Articles | Volume 11, issue 5
https://doi.org/10.5194/gmd-11-1873-2018
© Author(s) 2018. 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-11-1873-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
Mehmet Cüneyd Demirel
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
Department of Civil Engineering, Istanbul Technical University, 34469
Maslak, Istanbul, Turkey
Simon Stisen
Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, 1350, Denmark
Related authors
Søren J. Kragh, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 27, 2463–2478, https://doi.org/10.5194/hess-27-2463-2023, https://doi.org/10.5194/hess-27-2463-2023, 2023
Short summary
Short summary
This study investigates the precision of irrigation estimates from a global hotspot of unsustainable irrigation practice, the Indus and Ganges basins. We show that irrigation water use can be estimated with high precision by comparing satellite and rainfed hydrological model estimates of evapotranspiration. We believe that our work can support sustainable water resource management, as it addresses the uncertainty of a key component of the water balance that remains challenging to quantify.
Julian Koch, Lars Elsgaard, Mogens H. Greve, Steen Gyldenkærne, Cecilie Hermansen, Gregor Levin, Shubiao Wu, and Simon Stisen
Biogeosciences, 20, 2387–2403, https://doi.org/10.5194/bg-20-2387-2023, https://doi.org/10.5194/bg-20-2387-2023, 2023
Short summary
Short summary
Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The emissions are triggered by lowering the water table, which is a necessary step in order to make peatlands arable. Many countries aim at reducing their emissions by restoring peatlands, which can be achieved by stopping agricultural activities and thereby raising the water table. We estimate a total emission of 2.6 Mt CO2-eq for organic-rich peatlands in Denmark and a potential reduction of 77 %.
Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-142, https://doi.org/10.5194/hess-2023-142, 2023
Preprint under review for HESS
Short summary
Short summary
This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using either soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show, that the synergy from using two equally important components in joint approach within a baseline framework, yield better irrigation estimates.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-68, https://doi.org/10.5194/nhess-2023-68, 2023
Preprint under review for NHESS
Short summary
Short summary
In this study, we examine how future climate change may affect activity in landslides in Denmark using publicly available data. Our findings show that climate change will increase groundwater table depth, which will lead to increased landslide activity. During past events of extremely wet winters in this region, landslides caused damage to buildings and infrastructure.
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022, https://doi.org/10.5194/hess-26-5859-2022, 2022
Short summary
Short summary
Hydrological models at high spatial resolution are computationally expensive. However, outputs from such models, such as the depth of the groundwater table, are often desired in high resolution. We developed a downscaling algorithm based on machine learning that allows us 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
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.
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
Hafsa Mahmood, Ty P. A. Ferré, Raphael J. M. Schneider, Simon Stisen, Rasmus R. Frederiksen, and Anders V. Christiansen
EGUsphere, https://doi.org/10.5194/egusphere-2023-1872, https://doi.org/10.5194/egusphere-2023-1872, 2023
Short summary
Short summary
Temporal drain flow dynamics and understanding of their underlying controlling factors are important for water resource management in tile-drained agricultural areas. This study examine whether simpler, more efficient machine learning (ML) models can provide acceptable solutions compared to traditional physics based models. We predicted drain flow time series in multiple catchments subject to a range of climatic and landscape conditions.
Søren J. Kragh, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci., 27, 2463–2478, https://doi.org/10.5194/hess-27-2463-2023, https://doi.org/10.5194/hess-27-2463-2023, 2023
Short summary
Short summary
This study investigates the precision of irrigation estimates from a global hotspot of unsustainable irrigation practice, the Indus and Ganges basins. We show that irrigation water use can be estimated with high precision by comparing satellite and rainfed hydrological model estimates of evapotranspiration. We believe that our work can support sustainable water resource management, as it addresses the uncertainty of a key component of the water balance that remains challenging to quantify.
Julian Koch, Lars Elsgaard, Mogens H. Greve, Steen Gyldenkærne, Cecilie Hermansen, Gregor Levin, Shubiao Wu, and Simon Stisen
Biogeosciences, 20, 2387–2403, https://doi.org/10.5194/bg-20-2387-2023, https://doi.org/10.5194/bg-20-2387-2023, 2023
Short summary
Short summary
Utilizing peatlands for agriculture leads to large emissions of greenhouse gases worldwide. The emissions are triggered by lowering the water table, which is a necessary step in order to make peatlands arable. Many countries aim at reducing their emissions by restoring peatlands, which can be achieved by stopping agricultural activities and thereby raising the water table. We estimate a total emission of 2.6 Mt CO2-eq for organic-rich peatlands in Denmark and a potential reduction of 77 %.
