Articles | Volume 8, issue 7
Geosci. Model Dev., 8, 2009–2034, 2015
https://doi.org/10.5194/gmd-8-2009-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 2009–2034, 2015
https://doi.org/10.5194/gmd-8-2009-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Model description paper 08 Jul 2015
Model description paper | 08 Jul 2015
SPHY v2.0: Spatial Processes in HYdrology
W. Terink et al.
Related authors
Joris P. C. Eekhout, Johannes E. Hunink, Wilco Terink, and Joris de Vente
Hydrol. Earth Syst. Sci., 22, 5935–5946, https://doi.org/10.5194/hess-22-5935-2018, https://doi.org/10.5194/hess-22-5935-2018, 2018
Short summary
Short summary
Extreme weather, like heavy rainstorms and droughts, will become more frequent under climate change, jeopardizing water availability. We show that climate change in a large Mediterranean catchment leads to a redistribution of water from soil to reservoirs and to increased crop stress. Furthermore, increased soil erosion threatens the storage capacity of reservoirs. We conclude that climate change may affect rainfed and irrigated agriculture potential and threatens overall water security.
Joris P. C. Eekhout, Wilco Terink, and Joris de Vente
Earth Surf. Dynam., 6, 687–703, https://doi.org/10.5194/esurf-6-687-2018, https://doi.org/10.5194/esurf-6-687-2018, 2018
Short summary
Short summary
Climate change will likely increase soil erosion in many locations worldwide. This increase in erosion will have large-scale impacts, such as the siltation of reservoirs. We developed a new soil erosion model to evaluate these impacts, which has an advantage over existing models in that it includes most relevant processes: rainfall–runoff generation, vegetation development, and soil erosion and deposition. The model is suited to perform scenario studies on climate change and land management.
Maurice van Tiggelen, Paul C. J. P. Smeets, Carleen H. Reijmer, Bert Wouters, Jakob F. Steiner, Emile J. Nieuwstraten, Walter W. Immerzeel, and Michiel R. van den Broeke
The Cryosphere Discuss., https://doi.org/10.5194/tc-2020-378, https://doi.org/10.5194/tc-2020-378, 2021
Preprint under review for TC
Short summary
Short summary
We developed a method to estimate the aerodynamic properties of the Greenland ice sheet surface using either UAV or satellite remote sensing. We show that this new method is able to reproduce the important spatiotemporal variability in surface aerodynamic roughness, measured by the field observations. The new maps of surface roughness can be used in atmospheric models to improve simulations of surface turbulent heat fluxes, therefore surface energy-and-mass balance over rough ice worldwide.
Remco J. de Kok, Philip D. A. Kraaijenbrink, Obbe A. Tuinenburg, Pleun N. J. Bonekamp, and Walter W. Immerzeel
The Cryosphere, 14, 3215–3234, https://doi.org/10.5194/tc-14-3215-2020, https://doi.org/10.5194/tc-14-3215-2020, 2020
Short summary
Short summary
Glaciers worldwide are shrinking, yet glaciers in parts of High Mountain Asia are growing. Using models of the regional climate and glacier growth, we reproduce the observed patterns of glacier growth and shrinkage in High Mountain Asia of the last decades. Increases in snow, in part from water that comes from lowland agriculture, have probably been more important than changes in temperature to explain the growing glaciers. We now better understand changes in the crucial mountain water cycle.
Pleun N. J. Bonekamp, Chiel C. van Heerwaarden, Jakob F. Steiner, and Walter W. Immerzeel
The Cryosphere, 14, 1611–1632, https://doi.org/10.5194/tc-14-1611-2020, https://doi.org/10.5194/tc-14-1611-2020, 2020
Short summary
Short summary
Drivers controlling melt of debris-covered glaciers are largely unknown. With a 3D turbulence-resolving model the impact of surface properties of debris on micrometeorological variables and the conductive heat flux is shown. Also, we show ice cliffs are local melt hot spots and that turbulent fluxes and local heat advection amplify spatial heterogeneity on the surface.This work is important for glacier mass balance modelling and for the understanding of the evolution of debris-covered glaciers.
Teun van Woerkom, Jakob F. Steiner, Philip D. A. Kraaijenbrink, Evan S. Miles, and Walter W. Immerzeel
Earth Surf. Dynam., 7, 411–427, https://doi.org/10.5194/esurf-7-411-2019, https://doi.org/10.5194/esurf-7-411-2019, 2019
Short summary
Short summary
Using data obtained from multiple UAV flights over a debris-covered glacier in the Himalaya between 2013 and 2018, we show that the adjacent moraines erode at rates of up to 16 cm per year, contributing to this debris cover. With retreating ice and resulting instability of moraines, this causes the glacier to cover a narrow zone along the lateral moraines in ever-thicker layers of rocks, resulting in a possible future decrease of local melt.
Lu Li, Mingxi Shen, Yukun Hou, Chong-Yu Xu, Arthur F. Lutz, Jie Chen, Sharad K. Jain, Jingjing Li, and Hua Chen
Hydrol. Earth Syst. Sci., 23, 1483–1503, https://doi.org/10.5194/hess-23-1483-2019, https://doi.org/10.5194/hess-23-1483-2019, 2019
Short summary
Short summary
The study used an integrated glacio-hydrological model for the hydrological projections of the Himalayan Beas basin under climate change. It is very likely that the upper Beas basin will get warmer and wetter in the future. This loss in glacier area will result in a reduction in glacier discharge, while the future changes in total discharge are uncertain. The uncertainty in future hydrological change is not only from GCMs, but also from the bias-correction methods and hydrological modeling.
René Reijer Wijngaard, Hester Biemans, Arthur Friedrich Lutz, Arun Bhakta Shrestha, Philippus Wester, and Walter Willem Immerzeel
Hydrol. Earth Syst. Sci., 22, 6297–6321, https://doi.org/10.5194/hess-22-6297-2018, https://doi.org/10.5194/hess-22-6297-2018, 2018
Short summary
Short summary
This study assesses the combined impacts of climate change and socio-economic developments on the future water gap for the Indus, Ganges, and Brahmaputra river basins until the end of the 21st century. The results show that despite projected increases in surface water availability, the strong socio-economic development and associated increase in water demand will likely lead to an increase in the water gap, indicating that socio-economic changes will be the key driver in the evolving water gap.
Joris P. C. Eekhout, Johannes E. Hunink, Wilco Terink, and Joris de Vente
Hydrol. Earth Syst. Sci., 22, 5935–5946, https://doi.org/10.5194/hess-22-5935-2018, https://doi.org/10.5194/hess-22-5935-2018, 2018
Short summary
Short summary
Extreme weather, like heavy rainstorms and droughts, will become more frequent under climate change, jeopardizing water availability. We show that climate change in a large Mediterranean catchment leads to a redistribution of water from soil to reservoirs and to increased crop stress. Furthermore, increased soil erosion threatens the storage capacity of reservoirs. We conclude that climate change may affect rainfed and irrigated agriculture potential and threatens overall water security.
Fanny Brun, Patrick Wagnon, Etienne Berthier, Joseph M. Shea, Walter W. Immerzeel, Philip D. A. Kraaijenbrink, Christian Vincent, Camille Reverchon, Dibas Shrestha, and Yves Arnaud
The Cryosphere, 12, 3439–3457, https://doi.org/10.5194/tc-12-3439-2018, https://doi.org/10.5194/tc-12-3439-2018, 2018
Short summary
Short summary
On debris-covered glaciers, steep ice cliffs experience dramatically enhanced melt compared with the surrounding debris-covered ice. Using field measurements, UAV data and submetre satellite imagery, we estimate the cliff contribution to 2 years of ablation on a debris-covered tongue in Nepal, carefully taking into account ice dynamics. While they occupy only 7 to 8 % of the tongue surface, ice cliffs contributed to 23 to 24 % of the total tongue ablation.
Joris P. C. Eekhout, Wilco Terink, and Joris de Vente
Earth Surf. Dynam., 6, 687–703, https://doi.org/10.5194/esurf-6-687-2018, https://doi.org/10.5194/esurf-6-687-2018, 2018
Short summary
Short summary
Climate change will likely increase soil erosion in many locations worldwide. This increase in erosion will have large-scale impacts, such as the siltation of reservoirs. We developed a new soil erosion model to evaluate these impacts, which has an advantage over existing models in that it includes most relevant processes: rainfall–runoff generation, vegetation development, and soil erosion and deposition. The model is suited to perform scenario studies on climate change and land management.
Jakob F. Steiner, Philip D. A. Kraaijenbrink, Sergiu G. Jiduc, and Walter W. Immerzeel
The Cryosphere, 12, 95–101, https://doi.org/10.5194/tc-12-95-2018, https://doi.org/10.5194/tc-12-95-2018, 2018
Short summary
Short summary
Glaciers that once every few years or decades suddenly advance in length – also known as surging glaciers – are found in many glaciated regions in the world. In the Karakoram glacier tongues are additionally located at low altitudes and relatively close to human settlements. We investigate a very recent and extremely rapid surge in the region that has caused a lake to form in the main valley with possible risks for downstream communities.
