caused by events which have multiple drivers. Here we present a method to break down climate model biases in hazard indicators down to the bias caused by each driving variable. Using simplified fire and heat stress indicators driven by temperature and relative humidity as examples, we show how multivariate indicators may have complex biases and that the relationship between driving variables is a source of bias that must be considered in climate model bias corrections.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
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The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Fabiola Banfi and Carlo De Michele
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-357, https://doi.org/10.5194/tc-2020-357, 2021
Manuscript not accepted for further review
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Climate changes require a dynamic description of glaciers in hydrological models. In this study we focus on the local modeling of snow and firn. We tested our model at the site of Colle Gnifetti, 4400–4550 m a.s.l. The model shows that wind erodes all the precipitation of the cold months, while snow is in part conserved between May and September, since higher temperatures protect snow from erosion. We also compared modeled and observed firn density obtaining a satisfying agreement.
Katia Cugerone, Carlo De Michele, Antonio Ghezzi, Vorne Gianelle, and Stefania Gilardoni
Atmos. Chem. Phys., 18, 4831–4842, https://doi.org/10.5194/acp-18-4831-2018, https://doi.org/10.5194/acp-18-4831-2018, 2018
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The aerosol particle number size distributions (PNSDs) measured in one urban background site (Milan) and in one rural mountainous site (Oga San Colombano) have been studied and compared. Detailed statistical analyses have shown that a common empirical PNSD pattern exists, except for the urban winter data. In order to explain this phenomenon, we analysed the aerosol dynamics by considering the influence of primary aerosol components and the interaction with precipitation and high wind speed.
Francesco Avanzi, Alberto Bianchi, Alberto Cina, Carlo De Michele, Paolo Maschio, Diana Pagliari, Daniele Passoni, Livio Pinto, Marco Piras, and Lorenzo Rossi
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-57, https://doi.org/10.5194/tc-2017-57, 2017
Revised manuscript not accepted
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We compare three different instruments used to collect snow depth, i.e., photogrammetric surveys using Unmanned Aerial Systems (UAS), a 3D laser scanning, and manual probing. The relatively high density of manual data (135 pt over 6700 m2, i.e., 2 pt/100 m2) enables to assess the performance of UAS in capturing the marked spatial variability of snow. Results suggest that UAS represent a competitive choice among existing techniques for high-precision, high-resolution remote sensing of snow.
Francesco Avanzi, Hiroyuki Hirashima, Satoru Yamaguchi, Takafumi Katsushima, and Carlo De Michele
The Cryosphere, 10, 2013–2026, https://doi.org/10.5194/tc-10-2013-2016, https://doi.org/10.5194/tc-10-2013-2016, 2016
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We investigate capillary barriers and preferential flow in layered snow during nine cold laboratory experiments. The dynamics of each sample were replicated solving Richards equation within the 1-D multi-layer physically based SNOWPACK model. Results show that both processes affect the speed of water infiltration in stratified snow and are marked by a high degree of spatial variability at cm scale and complex 3-D patterns.
Carlo De Michele, Francesco Avanzi, Daniele Passoni, Riccardo Barzaghi, Livio Pinto, Paolo Dosso, Antonio Ghezzi, Roberto Gianatti, and Giacomo Della Vedova
The Cryosphere, 10, 511–522, https://doi.org/10.5194/tc-10-511-2016, https://doi.org/10.5194/tc-10-511-2016, 2016
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We investigate snow depth distribution at peak accumulation over a small Alpine area using photogrammetry-based surveys with a fixed wing unmanned aerial system. Results reveal that UAS estimations of point snow depth present an average difference with reference to manual measurements equal to -0.073 m. Moreover, in this case study snow depth standard deviation (hence coefficient of variation) increases with decreasing cell size, but it stabilizes for resolutions smaller than 1 m.
P. Da Ronco and C. De Michele
Hydrol. Earth Syst. Sci., 18, 4579–4600, https://doi.org/10.5194/hess-18-4579-2014, https://doi.org/10.5194/hess-18-4579-2014, 2014
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The negative impacts of cloud obstruction in snow mapping from MODIS and a new reliable cloud removal procedure for the Italian Alps.
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Subject: Urban Hydrology | Techniques and Approaches: Modelling approaches
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Wen Zhu, Tao Tao, Hexiang Yan, Jieru Yan, Jiaying Wang, Shuping Li, and Kunlun Xin
Hydrol. Earth Syst. Sci., 27, 2035–2050, https://doi.org/10.5194/hess-27-2035-2023, https://doi.org/10.5194/hess-27-2035-2023, 2023
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To provide a possibility for early warning and forecasting of ponding in the urban drainage system, an optimized long short-term memory (LSTM)-based model is proposed in this paper. It has a remarkable improvement compared to the models based on LSTM and convolutional neural network (CNN) structures. The performance of the corrected model is reliable if the number of monitoring sites is over one per hectare. Increasing the number of monitoring points further has little impact on the performance.
