Articles | Volume 13, issue 12
https://doi.org/10.5194/gmd-13-6111-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-6111-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
02 Dec 2020
Model description paper |
| 02 Dec 2020
| 02 Dec 2020
KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms
School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, United Kingdom
Related authors
Matthew Perks, Borbála Hortobágyi, Nick Everard, Susan Manson, Juliet Rowland, Andrew Large, and Andrew Russell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-213, https://doi.org/10.5194/hess-2024-213, 2025
Revised manuscript under review for HESS
Short summary
Short summary
Accurate river flow measurements are essential for understanding river processes. This study evaluates the freely-available software, KLT-IV, for automatic river surface velocity measurement. Analysing over 11,000 videos and comparing them with 303 traditional flow measurements, we find strong correlations (r² = 0.95–0.97) between KLT-IV and traditional methods. KLT-IV effectively estimates river flow with high accuracy, making it a valuable tool for autonomous water resource management.
Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, https://doi.org/10.5194/hess-25-5105-2021, 2021
Short summary
Short summary
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
Alonso Pizarro, Silvano F. Dal Sasso, Matthew T. Perks, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 24, 5173–5185, https://doi.org/10.5194/hess-24-5173-2020, https://doi.org/10.5194/hess-24-5173-2020, 2020
Short summary
Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
Matthew T. Perks, Silvano Fortunato Dal Sasso, Alexandre Hauet, Elizabeth Jamieson, Jérôme Le Coz, Sophie Pearce, Salvador Peña-Haro, Alonso Pizarro, Dariia Strelnikova, Flavia Tauro, James Bomhof, Salvatore Grimaldi, Alain Goulet, Borbála Hortobágyi, Magali Jodeau, Sabine Käfer, Robert Ljubičić, Ian Maddock, Peter Mayr, Gernot Paulus, Lionel Pénard, Leigh Sinclair, and Salvatore Manfreda
Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, https://doi.org/10.5194/essd-12-1545-2020, 2020
Short summary
Short summary
We present datasets acquired from seven countries across Europe and North America consisting of image sequences. These have been subjected to a range of pre-processing methods in preparation for image velocimetry analysis. These datasets and accompanying reference data are a resource that may be used for conducting benchmarking experiments, assessing algorithm performances, and focusing future software development.
Matthew T. Perks, Andrew J. Russell, and Andrew R. G. Large
Hydrol. Earth Syst. Sci., 20, 4005–4015, https://doi.org/10.5194/hess-20-4005-2016, https://doi.org/10.5194/hess-20-4005-2016, 2016
Short summary
Short summary
Unmanned aerial vehicles (UAVs) have the potential to capture information about the earth’s surface in dangerous and previously inaccessible locations. Here we present a method whereby image acquisition and subsequent analysis have enabled the highly dynamic and oft-immeasurable hydraulic phenomenon present during high-energy flash floods to be quantified at previously unattainable spatial and temporal resolutions.
Matthew Thomas Perks and Jeff Warburton
Earth Surf. Dynam., 4, 705–719, https://doi.org/10.5194/esurf-4-705-2016, https://doi.org/10.5194/esurf-4-705-2016, 2016
Short summary
Short summary
We appraise the success of a novel mitigation approach and subsequent adaptive management, designed to reduce the transfer of fine sediment in a small upland catchment in the UK. Analysis of the river response demonstrates that the fluvial sediment system has become more restrictive with reduced fine sediment transfer. The study demonstrates that channel reconfiguration can be effective in mitigating fine sediment flux in upland streams but the full value of this may take many years to achieve.
Matthew Perks, Borbála Hortobágyi, Nick Everard, Susan Manson, Juliet Rowland, Andrew Large, and Andrew Russell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-213, https://doi.org/10.5194/hess-2024-213, 2025
Revised manuscript under review for HESS
Short summary
Short summary
Accurate river flow measurements are essential for understanding river processes. This study evaluates the freely-available software, KLT-IV, for automatic river surface velocity measurement. Analysing over 11,000 videos and comparing them with 303 traditional flow measurements, we find strong correlations (r² = 0.95–0.97) between KLT-IV and traditional methods. KLT-IV effectively estimates river flow with high accuracy, making it a valuable tool for autonomous water resource management.
Robert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 25, 5105–5132, https://doi.org/10.5194/hess-25-5105-2021, https://doi.org/10.5194/hess-25-5105-2021, 2021
Short summary
Short summary
The rise of new technologies such as drones (unmanned aerial systems – UASs) has allowed widespread use of image velocimetry techniques in place of more traditional, usually slower, methods during hydrometric campaigns. In order to minimize the velocity estimation errors, one must stabilise the acquired videos. In this research, we compare the performance of different UAS video stabilisation tools and provide guidelines for their use in videos with different flight and ground conditions.
Alonso Pizarro, Silvano F. Dal Sasso, Matthew T. Perks, and Salvatore Manfreda
Hydrol. Earth Syst. Sci., 24, 5173–5185, https://doi.org/10.5194/hess-24-5173-2020, https://doi.org/10.5194/hess-24-5173-2020, 2020
Short summary
Short summary
An innovative approach is presented to optimise image-velocimetry performances for surface flow velocity estimates (and thus remotely sensed river discharges). Synthetic images were generated under different tracer characteristics using a numerical approach. Based on the results, the Seeding Distribution Index was introduced as a descriptor of the optimal portion of the video to analyse. A field case study was considered as a proof of concept of the proposed framework showing error reductions.