Emmy E. Stigter, Niko Wanders, Tuomo M. Saloranta, Joseph M. Shea, Marc F. P. Bierkens, and Walter W. Immerzeel
The Cryosphere, 11, 1647–1664, https://doi.org/10.5194/tc-11-1647-2017, https://doi.org/10.5194/tc-11-1647-2017, 2017
Koji Fujita, Hiroshi Inoue, Takeki Izumi, Satoru Yamaguchi, Ayako Sadakane, Sojiro Sunako, Kouichi Nishimura, Walter W. Immerzeel, Joseph M. Shea, Rijan B. Kayastha, Takanobu Sawagaki, David F. Breashears, Hiroshi Yagi, and Akiko Sakai
Nat. Hazards Earth Syst. Sci., 17, 749–764, https://doi.org/10.5194/nhess-17-749-2017, https://doi.org/10.5194/nhess-17-749-2017, 2017
Short summary
Short summary
We create multiple DEMs from photographs taken by helicopter and UAV and reveal the deposit volumes over the Langtang village, which was destroyed by avalanches induced by the Gorkha earthquake. Estimated snow depth in the source area is consistent with anomalously large snow depths observed at a neighboring glacier. Comparing with a long-term observational data, we conclude that this anomalous winter snow amplified the disaster induced by the 2015 Gorkha earthquake in Nepal.
Walter Immerzeel, Philip Kraaijenbrink, and Liss Andreassen
The Cryosphere Discuss., https://doi.org/10.5194/tc-2016-292, https://doi.org/10.5194/tc-2016-292, 2017
Revised manuscript not accepted
Short summary
Short summary
Unmanned Aerial Vehicles (UAV) have become increasingly popular in environmental monitoring. In this study we use a UAV to derive a very detailed digital elevation model (DEM) of Storbreen in Norway. We compare our results with a past DEM to derive the mass balance of this glacier. Our results confirm strong mass loss and retreat of continental glaciers in southern Norway and we conclude that UAVs are effective tools in stuyding mountain glaciers at a high level of detail.
Christian Vincent, Patrick Wagnon, Joseph M. Shea, Walter W. Immerzeel, Philip Kraaijenbrink, Dibas Shrestha, Alvaro Soruco, Yves Arnaud, Fanny Brun, Etienne Berthier, and Sonam Futi Sherpa
The Cryosphere, 10, 1845–1858, https://doi.org/10.5194/tc-10-1845-2016, https://doi.org/10.5194/tc-10-1845-2016, 2016
Short summary
Short summary
Approximately 25 % of the glacierized area in the Everest region is covered by debris, yet the surface mass balance of these glaciers has not been measured directly. From terrestrial photogrammetry and unmanned aerial vehicle (UAV) methods, this study shows that the ablation is strongly reduced by the debris cover. The insulating effect of the debris cover has a larger effect on total mass loss than the enhanced ice ablation due to supraglacial ponds and exposed ice cliffs.
W. W. Immerzeel, N. Wanders, A. F. Lutz, J. M. Shea, and M. F. P. Bierkens
Hydrol. Earth Syst. Sci., 19, 4673–4687, https://doi.org/10.5194/hess-19-4673-2015, https://doi.org/10.5194/hess-19-4673-2015, 2015
Short summary
Short summary
The water resources of the upper Indus river basin (UIB) are important for millions of people, yet little is known about the rain and snow fall in the high-altitude regions because of the inaccessibility, the climatic complexity and the lack of observations. In this study we use mass balance of glaciers to reconstruct the amount of precipitation in the UIB and we conclude that this amount is much higher than previously thought.
E. Collier, F. Maussion, L. I. Nicholson, T. Mölg, W. W. Immerzeel, and A. B. G. Bush
The Cryosphere, 9, 1617–1632, https://doi.org/10.5194/tc-9-1617-2015, https://doi.org/10.5194/tc-9-1617-2015, 2015
Short summary
Short summary
We investigate the impact of surface debris on glacier energy and mass fluxes and on atmosphere-glacier feedbacks in the Karakoram range, by including debris in an interactively coupled atmosphere-glacier model. The model is run from 1 May to 1 October 2004, with a simple specification of debris thickness. We find an appreciable reduction in ablation that exceeds 5m w.e. on glacier tongues, as well as significant alterations to near-surface air temperatures and boundary layer dynamics.
A. F. Lutz, W. W. Immerzeel, A. Gobiet, F. Pellicciotti, and M. F. P. Bierkens
Hydrol. Earth Syst. Sci., 17, 3661–3677, https://doi.org/10.5194/hess-17-3661-2013, https://doi.org/10.5194/hess-17-3661-2013, 2013
Related subject area
Hydrology
The global water resources and use model WaterGAP v2.2d: model description and evaluation
Shyft v4.8: a framework for uncertainty assessment and distributed hydrologic modeling for operational hydrology
A distributed simple dynamical systems approach (dS2 v1.0) for computationally efficient hydrological modelling at high spatio-temporal resolution
Simulating second-generation herbaceous bioenergy crop yield using the global hydrological model H08 (v.bio1)
KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms
Parametrization of lakes water dynamics in the ISBA-CTRIP land surface system (SURFEX v8.1)
Simulating human impacts on global water resources using VIC-5
The Ensemble Framework For Flash Flood Forecasting (EF5) v1.2: description and case study
ML-SWAN-v1: a hybrid machine learning framework for the concentration prediction and discovery of transport pathways of surface water nutrients
The latest improvements with SURFEX v8.0 of the Safran–Isba–Modcou hydrometeorological model for France
A multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0)
MFIT 1.0.0: Multi-Flow Inversion of Tracer breakthrough curves in fractured and karst aquifers
Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method
HydroMix v1.0: a new Bayesian mixing framework for attributing uncertain hydrological sources
TIER version 1.0: an open-source Topographically InformEd Regression (TIER) model to estimate spatial meteorological fields
Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0)
glmGUI v1.0: an R-based graphical user interface and toolbox for GLM (General Lake Model) simulations
The Canadian Hydrological Model (CHM) v1.0: a multi-scale, multi-extent, variable-complexity hydrological model – design and overview
WAYS v1: a hydrological model for root zone water storage simulation on a global scale
TOPMELT 1.0: a topography-based distribution function approach to snowmelt simulation for hydrological modelling at basin scale
MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement
Beo v1.0: numerical model of heat flow and low-temperature thermochronology in hydrothermal systems
A parallel workflow implementation for PEST version 13.6 in high-performance computing for WRF-Hydro version 5.0: a case study over the midwestern United States
r.sim.terrain 1.0: a landscape evolution model with dynamic hydrology
The probabilistic hydrological MARCSHYDRO (the MARkov Chain System) model: its structure and core version 0.2
A Python-enhanced urban land surface model SuPy (SUEWS in Python, v2019.2): development, deployment and demonstration
The multiscale routing model mRM v1.0: simple river routing at resolutions from 1 to 50 km
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations
Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model
DECIPHeR v1: Dynamic fluxEs and ConnectIvity for Predictions of HydRology
Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution
Discrete k-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change
Using observed river flow data to improve the hydrological functioning of the JULES land surface model (vn4.3) used for regional coupled modelling in Great Britain (UKC2)
A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)
V2Karst V1.1: a parsimonious large-scale integrated vegetation–recharge model to simulate the impact of climate and land cover change in karst regions
GSFLOW–GRASS v1.0.0: GIS-enabled hydrologic modeling of coupled groundwater–surface-water systems
Improvements to the hydrological processes of the Town Energy Balance model (TEB-Veg, SURFEX v7.3) for urban modelling and impact assessment
STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change
The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems
The Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements for new applications and reproducibility
Developing a global operational seasonal hydro-meteorological forecasting system: GloFAS-Seasonal v1.0
EcH2O-iso 1.0: water isotopes and age tracking in a process-based, distributed ecohydrological model
EDDA 2.0: integrated simulation of debris flow initiation and dynamics considering two initiation mechanisms
PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model
The design, deployment, and testing of kriging models in GEOframe with SIK-0.9.8
IPA (v1): a framework for agent-based modelling of soil water movement
Improved regional-scale groundwater representation by the coupling of the mesoscale Hydrologic Model (mHM v5.7) to the groundwater model OpenGeoSys (OGS)
The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models
tran-SAS v1.0: a numerical model to compute catchment-scale hydrologic transport using StorAge Selection functions
Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
KLT-IV v1.0 offers a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from both fixed and mobile remote sensing platforms. Platform motion can be accounted for using ground control points and/or stable features or a GPS device and inertial measurement unit sensor. Examples of the KLT-IV workflow are provided for two case studies where footage is acquired using unmanned aerial systems and fixed cameras.
Thibault Guinaldo, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-296, https://doi.org/10.5194/gmd-2020-296, 2020
Revised manuscript accepted for GMD
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Zhen Yin, Sebastien Strebelle, and Jef Caers
Geosci. Model Dev., 13, 651–672, https://doi.org/10.5194/gmd-13-651-2020, https://doi.org/10.5194/gmd-13-651-2020, 2020
Short summary
Short summary
We provide completely automated Bayesian evidential learning (AutoBEL) for geological uncertainty quantification. AutoBEL focuses on model falsification, global sensitivity analysis, and statistical learning for joint model uncertainty reduction by borehole data. Application shows fast and robust uncertainty reduction in geological models and predictions for large field cases, showing its applicability in subsurface applications, e.g., groundwater, oil, gas, and geothermal or mineral resources.
