Methods for assessment of models
15 May 2018
Methods for assessment of models
| 15 May 2018
The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models
Julian Koch et al.
Related authors
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-122, https://doi.org/10.5194/hess-2022-122, 2022
Preprint under review for HESS
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Hydrological models at high spatial resolution are computationally expensive. However, outputs from such models, such as the depth to the groundwater table, are often desired in high resolution. We developed a machine learning-based downscaling algorithm that allows to increase spatial resolution of hydrological model outputs, alleviating computational burden. We successfully applied the downscaling algorithm to the climate change-induced impacts on the groundwater table across Denmark.
Julian Koch, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg
Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, https://doi.org/10.5194/hess-23-4603-2019, 2019
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This study explores novel modelling avenues using machine learning in combination with process-based models to predict the shallow water table at high spatial resolution. Due to climate change and anthropogenic impacts, the shallow groundwater is rising in many parts of the world. In order to adapt to risks induced by groundwater flooding, new modelling tools need to emerge. In this study, we found that machine learning is capable of reaching the required accuracy and resolution.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
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Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Guiomar Ruiz-Pérez, Julian Koch, Salvatore Manfreda, Kelly Caylor, and Félix Francés
Hydrol. Earth Syst. Sci., 21, 6235–6251, https://doi.org/10.5194/hess-21-6235-2017, https://doi.org/10.5194/hess-21-6235-2017, 2017
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Plants are shaping the landscape and controlling the hydrological cycle, particularly in arid and semi-arid ecosystems. Remote sensing data appears as an appealing source of information for vegetation monitoring, in particular in areas with a limited amount of available field data. Here, we present an example of how remote sensing data can be exploited in a data-scarce basin. We propose a mathematical methodology that can be used as a springboard for future applications.
Gorka Mendiguren, Julian Koch, and Simon Stisen
Hydrol. Earth Syst. Sci., 21, 5987–6005, https://doi.org/10.5194/hess-21-5987-2017, https://doi.org/10.5194/hess-21-5987-2017, 2017
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The present study is focused on the spatial pattern evaluation of two models and describes the similarities and dissimilarities. It also discusses the factors that generate these patterns and proposes similar new approaches to minimize the differences. The study points towards a new approach in which the spatial component of the hydrological model is also calibrated and taken into account.
J. Koch, X. He, K. H. Jensen, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 18, 2907–2923, https://doi.org/10.5194/hess-18-2907-2014, https://doi.org/10.5194/hess-18-2907-2014, 2014
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-122, https://doi.org/10.5194/hess-2022-122, 2022
Preprint under review for HESS
Short summary
Short summary
Hydrological models at high spatial resolution are computationally expensive. However, outputs from such models, such as the depth to the groundwater table, are often desired in high resolution. We developed a machine learning-based downscaling algorithm that allows to increase spatial resolution of hydrological model outputs, alleviating computational burden. We successfully applied the downscaling algorithm to the climate change-induced impacts on the groundwater table across Denmark.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean-Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-597, https://doi.org/10.5194/hess-2021-597, 2021
Preprint under review for HESS
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Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Rena Meyer, Wenmin Zhang, Søren Julsgaard Kragh, Mie Andreasen, Karsten Høgh Jensen, Rasmus Fensholt, Simon Stisen, and Majken C. Looms
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-508, https://doi.org/10.5194/hess-2021-508, 2021
Revised manuscript accepted for HESS
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The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of high relevance for agriculture and water management. In this study, we investigate whether the established downscaling algorithm combining different satellite products to achieve medium scale soil moisture is applicable to higher resolution and if results can be improved by accounting for land cover types. Original satellite data and downscaled soil moisture are compared to ground observations.
E. Bontempo, M. C. Demirel, C. Corsini, F. Martins, and D. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 201–206, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, 2020
Raphael Schneider, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-685, https://doi.org/10.5194/hess-2019-685, 2020
Revised manuscript not accepted
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For groundwater models to deliver reliable results, their parameters often have to be estimated in an optimization process guided by some measure of model performance. In this context, we suggest the use of a novel performance metric, which is less prone to a fit to inadequate observations than the most frequently used metrics based on squared errors. Hence, calibration is more robust to deficiencies in model and observational data, which are common especially in larger scale models.
Kamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, and Eun-Sung Chung
Hydrol. Earth Syst. Sci., 23, 4803–4824, https://doi.org/10.5194/hess-23-4803-2019, https://doi.org/10.5194/hess-23-4803-2019, 2019
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This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
Julian Koch, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg
Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019, https://doi.org/10.5194/hess-23-4603-2019, 2019
Short summary
Short summary
This study explores novel modelling avenues using machine learning in combination with process-based models to predict the shallow water table at high spatial resolution. Due to climate change and anthropogenic impacts, the shallow groundwater is rising in many parts of the world. In order to adapt to risks induced by groundwater flooding, new modelling tools need to emerge. In this study, we found that machine learning is capable of reaching the required accuracy and resolution.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
Short summary
Short summary
Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Guiomar Ruiz-Pérez, Julian Koch, Salvatore Manfreda, Kelly Caylor, and Félix Francés
Hydrol. Earth Syst. Sci., 21, 6235–6251, https://doi.org/10.5194/hess-21-6235-2017, https://doi.org/10.5194/hess-21-6235-2017, 2017
Short summary
Short summary
Plants are shaping the landscape and controlling the hydrological cycle, particularly in arid and semi-arid ecosystems. Remote sensing data appears as an appealing source of information for vegetation monitoring, in particular in areas with a limited amount of available field data. Here, we present an example of how remote sensing data can be exploited in a data-scarce basin. We propose a mathematical methodology that can be used as a springboard for future applications.
Gorka Mendiguren, Julian Koch, and Simon Stisen
Hydrol. Earth Syst. Sci., 21, 5987–6005, https://doi.org/10.5194/hess-21-5987-2017, https://doi.org/10.5194/hess-21-5987-2017, 2017
Short summary
Short summary
The present study is focused on the spatial pattern evaluation of two models and describes the similarities and dissimilarities. It also discusses the factors that generate these patterns and proposes similar new approaches to minimize the differences. The study points towards a new approach in which the spatial component of the hydrological model is also calibrated and taken into account.