Søren Julsgaard Kragh, Jacopo Dari, Sara Modanesi, Christian Massari, Luca Brocca, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2023-142, https://doi.org/10.5194/hess-2023-142, 2023
Preprint under review for HESS
Short summary
Short summary
This study provides a comparison of methodologies to quantify irrigation to enhance regional irrigation estimates. To evaluate the methodologies, we compared various approaches to quantify irrigation using either soil moisture, evapotranspiration, or both within a novel baseline framework, together with irrigation estimates from other studies. We show, that the synergy from using two equally important components in joint approach within a baseline framework, yield better irrigation estimates.
Kristian Svennevig, Julian Koch, Marie Keiding, and Gregor Luetzenburg
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-68, https://doi.org/10.5194/nhess-2023-68, 2023
Preprint under review for NHESS
Short summary
Short summary
In this study, we examine how future climate change may affect activity in landslides in Denmark using publicly available data. Our findings show that climate change will increase groundwater table depth, which will lead to increased landslide activity. During past events of extremely wet winters in this region, landslides caused damage to buildings and infrastructure.
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022, https://doi.org/10.5194/hess-26-5859-2022, 2022
Short summary
Short summary
Hydrological models at high spatial resolution are computationally expensive. However, outputs from such models, such as the depth of the groundwater table, are often desired in high resolution. We developed a downscaling algorithm based on machine learning that allows us 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, Guillaume 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., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
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., 26, 3337–3357, https://doi.org/10.5194/hess-26-3337-2022, https://doi.org/10.5194/hess-26-3337-2022, 2022
Short summary
Short summary
The amount and spatio-temporal distribution of soil moisture, the water in the upper soil, is of great relevance for agriculture and water management. Here, we investigate whether the established downscaling algorithm combining different satellite products to estimate medium-scale soil moisture is applicable to higher resolutions and whether results can be improved by accounting for land cover types. Original satellite data and downscaled soil moisture are compared with 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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Related subject area
Hydrology
Enhancing the representation of water management in global hydrological models
NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process
Uncertainty estimation for a new exponential-filter-based long-term root-zone soil moisture dataset from Copernicus Climate Change Service (C3S) surface observations
Validating the Nernst–Planck transport model under reaction-driven flow conditions using RetroPy v1.0
DynQual v1.0: a high-resolution global surface water quality model
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Simulation of crop yield using the global hydrological model H08 (crp.v1)
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
pyESDv1.0.1: An open-source Python framework for empirical-statistical downscaling of climate information
Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Customized deep learning for precipitation bias correction and downscaling
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Regional coupled surface–subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency domain discharge data
Representing the impact of Rhizophora mangroves on flow and sediment transport in a hydrodynamic model (COAWST_rh v1.0): the importance of three-dimensional root system structures
Operational water forecast ability of the HRRR-iSnobal combination: an evaluation to adapt into production environments
Dynamic weighted ensemble of geoscientific models via automated machine learning-based classification
Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake
UniFHy v0.1.1: a community modelling framework for the terrestrial water cycle in Python
mesas.py v1.0: A flexible Python package for modeling solute transport and transit times using StorAge Selection functions
Basin-scale gyres and mesoscale eddies in large lakes: a novel procedure for their detection and characterization, assessed in Lake Geneva
SIMO v1.0: simplified model of the vertical temperature profile in a small, warm, monomictic lake
Thermal modeling of three lakes within the continuous permafrost zone in Alaska using the LAKE 2.0 model
Water balance model (WBM) v.1.0.0: a scalable gridded global hydrologic model with water-tracking functionality
Coupling a large-scale hydrological model (CWatM v1.1) with a high-resolution groundwater flow model (MODFLOW 6) to assess the impact of irrigation at regional scale
RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling
Developing a parsimonious canopy model (PCM v1.0) to predict forest gross primary productivity and leaf area index of deciduous broad-leaved forest
Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation
A physically based distributed karst hydrological model (QMG model-V1.0) for flood simulations
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
CREST-VEC: a framework towards more accurate and realistic flood simulation across scales
Rad-cGAN v1.0: Radar-based precipitation nowcasting model with conditional generative adversarial networks for multiple dam domains
The eWaterCycle platform for open and FAIR hydrological collaboration
Evaluating the Atibaia River hydrology using JULES6.1
A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector
CLIMFILL v0.9: a framework for intelligently gap filling Earth observations
Modeling subgrid lake energy balance in ORCHIDEE terrestrial scheme using the FLake lake model
Evaluating a reservoir parametrization in the vector-based global routing model mizuRoute (v2.0.1) for Earth system model coupling
Improved runoff simulations for a highly varying soil depth and complex terrain watershed in the Loess Plateau with the Community Land Model version 5
GSTools v1.3: a toolbox for geostatistical modelling in Python
AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
Modeling of streamflow in a 30 km long reach spanning 5 years using OpenFOAM 5.x
Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3
Effects of dimensionality on the performance of hydrodynamic models for stratified lakes and reservoirs
Computation of backwater effects in surface waters of lowland catchments including control structures – an efficient and re-usable method implemented in the hydrological open-source model Kalypso-NA (4.0)
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Daniel Boateng and Sebastian G. Mutz
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-67, https://doi.org/10.5194/gmd-2023-67, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
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 high-resolution accurate climate data.