Thomas Bueche, Marko Wenk, Benjamin Poschlod, Filippo Giadrossich, Mario Pirastru, and Mark Vetter
Geosci. Model Dev., 13, 565–580, https://doi.org/10.5194/gmd-13-565-2020, https://doi.org/10.5194/gmd-13-565-2020, 2020
Short summary
Short summary
The R-based graphical user interface glmGUI provides tools for pre- and postprocessing of General Lake Model (GLM) simulations. This includes an autocalibration, parameter sensitivity analysis, and several plot options. The model parameters can be analyzed and calibrated for the simulation output variables water temperature and lake level. The toolbox is tested for two sites (lake Ammersee, Germany, and lake Baratz, Italy).
Christopher B. Marsh, John W. Pomeroy, and Howard S. Wheater
Geosci. Model Dev., 13, 225–247, https://doi.org/10.5194/gmd-13-225-2020, https://doi.org/10.5194/gmd-13-225-2020, 2020
Short summary
Short summary
The Canadian Hydrological Model (CHM) is a next-generation distributed model. Although designed to be applied generally, it has a focus for application where cold-region processes, such as snowpacks, play a role in hydrology. A key feature is that it uses a multi-scale surface representation, increasing efficiency. It also enables algorithm comparisons in a flexible structure. Model philosophy, design, and several cold-region-specific examples are described.
Ganquan Mao and Junguo Liu
Geosci. Model Dev., 12, 5267–5289, https://doi.org/10.5194/gmd-12-5267-2019, https://doi.org/10.5194/gmd-12-5267-2019, 2019
Mattia Zaramella, Marco Borga, Davide Zoccatelli, and Luca Carturan
Geosci. Model Dev., 12, 5251–5265, https://doi.org/10.5194/gmd-12-5251-2019, https://doi.org/10.5194/gmd-12-5251-2019, 2019
Short summary
Short summary
This paper presents TOPMELT, a parsimonious snowpack simulation model integrated into a basin-scale hydrological model. TOPMELT implements the full spatial distribution of clear-sky potential solar radiation by means of a statistical representation: this approach reduces computational burden, which is a key potential advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The model is assessed by examining different resolutions of its domain.
Rui Wu, Lei Yang, Chao Chen, Sajjad Ahmad, Sergiu M. Dascalu, and Frederick C. Harris Jr.
Geosci. Model Dev., 12, 4115–4131, https://doi.org/10.5194/gmd-12-4115-2019, https://doi.org/10.5194/gmd-12-4115-2019, 2019
Short summary
Short summary
The paper mainly has two contributions. First, a post-processor framework is proposed to improve hydrologic model accuracy. The key is to characterize possible connections between model inputs and errors. Based on results, it is also possible to replace the time-consuming model calibration step using our post-processor framework. Second, a window selection method is proposed to handle nonstationary data. A window size is chosen containing stable data using a measure named
DSproposed by us.
Elco Luijendijk
Geosci. Model Dev., 12, 4061–4073, https://doi.org/10.5194/gmd-12-4061-2019, https://doi.org/10.5194/gmd-12-4061-2019, 2019
Short summary
Short summary
This paper presents a new model code that can be used to date the flow of hot fluids in the crust and the age of hot springs. It does so by modelling the thermal effects of fluid flow in the subsurface and by comparing the results with low-temperature thermochronology, which is a widely used method to quantify the temperature history of minerals and rocks. The model also demonstrates the effects of the depth and angle of permeable faults on temperatures of hot springs.
Jiali Wang, Cheng Wang, Vishwas Rao, Andrew Orr, Eugene Yan, and Rao Kotamarthi
Geosci. Model Dev., 12, 3523–3539, https://doi.org/10.5194/gmd-12-3523-2019, https://doi.org/10.5194/gmd-12-3523-2019, 2019
Short summary
Short summary
WRF-Hydro needs to be calibrated to optimize its output with respect to observations. However, when applied to a relatively large domain, both WRF-Hydro simulations and calibrations require intensive computing resources and are best performed in parallel. This study ported an independent calibration tool (parameter estimation tool – PEST) to high-performance computing clusters and adapted it to work with WRF-Hydro. The results show significant speedup for model calibration.
Brendan Alexander Harmon, Helena Mitasova, Anna Petrasova, and Vaclav Petras
Geosci. Model Dev., 12, 2837–2854, https://doi.org/10.5194/gmd-12-2837-2019, https://doi.org/10.5194/gmd-12-2837-2019, 2019
Short summary
Short summary
The numerical model, r.sim.terrain, simulates how overland flows of water and sediment shape topography over short periods of time. We tested the model by comparing runs of the simulation against a time series of airborne lidar surveys for our study landscape. Through these tests, we demonstrated that the model can simulate gully evolution including processes such as channel incision, channel widening, and the development of scour pits, rills, and depositional ridges.
Elena Shevnina and Andrey Silaev
Geosci. Model Dev., 12, 2767–2780, https://doi.org/10.5194/gmd-12-2767-2019, https://doi.org/10.5194/gmd-12-2767-2019, 2019
Short summary
Short summary
The paper provides a theory and assumptions behind an advance of frequency analysis (AFA) approach in long-term hydrological forecasting. In this paper, a new core of the probabilistic hydrological model MARkov Chain System (MARCSHYDRO) was introduced, together with the code and an example of a climate-scale prediction of an exceedance probability curve of river runoff with low computational costs.
Ting Sun and Sue Grimmond
Geosci. Model Dev., 12, 2781–2795, https://doi.org/10.5194/gmd-12-2781-2019, https://doi.org/10.5194/gmd-12-2781-2019, 2019
Short summary
Short summary
A Python-enhanced urban land surface model, SuPy (SUEWS in Python), is presented with its development (the SUEWS interface modification, F2PY configuration and Python frontend implementation), cross-platform deployment (PyPI, Python Package Index) and demonstration (online tutorials in Jupyter notebooks for users of different levels). SuPy represents a significant enhancement that supports existing and new model applications, reproducibility and enhanced functionality.
Stephan Thober, Matthias Cuntz, Matthias Kelbling, Rohini Kumar, Juliane Mai, and Luis Samaniego
Geosci. Model Dev., 12, 2501–2521, https://doi.org/10.5194/gmd-12-2501-2019, https://doi.org/10.5194/gmd-12-2501-2019, 2019
Short summary
Short summary
We present a model that aggregates simulated runoff along a river
(i.e. a routing model). The unique feature of the model is that it
can be run at multiple resolutions without any modifications to the
input data. The model internally (dis-)aggregates all input data to
the resolution given by the user. The model performance does not
depend on the chosen resolution. This allows efficient model
calibration at coarse resolution and subsequent model application at
fine resolution.
Wouter J. M. Knoben, Jim E. Freer, Keirnan J. A. Fowler, Murray C. Peel, and Ross A. Woods
Geosci. Model Dev., 12, 2463–2480, https://doi.org/10.5194/gmd-12-2463-2019, https://doi.org/10.5194/gmd-12-2463-2019, 2019
Short summary
Short summary
Computer models are used to predict river flows. A good model should represent the river basin to which it is applied so that flow predictions are as realistic as possible. However, many different computer models exist, and selecting the most appropriate model for a given river basin is not always easy. This study combines computer code for 46 different hydrological models into a single coding framework so that models can be compared in an objective way and we can learn about model differences.
Robert Reinecke, Laura Foglia, Steffen Mehl, Tim Trautmann, Denise Cáceres, and Petra Döll
Geosci. Model Dev., 12, 2401–2418, https://doi.org/10.5194/gmd-12-2401-2019, https://doi.org/10.5194/gmd-12-2401-2019, 2019
Short summary
Short summary
G³M is a new global groundwater model (http://globalgroundwatermodel.org) that simulates lateral and vertical flows as well as exchanges with surface water bodies like rivers, lakes, and wetlands for the whole globe except Antarctica and Greenland. The newly developed model framework enables an efficient integration into established global hydrological models. This paper presents the G³M concept and specific model design decisions together with first results under a naturalized equilibrium.
Gemma Coxon, Jim Freer, Rosanna Lane, Toby Dunne, Wouter J. M. Knoben, Nicholas J. K. Howden, Niall Quinn, Thorsten Wagener, and Ross Woods
Geosci. Model Dev., 12, 2285–2306, https://doi.org/10.5194/gmd-12-2285-2019, https://doi.org/10.5194/gmd-12-2285-2019, 2019
Short summary
Short summary
DECIPHeR (Dynamic fluxEs and ConnectIvity for Predictions of Hydrology) is a new modelling framework that can be applied from small catchment to continental scales for complex river basins. This paper describes the modelling framework and its key components and demonstrates the model’s ability to be applied across a large model domain. This work highlights the potential for catchment- to continental-scale predictions of streamflow to support robust environmental management and policy decisions.
