2018 CSDMS meeting-007: Difference between revisions

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|CSDMS meeting coauthor first name abstract=Andy
|CSDMS meeting coauthor first name abstract=Andrew D
|CSDMS meeting coauthor last name abstract=Wickert
|CSDMS meeting coauthor last name abstract=Wickert
|CSDMS meeting coauthor institute / Organization=University of Minnesota
|CSDMS meeting coauthor institute / Organization=University of Minnesota
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|CSDMS meeting coauthor first name abstract=Ying
|CSDMS meeting coauthor first name abstract=Ying Fan
|CSDMS meeting coauthor last name abstract=Reinfelder
|CSDMS meeting coauthor last name abstract=Reinfelder
|CSDMS meeting coauthor institute / Organization=Rutgers University
|CSDMS meeting coauthor institute / Organization=Rutgers University
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|CSDMS meeting coauthor country=Spain
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|CSDMS meeting coauthor email address=gonzalo.miguez@usc.es
|CSDMS meeting coauthor email address=gonzalo.miguez@usc.es
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|CSDMS meeting coauthor first name abstract=Crystal
|CSDMS meeting coauthor last name abstract=Ng
|CSDMS meeting coauthor institute / Organization=University of Minnesota
|CSDMS meeting coauthor town-city=Minneapolis
|CSDMS meeting coauthor country=United States
|State=Minnesota
|CSDMS meeting coauthor email address=gcng@umn.edu
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|CSDMS meeting coauthor first name abstract=Jerry
|CSDMS meeting coauthor last name abstract=Mitrovica
|CSDMS meeting coauthor institute / Organization=Harvard University
|CSDMS meeting coauthor town-city=Cambridge
|CSDMS meeting coauthor country=United States
|State=Massachusetts
|CSDMS meeting coauthor email address=jxm@eps.harvard.edu
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|CSDMS meeting coauthor first name abstract=Jacqueline
|CSDMS meeting coauthor last name abstract=Austermann
|CSDMS meeting coauthor institute / Organization=Columbia University
|CSDMS meeting coauthor town-city=New York
|CSDMS meeting coauthor country=United States
|State=New York
|CSDMS meeting coauthor email address=jackya@ldeo.columbia.edu
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{{CSDMS meeting abstract template 2018
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|CSDMS meeting abstract=Large-scale flow-routing algorithms efficiently route water to the ocean, often neglecting inland basins which may be able to form lakes. We combine groundwater and surface water routing components to allow visualisation of changing groundwater levels and lake locations and sizes through time. The groundwater component is based upon the model developed by Reinfelder et al (2013), and surface water is a simple downslope-flow algorithm. Our model requires as inputs topography, climatic data (recharge and winter temperature), and an approximation of hydraulic conductivity. The two outputs are depth to water table, and a surface water layer showing any lakes that would form under the starting conditions. The model can be run either to equilibrium in both surface and groundwater, or, if a starting depth to water table input is provided, the model can be run for any user-selected length of time with respect to groundwater movement. Since surface water movement is significantly faster than groundwater, it is always run to equilibrium. The model allows infiltration when surface water flows across cells that are not fully saturated in the groundwater, and it allows groundwater-fed lakes to form at locations where the topography and climate allow for this.  
|CSDMS meeting abstract=Large-scale flow-routing algorithms efficiently route water to the ocean, neglecting both inland basins that may be able to form lakes and changing groundwater storage. We add these elements of reality in a simplified and computationally-efficient way, combining groundwater and surface-water routing to simulate changing groundwater levels, surface-water flow pathways, and lake locations and extents through time. The groundwater component is based upon a linear-diffusive model for an unconfined aquifer developed by Reinfelder et al (2013), and surface water is routed through a simple downslope-flow algorithm that differs from most flow-routing algorithms in that it takes into account the elevation of the water surface, and not just the land surface. Our model requires as inputs topography, climatic data (P-ET and winter temperature), and an approximation of hydraulic conductivity based on topographic slope and mapped soils. The model outputs grids of depth to water table and thickness of surface water; the latter depicts any lakes that would form under the topographic and climatic conditions. The model can be run to equilibrium, or, if a starting depth to water table input is provided, for any user-selected length of time. Such solutions are transient only with respect to groundwater movementsurface-water flow is significantly faster, so it is always run to equilibrium. The model allows infiltration when surface water flows across cells that are not fully saturated in the groundwater, and it allows exfiltration and the formation of groundwater-fed lakes where convergent groundwater flow raises the water table above the land surface.  
We show sample results from this model on a test area. Future work using this model will include global model runs since the last glacial maximum, with ground truthing possible using past lake shoreline data. Changing depth to water table plus the surface water storage computed using this model allows computation of changing terrestrial water storage volume through time.
We show sample results from this model on a test area. Future work using this model will include global runs since the Last Glacial Maximum, with ground truthing possible using past lake shoreline data. Changing depth to water table plus the surface water storage computed using this model allows computation of changing terrestrial water storage volume through time.
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Revision as of 10:52, 1 April 2018





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Coupled groundwater and surface water modelling to visualise lake extent and total terrestrial water storage under a changing climate

Kerry Callaghan, University of Minnesota Minneapolis Minnesota, United States. calla350@umn.edu
Andrew D Wickert, University of Minnesota Minneapolis Minnesota, United States. awickert@umn.edu
Ying Fan Reinfelder, Rutgers University New Brunswick New Jersey, United States. yingfan@eps.rutgers.edu
Gonzalo Miguez-Macho, University of Santiago de Compostela Santiago de Compostela , Spain. gonzalo.miguez@usc.es


Large-scale flow-routing algorithms efficiently route water to the ocean, neglecting both inland basins that may be able to form lakes and changing groundwater storage. We add these elements of reality in a simplified and computationally-efficient way, combining groundwater and surface-water routing to simulate changing groundwater levels, surface-water flow pathways, and lake locations and extents through time. The groundwater component is based upon a linear-diffusive model for an unconfined aquifer developed by Reinfelder et al (2013), and surface water is routed through a simple downslope-flow algorithm that differs from most flow-routing algorithms in that it takes into account the elevation of the water surface, and not just the land surface. Our model requires as inputs topography, climatic data (P-ET and winter temperature), and an approximation of hydraulic conductivity based on topographic slope and mapped soils. The model outputs grids of depth to water table and thickness of surface water; the latter depicts any lakes that would form under the topographic and climatic conditions. The model can be run to equilibrium, or, if a starting depth to water table input is provided, for any user-selected length of time. Such solutions are transient only with respect to groundwater movement: surface-water flow is significantly faster, so it is always run to equilibrium. The model allows infiltration when surface water flows across cells that are not fully saturated in the groundwater, and it allows exfiltration and the formation of groundwater-fed lakes where convergent groundwater flow raises the water table above the land surface. We show sample results from this model on a test area. Future work using this model will include global runs since the Last Glacial Maximum, with ground truthing possible using past lake shoreline data. Changing depth to water table plus the surface water storage computed using this model allows computation of changing terrestrial water storage volume through time.