Qianqian Zhou, Shuai Teng, Zuxiang Situ, Xiaoting Liao, Junman Feng, Gongfa Chen, Jianliang Zhang, and Zonglei Lu
Hydrol. Earth Syst. Sci., 27, 1791–1808, https://doi.org/10.5194/hess-27-1791-2023, https://doi.org/10.5194/hess-27-1791-2023, 2023
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A deep-learning-based data-driven model for flood predictions in temporal and spatial dimensions, with the integration of a long short-term memory network, Bayesian optimization, and transfer learning is proposed. The model accurately predicts water depths and flood time series/dynamics for hyetograph inputs, with substantial improvements in computational time. With transfer learning, the model was well applied to a new case study and showed robust compatibility and generalization ability.
Ane LaBianca, Mette H. Mortensen, Peter Sandersen, Torben O. Sonnenborg, Karsten H. Jensen, and Jacob Kidmose
Hydrol. Earth Syst. Sci., 27, 1645–1666, https://doi.org/10.5194/hess-27-1645-2023, https://doi.org/10.5194/hess-27-1645-2023, 2023
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The study explores the effect of Anthropocene geology and the computational grid size on the simulation of shallow urban groundwater. Many cities are facing challenges with high groundwater levels close to the surface, yet urban planning and development seldom consider its impact on the groundwater resource. This study illustrates that the urban subsurface infrastructure significantly affects the groundwater flow paths and the residence time of shallow urban groundwater.
Bianca Rahill-Marier, Naresh Devineni, and Upmanu Lall
Hydrol. Earth Syst. Sci., 26, 5685–5695, https://doi.org/10.5194/hess-26-5685-2022, https://doi.org/10.5194/hess-26-5685-2022, 2022
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We present a new approach to modeling extreme regional rainfall by considering the spatial structure of extreme events. The developed models allow a probabilistic exploration of how the regional drainage network may respond to extreme rainfall events and provide a foundation for how future risks may be better estimated.
Matthew Preisser, Paola Passalacqua, R. Patrick Bixler, and Julian Hofmann
Hydrol. Earth Syst. Sci., 26, 3941–3964, https://doi.org/10.5194/hess-26-3941-2022, https://doi.org/10.5194/hess-26-3941-2022, 2022
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There is rising concern in numerous fields regarding the inequitable distribution of human risk to floods. The co-occurrence of river and surface flooding is largely excluded from leading flood hazard mapping services, therefore underestimating hazards. Using high-resolution elevation data and a region-specific social vulnerability index, we developed a method to estimate flood impacts at the household level in near-real time.
Vincent Pons, Rasmus Benestad, Edvard Sivertsen, Tone Merete Muthanna, and Jean-Luc Bertrand-Krajewski
Hydrol. Earth Syst. Sci., 26, 2855–2874, https://doi.org/10.5194/hess-26-2855-2022, https://doi.org/10.5194/hess-26-2855-2022, 2022
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Different models were developed to increase the temporal resolution of precipitation time series to minutes. Their applicability under climate change and their suitability for producing input time series for green infrastructure (e.g. green roofs) modelling were evaluated. The robustness of the model was validated against a range of European climates in eight locations in France and Norway. The future hydrological performances of green roofs were evaluated in order to improve design practice.
Elhadi Mohsen Hassan Abdalla, Vincent Pons, Virginia Stovin, Simon De-Ville, Elizabeth Fassman-Beck, Knut Alfredsen, and Tone Merete Muthanna
Hydrol. Earth Syst. Sci., 25, 5917–5935, https://doi.org/10.5194/hess-25-5917-2021, https://doi.org/10.5194/hess-25-5917-2021, 2021
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This study investigated the potential of using machine learning algorithms as hydrological models of green roofs across different climatic condition. The study provides comparison between conceptual and machine learning algorithms. Machine learning models were found to be accurate in simulating runoff from extensive green roofs.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
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This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Zhengzheng Zhou, James A. Smith, Mary Lynn Baeck, Daniel B. Wright, Brianne K. Smith, and Shuguang Liu
Hydrol. Earth Syst. Sci., 25, 4701–4717, https://doi.org/10.5194/hess-25-4701-2021, https://doi.org/10.5194/hess-25-4701-2021, 2021
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The role of rainfall space–time structure in flood response is an important research issue in urban hydrology. This study contributes to this understanding in small urban watersheds. Combining stochastically based rainfall scenarios with a hydrological model, the results show the complexities of flood response for various return periods, implying the common assumptions of spatially uniform rainfall in urban flood frequency are problematic, even for relatively small basin scales.