Katherine R. Barnhart, Rachel C. Glade, Charles M. Shobe, and Gregory E. Tucker
Geosci. Model Dev., 12, 1267–1297, https://doi.org/10.5194/gmd-12-1267-2019, https://doi.org/10.5194/gmd-12-1267-2019, 2019
Short summary
Short summary
Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution.
Taesam Lee and Vijay P. Singh
Geosci. Model Dev., 12, 1189–1207, https://doi.org/10.5194/gmd-12-1189-2019, https://doi.org/10.5194/gmd-12-1189-2019, 2019
Short summary
Short summary
A simple novel technique for simulating multisite occurrence of precipitation is proposed. The proposed technique employs the nonparametric approaches k-nearest neighbor and genetic algorithms. We tested this technique in various ways and proved that this simple technique can be useful and comparable to the existing one.
Alberto Martínez-de la Torre, Eleanor M. Blyth, and Graham P. Weedon
Geosci. Model Dev., 12, 765–784, https://doi.org/10.5194/gmd-12-765-2019, https://doi.org/10.5194/gmd-12-765-2019, 2019
Short summary
Short summary
Land–surface interactions with the atmosphere are key for weather and climate modelling studies, both in research and in the operational systems that provide scientific tools for decision makers. Regional assessments will be influenced by the characteristics of the land. We improved the representation of river flows in Great Britain by including a dependency on the terrain slope. This development will be reflected not only in river flows, but in the whole water cycle represented by the model.
Matthew R. Hipsey, Louise C. Bruce, Casper Boon, Brendan Busch, Cayelan C. Carey, David P. Hamilton, Paul C. Hanson, Jordan S. Read, Eduardo de Sousa, Michael Weber, and Luke A. Winslow
Geosci. Model Dev., 12, 473–523, https://doi.org/10.5194/gmd-12-473-2019, https://doi.org/10.5194/gmd-12-473-2019, 2019
Short summary
Short summary
The General Lake Model (GLM) has been developed to undertake simulation of a diverse range of wetlands, lakes, and reservoirs. The model supports the science needs of the Global Lake Ecological Observatory Network (GLEON), a network of lake sensors and researchers attempting to understand lake functioning and address questions about how lakes around the world vary in response to climate and land use change. The paper describes the science basis and application of the model.
Fanny Sarrazin, Andreas Hartmann, Francesca Pianosi, Rafael Rosolem, and Thorsten Wagener
Geosci. Model Dev., 11, 4933–4964, https://doi.org/10.5194/gmd-11-4933-2018, https://doi.org/10.5194/gmd-11-4933-2018, 2018
Short summary
Short summary
We propose the first large-scale vegetation–recharge model for karst regions (V2Karst), which enables the analysis of the impact of changes in climate and land cover on karst groundwater recharge. We demonstrate the plausibility of V2Karst simulations against observations at FLUXNET sites and of controlling modelled processes using sensitivity analysis. We perform virtual experiments to further test the model and gain insight into its sensitivity to precipitation pattern and vegetation cover.
G.-H. Crystal Ng, Andrew D. Wickert, Lauren D. Somers, Leila Saberi, Collin Cronkite-Ratcliff, Richard G. Niswonger, and Jeffrey M. McKenzie
Geosci. Model Dev., 11, 4755–4777, https://doi.org/10.5194/gmd-11-4755-2018, https://doi.org/10.5194/gmd-11-4755-2018, 2018
Short summary
Short summary
The profound importance of water has led to the development of increasingly complex hydrological models. However, implementing these models is usually time-consuming and requires specialized expertise, stymieing their widespread use to support science-driven decision-making. In response, we have developed GSFLOW–GRASS, a robust and comprehensive set of software tools that can be readily used to set up and execute GSFLOW, the U.S. Geological Survey's coupled groundwater–surface-water flow model.
Xenia Stavropulos-Laffaille, Katia Chancibault, Jean-Marc Brun, Aude Lemonsu, Valéry Masson, Aaron Boone, and Hervé Andrieu
Geosci. Model Dev., 11, 4175–4194, https://doi.org/10.5194/gmd-11-4175-2018, https://doi.org/10.5194/gmd-11-4175-2018, 2018
Short summary
Short summary
Integrating vegetation in urban planning is promoted to counter steer potential impacts of climate and demographic changes. Assessing the multiple benefits of such strategies on the urban microclimate requires a detailed coupling of both the water and energy transfers in numerical tools. In this respect, the representation of water-related processes in the urban subsoil of the existing model TEB-Veg has been improved. The new model thus allows a better evaluation of urban adaptation strategies.
Michael Bliss Singer, Katerina Michaelides, and Daniel E. J. Hobley
Geosci. Model Dev., 11, 3713–3726, https://doi.org/10.5194/gmd-11-3713-2018, https://doi.org/10.5194/gmd-11-3713-2018, 2018
Short summary
Short summary
For various applications, a regional or local characterization of rainfall is required, particularly at the watershed scale, where there is spatial heterogeneity. Furthermore, simple models are needed that can simulate various scenarios of climate change including changes in seasonal wetness and rainstorm intensity. To this end, we have developed the STOchastic Rainstorm Model (STORM). We explain its developments and data requirements, and illustrate how it simulates rainstorms over a basin.
Kristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, and Christa D. Peters-Lidard
Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, https://doi.org/10.5194/gmd-11-3605-2018, 2018
Short summary
Short summary
The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
Joseph J. Hamman, Bart Nijssen, Theodore J. Bohn, Diana R. Gergel, and Yixin Mao
Geosci. Model Dev., 11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, https://doi.org/10.5194/gmd-11-3481-2018, 2018
Short summary
Short summary
Variable Infiltration Capacity (VIC) is a widely used hydrologic model. This paper documents the development of VIC version 5, which includes a reconfiguration of the model source code to support a wider range of modeling applications. It also represents a significant step forward for the VIC user community in terms of support for a range of modeling applications, reproducibility, and scientific robustness.
Rebecca Emerton, Ervin Zsoter, Louise Arnal, Hannah L. Cloke, Davide Muraro, Christel Prudhomme, Elisabeth M. Stephens, Peter Salamon, and Florian Pappenberger
Geosci. Model Dev., 11, 3327–3346, https://doi.org/10.5194/gmd-11-3327-2018, https://doi.org/10.5194/gmd-11-3327-2018, 2018
Short summary
Short summary
Global overviews of upcoming flood and drought events are key for many applications from agriculture to disaster risk reduction. Seasonal forecasts are designed to provide early indications of such events weeks or even months in advance. This paper introduces GloFAS-Seasonal, the first operational global-scale seasonal hydro-meteorological forecasting system producing openly available forecasts of high and low river flow out to 4 months ahead.
Sylvain Kuppel, Doerthe Tetzlaff, Marco P. Maneta, and Chris Soulsby
Geosci. Model Dev., 11, 3045–3069, https://doi.org/10.5194/gmd-11-3045-2018, https://doi.org/10.5194/gmd-11-3045-2018, 2018
Short summary
Short summary
This paper presents a novel ecohydrological model in which both the fluxes of water and the relative concentration in stable isotopes (2H and 18O) can be simulated. Spatial heterogeneity, lateral transfers and plant-driven water use are incorporated. A thorough evaluation shows encouraging results using a wide range of in situ measurements from a Scottish catchment. The same modelling principles are then used to simulate how (and where) precipitation ages as water transits in the catchment.
Ping Shen, Limin Zhang, Hongxin Chen, and Ruilin Fan
Geosci. Model Dev., 11, 2841–2856, https://doi.org/10.5194/gmd-11-2841-2018, https://doi.org/10.5194/gmd-11-2841-2018, 2018
Short summary
Short summary
A rainstorm can trigger numerous debris flows. A difficult task in debris flow risk assessment is to identify debris flow initiation locations and volumes. This paper presents a new model to solve this problem by physically simulating the initiation of debris flows by hillslope bed erosion and transformation from slope failures. The sediment from these two initiation mechanisms joins the flow mixture, and the volume of the flow mixture increases along the flow path due to additional bed erosion.
Edwin H. Sutanudjaja, Rens van Beek, Niko Wanders, Yoshihide Wada, Joyce H. C. Bosmans, Niels Drost, Ruud J. van der Ent, Inge E. M. de Graaf, Jannis M. Hoch, Kor de Jong, Derek Karssenberg, Patricia López López, Stefanie Peßenteiner, Oliver Schmitz, Menno W. Straatsma, Ekkamol Vannametee, Dominik Wisser, and Marc F. P. Bierkens
Geosci. Model Dev., 11, 2429–2453, https://doi.org/10.5194/gmd-11-2429-2018, https://doi.org/10.5194/gmd-11-2429-2018, 2018
Short summary
Short summary
PCR-GLOBWB 2 is an integrated hydrology and water resource model that fully integrates water use simulation and consolidates all features that have been developed since PCR-GLOBWB 1 was introduced. PCR-GLOBWB 2 can have a global coverage at 5 arcmin resolution and supersedes PCR-GLOBWB 1, which has a resolution of 30 arcmin only. Comparing the 5 arcmin with 30 arcmin simulations using discharge data, we clearly find improvement in the model performance of the higher-resolution model.