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
Short summary
Short summary
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.
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023, https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
Short summary
Under-monitored regions like Africa need high-quality soil moisture predictions to help with food production, but it is not clear if soil moisture processes are similar enough around the world for data-driven models to maintain accuracy. We present a deep-learning-based soil moisture model that learns from both in situ data and satellite data and performs better than satellite products at the global scale. These results help us apply our model globally while better understanding its limitations.
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023, https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
Short summary
This paper introduces the SERGHEI framework and a solver for shallow-water problems. Such models, often used for surface flow and flood modelling, are computationally intense. In recent years the trends to increase computational power have changed, requiring models to adapt to new hardware and new software paradigms. SERGHEI addresses these challenges, allowing surface flow simulation to be enabled on the newest and upcoming consumer hardware and supercomputers very efficiently.
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023, https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Short summary
Richards' equation (RE) is used to describe the movement and storage of water in a soil profile and is a component of many hydrological and earth-system models. Solving RE numerically is challenging due to the non-linearities in the properties. Here, we present a simple but effective and mass-conservative solution to solving RE, which is ideal for teaching/learning purposes but also useful in prototype models that are used to explore alternative process representations.
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023, https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Short summary
Gridded precipitation datasets suffer from biases and coarse resolutions. We developed a customized deep learning (DL) model to bias-correct and downscale gridded precipitation data using radar observations. The results showed that the customized DL model can generate improved precipitation at fine resolutions where regular DL and statistical methods experience challenges. The new model can be used to improve precipitation estimates, especially for capturing extremes at smaller scales.
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023, https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary
Short summary
Predicting water resource evolution is a key challenge for the coming century.
Anthropogenic impacts on water resources, and particularly the effects of dams and reservoirs on river flows, are still poorly known and generally neglected in global hydrological studies. A parameterized reservoir model is reproduced to compute monthly releases in Spanish anthropized river basins. For global application, an exhaustive sensitivity analysis of the model parameters is performed on flows and volumes.
Nicolas Flipo, Nicolas Gallois, and Jonathan Schuite
Geosci. Model Dev., 16, 353–381, https://doi.org/10.5194/gmd-16-353-2023, https://doi.org/10.5194/gmd-16-353-2023, 2023
Short summary
Short summary
A new approach is proposed to fit hydrological or land surface models, which suffer from large uncertainties in terms of water partitioning between fast runoff and slow infiltration from small watersheds to regional or continental river basins. It is based on the analysis of hydrosystem behavior in the frequency domain, which serves as a basis for estimating water flows in the time domain with a physically based model. It opens the way to significant breakthroughs in hydrological modeling.
Masaya Yoshikai, Takashi Nakamura, Eugene C. Herrera, Rempei Suwa, Rene Rollon, Raghab Ray, Keita Furukawa, and Kazuo Nadaoka
EGUsphere, https://doi.org/10.5194/egusphere-2022-1350, https://doi.org/10.5194/egusphere-2022-1350, 2023
Short summary
Short summary
Due to complex root system structures, representing the impacts of Rhizophora mangroves on flow and sediment transport 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, which may provide a better understanding of sedimentary processes in Rhizophora mangrove forests.