Matthew T. Perks, Silvano Fortunato Dal Sasso, Alexandre Hauet, Elizabeth Jamieson, Jérôme Le Coz, Sophie Pearce, Salvador Peña-Haro, Alonso Pizarro, Dariia Strelnikova, Flavia Tauro, James Bomhof, Salvatore Grimaldi, Alain Goulet, Borbála Hortobágyi, Magali Jodeau, Sabine Käfer, Robert Ljubičić, Ian Maddock, Peter Mayr, Gernot Paulus, Lionel Pénard, Leigh Sinclair, and Salvatore Manfreda
Earth Syst. Sci. Data, 12, 1545–1559, https://doi.org/10.5194/essd-12-1545-2020, https://doi.org/10.5194/essd-12-1545-2020, 2020
Short summary
Short summary
We present datasets acquired from seven countries across Europe and North America consisting of image sequences. These have been subjected to a range of pre-processing methods in preparation for image velocimetry analysis. These datasets and accompanying reference data are a resource that may be used for conducting benchmarking experiments, assessing algorithm performances, and focusing future software development.
Matthew T. Perks, Andrew J. Russell, and Andrew R. G. Large
Hydrol. Earth Syst. Sci., 20, 4005–4015, https://doi.org/10.5194/hess-20-4005-2016, https://doi.org/10.5194/hess-20-4005-2016, 2016
Short summary
Short summary
Unmanned aerial vehicles (UAVs) have the potential to capture information about the earth’s surface in dangerous and previously inaccessible locations. Here we present a method whereby image acquisition and subsequent analysis have enabled the highly dynamic and oft-immeasurable hydraulic phenomenon present during high-energy flash floods to be quantified at previously unattainable spatial and temporal resolutions.
Matthew Thomas Perks and Jeff Warburton
Earth Surf. Dynam., 4, 705–719, https://doi.org/10.5194/esurf-4-705-2016, https://doi.org/10.5194/esurf-4-705-2016, 2016
Short summary
Short summary
We appraise the success of a novel mitigation approach and subsequent adaptive management, designed to reduce the transfer of fine sediment in a small upland catchment in the UK. Analysis of the river response demonstrates that the fluvial sediment system has become more restrictive with reduced fine sediment transfer. The study demonstrates that channel reconfiguration can be effective in mitigating fine sediment flux in upland streams but the full value of this may take many years to achieve.
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Nedal Aqel, Lea Reusser, Stephan Margreth, Andrea Carminati, and Peter Lehmann
Geosci. Model Dev., 17, 6949–6966, https://doi.org/10.5194/gmd-17-6949-2024, https://doi.org/10.5194/gmd-17-6949-2024, 2024
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The soil water potential (SWP) determines various soil water processes. Since remote sensing techniques cannot measure it directly, it is often deduced from volumetric water content (VWC) information. However, under dynamic field conditions, the relationship between SWP and VWC is highly ambiguous due to different factors that cannot be modeled with the classical approach. Applying a deep neural network with an autoencoder enables the prediction of the dynamic SWP.
Jenny Kupzig, Nina Kupzig, and Martina Flörke
Geosci. Model Dev., 17, 6819–6846, https://doi.org/10.5194/gmd-17-6819-2024, https://doi.org/10.5194/gmd-17-6819-2024, 2024
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Valid simulation results from global hydrological models (GHMs) are essential, e.g., to studying climate change impacts. Adapting GHMs to ungauged basins requires regionalization, enabling valid simulations. In this study, we highlight the impact of regionalization of GHMs on runoff simulations using an ensemble of regionalization methods for WaterGAP3. We have found that regionalization leads to temporally and spatially varying uncertainty, potentially reaching up to inter-model differences.
Dor Fridman, Mikhail Smilovic, Peter Burek, Sylvia Tramberend, and Taher Kahil
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-143, https://doi.org/10.5194/gmd-2024-143, 2024
Revised manuscript accepted for GMD
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Global hydrological models are applied at high spatial resolutions to quantify water availability and evaluate water scarcity mitigation options. Yet they mostly oversee important local processes. This paper presents and demonstrates the inclusion of wastewater treatment and reclamation into a global hydrological model. As a result model performance is improved, and models are capable to utilize treated wastewater as an alternative water source.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
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STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Lukas Riedel, Thomas Röösli, Thomas Vogt, and David N. Bresch
Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024, https://doi.org/10.5194/gmd-17-5291-2024, 2024
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River floods are among the most devastating natural hazards. We propose a flood model with a statistical approach based on openly available data. The model is integrated in a framework for estimating impacts of physical hazards. Although the model only agrees moderately with satellite-detected flood extents, we show that it can be used for forecasting the magnitude of flood events in terms of socio-economic impacts and for comparing these with past events.
