2018 CSDMS meeting-007: Difference between revisions

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|CSDMS meeting abstract title=Coupled groundwater and surface water modelling to visualise lake extent and total terrestrial water storage under a changing climate
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|CSDMS meeting coauthor first name abstract=Andy
|CSDMS meeting coauthor last name abstract=Wickert
|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=awickert@umn.edu
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|CSDMS meeting coauthor first name abstract=Ying
|CSDMS meeting coauthor last name abstract=Reinfelder
|CSDMS meeting coauthor institute / Organization=Rutgers University
|CSDMS meeting coauthor town-city=New Brunswick
|CSDMS meeting coauthor country=United States
|State=New Jersey
|CSDMS meeting coauthor email address=yingfan@eps.rutgers.edu
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|CSDMS meeting coauthor first name abstract=Gonzalo
|CSDMS meeting coauthor last name abstract=Miguez-Macho
|CSDMS meeting coauthor institute / Organization=University of Santiago de Compostela
|CSDMS meeting coauthor town-city=Santiago de Compostela
|CSDMS meeting coauthor country=Spain
|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
|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.
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.
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Revision as of 16:54, 31 March 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
Andy Wickert, University of Minnesota Minneapolis Minnesota, United States. awickert@umn.edu
Ying 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
Crystal Ng, University of Minnesota Minneapolis Minnesota, United States. gcng@umn.edu
Jerry Mitrovica, Harvard University Cambridge Massachusetts, United States. jxm@eps.harvard.edu
Jacqueline Austermann, Columbia University New York New York, United States. jackya@ldeo.columbia.edu


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. 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.


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