Joachim Meyer, John Horel, Patrick Kormos, Andrew Hedrick, Ernesto Trujillo, and S. McKenzie Skiles
Geosci. Model Dev., 16, 233–250, https://doi.org/10.5194/gmd-16-233-2023, https://doi.org/10.5194/gmd-16-233-2023, 2023
Short summary
Short summary
Freshwater resupply from seasonal snow in the mountains is changing. Current water prediction methods from snow rely on historical data excluding the change and can lead to errors. This work presented and evaluated an alternative snow-physics-based approach. The results in a test watershed were promising, and future improvements were identified. Adaptation to current forecast environments would improve resilience to the seasonal snow changes and helps ensure the accuracy of resupply forecasts.
Hao Chen, Tiejun Wang, Yonggen Zhang, Yun Bai, and Xi Chen
EGUsphere, https://doi.org/10.5194/egusphere-2022-1326, https://doi.org/10.5194/egusphere-2022-1326, 2023
Short summary
Short summary
Effectively assembling multiple models for approaching a benchmark solution remains a long-standing issue for various geoscience domains. We here proposed an automated machine learning-assisted ensemble framework (AutoML-Ens) that attempts to resolve this challenge. Results demonstrated 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.
Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman
Geosci. Model Dev., 16, 35–46, https://doi.org/10.5194/gmd-16-35-2023, https://doi.org/10.5194/gmd-16-35-2023, 2023
Short summary
Short summary
The risks brought by the proliferation of algal blooms motivate the improvement of bloom forecasting tools, but algal blooms are complexly controlled and difficult to predict. Given rapid growth of monitoring data and advances in computation, machine learning offers an alternative prediction methodology. This study tested various machine learning workflows in a dimictic mesotrophic lake and gave promising predictions of the seasonal variations and the timing of algal blooms.
Thibault Hallouin, Richard J. Ellis, Douglas B. Clark, Simon J. Dadson, Andrew G. Hughes, Bryan N. Lawrence, Grenville M. S. Lister, and Jan Polcher
Geosci. Model Dev., 15, 9177–9196, https://doi.org/10.5194/gmd-15-9177-2022, https://doi.org/10.5194/gmd-15-9177-2022, 2022
Short summary
Short summary
A new framework for modelling the water cycle in the land system has been implemented. It considers the hydrological cycle as three interconnected components, bringing flexibility in the choice of the physical processes and their spatio-temporal resolutions. It is designed to foster collaborations between land surface, hydrological, and groundwater modelling communities to develop the next-generation of land system models for integration in Earth system models.
Ciaran Harman and Esther Xu Fei
EGUsphere, https://doi.org/10.5194/egusphere-2022-1262, https://doi.org/10.5194/egusphere-2022-1262, 2022
Short summary
Short summary
Over the last 10 years scientists have developed a new way of modeling how material is transported through complex systems, called StorAge Selection. Here we present some new code implementing this method that is easy to use, but also flexible and very accurate. We show that for 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 people's code to the right answer in an important way: it conserves mass.
Seyed Mahmood Hamze-Ziabari, Ulrich Lemmin, Frédéric Soulignac, Mehrshad Foroughan, and David Andrew Barry
Geosci. Model Dev., 15, 8785–8807, https://doi.org/10.5194/gmd-15-8785-2022, https://doi.org/10.5194/gmd-15-8785-2022, 2022
Short summary
Short summary
A procedure combining numerical simulations, remote sensing, and statistical analyses is developed to detect large-scale current systems in large lakes. By applying this novel procedure in Lake Geneva, strategies for detailed transect field studies of the gyres and eddies were developed. Unambiguous field evidence of 3D gyre/eddy structures in full agreement with predictions confirmed the robustness of the proposed procedure.
Kristina Šarović, Melita Burić, and Zvjezdana B. Klaić
Geosci. Model Dev., 15, 8349–8375, https://doi.org/10.5194/gmd-15-8349-2022, https://doi.org/10.5194/gmd-15-8349-2022, 2022
Short summary
Short summary
We develop a simple 1-D model for the prediction of the vertical temperature profiles in small, warm lakes. The model uses routinely measured meteorological variables as well as UVB radiation and yearly mean temperature data. It can be used for the assessment of the onset and duration of lake stratification periods when water temperature data are unavailable, which can be useful for various lake studies performed in other scientific fields, such as biology, geochemistry, and sedimentology.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
Short summary
Short summary
Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Danielle S. Grogan, Shan Zuidema, Alex Prusevich, Wilfred M. Wollheim, Stanley Glidden, and Richard B. Lammers
Geosci. Model Dev., 15, 7287–7323, https://doi.org/10.5194/gmd-15-7287-2022, https://doi.org/10.5194/gmd-15-7287-2022, 2022
Short summary
Short summary
This paper describes the University of New Hampshire's water balance model (WBM). This model simulates the land surface components of the global water cycle and includes water extractions for use by humans for agricultural, domestic, and industrial purposes. A new feature is described that permits water source tracking through the water cycle, which has implications for water resource management. This paper was written to describe a long-used model and presents its first open-source version.