Robin Schwemmle, Hannes Leistert, Andreas Steinbrich, and Markus Weiler
Geosci. Model Dev., 17, 5249–5262, https://doi.org/10.5194/gmd-17-5249-2024, https://doi.org/10.5194/gmd-17-5249-2024, 2024
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The new process-based hydrological toolbox model, RoGeR (https://roger.readthedocs.io/), can be used to estimate the components of the hydrological cycle and the related travel times of pollutants through parts of the hydrological cycle. These estimations may contribute to effective water resources management. This paper presents the toolbox concept and provides a simple example of providing estimations to water resources management.
Sarah Hanus, Lilian Schuster, Peter Burek, Fabien Maussion, Yoshihide Wada, and Daniel Viviroli
Geosci. Model Dev., 17, 5123–5144, https://doi.org/10.5194/gmd-17-5123-2024, https://doi.org/10.5194/gmd-17-5123-2024, 2024
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This study presents a coupling of the large-scale glacier model OGGM and the hydrological model CWatM. Projected future increase in discharge is less strong while future decrease in discharge is stronger when glacier runoff is explicitly included in the large-scale hydrological model. This is because glacier runoff is projected to decrease in nearly all basins. We conclude that an improved glacier representation can prevent underestimating future discharge changes in large river basins.
M. Graham Clark and Sean K. Carey
Geosci. Model Dev., 17, 4911–4922, https://doi.org/10.5194/gmd-17-4911-2024, https://doi.org/10.5194/gmd-17-4911-2024, 2024
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This paper provides validation of the Canadian Small Lakes Model (CSLM) for estimating evaporation rates from reservoirs and a refactoring of the original FORTRAN code into MATLAB and Python, which are now stored in GitHub repositories. Here we provide direct observations of the surface energy exchange obtained with an eddy covariance system to validate the CSLM. There was good agreement between observations and estimations except under specific atmospheric conditions when evaporation is low.
Thibault Hallouin, François Bourgin, Charles Perrin, Maria-Helena Ramos, and Vazken Andréassian
Geosci. Model Dev., 17, 4561–4578, https://doi.org/10.5194/gmd-17-4561-2024, https://doi.org/10.5194/gmd-17-4561-2024, 2024
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The evaluation of the quality of hydrological model outputs against streamflow observations is widespread in the hydrological literature. In order to improve on the reproducibility of published studies, a new evaluation tool dedicated to hydrological applications is presented. It is open source and usable in a variety of programming languages to make it as accessible as possible to the community. Thus, authors and readers alike can use the same tool to produce and reproduce the results.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
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Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Qi Tang, Hugo Delottier, Wolfgang Kurtz, Lars Nerger, Oliver S. Schilling, and Philip Brunner
Geosci. Model Dev., 17, 3559–3578, https://doi.org/10.5194/gmd-17-3559-2024, https://doi.org/10.5194/gmd-17-3559-2024, 2024
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We have developed a new data assimilation framework by coupling an integrated hydrological model HydroGeoSphere with the data assimilation software PDAF. Compared to existing hydrological data assimilation systems, the advantage of our newly developed framework lies in its consideration of the physically based model; its large selection of different assimilation algorithms; and its modularity with respect to the combination of different types of observations, states and parameters.
Willem J. van Verseveld, Albrecht H. Weerts, Martijn Visser, Joost Buitink, Ruben O. Imhoff, Hélène Boisgontier, Laurène Bouaziz, Dirk Eilander, Mark Hegnauer, Corine ten Velden, and Bobby Russell
Geosci. Model Dev., 17, 3199–3234, https://doi.org/10.5194/gmd-17-3199-2024, https://doi.org/10.5194/gmd-17-3199-2024, 2024
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We present the wflow_sbm distributed hydrological model, recently released by Deltares, as part of the Wflow.jl open-source modelling framework in the programming language Julia. Wflow_sbm has a fast runtime, making it suitable for large-scale modelling. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets, which results in satisfactory to good performance (without much tuning). We show this for a number of specific cases.
Sanchit Minocha, Faisal Hossain, Pritam Das, Sarath Suresh, Shahzaib Khan, George Darkwah, Hyongki Lee, Stefano Galelli, Konstantinos Andreadis, and Perry Oddo
Geosci. Model Dev., 17, 3137–3156, https://doi.org/10.5194/gmd-17-3137-2024, https://doi.org/10.5194/gmd-17-3137-2024, 2024
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The Reservoir Assessment Tool (RAT) merges satellite data with hydrological models, enabling robust estimation of reservoir parameters like inflow, outflow, surface area, and storage changes around the world. Version 3.0 of RAT lowers the barrier of entry for new users and achieves scalability and computational efficiency. RAT 3.0 also facilitates open-source development of functions for continuous improvement to mobilize and empower the global water management community.
Heloisa Ehalt Macedo, Bernhard Lehner, Jim Nicell, and Günther Grill
Geosci. Model Dev., 17, 2877–2899, https://doi.org/10.5194/gmd-17-2877-2024, https://doi.org/10.5194/gmd-17-2877-2024, 2024
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Treated and untreated wastewaters are sources of contaminants of emerging concern. HydroFATE, a new global model, estimates their concentrations in surface waters, identifying streams that are most at risk and guiding monitoring/mitigation efforts to safeguard aquatic ecosystems and human health. Model predictions were validated against field measurements of the antibiotic sulfamethoxazole, with predicted concentrations exceeding ecological thresholds in more than 400 000 km of rivers worldwide.