Marialaura Bancheri, Francesco Serafin, Michele Bottazzi, Wuletawu Abera, Giuseppe Formetta, and Riccardo Rigon
Geosci. Model Dev., 11, 2189–2207, https://doi.org/10.5194/gmd-11-2189-2018, https://doi.org/10.5194/gmd-11-2189-2018, 2018
Short summary
Short summary
This paper presents a new modeling package for the spatial interpolation of environmental variables. It includes 11 theoretical semivariogram models and four types of Kriging interpolations. To test the performances of the package, two applications are performed: the interpolation of 1 year of temperatures
and a rainfall event. Both interpolations gave good results. In comparison with gstat, the SIK package proved to be a good alternative, regarding both the easiness of use and the accuracy.
Benjamin Mewes and Andreas H. Schumann
Geosci. Model Dev., 11, 2175–2187, https://doi.org/10.5194/gmd-11-2175-2018, https://doi.org/10.5194/gmd-11-2175-2018, 2018
Miao Jing, Falk Heße, Rohini Kumar, Wenqing Wang, Thomas Fischer, Marc Walther, Matthias Zink, Alraune Zech, Luis Samaniego, Olaf Kolditz, and Sabine Attinger
Geosci. Model Dev., 11, 1989–2007, https://doi.org/10.5194/gmd-11-1989-2018, https://doi.org/10.5194/gmd-11-1989-2018, 2018
Julian Koch, Mehmet Cüneyd Demirel, and Simon Stisen
Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, https://doi.org/10.5194/gmd-11-1873-2018, 2018
Short summary
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.
Paolo Benettin and Enrico Bertuzzo
Geosci. Model Dev., 11, 1627–1639, https://doi.org/10.5194/gmd-11-1627-2018, https://doi.org/10.5194/gmd-11-1627-2018, 2018
Short summary
Short summary
Solutes introduced in the environment are transported by water to streams and lakes. The tran-SAS package includes a set of codes to model this process for entire watersheds by using the concept of water residence times, i.e. the time that water takes to move through the landscape. Results show that the model is implemented efficiently and it can be used to simulate solute transport in a number of different conditions.
Léonard Santos, Guillaume Thirel, and Charles Perrin
Geosci. Model Dev., 11, 1591–1605, https://doi.org/10.5194/gmd-11-1591-2018, https://doi.org/10.5194/gmd-11-1591-2018, 2018
Short summary
Short summary
Many rainfall–runoff models are based on stores. However, the differential equations that describe the stores' evolution are rarely presented in literature.
This represents an issue when the temporal resolution changes. In this work, we propose and evaluate a state-space version of a simple rainfall–runoff model within a robust resolution scheme. The results show that the proposed model performs equally well or slightly better than the original one and is independent of the temporal resolution.
Cited articles
Abbott, M., Bathurst, J., Cunge, J., O'Connell, P., and Rasmussen, J.: An introduction to the European Hydrological System – Systeme Hydrologique Europeen, "SHE", 2: Structure of a physically-based, distributed modelling system, J. Hydrol., 87, 61–77, 1986.
ADB: Consultant's Report Regional Technical Assistance: Water and Adaptation Interventions in Central and West Asia, Tech. rep., 2012.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop evapotranspiration – Guidelines for computing crop water requirements, FAO Irrigation and drainage paper, 56, 1998.
Andersson, E.: User guide to ECMWF forecast products. Version 1.1, Tech. rep., ECMWF, available at: http://old.ecmwf.int/products/forecasts/guide/user_guide.pdf (last access: 02 August 2014), 2013.
Bartholomeus, R. P., Witte, J.-P. M., van Bodegom, P. M., van Dam, J. C., and Aerts, R.: Critical soil conditions for oxygen stress to plant roots: Substituting the Feddes-function by a process-based model, J. Hydrol., 360, 147–165, https://doi.org/10.1016/j.jhydrol.2008.07.029, 2008.
Bastiaanssen, W., Allen, R., Droogers, P., Da'Urso, G., and Steduto, P.: Twenty-five years modeling irrigated and drained soils: State of the art, Agr. Water Manage., 92, 111–125, https://doi.org/10.1016/j.agwat.2007.05.013, 2007.
Batjes, N., Dijkshoorn, K., van Engelen, V., Fischer, G., Jones, A., Montanarella, L., Petri, M., Prieler, S., Teixeira, E., Wiberg, D., and Shi, X.: Harmonized World Soil Database (version 1.1), Tech. rep., FAO and IIASA, Rome, Italy and Laxenburg, Austria, 2009.
Batjes, N., Dijkshoorn, K., van Engelen, V., Fischer, G., Jones, A., Montanarella, L., Petri, M., Prieler, S., Teixeira, E., Wiberg, D., and Shi, X.: Harmonized World Soil Database (version 1.2), Tech. rep., FAO and IIASA, Rome, Italy and Laxenburg, Austria, 2012.
Beven, K.: Kinematic subsurface stormflow, Water Resour. Res., 17, 1419–1424, https://doi.org/10.1029/WR017i005p01419, 1981.
Beven, K.: Robert E. Horton's perceptual model of infiltration processes, Hydrol. Process., 18, 3447–3460, https://doi.org/10.1002/hyp.5740, 2004.
Beven, K. and Germann, P.: Macropores and water flow in soils, Water Resour. Res., 18, 1311–1325, https://doi.org/10.1029/WR018i005p01311, 1982.
Bierkens, M. F. P. and van Beek, L. P. H.: Seasonal Predictability of European Discharge: NAO and Hydrological Response Time, J. Hydrometeorol., 10, 953–968, https://doi.org/10.1175/2009JHM1034.1, 2009.
Biswas, A. K. and Tortajada, C.: Future Water Governance: Problems and Perspectives, Int. J. Water Resour. D., 26, 129–139, https://doi.org/10.1080/07900627.2010.488853, 2010.
Bontemps, S., Defourny, P., van Bogaert, E., Arino, O., Kalogirou, V., and Ramos Perez, J.: GLOBCOVER 2009. Products Description and Validation Report, Tech. rep., ESA, 2011.
Bowling, L. C., Pomeroy, J. W., and Lettenmaier, D. P.: Parameterization of Blowing-Snow Sublimation in a Macroscale Hydrology Model, J. Hydrometeorol., 5, 745–762, https://doi.org/10.1175/1525-7541(2004)005<0745:POBSIA>2.0.CO;2, 2004.
Bracken, C., Rajagopalan, B., and Prairie, J.: A multisite seasonal ensemble streamflow forecasting technique, Water Resour. Res., 46, https://doi.org/10.1029/2009WR007965, 2010.
Bramer, L. M., Hornbuckle, B. K., and Caragea, P. C.: How Many Measurements of Soil Moisture within the Footprint of a Ground-Based Microwave Radiometer Are Required to Account for Meter-Scale Spatial Variability?, Vadose Zone J., 12, 3, https://doi.org/10.2136/vzj2012.0100, 2013.
Brutsaert, W.: De Saint-Venant Equations Experimentally Verified, J. Hydr. Eng. Div.-ASCE, 97, 1387–1401, 1971.
Brutsaert, W.: Hydrology. An introduction., Cambridge University Press, Cambridge, 2005.
Carlson, T. N. and Ripley, D. A.: On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sens. Environ., 62, 241–252, https://doi.org/10.1016/S0034-4257(97)00104-1, 1997.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015a.
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Gochis, D. J., Rasmussen, R. M., Tarboton, D. G., Mahat, V., Flerchinger, G. N., and Marks, D. G.: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies, Water Resour. Res., 51, 2515–2542, https://doi.org/10.1002/2015WR017200, 2015b.
Contreras, S., Hunink, J., and Baille, A.: Building a Watershed Information System for the Campo de Cartagena basin (Spain) integrating hydrological modeling and remote sensing. FutureWater Report 125., Tech. rep., FutureWater, 2014.
Corradini, C., Morbidelli, R., and Melone, F.: On the interaction between infiltration and Hortonian runoff, J. Hydrol., 204, 52–67, https://doi.org/10.1016/S0022-1694(97)00100-5, 1998.
Dai, A.: Drought under global warming: a review, Wiley Interdisciplinary Reviews: Climate Change, 2, 45–65, https://doi.org/10.1002/wcc.81, 2011.
de Jong, S. and Jetten, V.: Distributed, quantitative assessment of canopy storage capacity by Hyperspectral Remote Sensing, available at: http://www.geo.uu.nl/dejong/pdf-files/Interception-by-RS.pdf (last access: 13 November 2014), 2010.
Deb, S. and Shukla, M.: Soil hydrology, land use and agriculture: measurement and modelling, Las Cruces, https://doi.org/10.1079/9781845937973.0000, 2011.
Droogers, P. and Aerts, J.: Adaptation strategies to climate change and climate variability: A comparative study between seven contrasting river basins, Phys. Chem. Earth Pt. A/B/C, 30, 339–346, https://doi.org/10.1016/j.pce.2005.06.015, 2005.
Droogers, P. and Bouma, J.: Simulation modelling for water governance in basins, Int. J. Water Resour. D., 30, 475–494, https://doi.org/10.1080/07900627.2014.903771, 2014.