Luca Guillaumot, Mikhail Smilovic, Peter Burek, Jens de Bruijn, Peter Greve, Taher Kahil, and Yoshihide Wada
Geosci. Model Dev., 15, 7099–7120, https://doi.org/10.5194/gmd-15-7099-2022, https://doi.org/10.5194/gmd-15-7099-2022, 2022
Short summary
Short summary
We develop and test the first large-scale hydrological model at regional scale with a very high spatial resolution that includes a water management and groundwater flow model. This study infers the impact of surface and groundwater-based irrigation on groundwater recharge and on evapotranspiration in both irrigated and non-irrigated areas. We argue that water table recorded in boreholes can be used as validation data if water management is well implemented and spatial resolution is ≤ 100 m.
Robert Chlumsky, James R. Craig, Simon G. M. Lin, Sarah Grass, Leland Scantlebury, Genevieve Brown, and Rezgar Arabzadeh
Geosci. Model Dev., 15, 7017–7030, https://doi.org/10.5194/gmd-15-7017-2022, https://doi.org/10.5194/gmd-15-7017-2022, 2022
Short summary
Short summary
We introduce the open-source RavenR package, which has been built to support the use of the hydrologic modelling framework Raven. The R package contains many functions that may be useful in each step of the model-building process, including preparing model input files, running the model, and analyzing the outputs. We present six reproducible use cases of the RavenR package for the Liard River basin in Canada to demonstrate how it may be deployed.
Bahar Bahrami, Anke Hildebrandt, Stephan Thober, Corinna Rebmann, Rico Fischer, Luis Samaniego, Oldrich Rakovec, and Rohini Kumar
Geosci. Model Dev., 15, 6957–6984, https://doi.org/10.5194/gmd-15-6957-2022, https://doi.org/10.5194/gmd-15-6957-2022, 2022
Short summary
Short summary
Leaf area index (LAI) and gross primary productivity (GPP) are crucial components to carbon cycle, and are closely linked to water cycle in many ways. We develop a Parsimonious Canopy Model (PCM) to simulate GPP and LAI at stand scale, and show its applicability over a diverse range of deciduous broad-leaved forest biomes. With its modular structure, the PCM is able to adapt with existing data requirements, and run in either a stand-alone mode or as an interface linked to hydrologic models.
Stefania Camici, Gabriele Giuliani, Luca Brocca, Christian Massari, Angelica Tarpanelli, Hassan Hashemi Farahani, Nico Sneeuw, Marco Restano, and Jérôme Benveniste
Geosci. Model Dev., 15, 6935–6956, https://doi.org/10.5194/gmd-15-6935-2022, https://doi.org/10.5194/gmd-15-6935-2022, 2022
Short summary
Short summary
This paper presents an innovative approach, STREAM (SaTellite-based Runoff Evaluation And Mapping), to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and terrestrial total water storage anomalies. Potentially useful for multiple operational and scientific applications, the added value of the STREAM approach is the ability to increase knowledge on the natural processes, human activities, and their interactions on the land.
Ji Li, Daoxian Yuan, Fuxi Zhang, Jiao Liu, and Mingguo Ma
Geosci. Model Dev., 15, 6581–6600, https://doi.org/10.5194/gmd-15-6581-2022, https://doi.org/10.5194/gmd-15-6581-2022, 2022
Short summary
Short summary
A new karst hydrological model (the QMG model) is developed to simulate and predict the floods in karst trough valley basins. Unlike the complex structure and parameters of current karst groundwater models, this model has a simple double-layered structure with few parameters and decreases the demand for modeling data in karst areas. The flood simulation results based on the QMG model of the Qingmuguan karst trough valley basin are satisfactory, indicating the suitability of the model simulation.