Pedro Felipe Arboleda-Obando, Agnès Ducharne, Zun Yin, and Philippe Ciais
Geosci. Model Dev., 17, 2141–2164, https://doi.org/10.5194/gmd-17-2141-2024, https://doi.org/10.5194/gmd-17-2141-2024, 2024
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We show a new irrigation scheme included in the ORCHIDEE land surface model. The new irrigation scheme restrains irrigation due to water shortage, includes water adduction, and represents environmental limits and facilities to access water, due to representing infrastructure in a simple way. Our results show that the new irrigation scheme helps simulate acceptable land surface conditions and fluxes in irrigated areas, even if there are difficulties due to shortcomings and limited information.
Guoqiang Tang, Andrew W. Wood, Andrew J. Newman, Martyn P. Clark, and Simon Michael Papalexiou
Geosci. Model Dev., 17, 1153–1173, https://doi.org/10.5194/gmd-17-1153-2024, https://doi.org/10.5194/gmd-17-1153-2024, 2024
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Ensemble geophysical datasets are crucial for understanding uncertainties and supporting probabilistic estimation/prediction. However, open-access tools for creating these datasets are limited. We have developed the Python-based Geospatial Probabilistic Estimation Package (GPEP). Through several experiments, we demonstrate GPEP's ability to estimate precipitation, temperature, and snow water equivalent. GPEP will be a useful tool to support uncertainty analysis in Earth science applications.
Atabek Umirbekov, Richard Essery, and Daniel Müller
Geosci. Model Dev., 17, 911–929, https://doi.org/10.5194/gmd-17-911-2024, https://doi.org/10.5194/gmd-17-911-2024, 2024
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We present a parsimonious snow model which simulates snow mass without the need for extensive calibration. The model is based on a machine learning algorithm that has been trained on diverse set of daily observations of snow accumulation or melt, along with corresponding climate and topography data. We validated the model using in situ data from numerous new locations. The model provides a promising solution for accurate snow mass estimation across regions where in situ data are limited.
Ciaran J. Harman and Esther Xu Fei
Geosci. Model Dev., 17, 477–495, https://doi.org/10.5194/gmd-17-477-2024, https://doi.org/10.5194/gmd-17-477-2024, 2024
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Over the last 10 years, scientists have developed StorAge Selection: a new way of modeling how material is transported through complex systems. Here, we present some new, easy-to-use, flexible, and very accurate code for implementing this method. We show that, in cases where we know exactly what the answer should be, our code gets the right answer. We also show that our code is closer than some other codes to the right answer in an important way: it conserves mass.
Lele Shu, Paul Ullrich, Xianhong Meng, Christopher Duffy, Hao Chen, and Zhaoguo Li
Geosci. Model Dev., 17, 497–527, https://doi.org/10.5194/gmd-17-497-2024, https://doi.org/10.5194/gmd-17-497-2024, 2024
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Our team developed rSHUD v2.0, a toolkit that simplifies the use of the SHUD, a model simulating water movement in the environment. We demonstrated its effectiveness in two watersheds, one in the USA and one in China. The toolkit also facilitated the creation of the Global Hydrological Data Cloud, a platform for automatic data processing and model deployment, marking a significant advancement in hydrological research.
Jarno Verkaik, Edwin H. Sutanudjaja, Gualbert H. P. Oude Essink, Hai Xiang Lin, and Marc F. P. Bierkens
Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, https://doi.org/10.5194/gmd-17-275-2024, 2024
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This paper presents the parallel PCR-GLOBWB global-scale groundwater model at 30 arcsec resolution (~1 km at the Equator). Named GLOBGM v1.0, this model is a follow-up of the 5 arcmin (~10 km) model, aiming for a higher-resolution simulation of worldwide fresh groundwater reserves under climate change and excessive pumping. For a long transient simulation using a parallel prototype of MODFLOW 6, we show that our implementation is efficient for a relatively low number of processor cores.
Han Qiu, Gautam Bisht, Lingcheng Li, Dalei Hao, and Donghui Xu
Geosci. Model Dev., 17, 143–167, https://doi.org/10.5194/gmd-17-143-2024, https://doi.org/10.5194/gmd-17-143-2024, 2024
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We developed and validated an inter-grid-cell lateral groundwater flow model for both saturated and unsaturated zone in the ELMv2.0 framework. The developed model was benchmarked against PFLOTRAN, a 3D subsurface flow and transport model and showed comparable performance with PFLOTRAN. The developed model was also applied to the Little Washita experimental watershed. The spatial pattern of simulated groundwater table depth agreed well with the global groundwater table benchmark dataset.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev., 16, 6479–6514, https://doi.org/10.5194/gmd-16-6479-2023, https://doi.org/10.5194/gmd-16-6479-2023, 2023
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We present an open-source Python framework for performing empirical-statistical downscaling of climate information, such as precipitation. The user-friendly package comprises all the downscaling cycles including data preparation, model selection, training, and evaluation, designed in an efficient and flexible manner, allowing for quick and reproducible downscaling products. The framework would contribute to climate change impact assessments by generating accurate high-resolution climate data.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
Geosci. Model Dev., 16, 5847–5863, https://doi.org/10.5194/gmd-16-5847-2023, https://doi.org/10.5194/gmd-16-5847-2023, 2023
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Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow in hydrodynamic models has been challenging. This study presents a new drag and turbulence model that leverages an empirical model for root systems. The model can be applied without rigorous measurements of root structures and showed high performance in flow simulations; this may provide a better understanding of hydrodynamics and related transport processes in Rhizophora mangrove forests.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
Geosci. Model Dev., 16, 5685–5701, https://doi.org/10.5194/gmd-16-5685-2023, https://doi.org/10.5194/gmd-16-5685-2023, 2023
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Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here propose an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrate the great potential of AutoML-Ens for improving estimations due to its two unique features, i.e., assigning dynamic weights for candidate models and taking full advantage of AutoML-assisted workflow.