Yangzi Qiu, Igor da Silva Rocha Paz, Feihu Chen, Pierre-Antoine Versini, Daniel Schertzer, and Ioulia Tchiguirinskaia
Hydrol. Earth Syst. Sci., 25, 3137–3162, https://doi.org/10.5194/hess-25-3137-2021, https://doi.org/10.5194/hess-25-3137-2021, 2021
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Our original research objective is to investigate the uncertainties of the hydrological responses of nature-based solutions (NBSs) that result from the multiscale space variability in both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability in rainfall and the spatial arrangement of NBS can generate uncertainties of peak flow and total runoff volume estimations in NBS scenarios.
Kaihua Guo, Mingfu Guan, and Dapeng Yu
Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, https://doi.org/10.5194/hess-25-2843-2021, 2021
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This study presents a comprehensive review of models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. It explores the advantages and limitations of existing models and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.
Everett Snieder, Karen Abogadil, and Usman T. Khan
Hydrol. Earth Syst. Sci., 25, 2543–2566, https://doi.org/10.5194/hess-25-2543-2021, https://doi.org/10.5194/hess-25-2543-2021, 2021
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Flow distributions are highly skewed, resulting in low prediction accuracy of high flows when using artificial neural networks for flood forecasting. We investigate the use of resampling and ensemble techniques to address the problem of skewed datasets to improve high flow prediction. The methods are implemented both independently and in combined, hybrid techniques. This research presents the first analysis of the effects of combining these methods on high flow prediction accuracy.
Ico Broekhuizen, Günther Leonhardt, Jiri Marsalek, and Maria Viklander
Hydrol. Earth Syst. Sci., 24, 869–885, https://doi.org/10.5194/hess-24-869-2020, https://doi.org/10.5194/hess-24-869-2020, 2020
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Urban drainage models are usually calibrated using a few events so that they accurately represent a real-world site. This paper compares 14 single- and two-stage strategies for selecting these events and found significant variation between them in terms of model performance and the obtained values of model parameters. Calibrating parameters for green and impermeable areas in two separate stages improved model performance in the validation period while making calibration easier and faster.
Xuehong Zhu, Qiang Dai, Dawei Han, Lu Zhuo, Shaonan Zhu, and Shuliang Zhang
Hydrol. Earth Syst. Sci., 23, 3353–3372, https://doi.org/10.5194/hess-23-3353-2019, https://doi.org/10.5194/hess-23-3353-2019, 2019
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Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.
Joong Gwang Lee, Christopher T. Nietch, and Srinivas Panguluri
Hydrol. Earth Syst. Sci., 22, 2615–2635, https://doi.org/10.5194/hess-22-2615-2018, https://doi.org/10.5194/hess-22-2615-2018, 2018
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This paper demonstrates an approach to spatial discretization for analyzing green infrastructure (GI) using SWMM. Besides DCIA, pervious buffers should be identified for GI modeling. Runoff contributions from different spatial components and flow pathways would impact GI performance. The presented approach can reduce the number of calibration parameters and apply scale–independently to a watershed scale. Hydrograph separation can add insights for developing GI scenarios.
Elena Cristiano, Marie-Claire ten Veldhuis, Santiago Gaitan, Susana Ochoa Rodriguez, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 22, 2425–2447, https://doi.org/10.5194/hess-22-2425-2018, https://doi.org/10.5194/hess-22-2425-2018, 2018
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In this work we investigate the influence rainfall and catchment scales have on hydrological response. This problem is quite relevant in urban areas, where the response is fast due to the high degree of imperviousness. We presented a new approach to classify rainfall variability in space and time and use this classification to investigate rainfall aggregation effects on urban hydrological response. This classification allows the spatial extension of the main core of the storm to be identified.
Suresh Hettiarachchi, Conrad Wasko, and Ashish Sharma
Hydrol. Earth Syst. Sci., 22, 2041–2056, https://doi.org/10.5194/hess-22-2041-2018, https://doi.org/10.5194/hess-22-2041-2018, 2018
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The study examines the impact of higher temperatures expected in a future climate on how rainfall varies with time during severe storm events. The results show that these impacts increase future flood risk in urban environments and that current design guidelines need to be adjusted so that effective adaptation measures can be implemented.
Stephanie Clark, Ashish Sharma, and Scott A. Sisson
Hydrol. Earth Syst. Sci., 22, 1793–1810, https://doi.org/10.5194/hess-22-1793-2018, https://doi.org/10.5194/hess-22-1793-2018, 2018
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This study investigates global patterns relating urban river flood impacts to socioeconomic development and changing hydrologic conditions, and comparisons are provided between 98 individual cities. This paper condenses and communicates large amounts of information to accelerate the understanding of relationships between local urban conditions and global processes, and to potentially motivate knowledge transfer between decision-makers facing similar circumstances.