Droogers, P. and Immerzeel, W. W.: Wat is het beste model?, H2O Tijdschrift voor watervoorziening en waterbeheer, 4, 38–41, 2010.
Droogers, P., Immerzeel, W. W., Terink, W., Hoogeveen, J., Bierkens, M. F. P., van Beek, L. P. H., and Debele, B.: Water resources trends in Middle East and North Africa towards 2050, Hydrol. Earth Syst. Sci., 16, 3101–3114, https://doi.org/10.5194/hess-16-3101-2012, 2012.
EEA: EU-DEM layers, Copernicus data and information funded by the European Union, European Environmental Agency, Tech. rep., 2014.
Endrizzi, S., Dall' Amico, M., Gruber, S., and Rigon, R.: GEOtop Users Manual. User Manual Version 1.0, Tech. rep., Department of Physical Geography, University of Zurich, Zurich, 2011.
Endrizzi, S., Gruber, S., Dall'Amico, M., and Rigon, R.: GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects, Geosci. Model Dev., 7, 2831–2857, https://doi.org/10.5194/gmd-7-2831-2014, 2014.
EPA: Modeling at EPA, available at: http://www.epa.gov/epahome/models.htm (last access: 30 September 2014), 2014.
Essery, R., Morin, S., Lejeune, Y., and B Ménard, C.: A comparison of 1701 snow models using observations from an alpine site, Adv. Water Resour., 55, 131–148, https://doi.org/10.1016/j.advwatres.2012.07.013, 2013.
FAO: FAO Water. Crop Water Information, available at: http://www.fao.org/nr/water/cropinfo.html (last access: 09 June 2014), 2013.
Feddes, R., Kowalik, P., and Zaradny, H.: Simulation of field water use and crop yield. Simulation Monographs, Pudoc, Wageningen University, 1978.
Finger, D., Pellicciotti, F., Konz, M., Rimkus, S., and Burlando, P.: The value of glacier mass balance, satellite snow cover images, and hourly discharge for improving the performance of a physically based distributed hydrological model, Water Resour. Res., 47, W07519, https://doi.org/10.1029/2010WR009824, 2011.
Foglia, L., Hill, M. C., Mehl, S. W., and Burlando, P.: Sensitivity analysis, calibration, and testing of a distributed hydrological model using error-based weighting and one objective function, Water Resour. Res., 45, W06427, https://doi.org/10.1029/2008WR007255, 2009.
Gat, J. R., Bowser, C. J., and Kendall, C.: The contribution of evaporation from the Great Lakes to the continental atmosphere: estimate based on stable isotope data, Geophys. Res. Lett., 21, 557–560, https://doi.org/10.1029/94GL00069, 1994.
Gill, M. A.: Flood routing by the Muskingum method, J. Hydrol., 36, 353–363, https://doi.org/10.1016/0022-1694(78)90153-1, 1978.
Gopalan, K., Wang, N.-Y., Ferraro, R., and Liu, C.: Status of the TRMM 2A12 Land Precipitation Algorithm, J. Atmos. Ocean. Tech., 27, 1343–1354, https://doi.org/10.1175/2010JTECHA1454.1, 2010.
Goward, S. N. and Huemmrich, K. F.: Vegetation canopy PAR absorptance and the normalized difference vegetation index: An assessment using the SAIL model, Remote Sens. Environ., 39, 119–140, https://doi.org/10.1016/0034-4257(92)90131-3, 1992.
Grantz, K., Rajagopalan, B., Clark, M., and Zagona, E.: A technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts, Water Resour. Res., 41, https://doi.org/10.1029/2004WR003467, 2005.
Groot Zwaaftink, C. D., Mott, R., and Lehning, M.: Seasonal simulation of drifting snow sublimation in Alpine terrain, Water Resour. Res., 49, 1581–1590, https://doi.org/10.1002/wrcr.20137, 2013.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, https://doi.org/10.1016/S0034-4257(02)00095-0, 2002.
Hargreaves, G. H. and Samani, Z. A.: Reference Crop Evapotranspiration from Temperature, Appl. Eng. Agric., 1, 96–99, https://doi.org/10.13031/2013.26773, 1985.
HEC: Hydrologic Engineering Center (HEC) computer software for hydrologic engineering and planning analysis, available at: http://www.hec.usace.army.mil/software/ (last access: 03 September 2014), 2014.
Hewlett, J.: Soil moisture as a source of base flow from steep mountain watershed, Tech. rep., US forest Service, Southeastern Forest Experiment Station, Asheville, North Carolina, 1961.
Heynen, M., Pellicciotti, F., and Carenzo, M.: Parameter sensitivity of a distributed enhanced temperature-index melt model, Ann. Glaciol., 54, 311–321, https://doi.org/10.3189/2013AoG63A537, 2013.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A.: Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25, 1965–1978, https://doi.org/10.1002/joc.1276, 2005.
Hock, R.: Temperature index melt modelling in mountain areas, J. Hydrol., 282, 104–115, https://doi.org/10.1016/S0022-1694(03)00257-9, 2003.
Hock, R.: Glacier melt: a review of processes and their modelling, Prog. Phys. Geog., 29, 362–391, https://doi.org/10.1191/0309133305pp453ra, 2005.
Hooghoudt, S.: Bijdragen tot de kennis van eenige natuurkundige grootheden van den grond. No. 7. Algemeene beschouwing van het probleem van de detailontwatering en de infiltratie door middel van parallel loopende drains, greppels, slooten en kanalen, Versl. Landbouwkd. Onderz., 46, 515–707, 1940.
Hunink, J., Niadas, I., Antonaropoulos, P., Droogers, P., and de Vente, J.: Targeting of intervention areas to reduce reservoir sedimentation in the Tana catchment (Kenya) using SWAT, Hydrolog. Sci. J., 58, 600–614, https://doi.org/10.1080/02626667.2013.774090, 2013.
Immerzeel, 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.
Immerzeel, W., Lutz, A., and Droogers, P.: Climate Change Impacts on the Upstream Water Resources of the Amu and Syr Darya River Basins, Tech. rep., FutureWater, Wageningen, 2012.
Immerzeel, W. W. and Bierkens, M. F. P.: Asia's water balance, Nat. Geosci., 5, 841–842, https://doi.org/10.1038/ngeo1643, 2012.
Immerzeel, W. W., Droogers, P., de Jong, S. M., and Bierkens, M. F. P.: Large-scale monitoring of snow cover and runoff simulation in Himalayan river basins using remote sensing, Remote Sens. Environ., 113, 40–49, https://doi.org/10.1016/j.rse.2008.08.010, 2009.
Immerzeel, W. W., van Beek, L. P. H., and Bierkens, M. F. P.: Climate change will affect the Asian water towers, Science, 328, 1382–1385, https://doi.org/10.1126/science.1183188, 2010.
Immerzeel, W. W., Beek, L. P. H., Konz, M., Shrestha, A. B., and Bierkens, M. F. P.: Hydrological response to climate change in a glacierized catchment in the Himalayas, Climatic Change, 110, 721–736, https://doi.org/10.1007/s10584-011-0143-4, 2011.
Irrisoft: Database and on-line Applications in Irrigation, Drainage & Hydrology, available at: http://www.irrisoft.org (last access: 07 May 2014), 2014.
Karssenberg, D.: The value of environmental modelling languages for building distributed hydrological models, Hydrol. Process., 16, 2751–2766, https://doi.org/10.1002/hyp.1068, 2002.
Karssenberg, D., Burrough, P. A., Sluiter, R., and de Jong, K.: The PCRaster Software and Course Materials for Teaching Numerical Modelling in the Environmental Sciences, T. GIS, 5, 99–110, https://doi.org/10.1111/1467-9671.00070, 2001.
Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K., and Bierkens, M. F.: A software framework for construction of process-based stochastic spatio-temporal models and data assimilation, Environ. Model. Softw., 25, 489–502, https://doi.org/10.1016/j.envsoft.2009.10.004, 2010.
Kauffman, S., Droogers, P., Hunink, J., Mwaniki, B., Muchena, F., Gicheru, P., Bindraban, P., Onduru, D., Cleveringa, R., and Bouma, J.: Green Water Credits – exploring its potential to enhance ecosystem services by reducing soil erosion in the Upper Tana basin, Kenya, International Journal of Biodiversity Science, Ecosystem Services & Management, 10, 133–143, https://doi.org/10.1080/21513732.2014.890670, 2014.
Kokkonen, T., Koivusalo, H., Jakeman, A., and Norton, J.: Construction of a Degree-Day Snow Model in the Light of the "Ten Iterative Steps in Model Development", in: Proceedings of the iEMSs Third Biennial Meeting: Summit on Environmental Modelling and Software, Environmental Modelling and Software Society, Burlington, USA, 2006.
Kozak, J. A., Ahuja, L. R., Green, T. R., and Ma, L.: Modelling crop canopy and residue rainfall interception effects on soil hydrological components for semi-arid agriculture, Hydrol. Process., 21, 229–241, https://doi.org/10.1002/hyp.6235, 2007.