Guta Wakbulcho Abeshu, Fuqiang Tian, Thomas Wild, Mengqi Zhao, Sean Turner, A. F. M. Kamal Chowdhury, Chris R. Vernon, Hongchang Hu, Yuan Zhuang, Mohamad Hejazi, and Hong-Yi Li
Geosci. Model Dev., 16, 5449–5472, https://doi.org/10.5194/gmd-16-5449-2023, https://doi.org/10.5194/gmd-16-5449-2023, 2023
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Most existing global hydrologic models do not explicitly represent hydropower reservoirs. We are introducing a new water management module to Xanthos that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We show that this explicit representation of hydropower reservoirs can lead to a significantly more realistic simulation of reservoir storage and releases in over 44 % of the hydropower reservoirs included in this study.
Javier Diez-Sierra, Salvador Navas, and Manuel del Jesus
Geosci. Model Dev., 16, 5035–5048, https://doi.org/10.5194/gmd-16-5035-2023, https://doi.org/10.5194/gmd-16-5035-2023, 2023
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NEOPRENE is an open-source, freely available library allowing scientists and practitioners to generate synthetic time series and maps of rainfall. These outputs will help to explore plausible events that were never observed in the past but may occur in the near future and to generate possible future events under climate change conditions. The paper shows how to use the library to downscale daily precipitation and how to use synthetic generation to improve our characterization of extreme events.
Adam Pasik, Alexander Gruber, Wolfgang Preimesberger, Domenico De Santis, and Wouter Dorigo
Geosci. Model Dev., 16, 4957–4976, https://doi.org/10.5194/gmd-16-4957-2023, https://doi.org/10.5194/gmd-16-4957-2023, 2023
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We apply the exponential filter (EF) method to satellite soil moisture retrievals to estimate the water content in the unobserved root zone globally from 2002–2020. Quality assessment against an independent dataset shows satisfactory results. Error characterization is carried out using the standard uncertainty propagation law and empirically estimated values of EF model structural uncertainty and parameter uncertainty. This is followed by analysis of temporal uncertainty variations.
Po-Wei Huang, Bernd Flemisch, Chao-Zhong Qin, Martin O. Saar, and Anozie Ebigbo
Geosci. Model Dev., 16, 4767–4791, https://doi.org/10.5194/gmd-16-4767-2023, https://doi.org/10.5194/gmd-16-4767-2023, 2023
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Water in natural environments consists of many ions. Ions are electrically charged and exert electric forces on each other. We discuss whether the electric forces are relevant in describing mixing and reaction processes in natural environments. By comparing our computer simulations to lab experiments in literature, we show that the electric interactions between ions can play an essential role in mixing and reaction processes, in which case they should not be neglected in numerical modeling.
Edward R. Jones, Marc F. P. Bierkens, Niko Wanders, Edwin H. Sutanudjaja, Ludovicus P. H. van Beek, and Michelle T. H. van Vliet
Geosci. Model Dev., 16, 4481–4500, https://doi.org/10.5194/gmd-16-4481-2023, https://doi.org/10.5194/gmd-16-4481-2023, 2023
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DynQual is a new high-resolution global water quality model for simulating total dissolved solids, biological oxygen demand and fecal coliform as indicators of salinity, organic pollution and pathogen pollution, respectively. Output data from DynQual can supplement the observational record of water quality data, which is highly fragmented across space and time, and has the potential to inform assessments in a broad range of fields including ecological, human health and water scarcity studies.
Hugo Delottier, John Doherty, and Philip Brunner
Geosci. Model Dev., 16, 4213–4231, https://doi.org/10.5194/gmd-16-4213-2023, https://doi.org/10.5194/gmd-16-4213-2023, 2023
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Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290, https://doi.org/10.5194/gmd-16-3275-2023, https://doi.org/10.5194/gmd-16-3275-2023, 2023
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Simultaneously simulating food production and the requirements and availability of water resources in a spatially explicit manner within a single framework remains challenging on a global scale. Here, we successfully enhanced the global hydrological model H08 that considers human water use and management to simulate the yields of four major staple crops: maize, wheat, rice, and soybean. Our improved model will be beneficial for advancing global food–water nexus studies in the future.
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163, https://doi.org/10.5194/gmd-16-3137-2023, https://doi.org/10.5194/gmd-16-3137-2023, 2023
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Water and pesticide transfer models are complex and should be simplified to be used in decision support. Indeed, these models simulate many spatial processes in interaction, involving a large number of parameters. Sensitivity analysis allows us to select the most influential input parameters, but it has to be adapted to spatial modelling. This study will identify relevant methods that can be transposed to any hydrological and water quality model and improve the fate of pesticide knowledge.