R. Guzinski, H. Nieto, S. Stisen, and R. Fensholt
Hydrol. Earth Syst. Sci., 19, 2017–2036, https://doi.org/10.5194/hess-19-2017-2015, https://doi.org/10.5194/hess-19-2017-2015, 2015
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The study compared evapotranspiration (ET) modelled by two remote sensing models and one hydrological model in a river catchment in Denmark. The results show that the spatial patterns of ET produced by the remote sensing models are more similar to each other than to the fluxes produced by the hydrological model. This indicates potential benefits to the hydrological modelling community from integrating spatial information derived through remote sensing methodology into the hydrological models.
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
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This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
H. Ajami, J. P. Evans, M. F. McCabe, and S. Stisen
Hydrol. Earth Syst. Sci., 18, 5169–5179, https://doi.org/10.5194/hess-18-5169-2014, https://doi.org/10.5194/hess-18-5169-2014, 2014
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A new hybrid approach was developed to reduce the computational burden of the spin-up procedure by using a combination of model simulations and an empirical depth-to-water table function. Results illustrate that the hybrid approach reduced the spin-up period required for an integrated groundwater--surface water--land surface model (ParFlow.CLM) by up to 50%. The methodology is applicable to other coupled or integrated modeling frameworks when initialization from an equilibrium state is required.
J. Koch, X. He, K. H. Jensen, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 18, 2907–2923, https://doi.org/10.5194/hess-18-2907-2014, https://doi.org/10.5194/hess-18-2907-2014, 2014
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Sandra Hellmers and Peter Fröhle
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Mathias Bavay, Michael Reisecker, Thomas Egger, and Daniela Korhammer
Geosci. Model Dev., 15, 365–378, https://doi.org/10.5194/gmd-15-365-2022, https://doi.org/10.5194/gmd-15-365-2022, 2022
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Most users struggle with the configuration of numerical models. This can be improved by relying on a GUI, but this requires a significant investment and a specific skill set and does not fit with the daily duties of model developers, leading to major maintenance burdens. Inishell generates a GUI on the fly based on an XML description of the required configuration elements, making maintenance very simple. This concept has been shown to work very well in our context.
Vladimir Mirlas, Yaakov Anker, Asher Aizenkod, and Naftali Goldshleger
Geosci. Model Dev., 15, 129–143, https://doi.org/10.5194/gmd-15-129-2022, https://doi.org/10.5194/gmd-15-129-2022, 2022
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Niccolò Tubini and Riccardo Rigon
Geosci. Model Dev., 15, 75–104, https://doi.org/10.5194/gmd-15-75-2022, https://doi.org/10.5194/gmd-15-75-2022, 2022
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This paper presents WHETGEO and its 1D deployment: a new physically based model simulating the water and energy budgets in a soil column. WHETGEO-1D is intended to be the first building block of a new customisable land-surface model that is integrated with process-based hydrology. WHETGEO is developed as an open-source code and is fully integrated into the GEOframe/OMS3 system, allowing the use of the many ancillary tools it provides.
Tobias Stacke and Stefan Hagemann
Geosci. Model Dev., 14, 7795–7816, https://doi.org/10.5194/gmd-14-7795-2021, https://doi.org/10.5194/gmd-14-7795-2021, 2021
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HydroPy is a new version of an established global hydrology model. It was rewritten from scratch and adapted to a modern object-oriented infrastructure to facilitate its future development and application. With this study, we provide a thorough documentation and evaluation of our new model. At the same time, we open our code base and publish the model's source code in a public software repository. In this way, we aim to contribute to increasing transparency and reproducibility in science.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-405, https://doi.org/10.5194/gmd-2021-405, 2021
Revised manuscript accepted for GMD
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This study aimed to develop a radar-based precipitation nowcasting model using an advanced machine learning technique, conditional generative adversarial network. The precipitation nowcasting model developed in this study was trained with a radar reflectivity map of the Soyang-gang Dam region in South Korea. We showed that the model can be successfully applied to precipitation nowcasting with longer lead times, and using the transfer learning approach it shows good performance in other regions.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
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Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Marco Toffolon, Luca Cortese, and Damien Bouffard
Geosci. Model Dev., 14, 7527–7543, https://doi.org/10.5194/gmd-14-7527-2021, https://doi.org/10.5194/gmd-14-7527-2021, 2021
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The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0 °C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.
Marco De Lucia, Michael Kühn, Alexander Lindemann, Max Lübke, and Bettina Schnor
Geosci. Model Dev., 14, 7391–7409, https://doi.org/10.5194/gmd-14-7391-2021, https://doi.org/10.5194/gmd-14-7391-2021, 2021
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POET is a parallel reactive transport simulator which implements a mechanism to store and reuse previous results of geochemical simulations through distributed hash tables. POET parallelizes chemistry using a master/worker design with noncontiguous grid partitions to maximize its efficiency and load balance on shared-memory machines and compute clusters.
Mary M. F. O'Neill, Danielle T. Tijerina, Laura E. Condon, and Reed M. Maxwell
Geosci. Model Dev., 14, 7223–7254, https://doi.org/10.5194/gmd-14-7223-2021, https://doi.org/10.5194/gmd-14-7223-2021, 2021
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Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.
Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem
Geosci. Model Dev., 14, 7287–7307, https://doi.org/10.5194/gmd-14-7287-2021, https://doi.org/10.5194/gmd-14-7287-2021, 2021
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Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
Rolf Hut, Niels Drost, Nick van de Giesen, Ben van Werkhoven, Banafsheh Abdollahi, Jerom Aerts, Thomas Albers, Fakhereh Alidoost, Bouwe Andela, Jaro Camphuijsen, Yifat Dzigan, Ronald van Haren, Eric Hutton, Peter Kalverla, Maarten van Meersbergen, Gijs van den Oord, Inti Pelupessy, Stef Smeets, Stefan Verhoeven, Martine de Vos, and Berend Weel
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-344, https://doi.org/10.5194/gmd-2021-344, 2021
Revised manuscript accepted for GMD
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With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science. eWatercyle gives easy access to well known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research.