Krysanova, V., Müller-Wohlfeil, D.-I., and Becker, A.: Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds, Ecol. Model., 106, 261–289, 1998.
Krysanova, V., Wechsung, F., Arnold, J., Srinivasan, R., and Williams, J.: PIK Report Nr. 69 "SWIM (Soil and Water Integrated Model), User Manual", Tech. rep., Potsdam Institute for Climate Impact Research, Potsdam, 2000.
Krysanova, V., Hattermann, F., Huang, S., Hesse, C., Vetter, T., Liersch, S., Koch, H., and Kundzewicz, Z. W.: Modelling climate and land-use change impacts with SWIM: lessons learnt from multiple applications, Hydrolog. Sci. J., 60, 606–635, https://doi.org/10.1080/02626667.2014.925560, 2015.
Lall, U.: Debates – The future of hydrological sciences: A (common) path forward? One water. One world. Many climes. Many souls, Water Resour. Res., 50, 5335–5341, https://doi.org/10.1002/2014WR015402, 2014.
Lambert, J., Daroussin, J., Eimberck, M., Le Bas, C., Jamagne, M., King, D., and Montanarella, L.: Soil Geographical Database for Eurasia & The Mediterranean. Instructions Guide for Elaboration at scale 1:1,000,000 version 4.0. EUR 20422 EN., Tech. rep., JRC, Ispra, Italy, 2003.
Lenaerts, J. T. M., van den Broeke, M. R., Déry, S. J., König-Langlo, G., Ettema, J., and Munneke, P. K.: Modelling snowdrift sublimation on an Antarctic ice shelf, The Cryosphere, 4, 179–190, https://doi.org/10.5194/tc-4-179-2010, 2010.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple hydrologically based model of land surface water and energy fluxes for general circulation models, 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture parameterization of the VIC-2L model: Evaluation and modification, Global Planet. Change, 13, 195–206, https://doi.org/10.1016/0921-8181(95)00046-1, 1996.
Lindström, G., Pers, C., Rosberg, J., Strömqvist, J., and Arheimer, B.: Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales, Hydrol. Res., 41, 295–319, https://doi.org/10.2166/nh.2010.007, 2010.
Liu, Y., Gupta, H., Springer, E., and Wagener, T.: Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management, Environ. Model. Softw., 23, 846–858, https://doi.org/10.1016/j.envsoft.2007.10.007, 2008.
Lutz, A. F., Droogers, P., and Immerzeel, W.: Climate Change Impact and Adaptation on the Water Resources in the Amu Darya and Syr Darya River Basins, Tech. rep., FutureWater, Wageningen, 2012.
Lutz, A. F., Immerzeel, W. W., Gobiet, A., Pellicciotti, F., and Bierkens, M. F. P.: Comparison of climate change signals in CMIP3 and CMIP5 multi-model ensembles and implications for Central Asian glaciers, Hydrol. Earth Syst. Sci., 17, 3661–3677, https://doi.org/10.5194/hess-17-3661-2013, 2013.
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B., and Bierkens, M. F. P.: Consistent increase in High Asia's runoff due to increasing glacier melt and precipitation, Nature Climate Change, 4, 587–592, https://doi.org/10.1038/nclimate2237, 2014a.
Lutz, A. F., Immerzeel, W., and Kraaijenbrink, P.: Gridded Meteorological Datasets and Hydrological Modelling in the Upper Indus Basin. FutureWater Report 130, Tech. rep., FutureWater, Wageningen, the Netherlands, 2014b.
MacDonald, M. K., Pomeroy, J. W., and Pietroniro, A.: Parameterizing redistribution and sublimation of blowing snow for hydrological models: tests in a mountainous subarctic catchment, Hydrol. Process., 23, 2570–2583, https://doi.org/10.1002/hyp.7356, 2009.
Manning, R.: On the flow of water in open channels and pipes, Trans. Inst. Civ. Eng. Ireland, 20, 161–207, 1989.
McPhee, J., Rubio-Alvarez, E., Meza, R., Ayala, A., Vargas, X., and Vicuna, S.: An approach to estimating hydropower impacts of climate change from a regional perspective, Watershed Management, 2010, 13–24, https://doi.org/10.1061/41143(394)2, 2010.
Meehl, G. A., Covey, C., Taylor, K. E., Delworth, T., Stouffer, R. J., Latif, M., McAvaney, B., and Mitchell, J. F. B.: THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research, B. Am. Meteorol. Soc., 88, 1383–1394, https://doi.org/10.1175/BAMS-88-9-1383, 2007.
Mendoza, P. A., McPhee, J., and Vargas, X.: Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment, Water Resour. Res., 48, W09532, https://doi.org/10.1029/2011WR011089, 2012.
Morris, E. M. and Woolhiser, D. A.: Unsteady one-dimensional flow over a plane: Partial equilibrium and recession hydrographs, Water Resour. Res., 16, 355–360, https://doi.org/10.1029/WR016i002p00355, 1980.
Myneni, R. and Williams, D.: On the relationship between FAPAR and NDVI, Remote Sens. Environ., 49, 200–211, https://doi.org/10.1016/0034-4257(94)90016-7, 1994.
Nash, J. and Sutcliffe, J.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and Water Assessment Tool (SWAT). Theoretical Documentation, version 2009, Tech. rep., Texas Water Resources Institute, College Station, Texas, available at: http://twri.tamu.edu/reports/2011/tr406.pdf (last access: 04 June 2014), 2009.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Oogathoo, S., Prasher, S., Rudra, R., and Patel, R.: Calibration and validation of the MIKE SHE model in Canagagigue Creek watershed, in: Agricultural and biosystems engineering for a sustainable world. International Conference on Agricultural Engineering, Hersonissos, Crete, Greece, 2008.
Parajka, J. and Blöschl, G.: Spatio-temporal combination of MODIS images – potential for snow cover mapping, Water Resour. Res., 44, W03406, https://doi.org/10.1029/2007WR006204, 2008.
Park, C. C.: World-wide variations in hydraulic geometry exponents of stream channels: An analysis and some observations, J. Hydrol., 33, 133–146, https://doi.org/10.1016/0022-1694(77)90103-2, 1977.
Pechlivanidis, I., Jackson, B., McIntyre, N., and Weather, H.: Catchment scale hydrological modelling: a review of model types, calibration approaches and uncertainty analysis methods in the context of recent developments in technology and applications, Global NEST Journal, 13, 193–214, 2011.
Pellicciotti, F., Brock, B., Strasser, U., Burlando, P., Funk, M., and Corripio, J.: An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d'Arolla, Switzerland, J. Glaciol., 51, 573–587, https://doi.org/10.3189/172756505781829124, 2005.
Peng, D., Zhang, B., and Liu, L.: Comparing spatiotemporal patterns in Eurasian FPAR derived from two NDVI-based methods, International Journal of Digital Earth, 5, 283–298, https://doi.org/10.1080/17538947.2011.598193, 2012.
Piechota, T. and Chiew, F.: Seasonal streamflow forecasting in eastern Australia and the El Niño – Southern Oscillation, Water Resour. Res., 34, 3035–3044, 1998.
Pomeroy, J. W., Gray, D. M., Brown, T., Hedstrom, N. R., Quinton, W. L., Granger, R. J., and Carey, S. K.: The cold regions hydrological model: A platform for basing process representation and model structure on physical evidence, Hydrol. Process., 21, 2650–2667, https://doi.org/10.1002/hyp.6787, 2007.
Rafn, E. B., Contor, B., and Ames, D. P.: Evaluation of a Method for Estimating Irrigated Crop-Evapotranspiration Coefficients from Remotely Sensed Data in Idaho, J. Irrig. Drain. E.-ASCE, 134, 722–729, https://doi.org/10.1061/(ASCE)0733-9437(2008)134:6(722), 2008.
Ragettli, S. and Pellicciotti, F.: Calibration of a physically based, spatially distributed hydrological model in a glacierized basin: On the use of knowledge from glaciometeorological processes to constrain model parameters, Water Resour. Res., 48, W03509, https://doi.org/10.1029/2011WR010559, 2012.
Ragettli, S., Cortés, G., Mcphee, J., and Pellicciotti, F.: An evaluation of approaches for modelling hydrological processes in high-elevation, glacierized Andean watersheds, Hydrol. Process., 28, 5674–5695, https://doi.org/10.1002/hyp.10055, 2014.
Ragettli, S., Pellicciotti, F., Immerzeel, W., Miles, E., Petersen, L., Heynen, M., Shea, J. M., Stumm, D., Joshi, S., and Shrestha, A.: Unraveling the hydrology of a Himalayan watershed through integration of high resolution in-situ data and remote sensing with an advanced simulation model, Adv. Water Resour., 78, 94–111, https://doi.org/10.1016/j.advwatres.2015.01.013, 2015.
Refshaard, J. and Storm, B.: MIKE SHE, Danish Hydraulic Institute, Horsholm, 1995.
Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E.: A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin, Water Resour. Res., 42, W09404, https://doi.org/10.1029/2005WR004653, 2006.
Reid, T. D., Carenzo, M., Pellicciotti, F., and Brock, B. W.: Including debris cover effects in a distributed model of glacier ablation, J. Geophys. Res.-Atmos., 117, D18105, https://doi.org/10.1029/2012JD017795, 2012.