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937, https://doi.org/10.5194/gmd-16-2915-2023, https://doi.org/10.5194/gmd-16-2915-2023, 2023
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In this study, we developed a novel modeling system called iHydroSlide3D v1.0 by coupling a modified a 3D landslide model with a distributed hydrology model. The model is able to apply flexibly different simulating resolutions for hydrological and slope stability submodules and gain a high computational efficiency through parallel computation. The test results in the Yuehe River basin, China, show a good predicative capability for cascading flood–landslide events.
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454, https://doi.org/10.5194/gmd-16-2437-2023, https://doi.org/10.5194/gmd-16-2437-2023, 2023
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We present a computer simulation model of the hydrological system and human system, which can simulate the behaviour of individual farmers and their interactions with the water system at basin scale to assess how the systems have evolved and are projected to evolve in the future. For example, we can simulate the effect of subsidies provided on investment in adaptation measures and subsequent effects in the hydrological system, such as a lowering of the groundwater table or reservoir level.
Matthew D. Wilson and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023, https://doi.org/10.5194/gmd-16-2415-2023, 2023
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During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
Bibi S. Naz, Wendy Sharples, Yueling Ma, Klaus Goergen, and Stefan Kollet
Geosci. Model Dev., 16, 1617–1639, https://doi.org/10.5194/gmd-16-1617-2023, https://doi.org/10.5194/gmd-16-1617-2023, 2023
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It is challenging to apply a high-resolution integrated land surface and groundwater model over large spatial scales. In this paper, we demonstrate the application of such a model over a pan-European domain at 3 km resolution and perform an extensive evaluation of simulated water states and fluxes by comparing with in situ and satellite data. This study can serve as a benchmark and baseline for future studies of climate change impact projections and for hydrological forecasting.
Cited articles
Altena, B. and Kääb, A.: Weekly Glacier Flow Estimation from Dense Satellite
Time Series Using Adapted Optical Flow Technology, Front. Earth
Sci., 5, 53, https://doi.org/10.3389/feart.2017.00053, 2017. a
Bandini, F., Sunding, T. P., Linde, J., Smith, O., Jensen, I. K., Köppl,
C. J., Butts, M., and Bauer-Gottwein, P.: Unmanned Aerial System (UAS)
observations of water surface elevation in a small stream: Comparison of
radar altimetry, LIDAR and photogrammetry techniques, Remote Sens.
Environ., 237, 111487, https://doi.org/10.1016/j.rse.2019.111487, 2020. a
Borga, M., Anagnostou, E., Blöschl, G., and Creutin, J.-D.: Flash flood
forecasting, warning and risk management: the HYDRATE project, Environ.
Sci. Pol., 14, 834–844,
https://doi.org/10.1016/j.envsci.2011.05.017, 2011. a
Buchanan, T. J. and Somers, W. P.: Discharge measurements at gaging stations, U.S. Geological Survey Techniques of Water-Resources Investigations, book 3, chap. A8, 65 pp.,
available at: https://pubs.usgs.gov/twri/twri3a8/ (last access: 29 November 2020), 1969. a
Charlton, M. E., Large, A. R. G., and Fuller, I. C.: Application of airborne
LiDAR in river environments: the River Coquet, Northumberland, UK, Earth
Surf. Proc. Land., 28, 299–306, https://doi.org/10.1002/esp.482, 2003. a
Cosgrove, W. J. and Loucks, D. P.: Water management: Current and future
challenges and research directions, Water Resour. Res., 51, 4823–4839,
https://doi.org/10.1002/2014WR016869, 2015. a
Coxon, G., Freer, J., Westerberg, I. K., Wagener, T., Woods, R., and Smith,
P. J.: A novel framework for discharge uncertainty quantification applied to
500 UK gauging stations, Water Resour. Res., 51, 5531–5546,
https://doi.org/10.1002/2014WR016532, 2015. a
Creutin, J., Muste, M., Bradley, A., Kim, S., and Kruger, A.: River gauging
using PIV techniques: a proof of concept experiment on the Iowa River,
J. Hydrol., 277, 182–194, 2003. a
Dale, J., Scott, D., Dwyer, D., and Thornton, J.: Target tracking, moving
target detection, stabilisation and enhancement of airborne video, in:
Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and
Applications II, vol. 5787, 154–165, International Society for Optics
and Photonics, 2005. a
Detert, M., Johnson, E. D., and Weitbrecht, V.: Proof‐of‐concept for
low‐cost and noncontact synoptic airborne river flow measurements,
Int. J. Remote Sens., 38, 2780–2807,
https://doi.org/10.1080/01431161.2017.1294782, 2017. a
Eltner, A., Sardemann, H., and Grundmann, J.: Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery, Hydrol. Earth Syst. Sci., 24, 1429–1445, https://doi.org/10.5194/hess-24-1429-2020, 2020. a, b
Fiji: ImageJ, available at: https://imagej.net/Fiji (last access: 29 November 2020), 2020. a
Fujita, I. and Kunita, Y.: Application of aerial LSPIV to the 2002 flood of the Yodo River using a helicopter mounted high density video camera, J. Hydroenviron. Res., 5, 323–331,
https://doi.org/10.1016/j.jher.2011.05.003, 2011. a, b
Fujita, I., Muste, M., and Kruger, A.: Large-scale particle image velocimetry
for flow analysis in hydraulic engineering applications, J. Hydraul.