Marco Dal Molin, Dmitri Kavetski, and Fabrizio Fenicia
Geosci. Model Dev., 14, 7047–7072, https://doi.org/10.5194/gmd-14-7047-2021, https://doi.org/10.5194/gmd-14-7047-2021, 2021
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This paper introduces SuperflexPy, an open-source Python framework for building flexible conceptual hydrological models. SuperflexPy is available as open-source code and can be used by the hydrological community to investigate improved process representations, for model comparison, and for operational work.
E. Andrés Quichimbo, Michael Bliss Singer, Katerina Michaelides, Daniel E. J. Hobley, Rafael Rosolem, and Mark O. Cuthbert
Geosci. Model Dev., 14, 6893–6917, https://doi.org/10.5194/gmd-14-6893-2021, https://doi.org/10.5194/gmd-14-6893-2021, 2021
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Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Nathaniel W. Chaney, Laura Torres-Rojas, Noemi Vergopolan, and Colby K. Fisher
Geosci. Model Dev., 14, 6813–6832, https://doi.org/10.5194/gmd-14-6813-2021, https://doi.org/10.5194/gmd-14-6813-2021, 2021
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Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decades, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between tiling schemes and river networks in the HydroBlocks land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.
Dejian Zhang, Bingqing Lin, Jiefeng Wu, and Qiaoying Lin
Geosci. Model Dev., 14, 5915–5925, https://doi.org/10.5194/gmd-14-5915-2021, https://doi.org/10.5194/gmd-14-5915-2021, 2021
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GP-SWAT is a two-layer model parallelization tool for a SWAT model based on the graph-parallel Pregel algorithm. It can be employed to perform both individual and iterative model parallelization, endowing it with a range of possible applications and great flexibility in maximizing performance. As a flexible and scalable tool, it can run in diverse environments, ranging from a commodity computer with a Microsoft Windows, Mac or Linux OS to a Spark cluster consisting of a large number of nodes.
Daisuke Tokuda, Hyungjun Kim, Dai Yamazaki, and Taikan Oki
Geosci. Model Dev., 14, 5669–5693, https://doi.org/10.5194/gmd-14-5669-2021, https://doi.org/10.5194/gmd-14-5669-2021, 2021
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We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
Marko Kallio, Joseph H. A. Guillaume, Vili Virkki, Matti Kummu, and Kirsi Virrantaus
Geosci. Model Dev., 14, 5155–5181, https://doi.org/10.5194/gmd-14-5155-2021, https://doi.org/10.5194/gmd-14-5155-2021, 2021
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Different runoff and streamflow products are freely available but may come with unsuitable spatial units. On the other hand, starting a new modelling exercise may require considerable resources. Hydrostreamer improves the usability of existing runoff products, allowing runoff and streamflow estimates at the desired spatial units with minimal data requirements and intuitive workflow. The case study shows that Hydrostreamer performs well compared to benchmark products and observation data.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-170, https://doi.org/10.5194/gmd-2021-170, 2021
Revised manuscript accepted for GMD
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Land surface models allowing us to understand and investigate the cause and effect of environmental processes changes. Therefore, this type of model is increasingly used for hydrological assessments. Here we explore the possibility of this approach using a case study in the Atibaia river basin, which serves as a major water supply for metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to predict the basin hydrology.
Peter Uhe, Daniel Mitchell, Paul D. Bates, Nans Addor, Jeff Neal, and Hylke E. Beck
Geosci. Model Dev., 14, 4865–4890, https://doi.org/10.5194/gmd-14-4865-2021, https://doi.org/10.5194/gmd-14-4865-2021, 2021
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We present a cascade of models to compute high-resolution river flooding. This takes meteorological inputs, e.g., rainfall and temperature from observations or climate models, and takes them through a series of modeling steps. This is relevant to evaluating current day and future flood risk and impacts. The model framework uses global data sets, allowing it to be applied anywhere in the world.
Marco De Lucia and Michael Kühn
Geosci. Model Dev., 14, 4713–4730, https://doi.org/10.5194/gmd-14-4713-2021, https://doi.org/10.5194/gmd-14-4713-2021, 2021
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DecTree evaluates a hierarchical coupling method for reactive transport simulations in which pre-trained surrogate models are used to speed up the geochemical subprocess, and equation-based
full-physicssimulations are called only if the surrogate predictions are implausible. Furthermore, we devise and evaluate a decision tree surrogate approach designed to inject domain knowledge of the surrogate by defining engineered features based on law of mass action or stoichiometric reaction equations.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
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We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, https://doi.org/10.5194/gmd-14-3577-2021, 2021
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LISFLOOD-FP has been extended with new shallow-water solvers – DG2 and FV1 – for modelling all types of slow- or fast-moving waves over any smooth or rough surface. Using GPU parallelisation, FV1 is faster than the simpler ACC solver on grids with millions of elements. The DG2 solver is notably effective on coarse grids where river channels are hard to capture, improving predicted river levels and flood water depths. This marks a new step towards real-world DG2 flood inundation modelling.
Chiranjib Chaudhuri, Annie Gray, and Colin Robertson
Geosci. Model Dev., 14, 3295–3315, https://doi.org/10.5194/gmd-14-3295-2021, https://doi.org/10.5194/gmd-14-3295-2021, 2021
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A flood risk estimation model for two study watersheds in Canada and an interactive visualization platform using publicly available hydrometric data are presented. The risk model uses a height above nearest drainage (HAND)-based solution for Manning’s formula and is implemented on a big-data discrete global grid system framework. Overall, the novel data model decreases processing time and provides easy parallelization, resulting in performance gains in online flood analytics.
