A GIS-based model for simulating the hydrological effects of land use changes on karst systems – The integration of the LuKARS model into FREEWAT

Bittner, Daniel; Rychlik, Ayla; Klöffel, Tobias; Leuteritz, Anna; Disse, Markus; Chiogna, Gabriele

doi:10.1016/j.envsoft.2020.104682

Cited by 16 publications

(10 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dominant parameter contributions at Scale 0, that is, the recharge coefficient of Hyd 1, 2, and 3 ( is E k , Figures 6b and 7a), are similar to those found in the dominant eigenvectors of the complete discharge signal (Teixeira Parente, . As the major volume of the Kerschbaum spring discharge originates from baseflow (Bittner, Rychlik, et al, 2020), we argue that this is the reason why those parameters controlling the modeled baseflow, that is, is E k of each hydrotope, are most sensitive at Scale 0. Moreover, is E k of Hyd 2 is most sensitive since Hyd 2 is the largest hydrotope in the area and contributes most to the groundwater recharge.…”

Section: Hydrological Interpretationmentioning

confidence: 96%

“…The LuKARS model was developed by Bittner et al (2018) to investigate how mining activities in the recharge area affect the quantity of discharge in the Kerschbaum spring. A GUI for the model is available as open source plugin for FREEWAT (Rossetto et al, 2018) in QGIS (Bittner, Rychlik, et al, 2020). The model is based on the implementation of hydrotopes, that is, areas with homogeneous soil and land use characteristics (Arnold et al, 1998), shown in Figures 2a and 2b.…”

Section: Kerschbaum Spring Lukars Modelmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces

Bittner

Engel

Wohlmuth

et al. 2021

Water Resources Research

Self Cite

View full text Add to dashboard Cite

Quantifying parametric uncertainty is important since the reliability of simulation results strongly depends on the parametrization of a model (Beven, 1995;Reinecke et al., 2019). Diagnostic model analyses are helpful to investigate parameter and process dynamics as well as to better understand process representations in hydrological models (Guse et al., 2016;Pfannerstill et al., 2015). The problem of equifinal parameter sets is well known in hydrology (Beven, 2006;Her & Chaubey, 2015;Kelleher et al., 2017) and time series of different variables (e.g., discharge, groundwater levels, and snow depth) can be used to constrain model parameters according to the physical processes that they describe (

show abstract

Section: Hydrological Interpretationmentioning

confidence: 96%

Section: Kerschbaum Spring Lukars Modelmentioning

confidence: 99%

Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces

Bittner

Engel

Wohlmuth

et al. 2021

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thus, an approach to share environmental models on the web coupling OpenMI and WPS was suggested by Zhang et al [108]. In addition, the constant updating of applications is required in open-source tools, and several plugins have been used and updated, such as FREEWAT (FREE and Open Source Software Tools for WATer Resource Management) that was created in 2015 [109] and integrated with a user-friendly modeling environment that integrates the LuKARS (Land use change modeling in KARSt systems) model [14]. In addition, recent technologies regarding the Artificial Intelligence (AI) methods have been growing in this context.…”

Section: Environmental Gis Applicationsmentioning

confidence: 99%

“…Most recent studies combined existent plugins with new ones. For instance, Bittner et al [14] integrated the LuKARS (Land use change modeling in KARSt systems) model into the already existent FREEWAT (FREE and Open Source Software Tools for WATer Resource Management) framework. With the recent advances in remote sensing techniques and Machine Learning (ML) algorithms, Bragagnolo et al [15] presented r.landslide, a free and open-source add-on to the open-source GRASS software for landslide susceptibility mapping, which works with Artificial Neural Network (ANN), environmental parameters and landslide databases.…”

Section: Introductionmentioning

confidence: 99%

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

Duarte

Teodoro

2021

Geomatics

View full text Add to dashboard Cite

Background: The advent of Geographical Information Systems (GIS) has changed the way people think and interact with the world. The main objectives of this paper are: (i) to provide an overview of 10 years (2010–2020) regarding the creation/development of GIS open-source applications; and (ii) to evaluate the GIS open-source plugins for environmental science. Methods: In the first objective, we evaluate the publications regarding the development of GIS open-source geospatial software in the last 10 years, considering desktop, web GIS and mobile applications, so that we can analyze the impact of this type of application for different research areas. In the second objective, we analyze the development of GIS open-source applications in the field of environmental sciences (with more focus on QGIS plugins) in the last 10 years and discuss the applicability and usability of these GIS solutions in different environmental domains. A bibliometric analysis was performed using Web of Science database and VOSViewer software. Conclusions: We concluded that, in general, the development of GIS open-source applications has increased in the last 10 years, especially GIS mobile applications, since the big data and Internet of Things (IoT) era, which was expected given the new advanced technologies available in every area, especially in GIS.

show abstract

“…Given the natural characteristics of the springshed (forested catchment, elevation between 415 and 969 m asl, annual mean temperature of 8°C), evapotranspiration, interception and snowmelt representations are expected to have an important influence on the modeled spring discharge (Bittner et al 2018). However, since no direct measurements for these input data are available, simple algorithms have to be applied for computing the input time series for the LuKARS model (Bittner et al 2020a).…”

Section: Introductionmentioning

confidence: 99%

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

2021

Self Cite

View full text Add to dashboard Cite

Uncertainties in hydrologic model outputs can arise for many reasons such as structural, parametric and input uncertainty. Identification of the sources of uncertainties and the quantification of their impacts on model results are important to appropriately reproduce hydrodynamic processes in karst aquifers and to support decision-making. The present study investigates the time-dependent relevance of model input uncertainties, defined as the conceptual uncertainties affecting the representation and parameterization of processes relevant for groundwater recharge, i.e. interception, evapotranspiration and snow dynamic, on the lumped karst model LuKARS. A total of nine different models are applied, three to compute interception (DVWK, Gash and Liu), three to compute evapotranspiration (Thornthwaite, Hamon and Oudin) and three to compute snow processes (Martinec, Girons Lopez and Magnusson). All the input model combinations are tested for the case study of the Kerschbaum spring in Austria. The model parameters are kept constant for all combinations. While parametric uncertainties computed for the same model in previous studies do not show pronounced temporal variations, the results of the present work show that input uncertainties are seasonally varying. Moreover, the input uncertainties of evapotranspiration and snowmelt are higher than the interception uncertainties. The results show that the importance of a specific process for groundwater recharge can be estimated from the respective input uncertainties. These findings have practical implications as they can guide researchers to obtain relevant field data to improve the representation of different processes in lumped parameter models and to support model calibration.

show abstract

A GIS-based model for simulating the hydrological effects of land use changes on karst systems – The integration of the LuKARS model into FREEWAT

Cited by 16 publications

References 39 publications

Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces

Temporal Scale‐Dependent Sensitivity Analysis for Hydrological Model Parameters Using the Discrete Wavelet Transform and Active Subspaces

GIS Open-Source Plugins Development: A 10-Year Bibliometric Analysis on Scientific Literature

Unraveling the time-dependent relevance of input model uncertainties for a lumped hydrologic model of a pre-alpine karst system

Contact Info

Product

Resources

About