Res., 36, 397–414, https://doi.org/10.1080/00221689809498626, 1998. a
Fujita, I., Watanabe, H., and Tsubaki, R.: Development of a non-intrusive and
efficient flow monitoring technique: The space-time image velocimetry (STIV),
Int. J. River Basin Manage., 5, 105–114, 2007. a
Fujita, I., Notoya, Y., Tani, K., and Tateguchi, S.: Efficient and accurate
estimation of water surface velocity in STIV, Environ. Fluid Mech.,
19, 1363–1378, 2019. a
Fulford, J. M. and Sauer, V. B.: Comparison of velocity interpolation methods
for computing open-channel discharge, US Geol. Surv. Water Supply Pap, 2290,
139–144, 1986. a
Hannah, D. M., Demuth, S., van Lanen, H. A. J., Looser, U., Prudhomme, C.,
Rees, G., Stahl, K., and Tallaksen, L. M.: Large-scale river flow archives:
importance, current status and future needs, Hydrol. Process., 25,
1191–1200, https://doi.org/10.1002/hyp.7794, 2011. a
Heikkilä, J. and Silvén, O.: A four-step camera calibration procedure
with implicit image, Correction, Infotech Oulu and Department of Electrical
Engineering, University of Oulu, Oulu, Finland, 2014. a
Huang, Z.-C., Yeh, C.-Y., Tseng, K.-H., and Hsu, W.-Y.: A UAV–RTK Lidar
System for Wave and Tide Measurements in Coastal Zones, J.
Atmos. Ocean. Tech., 35, 1557–1570, https://doi.org/10.1175/JTECH-D-17-0199.1, 2018. a
Jodeau, M., Hauet, A., Paquier, A., Le Coz, J., and Dramais, G.: Application
and evaluation of LS-PIV technique for the monitoring of river surface
velocities in high flow conditions, Flow Meas. Instrum., 19,
117–127, 2008. a
Kalal, Z., Mikolajczyk, K., and Matas, J.: Forward-backward error: Automatic
detection of tracking failures, in: 2010 20th International Conference on
Pattern Recognition, 2756–2759, 2010. a
Kim, Y., Muste, M., Hauet, A., Krajewski, W. F., Kruger, A., and Bradley, A.:
Stream discharge using mobile large-scale particle image velocimetry: A proof
of concept, Water Resour. Res., 44, W09502, https://doi.org/10.1029/2006WR005441, 2008. a
Le Coz, J., Pierrefeu, G., Jodeau, M., and Paquier, A.: Mean vertical velocity profiles from ADCP river discharge measurement datasets, in: 32nd Congress of IAHR, Venice, ITA, 1–6 July 2007, p. 10, 2007. a
Le Coz, J., Pierrefeu, G., and Paquier, A.: Evaluation of river discharges
monitored by a fixed side-looking Doppler profiler, Water Resour. Res.,
44, W00D09, https://doi.org/10.1029/2008WR006967, 2008. a, b
Le Coz, J., Hauet, A., Pierrefeu, G., Dramais, G., and Camenen, B.: Performance of image-based velocimetry (LSPIV) applied to flash-flood discharge measurements in Mediterranean rivers, J. Hydrol., 394, 42–52,
https://doi.org/10.1016/j.jhydrol.2010.05.049, 2010. a, b
Legleiter, C. J. and Kinzel, P. J.: Inferring Surface Flow Velocities in
Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a
Helicopter, Remote Sens., 12, 1282, https://doi.org/10.3390/rs12081282, 2020. a, b
Leitão, J. P., Peña-Haro, S., Lüthi, B., Scheidegger, A., and de Vitry,
M. M.: Urban overland runoff velocity measurement with consumer-grade
surveillance cameras and surface structure image velocimetry, J.