Axel Schaffitel, Tobias Schuetz, and Markus Weiler
Geosci. Model Dev., 14, 2127–2142, https://doi.org/10.5194/gmd-14-2127-2021, https://doi.org/10.5194/gmd-14-2127-2021, 2021
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This paper presents FluSM, an algorithm to derive the water balance from soil moisture and metrological measurements. This data-driven water balance framework uses soil moisture as an input and therefore is applicable for cases with unclear processes and lacking parameters. In a case study, we apply FluSM to derive the water balance of 15 different permeable pavements under field conditions. These findings are of special interest for urban hydrology.
Thibault Guinaldo, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux
Geosci. Model Dev., 14, 1309–1344, https://doi.org/10.5194/gmd-14-1309-2021, https://doi.org/10.5194/gmd-14-1309-2021, 2021
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Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services such as freshwater supply. Despite the impact of lakes on the water cycle, they are generally not considered in global hydrological studies. Based on a model called MLake, we assessed both the importance of lakes in simulating river flows at global scale and the value of their level variations for water resource management.
Hannes Müller Schmied, Denise Cáceres, Stephanie Eisner, Martina Flörke, Claudia Herbert, Christoph Niemann, Thedini Asali Peiris, Eklavyya Popat, Felix Theodor Portmann, Robert Reinecke, Maike Schumacher, Somayeh Shadkam, Camelia-Eliza Telteu, Tim Trautmann, and Petra Döll
Geosci. Model Dev., 14, 1037–1079, https://doi.org/10.5194/gmd-14-1037-2021, https://doi.org/10.5194/gmd-14-1037-2021, 2021
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In a globalized world with large flows of virtual water between river basins and international responsibilities for the sustainable development of the Earth system and its inhabitants, quantitative estimates of water flows and storages and of water demand by humans are required. Global hydrological models such as WaterGAP are developed to provide this information. Here we present a thorough description, evaluation and application examples of the most recent model version, WaterGAP v2.2d.
John F. Burkhart, Felix N. Matt, Sigbjørn Helset, Yisak Sultan Abdella, Ola Skavhaug, and Olga Silantyeva
Geosci. Model Dev., 14, 821–842, https://doi.org/10.5194/gmd-14-821-2021, https://doi.org/10.5194/gmd-14-821-2021, 2021
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We present a new hydrologic modeling framework for interactive development of inflow forecasts for hydropower production planning and other operational environments (e.g., flood forecasting). The software provides a Python user interface with an application programming interface (API) for a computationally optimized C++ model engine, giving end users extensive control over the model configuration in real time during a simulation. This provides for extensive experimentation with configuration.
Joost Buitink, Lieke A. Melsen, James W. Kirchner, and Adriaan J. Teuling
Geosci. Model Dev., 13, 6093–6110, https://doi.org/10.5194/gmd-13-6093-2020, https://doi.org/10.5194/gmd-13-6093-2020, 2020
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This paper presents a new distributed hydrological model: the distributed simple dynamical systems (dS2) model. The model is built with a focus on computational efficiency and is therefore able to simulate basins at high spatial and temporal resolution at a low computational cost. Despite the simplicity of the model concept, it is able to correctly simulate discharge in both small and mesoscale basins.
Zhipin Ai, Naota Hanasaki, Vera Heck, Tomoko Hasegawa, and Shinichiro Fujimori
Geosci. Model Dev., 13, 6077–6092, https://doi.org/10.5194/gmd-13-6077-2020, https://doi.org/10.5194/gmd-13-6077-2020, 2020
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Incorporating bioenergy crops into the well-established global hydrological models is seldom seen today. Here, we successfully enhance a state-of-the-art global hydrological model H08 to simulate bioenergy crop yield. We found that unconstrained irrigation more than doubled the yield under rainfed conditions while simultaneously reducing the water use efficiency by 32 % globally. Our enhanced model provides a new tool for the future assessment of bioenergy–water tradeoffs.
Matthew T. Perks
Geosci. Model Dev., 13, 6111–6130, https://doi.org/10.5194/gmd-13-6111-2020, https://doi.org/10.5194/gmd-13-6111-2020, 2020
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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.
Bram Droppers, Wietse H. P. Franssen, Michelle T. H. van Vliet, Bart Nijssen, and Fulco Ludwig
Geosci. Model Dev., 13, 5029–5052, https://doi.org/10.5194/gmd-13-5029-2020, https://doi.org/10.5194/gmd-13-5029-2020, 2020
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Our study aims to include both both societal and natural water requirements and uses into a hydrological model in order to enable worldwide assessments of sustainable water use. The model was extended to include irrigation, domestic, industrial, energy, and livestock water uses as well as minimum flow requirements for natural systems. Initial results showed competition for water resources between society and nature, especially with respect to groundwater withdrawals.
Zachary L. Flamig, Humberto Vergara, and Jonathan J. Gourley
Geosci. Model Dev., 13, 4943–4958, https://doi.org/10.5194/gmd-13-4943-2020, https://doi.org/10.5194/gmd-13-4943-2020, 2020
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The Ensemble Framework For Flash Flood Forecasting (EF5) is used in the US National Weather Service for operational monitoring and short-term forecasting of flash floods. This article describes the hydrologic models supported by the framework and evaluates their accuracy by comparing simulations of streamflow from 2001 to 2011 at 4 366 observation sites with catchments less than 1000 km2. Overall, the uncalibrated models reasonably simulate flash flooding events.
Benya Wang, Matthew R. Hipsey, and Carolyn Oldham
Geosci. Model Dev., 13, 4253–4270, https://doi.org/10.5194/gmd-13-4253-2020, https://doi.org/10.5194/gmd-13-4253-2020, 2020
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Surface water nutrients are essential to manage water quality, but it is hard to analyse trends. We developed a hybrid model and compared with other models for the prediction of six different nutrients. Our results showed that the hybrid model had significantly higher accuracy and lower prediction uncertainty for almost all nutrient species. The hybrid model provides a flexible method to combine data of varied resolution and quality and is accurate for the prediction of nutrient concentrations.