Rigon, R., Bertoldi, G., and Over, T. M.: GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets, J. Hydrometeorol., 7, 371–388, https://doi.org/10.1175/JHM497.1, 2006.
Rockström, J., Falkenmark, M., Lannerstad, M., and Karlberg, L.: The planetary water drama: Dual task of feeding humanity and curbing climate change, Geophys. Res. Lett., 39, L15401, https://doi.org/10.1029/2012GL051688, 2012.
Rollenbeck, R. and Bendix, J.: Rainfall distribution in the Andes of southern Ecuador derived from blending weather radar data and meteorological field observations, Atmos. Res., 99, 277–289, https://doi.org/10.1016/j.atmosres.2010.10.018, 2011.
Samain, B., Simons, G. W. H., Voogt, M. P., Defloor, W., Bink, N.-J., and Pauwels, V. R. N.: Consistency between hydrological model, large aperture scintillometer and remote sensing based evapotranspiration estimates for a heterogeneous catchment, Hydrol. Earth Syst. Sci., 16, 2095–2107, https://doi.org/10.5194/hess-16-2095-2012, 2012.
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, 2010.
Sangrey, D. A., Harrop-Williams, K. O., and Klaiber, J. A.: Predicting Ground-Water Response to Precipitation, J. Geotech. Eng.-ASCE, 110, 957–975, https://doi.org/10.1061/(ASCE)0733-9410(1984)110:7(957), 1984.
Schaner, N., Voisin, N., Nijssen, B., and Lettenmaier, D. P.: The contribution of glacier melt to streamflow, Environ. Res. Lett., 7, 034029, https://doi.org/10.1088/1748-9326/7/3/034029, 2012.
Schmitz, O., Karssenberg, D., van Deursen, W., and Wesseling, C.: Linking external components to a spatio-temporal modelling framework: Coupling MODFLOW and PCRaster, Environ. Model. Softw., 24, 1088–1099, https://doi.org/10.1016/j.envsoft.2009.02.018, 2009.
Schmitz, O., Karssenberg, D., de Jong, K., de Kok, J.-L., and de Jong, S. M.: Map algebra and model algebra for integrated model building, Environ. Model. Softw., 48, 113–128, https://doi.org/10.1016/j.envsoft.2013.06.009, 2013.
Sellers, P. J., Tucker, C. J., Collatz, G. J., Los, S. O., Justice, C. O., Dazlich, D. A., and Randall, D. A.: A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data, J. Climate, 9, 706–737, https://doi.org/10.1175/1520-0442(1996)009<0706:ARLSPF>2.0.CO;2, 1996.
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006.
Singh, P. and Kumar, N.: Impact assessment of climate change on the hydrological response of a snow and glacier melt runoff dominated Himalayan river, J. Hydrol., 193, 316–350, https://doi.org/10.1016/S0022-1694(96)03142-3, 1997.
Sloan, P. G. and Moore, I. D.: Modeling subsurface stormflow on steeply sloping forested watersheds, Water Resour. Res., 20, 1815–1822, https://doi.org/10.1029/WR020i012p01815, 1984.
Smedema, L. and Rycroft, D.: Land Drainage: Planning and Design of Agricultural Drainage Systems, Cornell University Press, 1983.
Sorg, A., Bolch, T., Stoffel, M., Solomina, O., and Beniston, M.: Climate change impacts on glaciers and runoff in Tien Shan (Central Asia), Nature Climate Change, 2, 725–731, https://doi.org/10.1038/nclimate1592, 2012.
Sperna Weiland, F. C., van Beek, L. P. H., Kwadijk, J. C. J., and Bierkens, M. F. P.: The ability of a GCM-forced hydrological model to reproduce global discharge variability, Hydrol. Earth Syst. Sci., 14, 1595–1621, https://doi.org/10.5194/hess-14-1595-2010, 2010.
Strasser, U., Bernhardt, M., Weber, M., Liston, G. E., and Mauser, W.: Is snow sublimation important in the alpine water balance?, The Cryosphere, 2, 53–66, https://doi.org/10.5194/tc-2-53-2008, 2008.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the Experiment Design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, https://doi.org/10.1016/0034-4257(79)90013-0, 1979.
USGS: Landsat 8: U.S. Geological Survey Fact Sheet 2013–3060, Tech. rep., available at: http://pubs.usgs.gov/fs/2013/3060/ (last access: 15 June 2014), 2013.
USGS: Water Resources Applications Software, available at: http://water.usgs.gov/software/lists/alphabetical (last access: 30 April 2014), 2014.
van Beek, L. and Bierkens, M.: The Global Hydrological Model PCR-GLOBWB: Conceptualization, Parameterization and Verification, Tech. rep., Department of Physical Geography, Utrecht University, Utrecht, available at: http://vanbeek.geo.uu.nl/suppinfo/vanbeekbierkens2009.pdf (last access: 24 November 2014), 2008.
van Dam, J. C., Huygen, J., Wesseling, J. G., Feddes, R. A., Kabat, P., van Walsum, P. E. V., Groenendijk, P., and van Diepen, C. A.: Theory of SWAP version 2.0. Simulation of water flow, solute transport and plant growth in the Soil-Water-Atmosphere-Plant environment, Tech. rep., Department Water Resources, Wageningen Agricultural University, 1997.
Van Der Knijff, J. M., Younis, J., and De Roo, A. P. J.: LISFLOOD: a GIS-based distributed model for river basin scale water balance and flood simulation, Int. J. Geogr. Inf. Sci., 24, 189–212, https://doi.org/10.1080/13658810802549154, 2010.
Van der Kwaak, J. E. and Loague, K.: Hydrologic-response simulations for the R-5 catchment with a comprehensive physics-based model, Water Resour. Res., 37, 999–1013, https://doi.org/10.1029/2000WR900272, 2001.
Van Genuchten, M.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898, 1980.
Venetis, C.: A STUDY ON THE RECESSION OF UNCONFINED ACQUIFERS, International Association of Scientific Hydrology. Bulletin, 14, 119–125, https://doi.org/10.1080/02626666909493759, 1969.
Verbunt, M., Gurtz, J., Jasper, K., Lang, H., Warmerdam, P., and Zappa, M.: The hydrological role of snow and glaciers in alpine river basins and their distributed modeling, J. Hydrol., 282, 36–55, https://doi.org/10.1016/S0022-1694(03)00251-8, 2003.
Vicuña, S., Garreaud, R. D., and McPhee, J.: Climate change impacts on the hydrology of a snowmelt driven basin in semiarid Chile, Climatic Change, 105, 469–488, https://doi.org/10.1007/s10584-010-9888-4, 2011.
Von Hoyningen-Huene, J.: Die Interzeption des Niederschlags in landwirtschaftlichen Pflanzenbeständen, Arbeitsbericht Deutscher Verband fur Wasserwirtschaft und Kulturbau, DWK, 1981.
Wada, Y., Van Beek, L. P. H., Van Kempen, C. M., Reckman, J. W. T. M., Vasak, S., and Bierkens, M. F. P.: Global depletion of groundwater resources, Geophys. Res. Lett., 37, L20402, https://doi.org/10.1029/2010GL044571, 2010.
Wagener, T., Sivapalan, M., Troch, P. A., McGlynn, B. L., Harman, C. J., Gupta, H. V., Kumar, P., Rao, P. S. C., Basu, N. B., and Wilson, J. S.: The future of hydrology: An evolving science for a changing world, Water Resour. Res., 46, W05301, https://doi.org/10.1029/2009WR008906, 2010.
Wagner, P. D., Fiener, P., Wilken, F., Kumar, S., and Schneider, K.: Comparison and evaluation of spatial interpolation schemes for daily rainfall in data scarce regions, J. Hydrol., 464–465, 388–400, https://doi.org/10.1016/j.jhydrol.2012.07.026, 2012.
Wheeler, T. and von Braun, J.: Climate change impacts on global food security, Science, 341, 508–513, https://doi.org/10.1126/science.1239402, 2013.
Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed hydrology-vegetation model for complex terrain, Water Resour. Res., 30, 1665–1680, https://doi.org/10.1029/94WR00436, 1994.
Williams, J.: HYMO flood routing, J. Hydrol., 26, 17–27, https://doi.org/10.1016/0022-1694(75)90122-5, 1975.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and Kitoh, A.: APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges, B. Am. Meteorol. Soc., 93, 1401–1415, https://doi.org/10.1175/BAMS-D-11-00122.1, 2012.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(7271 KB) - Full-text XML
Short summary
This paper introduces the Spatial Processes in HYdrology (SPHY) model (v2.0), its underlying concepts, and some example applications. SPHY has the flexibility to be applied in a wide range of hydrologic applications, on various scales, and can easily be implemented. The most relevant hydrologic processes integrated in the SPHY model are rainfall--runoff, cryosphere processes, evapotranspiration processes, the dynamic evolution of evolution of vegetation cover, and lake/reservoir outflow.
This paper introduces the Spatial Processes in HYdrology (SPHY) model (v2.0), its underlying...