Luca Trotter, Wouter J. M. Knoben, Keirnan J. A. Fowler, Margarita Saft, and Murray C. Peel
Geosci. Model Dev., 15, 6359–6369, https://doi.org/10.5194/gmd-15-6359-2022, https://doi.org/10.5194/gmd-15-6359-2022, 2022
Short summary
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
Zhi Li, Shang Gao, Mengye Chen, Jonathan Gourley, Naoki Mizukami, and Yang Hong
Geosci. Model Dev., 15, 6181–6196, https://doi.org/10.5194/gmd-15-6181-2022, https://doi.org/10.5194/gmd-15-6181-2022, 2022
Short summary
Short summary
Operational streamflow prediction at a continental scale is critical for national water resources management. However, limited computational resources often impede such processes, with streamflow routing being one of the most time-consuming parts. This study presents a recent development of a hydrologic system that incorporates a vector-based routing scheme with a lake module that markedly speeds up streamflow prediction. Moreover, accuracy is improved and flood false alarms are mitigated.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
Short summary
Short summary
Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
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., 15, 5371–5390, https://doi.org/10.5194/gmd-15-5371-2022, https://doi.org/10.5194/gmd-15-5371-2022, 2022
Short summary
Short summary
With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform 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.
Hsi-Kai Chou, Ana Maria Heuminski de Avila, and Michaela Bray
Geosci. Model Dev., 15, 5233–5240, https://doi.org/10.5194/gmd-15-5233-2022, https://doi.org/10.5194/gmd-15-5233-2022, 2022
Short summary
Short summary
Land surface models allow us to understand and investigate the cause and effect of environmental process 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 the metropolitan regions of Campinas and São Paulo, Brazil. We evaluated the model performance and use the model to simulate the basin hydrology.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
Short summary
Short summary
Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Verena Bessenbacher, Sonia Isabelle Seneviratne, and Lukas Gudmundsson
Geosci. Model Dev., 15, 4569–4596, https://doi.org/10.5194/gmd-15-4569-2022, https://doi.org/10.5194/gmd-15-4569-2022, 2022
Short summary
Short summary
Earth observations have many missing values. They are often filled using information from spatial and temporal contexts that mostly ignore information from related observed variables. We propose the gap-filling method CLIMFILL that additionally uses information from related variables. We test CLIMFILL using gap-free reanalysis data of variables related to soil–moisture climate interactions. CLIMFILL creates estimates for the missing values that recover the original dependence structure.
Anthony Bernus and Catherine Ottlé
Geosci. Model Dev., 15, 4275–4295, https://doi.org/10.5194/gmd-15-4275-2022, https://doi.org/10.5194/gmd-15-4275-2022, 2022
Short summary
Short summary
The lake model FLake was coupled to the ORCHIDEE land surface model to simulate lake energy balance at global scale with a multi-tile approach. Several simulations were performed with various atmospheric reanalyses and different lake depth parameterizations. The simulated lake surface temperature showed good agreement with observations (RMSEs of the order of 3 °C). We showed the large impact of the atmospheric forcing on lake temperature. We highlighted systematic errors on ice cover phenology.
Inne Vanderkelen, Shervan Gharari, Naoki Mizukami, Martyn P. Clark, David M. Lawrence, Sean Swenson, Yadu Pokhrel, Naota Hanasaki, Ann van Griensven, and Wim Thiery
Geosci. Model Dev., 15, 4163–4192, https://doi.org/10.5194/gmd-15-4163-2022, https://doi.org/10.5194/gmd-15-4163-2022, 2022
Short summary
Short summary
Human-controlled reservoirs have a large influence on the global water cycle. However, dam operations are rarely represented in Earth system models. We implement and evaluate a widely used reservoir parametrization in a global river-routing model. Using observations of individual reservoirs, the reservoir scheme outperforms the natural lake scheme. However, both schemes show a similar performance due to biases in runoff timing and magnitude when using simulated runoff.
Jiming Jin, Lei Wang, Jie Yang, Bingcheng Si, and Guo-Yue Niu
Geosci. Model Dev., 15, 3405–3416, https://doi.org/10.5194/gmd-15-3405-2022, https://doi.org/10.5194/gmd-15-3405-2022, 2022
Short summary
Short summary
This study aimed to improve runoff simulations and explore deep soil hydrological processes for a highly varying soil depth and complex terrain watershed in the Loess Plateau, China. The actual soil depths and river channels were incorporated into the model to better simulate the runoff in this watershed. The soil evaporation scheme was modified to better describe the evaporation processes. Our results showed that the model significantly improved the runoff simulations.