Patrick Le Moigne, François Besson, Eric Martin, Julien Boé, Aaron Boone, Bertrand Decharme, Pierre Etchevers, Stéphanie Faroux, Florence Habets, Matthieu Lafaysse, Delphine Leroux, and Fabienne Rousset-Regimbeau
Geosci. Model Dev., 13, 3925–3946, https://doi.org/10.5194/gmd-13-3925-2020, https://doi.org/10.5194/gmd-13-3925-2020, 2020
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The study describes how a hydrometeorological model, operational at Météo-France, has been improved. Particular emphasis is placed on the impact of climatic data, surface, and soil parametrizations on the model results. Model simulations and evaluations carried out on a variety of measurements of river flows and snow depths are presented. All improvements in climate, surface data, and model physics have a positive impact on system performance.
Yilin Fang, Xingyuan Chen, Jesus Gomez Velez, Xuesong Zhang, Zhuoran Duan, Glenn E. Hammond, Amy E. Goldman, Vanessa A. Garayburu-Caruso, and Emily B. Graham
Geosci. Model Dev., 13, 3553–3569, https://doi.org/10.5194/gmd-13-3553-2020, https://doi.org/10.5194/gmd-13-3553-2020, 2020
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Surface water quality along river corridors can be improved by the area of the stream bed and stream bank in which stream water mixes with shallow groundwater or hyporheic zones (HZs). These zones are ubiquitous and dominated by microorganisms that can process the dissolved nutrients exchanged at this interface of these zones. The modulation of surface water quality can be simulated by connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer representation.
Jacques Bodin
Geosci. Model Dev., 13, 2905–2924, https://doi.org/10.5194/gmd-13-2905-2020, https://doi.org/10.5194/gmd-13-2905-2020, 2020
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Fractured and karst aquifers constitute important groundwater reservoirs worldwide but are particularly vulnerable to anthropogenic pollution. MFIT is a new GUI-based software for the analytical modeling of artificial tracer tests in such media. It integrates four transport models that are all capable of simulating complex (multimodal and/or heavy-tailed) tracer breakthrough curve responses and includes advanced tools for the automatic calibration and uncertainty analysis of model parameters.
Lele Shu, Paul A. Ullrich, and Christopher J. Duffy
Geosci. Model Dev., 13, 2743–2762, https://doi.org/10.5194/gmd-13-2743-2020, https://doi.org/10.5194/gmd-13-2743-2020, 2020
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Hydrologic modeling is an essential strategy for understanding and predicting natural flows. The paper introduces the design of Simulator for Hydrologic Unstructured Domains (SHUD), from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-Catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed.
Harsh Beria, Joshua R. Larsen, Anthony Michelon, Natalie C. Ceperley, and Bettina Schaefli
Geosci. Model Dev., 13, 2433–2450, https://doi.org/10.5194/gmd-13-2433-2020, https://doi.org/10.5194/gmd-13-2433-2020, 2020
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We develop a Bayesian mixing model to address the issue of small sample sizes to describe different sources in hydrological mixing applications. Using composite likelihood functions, the model accounts for an often overlooked bias arising due to unweighted mixing. We test the model efficacy using a series of statistical benchmarking tests and demonstrate its real-life applicability by applying it to a Swiss Alpine catchment to obtain the proportion of groundwater recharged from rain vs. snow.
Andrew J. Newman and Martyn P. Clark
Geosci. Model Dev., 13, 1827–1843, https://doi.org/10.5194/gmd-13-1827-2020, https://doi.org/10.5194/gmd-13-1827-2020, 2020
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This paper introduces the Topographically InformEd Regression (TIER) model, which uses terrain attributes to turn observations of precipitation and temperature into spatial maps. TIER allows our understanding of complex atmospheric processes such as terrain-enhanced precipitation to be modeled in a very simple way. TIER lets users change the model so they can experiment with different ways of making maps. A key conclusion is that small changes in TIER will change the final map.
Cited articles
Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N.,
Erechtchoukova, M., Hildebrandt, A., Hoffman, F., Jackisch, C., Khaiter, P.,
Mannina, G., Matsunaga, T., Purucker, S. T., Rivington, M., and Samaniego,
L.: Technical assessment and evaluation of environmental models and software:
Letter to the Editor, Environ. Model. Softw., 26, 328–336,
https://doi.org/10.1016/j.envsoft.2010.08.004, 2011.
Bennett, N. D., Croke, B. F. W., Guariso, G., Guillaume, J. H. A., Hamilton,
S. H., Jakeman, A. J., Marsili-Libelli, S., Newham, L. T. H., Norton, J. P.,
Perrin, C., Pierce, S. A., Robson, B., Seppelt, R., Voinov, A. A., Fath, B.
D., and Andreassian, V.: Characterising performance of environmental models,
Environ. Model. Softw., 40, 1–20, https://doi.org/10.1016/j.envsoft.2012.09.011, 2013.
Brown, B. G., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T.,
Ahijevych, D., and Jensen, T.: The Model Evaluation Tools (MET): Community
tools for forecast evaluation, in: Preprints, 25th Conf. on International
Interactive Information and Processing Systems (IIPS) for Meteorology,
Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc. A, Vol. 9, 2009.
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple
working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301,
https://doi.org/10.1029/2010WR009827, 2011.
Cloke, H. L. and Pappenberger, F.: Evaluating forecasts of extreme events
for hydrological applications: An approach for screening unfamiliar
performance measures, Meteorol. Appl., 15, 181–197, 2008.
Corbari, C. and Mancini, M.: Calibration and Validation of a Distributed
Energy–Water Balance Model Using Satellite Data of Land Surface Temperature
and Ground Discharge Measurements, J. Hydrometeorol., 15, 376–392,
https://doi.org/10.1175/JHM-D-12-0173.1, 2014.
Cuntz, M., Mai, J., Zink, M., Thober, S., Kumar, R., Schäfer, D.,
Schrön, M., Craven, J., Rakovec, O., Spieler, D., Prykhodko, V.,
Dalmasso, G., Musuuza, J., Langenberg, B., Attinger, S., and Samaniego, L.:
Computationally inexpensive identification of noninformative model parameters
by sequential screening, Water Resour. Res., 51, 6417–6441,
https://doi.org/10.1002/2015WR016907, 2015.