Abdellah Ichiba, Auguste Gires, Ioulia Tchiguirinskaia, Daniel Schertzer, Philippe Bompard, and Marie-Claire Ten Veldhuis
Hydrol. Earth Syst. Sci., 22, 331–350, https://doi.org/10.5194/hess-22-331-2018, https://doi.org/10.5194/hess-22-331-2018, 2018
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This paper proposes a two-step investigation to illustrate the extent of scale effects in urban hydrology. First, fractal tools are used to highlight the scale dependency observed within GIS data inputted in urban hydrological models. Then an intensive multi-scale modelling work was carried out to confirm effects on model performances. The model was implemented at 17 spatial resolutions ranging from 100 to 5 m. Results allow the understanding of scale challenges in hydrology modelling.
Per Skougaard Kaspersen, Nanna Høegh Ravn, Karsten Arnbjerg-Nielsen, Henrik Madsen, and Martin Drews
Hydrol. Earth Syst. Sci., 21, 4131–4147, https://doi.org/10.5194/hess-21-4131-2017, https://doi.org/10.5194/hess-21-4131-2017, 2017
Elena Cristiano, Marie-Claire ten Veldhuis, and Nick van de Giesen
Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, https://doi.org/10.5194/hess-21-3859-2017, 2017
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In the last decades, new instruments were developed to measure rainfall and hydrological processes at high resolution. Weather radars are used, for example, to measure how rainfall varies in space and time. At the same time, new models were proposed to reproduce and predict hydrological response, in order to prevent flooding in urban areas. This paper presents a review of our current knowledge of rainfall and hydrological processes in urban areas, focusing on their variability in time and space.
Chiara Arrighi, Hocine Oumeraci, and Fabio Castelli
Hydrol. Earth Syst. Sci., 21, 515–531, https://doi.org/10.5194/hess-21-515-2017, https://doi.org/10.5194/hess-21-515-2017, 2017
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In developed countries, the majority of fatalities during floods occurs as a consequence of inappropriate high-risk behaviour such as walking or driving in floodwaters. This work addresses pedestrians' instability in floodwaters. It analyses both the contribution of flood and human physical characteristics in the loss of stability highlighting the key role of subject height (submergence) and flow regime. The method consists of a re-analysis of experiments and numerical modelling.
Hjalte Jomo Danielsen Sørup, Stylianos Georgiadis, Ida Bülow Gregersen, and Karsten Arnbjerg-Nielsen
Hydrol. Earth Syst. Sci., 21, 345–355, https://doi.org/10.5194/hess-21-345-2017, https://doi.org/10.5194/hess-21-345-2017, 2017
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In this study we propose a methodology changing present-day precipitation time series to reflect future changed climate. Present-day time series have a much finer resolution than what is provided by climate models and thus have a much broader application range. The proposed methodology is able to replicate most expectations of climate change precipitation. These time series can be used to run fine-scale hydrological and hydraulic models and thereby assess the influence of climate change on them.
Tsun-Hua Yang, Gong-Do Hwang, Chin-Cheng Tsai, and Jui-Yi Ho
Hydrol. Earth Syst. Sci., 20, 4731–4745, https://doi.org/10.5194/hess-20-4731-2016, https://doi.org/10.5194/hess-20-4731-2016, 2016
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Taiwan continues to suffer from floods. This study proposes the integration of rainfall thresholds and ensemble precipitation forecasts to provide probabilistic urban inundation forecasts. Utilization of ensemble precipitation forecasts can extend forecast lead times to 72 h, preceding peak flows and allowing response agencies to take necessary preparatory measures. This study also develops a hybrid of real-time observation and rainfall forecasts to improve the first 24 h inundation forecasts.
Christopher A. Sanchez, Benjamin L. Ruddell, Roy Schiesser, and Venkatesh Merwade
Hydrol. Earth Syst. Sci., 20, 1289–1299, https://doi.org/10.5194/hess-20-1289-2016, https://doi.org/10.5194/hess-20-1289-2016, 2016
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The use of authentic learning activities is especially important for place-based geosciences like hydrology, where professional breadth and technical depth are critical for practicing hydrologists. The current study found that integrating computerized learning content into the learning experience, using only a simple spreadsheet tool and readily available hydrological data, can effectively bring the "real world" into the classroom and provide an enriching educational experience.
G. Bruni, R. Reinoso, N. C. van de Giesen, F. H. L. R. Clemens, and J. A. E. ten Veldhuis
Hydrol. Earth Syst. Sci., 19, 691–709, https://doi.org/10.5194/hess-19-691-2015, https://doi.org/10.5194/hess-19-691-2015, 2015
J. Herget, T. Roggenkamp, and M. Krell
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Hydrol. Earth Syst. Sci., 15, 3115–3122, https://doi.org/10.5194/hess-15-3115-2011, https://doi.org/10.5194/hess-15-3115-2011, 2011
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