Hydrol., 565, 791–804, https://doi.org/10.1016/j.jhydrol.2018.09.001, 2018. a
Lewis, Q. W. and Rhoads, B. L.: LSPIV Measurements of Two-Dimensional Flow
Structure in Streams Using Small Unmanned Aerial Systems: 1. Accuracy
Assessment Based on Comparison With Stationary Camera Platforms and In-Stream
Velocity Measurements, Water Resour. Res., 54, 8000–8018,
https://doi.org/10.1029/2018WR022550, 2018. a, b
Lin, D., Grundmann, J., and Eltner, A.: Evaluating Image Tracking Approaches
for Surface Velocimetry With Thermal Tracers, Water Resour. Res., 55,
3122–3136, https://doi.org/10.1029/2018WR024507, 2019. a
Lucas, B. D. and Kanade, T.: An iterative image registration technique
with an application to stereo vision, IJCAI'81: Proceedings of the 7th international joint conference on Artificial intelligence, 21, 674–679, 981. a
Luo, S., Sun, Y., Shen, I., Chen, B., and Chuang, Y.: Geometrically
Consistent Stereoscopic Image Editing Using Patch-Based Synthesis, IEEE
Transactions on Visualization and Computer Graphics, 21, 56–67, 2015. a
Maddock, I.: The importance of physical habitat assessment for evaluating river health, Freshw. Biol., 41, 373–391,
https://doi.org/10.1046/j.1365-2427.1999.00437.x, 1999. a
Messerli, A. and Grinsted, A.: Image georectification and feature tracking toolbox: ImGRAFT, Geosci. Instrum. Method. Data Syst., 4, 23–34, https://doi.org/10.5194/gi-4-23-2015, 2015. a
Muste, M., Fujita, I., and Hauet, A.: Large-scale particle image velocimetry
for measurements in riverine environments, Water Resour. Res., 44, W00D19,
https://doi.org/10.1029/2008WR006950, 2008. a
Patalano, A., García, C. M., and Rodríguez, A.: Rectification of Image
Velocity Results (RIVeR): A simple and user-friendly toolbox for large scale
water surface Particle Image Velocimetry (PIV) and Particle Tracking
Velocimetry (PTV), Comput. Geosci., 109, 323–330,
https://doi.org/10.1016/j.cageo.2017.07.009, 2017. a
Pearce, S., Ljubičić, R., Peña-Haro, S., Perks, M., Tauro, F.,
Pizarro, A., Dal Sasso, S. F., Strelnikova, D., Grimaldi, S., Maddock, I.,
Paulus, G., Plavšić, J., Prodanović, D., and Manfreda, S.: An Evaluation of Image Velocimetry Techniques under Low Flow
Conditions and High Seeding Densities Using Unmanned Aerial Systems, Remote
Sensing, 12, 232, https://doi.org/10.3390/rs12020232, 2020. a, b, c
Pix4D: Pix4D GSD Calculator, available at: https://s3.amazonaws.com/mics.pix4d.com/KB/documents/Pix4d_GSD_Calculator.xlsx, last access: 22 September 2020. a
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M.,
Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., and Cardona, A.:
Fiji: an open-source platform for biological-image analysis, Nat, methods,
9, 676–682, 2012. a
Schowengerdt, R. A.: Remote sensing: models and methods for image processing,
Elsevier, 2006. a
Shi, J. and Tomasi, C.: Computer vision and pattern recognition, in:
Proceedings CVPR'94, 593–600,
1994. a
StackOverflowMATLABchat: mlapptools: MATLAB class containing methods for
programmatic uifigure modification, GitHub, available at:
https://github.com/StackOverflowMATLABchat/mlapptools (last access: 29 November 2020), 2016. a
Stokstad, E.: Scarcity of Rain, Stream Gages Threatens Forecasts, Science, 285, 1199–1200, https://doi.org/10.1126/science.285.5431.1199, 1999. a
Strelnikova, D., Paulus, G., Käfer, S., Anders, K.-H., Mayr, P., Mader, H.,
Scherling, U., and Schneeberger, R.: Drone-Based Optical Measurements of
Heterogeneous Surface Velocity Fields around Fish Passages at Hydropower
Dams, Remote Sens., 12, 384, https://doi.org/10.3390/rs12030384, 2020. a, b
Tauro, F., Tosi, F., Mattoccia, S., Toth, E., Piscopia, R., and Grimaldi, S.:
Optical tracking velocimetry (OTV): leveraging optical flow and
trajectory-based filtering for surface streamflow observations, Remote
Sensing, 10, 2010, https://doi.org/10.3390/rs10122010, 2018. a, b
Tauro, F., Piscopia, R., and Grimaldi, S.: PTV-Stream: A simplified particle
tracking velocimetry framework for stream surface flow monitoring, Catena,
172, 378–386, 2019. a
Thielicke, W. and Stamhuis, E.: PIVlab – Towards User-friendly, Affordable
and Accurate Digital Particle Image Velocimetry in MATLAB, J. Open
Res. Softw., 2, e30, https://doi.org/10.5334/jors.bl, 2014. a
Tomasi, C. and Kanade, T.: Detection and Tracking of Point Features, Technical Report CMU-CS-91-132, 1991. a
Welber, M., Le Coz, J., Laronne, J. B., Zolezzi, G., Zamler, D., Dramais, G.,
Hauet, A., and Salvaro, M.: Field assessment of noncontact stream gauging
using portable surface velocity radars (SVR), Water Resour. Res., 52,
1108–1126, https://doi.org/10.1002/2015WR017906, 2016. a, b
Westerweel, J. and Scarano, F.: Universal outlier detection for PIV data,
Exp. Fluid, 39, 1096–1100, 2005. a
Wilcock, P. R.: Estimating Local Bed Shear Stress from Velocity Observations,
Water Resour. Res., 32, 3361–3366, https://doi.org/10.1029/96WR02277,
1996. a
Short summary
KLT-IV v1.0 offers a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from both fixed and mobile remote sensing platforms. Platform motion can be accounted for using ground control points and/or stable features or a GPS device and inertial measurement unit sensor. Examples of the KLT-IV workflow are provided for two case studies where footage is acquired using unmanned aerial systems and fixed cameras.
KLT-IV v1.0 offers a user-friendly graphical interface for the determination of river flow...