Dawson, C. W., Abrahart, R. J., and See, L. M.: HydroTest: A web-based
toolbox of evaluation metrics for the standardised assessment of hydrological
forecasts, Environ. Modell. Softw., 22, 1034–1052,
https://doi.org/10.1016/j.envsoft.2006.06.008, 2007.
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen,
S.: Combining satellite data and appropriate objective functions for improved
spatial pattern performance of a distributed hydrologic model, Hydrol. Earth
Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018a.
Demirel, M. C., Stisen, S., and Koch, J.: SPAEF: SPAtial EFficiency,
https://doi.org/10.5281/ZENODO.1158890, 2018b.
Doherty, J.: PEST: Model Independent Parameter Estimation. Fifth Edition of
User Manual, Watermark Numerical Computing, Brisbane, 2005.
Dorninger, M., Mittermaier, M. P., Gilleland, E., Ebert, E. E., Brown, B.
G., and Wilson, L. J.: MesoVICT: Mesoscale Verification Inter-Comparison over Complex Terrain. NCAR Technical Note NCAR/TN-505+STR, 23 pp., https://doi.org/10.5065/D6416V21, 2013.
Duan, Q. Y., Gupta, V. K., and Sorooshian, S.: Shuffled complex evolution
approach for effective and efficient global minimization, J. Optimiz. Theory
App., 76, 501–521, https://doi.org/10.1007/BF00939380, 1993.
Gilleland, E., Ahijevych, D., Brown, B. G., Casati, B., and Ebert, E. E.:
Intercomparison of Spatial Forecast Verification Methods, Weather Forecast.,
24, 1416–1430, 2009.
Gilleland, E., Bukovsky, M., Williams, C. L., McGinnis, S., Ammann, C. M.,
Brown, B. G., and Mearns, L. O.: Evaluating NARCCAP model performance for
frequencies of severe-storm environments, Adv. Stat. Clim. Meteorol.
Oceanogr., 2, 137–153, https://doi.org/10.5194/ascmo-2-137-2016, 2016.
Glaser, B., Klaus, J., Frei, S., Frentress, J., Pfister, L., and Hopp, L.: On
the value of surface saturated area dynamics mapped with thermal infrared
imagery for modeling the hillslope-riparian-stream continuum, Water Resour.
Res., 52, 8317–8342, https://doi.org/10.1002/2015WR018414, 2016.
Grayson, R. and Blöschl, G.: Spatial patterns in catchment hydrology:
observations and modelling, Cambridge University Press, 2001.
Grayson, R. B., Blöschl, G., Western, A. W., and McMahon, T. A.: Advances
in the use of observed spatial patterns of catchment hydrological response,
Adv. Water Resour., 25, 1313–1334, https://doi.org/10.1016/s0309-1708(02)00060-x, 2002.
Gupta, H. V., Wagener, T., and Liu, Y. Q.: Reconciling theory with
observations: elements of a diagnostic approach to model evaluation, Hydrol.
Process., 22, 3802–3813, https://doi.org/10.1002/Hyp.6989, 2008.
Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., and Ye, M.: Towards
a comprehensive assessment of model structural adequacy, Water Resour. Res.,
48, W08301,
https://doi.org/10.1029/2011WR011044, 2012.
Hagen, A.: Fuzzy set approach to assessing similarity of categorical maps,
Int. J. Geogr. Inf. Sci., 17, 235–249, https://doi.org/10.1080/13658810210157822, 2003.
Hagen, A. and Martens, P.: Map comparison methods for comprehensive assessment
of geosimulation models, International Conference on Computational
Science and Its Applications, Springer, Berlin, Heidelberg, 2008.
Herrera-Estrada, J. E., Satoh, Y., and Sheffield, J.: Spatiotemporal dynamics
of global drought, Geophys. Res. Lett., 44, 2254–2263,
https://doi.org/10.1002/2016GL071768, 2017.
Hovadik, J. M. and Larue, D. K.: Static characterizations of reservoirs:
refining the concepts of connectivity and continuity, Petrol. Geosci., 13,
195–211, 2007.
Immerzeel, W. W. and Droogers, P.: Calibration of a distributed hydrological
model based on satellite evapotranspiration, J. Hydrol., 349, 411–424,
https://doi.org/10.1016/j.jhydrol.2007.11.017, 2008.
Jensen, K. H. and Illangasekare, T. H.: HOBE: A Hydrological Observatory,
Vadose Zone J., 10, 1–7, https://doi.org/10.2136/vzj2011.0006, 2011.
Kling, H. and Gupta, H.: On the development of regionalization relationships
for lumped watershed models: The impact of ignoring sub-basin scale
variability, J. Hydrol., 373, 337–351, https://doi.org/10.1016/j.jhydrol.2009.04.031,
2009.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425,
264–277, https://doi.org/10.1016/J.JHYDROL.2012.01.011, 2012.
Koch, J.: SEEM: Spatial Evaluation of Environmental Models,
https://doi.org/10.5281/zenodo.1154614, 2018.
Koch, J. and Stisen, S.: Citizen science: A new perspective to advance
spatial pattern evaluation in hydrology, PLoS One, 12, 1–20,
https://doi.org/10.1371/journal.pone.0178165, 2017.
Koch, J., He, X., Jensen, K. H., and Refsgaard, J. C.: Challenges in
conditioning a stochastic geological model of a heterogeneous glacial aquifer
to a comprehensive soft data set, Hydrol. Earth Syst. Sci., 18, 2907–2923,
https://doi.org/10.5194/hess-18-2907-2014, 2014.
Koch, J., Jensen, K. H., and Stisen, S.: Toward a true spatial model
evaluation in distributed hydrological modeling: Kappa statistics, Fuzzy
theory, and EOF-analysis benchmarked by the human perception and evaluated
against a modeling case study, Water Resour. Res., 51, 1225–1246,
https://doi.org/10.1002/2014WR016607, 2015.