Sebastian Müller, Lennart Schüler, Alraune Zech, and Falk Heße
Geosci. Model Dev., 15, 3161–3182, https://doi.org/10.5194/gmd-15-3161-2022, https://doi.org/10.5194/gmd-15-3161-2022, 2022
Short summary
Short summary
The GSTools package provides a Python-based platform for geoostatistical applications. Salient features of GSTools are its random field generation, its kriging capabilities and its versatile covariance model. It is furthermore integrated with other Python packages, like PyKrige, ogs5py or scikit-gstat, and provides interfaces to meshio and PyVista. Four presented workflows showcase the abilities of GSTools.
Ather Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, and Kyung Hwa Cho
Geosci. Model Dev., 15, 3021–3039, https://doi.org/10.5194/gmd-15-3021-2022, https://doi.org/10.5194/gmd-15-3021-2022, 2022
Short summary
Short summary
The field of artificial intelligence has shown promising results in a wide variety of fields including hydrological modeling. However, developing and testing hydrological models with artificial intelligence techniques require expertise from diverse fields. In this study, we developed an open-source framework based upon the python programming language to simplify the process of the development of hydrological models of time series data using machine learning.
Yunxiang Chen, Jie Bao, Yilin Fang, William A. Perkins, Huiying Ren, Xuehang Song, Zhuoran Duan, Zhangshuan Hou, Xiaoliang He, and Timothy D. Scheibe
Geosci. Model Dev., 15, 2917–2947, https://doi.org/10.5194/gmd-15-2917-2022, https://doi.org/10.5194/gmd-15-2917-2022, 2022
Short summary
Short summary
Climate change affects river discharge variations that alter streamflow. By integrating multi-type survey data with a computational fluid dynamics tool, OpenFOAM, we show a workflow that enables accurate and efficient streamflow modeling at 30 km and 5-year scales. The model accuracy for water stage and depth average velocity is −16–9 cm and 0.71–0.83 in terms of mean error and correlation coefficients. This accuracy indicates the model's reliability for evaluating climate impact on rivers.
Marcela Silva, Ashley M. Matheny, Valentijn R. N. Pauwels, Dimetre Triadis, Justine E. Missik, Gil Bohrer, and Edoardo Daly
Geosci. Model Dev., 15, 2619–2634, https://doi.org/10.5194/gmd-15-2619-2022, https://doi.org/10.5194/gmd-15-2619-2022, 2022
Short summary
Short summary
Our study introduces FETCH3, a ready-to-use, open-access model that simulates the water fluxes across the soil, roots, and stem. To test the model capabilities, we tested it against exact solutions and a case study. The model presented considerably small errors when compared to the exact solutions and was able to correctly represent transpiration patterns when compared to experimental data. The results show that FETCH3 can correctly simulate above- and below-ground water transport.
Mayra Ishikawa, Wendy Gonzalez, Orides Golyjeswski, Gabriela Sales, J. Andreza Rigotti, Tobias Bleninger, Michael Mannich, and Andreas Lorke
Geosci. Model Dev., 15, 2197–2220, https://doi.org/10.5194/gmd-15-2197-2022, https://doi.org/10.5194/gmd-15-2197-2022, 2022
Short summary
Short summary
Reservoir hydrodynamics is often described in numerical models differing in dimensionality. 1D and 2D models assume homogeneity along the unresolved dimension. We compare the performance of models with 1 to 3 dimensions. All models presented reasonable results for seasonal temperature dynamics. Neglecting longitudinal transport resulted in the largest deviations in temperature. Flow velocity could only be reproduced by the 3D model. Results support the selection of models and their assessment.
Sandra Hellmers and Peter Fröhle
Geosci. Model Dev., 15, 1061–1077, https://doi.org/10.5194/gmd-15-1061-2022, https://doi.org/10.5194/gmd-15-1061-2022, 2022
Short summary
Short summary
A hydrological method to compute backwater effects in surface water streams and on adjacent lowlands caused by mostly complex flow control systems is presented. It enables transfer of discharges to water levels and calculation of backwater volume routing along streams and lowland areas by balancing water level slopes. The developed, implemented and evaluated method extends the application range of hydrological models significantly for flood-routing simulation in backwater-affected catchments.
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...