Koch, J., Cornelissen, T., Fang, Z., Bogena, H., Diekkrüger, B., Kollet,
S., and Stisen, S.: Inter-comparison of three distributed hydrological models
with respect to seasonal variability of soil moisture patterns at a small
forested catchment, J. Hydrol., 533, 234–249,
https://doi.org/10.1016/j.jhydrol.2015.12.002, 2016a.
Koch, J., Siemann, A., Stisen, S., and Sheffield, J.: Spatial validation of
large scale land surface models against monthly land surface temperature
patterns using innovative performance metrics, J. Geophys. Res.-Atmos., 121,
5430–5452, https://doi.org/10.1002/2015JD024482, 2016b.
Koch, J., Mendiguren, G., Mariethoz, G., and Stisen, S.: Spatial sensitivity
analysis of simulated land-surface patterns in a catchment model using a set
of innovative spatial performance metrics, J. Hydrometeorol., 18, 1121–1142,
JHM-D-16-0148.1, https://doi.org/10.1175/JHM-D-16-0148.1, 2017.
Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency criteria for hydrological model assessment, Adv. Geosci., 5, 89–97, https://doi.org/10.5194/adgeo-5-89-2005, 2005.
Kuhnert, M., Voinov, A., and Seppelt, R.: Comparing raster map comparison
algorithms for spatial modeling and analysis, Photogramm. Eng. Remote
Sensing, 71, 975–984, 2005.
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.
Kumar, R., Samaniego, L., and Attinger, S.: Implications of distributed
hydrologic model parameterization on water fluxes at multiple scales and
locations, Water Resour. Res., 49, 360–379, https://doi.org/10.1029/2012WR012195, 2013.
Kumar, S. V., Peters-Lidard, C. D., Santanello, J., Harrison, K., Liu, Y.,
and Shaw, M.: Land surface Verification Toolkit (LVT) – a generalized
framework for land surface model evaluation, Geosci. Model Dev., 5, 869–886,
https://doi.org/10.5194/gmd-5-869-2012, 2012.
McCabe, M. F., Wood, E. F., Wjcik, R., Pan, M., Sheffield, J., Gao, H., and
Su, H.: Hydrological consistency using multi-sensor remote sensing data for
water and energy cycle studies, Remote Sens. Environ., 112, 430–444,
https://doi.org/10.1016/j.rse.2007.03.027, 2008.
McCabe, M. F., Rodell, M., Alsdorf, D. E., Miralles, D. G., Uijlenhoet, R.,
Wagner, W., Lucieer, A., Houborg, R., Verhoest, N. E. C., Franz, T. E., Shi,
J., Gao, H., and Wood, E. F.: The future of Earth observation in hydrology,
Hydrol. Earth Syst. Sci., 21, 3879–3914,
https://doi.org/10.5194/hess-21-3879-2017, 2017.
Mendiguren, G., Koch, J., and Stisen, S.: Spatial pattern evaluation of a
calibrated national hydrological model – a remote-sensing-based diagnostic
approach, Hydrol. Earth Syst. Sci., 21, 5987–6005,
https://doi.org/10.5194/hess-21-5987-2017, 2017.
Mendoza, P. A., Clark, M. P., Barlage, M., Rajagopalan, B., Samaniego, L.,
Abramowitz, G., and Gupta, H.: Are we unnecessarily constraining the agility
of complex process-based models?, Water Resour. Res., 51, 716–728,
https://doi.org/10.1002/2014WR015820, 2015.
Mittermaier, M., Roberts, N. and Thompson, S. A.: A long-term assessment of
precipitation forecast skill using the Fractions Skill Score, Meteorol.
Appl., 20, 176–186, https://doi.org/10.1002/met.296, 2013.
Mizukami, N., Clark, M. P., Newman, A. J., Wood, A. W., Gutmann, E. D.,
Nijssen, B., Rakovec, O,. and Samaniego, L.: Towards seamless large-domain
parameter estimation for hydrologic models, Water Resour. Res., 53,
8020–8040, https://doi.org/10.1002/2017WR020401, 2017.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and
Veith, T. L.: Model Evaluation Guidelines for Systematic Quantification of
Accuracy in Watershed Simulations, T. ASABE, 50, 885–900,
https://doi.org/10.13031/2013.23153, 2007.
Norman, J. M., Kustas, W. P., and Humes, K. S.: Source approach for
estimating soil and vegetation energy fluxes in observations of directional
radiometric surface temperature, Agr. Forest Meteorol., 77, 263–293,
https://doi.org/10.1016/0168-1923(95)02265-Y, 1995.
Orth, R., Dutra, E., Trigo, I. F., and Balsamo, G.: Advancing land surface
model development with satellite-based Earth observations, Hydrol. Earth
Syst. Sci., 21, 2483–2495, https://doi.org/10.5194/hess-21-2483-2017, 2017.
Pokhrel, P. and Gupta, H. V.: On the ability to infer spatial catchment
variability using streamflow hydrographs, Water Resour. Res., 47, W08534,
https://doi.org/10.1029/2010wr009873, 2011.
Refsgaard, J. C. and Henriksen, H. J.: Modelling guidelines – Terminology
and guiding principles, Adv. Water Resour., 27, 71–82,
https://doi.org/10.1016/j.advwatres.2003.08.006, 2004.
Refsgaard, J. C., Auken, E., Bamberg, C. A., Christensen, B. S. B., Clausen,
T., Dalgaard, E., Effersø, F., Ernstsen, V., Gertz, F., Hansen, A. L., He,
X., Jacobsen, B. H., Jensen, K. H., Jørgensen, F., Jørgensen, L. F.,
Koch, J., Nilsson, B., Petersen, C., De Schepper, G., Schamper, C.,
Sørensen, K. I., Therrien, R., Thirup, C., and Viezzoli, A.: Nitrate
reduction in geologically heterogeneous catchments – A framework for
assessing the scale of predictive capability of hydrological models, Sci.
Total Environ., 468–469, 1278–1288, https://doi.org/10.1016/j.scitotenv.2013.07.042,
2014.
Renard, P. and Allard, D.: Connectivity metrics for subsurface flow and
transport, Adv. Water Resour., 51, 168–196,
https://doi.org/10.1016/j.advwatres.2011.12.001, 2013.
Roberts, N.: Assessing the spatial and temporal variation in the skill of
precipitation forecasts from an NWP model, Meteorol. Appl., 15, 163–169,
2008.
Roberts, N. M. and Lean, H. W.: Scale-Selective Verification of Rainfall
Accumulations from High-Resolution Forecasts of Convective Events, Mon.
Weather Rev., 136, 78–97, https://doi.org/10.1175/2007MWR2123.1, 2008.
Rongier, G., Collon, P., Renard, P., Straubhaar, J., and Sausse, J.:
Comparing connected structures in ensemble of random fields, Adv. Water
Resour., 96, 145–169, https://doi.org/10.1016/j.advwatres.2016.07.008, 2016.
Ruiz-Pérez, G., González-Sanchis, M., Del Campo, A. D., and
Francés, F.: Can a parsimonious model implemented with satellite data be
used for modelling the vegetation dynamics and water cycle in
water-controlled environments?, Ecol. Modell., 324, 45–53,
https://doi.org/10.1016/j.ecolmodel.2016.01.002, 2016.
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, 2010a.
Samaniego, L., Bardossy, A., and Kumar, R.: Streamflow prediction in ungauged
catchments using copula-based dissimilarity measures, Water Resour. Res., 46,
W02506,
https://doi.org/10.1029/2008WR007695, 2010b.
Samaniego, L., Kumar, R., Mai, J., Zink, M., Thober, S., Cuntz, M., Rakovec,
O., Schäfer, D., Schrön, M., Brenner, J., Demirel, C. M., Kaluza, M.,
Langenberg, B., Stisen, S., and Attinger, S.: mesoscale Hydrologic Model,
https://doi.org/10.5281/ZENODO.1069203, 2017a.
Samaniego, L., Kumar, R., Thober, S., Rakovec, O., Zink, M., Wanders, N.,
Eisner, S., Müller Schmied, H., Sutanudjaja, E. H., Warrach-Sagi, K., and
Attinger, S.: Toward seamless hydrologic predictions across spatial scales,
Hydrol. Earth Syst. Sci., 21, 4323–4346,
https://doi.org/10.5194/hess-21-4323-2017, 2017b.
Schaefi, B. and Gupta, H. V.: Do Nash values have value?, Hydrol. Process.,
21, 2075–2080, 2007.
Schalge, B., Rihani, J., Baroni, G., Erdal, D., Geppert, G., Haefliger, V.,
Haese, B., Saavedra, P., Neuweiler, I., Hendricks Franssen, H.-J., Ament, F.,
Attinger, S., Cirpka, O. A., Kollet, S., Kunstmann, H., Vereecken, H., and
Simmer, C.: High-Resolution Virtual Catchment Simulations of the
Subsurface-Land Surface-Atmosphere System, Hydrol. Earth Syst. Sci. Discuss.,
https://doi.org/10.5194/hess-2016-557, 2016.
Schuurmans, J. M., van Geer, F. C., and Bierkens, M. F. P.: Remotely sensed
latent heat fluxes for model error diagnosis: a case study, Hydrol. Earth
Syst. Sci., 15, 759–769, https://doi.org/10.5194/hess-15-759-2011, 2011.
Stisen, S., McCabe, M. F., Refsgaard, J. C., Lerer, S., and Butts, M. B.:
Model parameter analysis using remotely sensed pattern information in a
multi-constraint framework, J. Hydrol., 409, 337–349,
https://doi.org/10.1016/j.jhydrol.2011.08.030, 2011.
Stisen, S., Sonnenborg, T. O., Refsgaard, J. C., Koch, J., Bircher, S., and
Jensen, K. H.: Moving beyond runoff calibration – Multi-constraint
optimization of a surface-subsurface-atmosphere model, Hydrol. Process.,
in revision, 2018.
Swain, M. J. and Ballard, D. H.: Color indexing, Int. J. Comput. Vis., 7,
11–32, https://doi.org/10.1007/BF00130487, 1991.
Terink, W., Lutz, A. F., Simons, G. W. H., Immerzeel, W. W., and Droogers, P.: SPHY v2.0: Spatial Processes in HYdrology, Geosci. Model Dev., 8, 2009–2034, https://doi.org/10.5194/gmd-8-2009-2015, 2015.
van Griensven, A., Meixner, T., Grunwald, S., Bishop, T., Diluzio, M., and
Srinivasan, R.: A global sensitivity analysis tool for the parameters of
multi-variable catchment models, J. Hydrol., 324, 10–23,
https://doi.org/10.1016/j.jhydrol.2005.09.008, 2006.
Vereecken, H., Pachepsky, Y., Simmer, C., Rihani, J., Kunoth, A., Korres,
W., Graf, A., Franssen, H. J.-H., Thiele-Eich, I., and Shao, Y.: On the role
of patterns in understanding the functioning of soil-vegetation-atmosphere
systems, J. Hydrol., 542, 63–86, https://doi.org/10.1016/j.jhydrol.2016.08.053, 2016.
Wealands, S. R., Grayson, R. B., and Walker, J. P.: Quantitative comparison
of spatial fields for hydrological model assessment – some promising
approaches, Adv. Water Resour., 28, 15–32,
https://doi.org/10.1016/j.advwatres.2004.10.001, 2005.
Western, A. W., Blöschl, G., and Grayson, R. B.: Toward capturing
hydrologically significant connectivity in spatial patterns, Water Resour.
Res., 37, 83–97, 2001.
Wolff, J. K., Harrold, M., Fowler, T., Gotway, J. H., Nance, L., and Brown,
B. G.: Beyond the Basics: Evaluating Model-Based Precipitation Forecasts
Using Traditional, Spatial, and Object-Based Methods, Weather Forecast., 29,
1451–1472, https://doi.org/10.1175/WAF-D-13-00135.1, 2014.
Short summary
Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.
Our work addresses a key challenge in earth system modelling: how to optimally exploit the...