[[Category:Task]]

== Overview ==
:The General Lake Model (GLM) is a 1D lake water balance and vertical stratification model. GLM has been designed to be an open-source community model suited to environmental modeling studies where simulation of lakes or reservoirs is required. GLM was developed by the [http://aed.see.uwa.edu.au/research/models/GLM/ University of Western Australia's Aquatic Eco-Dynamics group] 

Here is an [http://community.gleon.org/research/projects/general-lake-model an overview of the model.]

== Concept ==

:[From AED website] GLM computes vertical profiles of temperature, salinity and density by accounting for the effect of inflows/outflows, mixing and surface heating and cooling, including the effect of ice cover on heating and mixing of the lake. Since the model is one-dimensional it assumes no horizontal variability so users must ensure the lake conditions match this one-dimensional assumption. The model is ideally suited to long-term investigations ranging from seasons to decades, and for coupling with biogeochemical models to explore the role that stratification and vertical mixing play on the dynamics of lake ecosystem.

:GLM incorporates a flexible Lagrangian layer structure similar to the approach of several 1-D lake model designs (Imberger and Patterson 1981; Hamilton and Schladow 1997). The Lagrangian approach was originally introduced in the model DYRESM developed by the Centre for Water Research and allows for layers to change thickness by contracting and expanding in response to inflows, outflows, mixing and surface mass fluxes. When sufficient energy becomes available to over come density gradients, two layers will merge thus accounting for the process of mixing. Layer thicknesses are adjusted by the model in order to sufficiently resolve the vertical density gradient. Unlike the fixed grid design where mixing algorithms are typically based on vertical velocities, numerical diffusion of the thermocline is limited, making the GLM approach particularly suited to long-term investigations.

:Although GLM is a new light-weight model code, many of the heating and mixing algorithms have been based on equations presented by Hamilton and Schladow (1997). GLM has been written with a modernized code structure and features a number of customizations to make the model easy and efficient to use. The model integrates with LakeAnalyser (LA) for derivation of numerous metrics of relevance to lake hydrodynamics and may also be integrated within a Markov Chain Monte Carlo (MCMC) algorithm.

== Software ==
Here is detailed documentation about the [[GLM_Software | GLM Software]].



{{#set:
StartDate=2014-06-20|
SubTask=Document_RGLM_tools}}

Document GLM lake model

2015-04-23T18:05:00Z

Hilary: Set PropertyValue: StartDate = 2014-05-01

[[Category:Task]]

== Overview ==
:The General Lake Model (GLM) is a 1D lake water balance and vertical stratification model. GLM has been designed to be an open-source community model suited to environmental modeling studies where simulation of lakes or reservoirs is required. GLM was developed by the [http://aed.see.uwa.edu.au/research/models/GLM/ University of Western Australia's Aquatic Eco-Dynamics group] 

Here is an [http://community.gleon.org/research/projects/general-lake-model an overview of the model.]

== Concept ==

:[From AED website] GLM computes vertical profiles of temperature, salinity and density by accounting for the effect of inflows/outflows, mixing and surface heating and cooling, including the effect of ice cover on heating and mixing of the lake. Since the model is one-dimensional it assumes no horizontal variability so users must ensure the lake conditions match this one-dimensional assumption. The model is ideally suited to long-term investigations ranging from seasons to decades, and for coupling with biogeochemical models to explore the role that stratification and vertical mixing play on the dynamics of lake ecosystem.

:GLM incorporates a flexible Lagrangian layer structure similar to the approach of several 1-D lake model designs (Imberger and Patterson 1981; Hamilton and Schladow 1997). The Lagrangian approach was originally introduced in the model DYRESM developed by the Centre for Water Research and allows for layers to change thickness by contracting and expanding in response to inflows, outflows, mixing and surface mass fluxes. When sufficient energy becomes available to over come density gradients, two layers will merge thus accounting for the process of mixing. Layer thicknesses are adjusted by the model in order to sufficiently resolve the vertical density gradient. Unlike the fixed grid design where mixing algorithms are typically based on vertical velocities, numerical diffusion of the thermocline is limited, making the GLM approach particularly suited to long-term investigations.

:Although GLM is a new light-weight model code, many of the heating and mixing algorithms have been based on equations presented by Hamilton and Schladow (1997). GLM has been written with a modernized code structure and features a number of customizations to make the model easy and efficient to use. The model integrates with LakeAnalyser (LA) for derivation of numerous metrics of relevance to lake hydrodynamics and may also be integrated within a Markov Chain Monte Carlo (MCMC) algorithm.

== Software ==
Here is detailed documentation about the [[GLM_Software | GLM Software]].



{{#set:
StartDate=2014-05-01|
SubTask=Document_RGLM_tools}}

2015-04-23T13:47:28Z

Hilary: Set PropertyValue: StartDate = 2015-04-22

[[Category:Task]]


{{#set:
StartDate=2015-04-22|
Type=Low}}

Construct catchment mesh

2015-04-23T13:47:21Z

Hilary: Set PropertyValue: Type = low

[[Category:Task]]


{{#set:|
Type=Low}}

Construct catchment mesh

2015-04-23T13:47:07Z

Hilary: Creating new page with Category: Task

[[Category:Task]]

Set up PIHM for NTL

2015-04-23T13:47:07Z

Hilary: Added PropertyValue: SubTask = Construct catchment mesh

[[Category:Task]]
== Setup PIHM for North Temperate Lakes ==

Plans for how to integrate lakes with PIHM, progressively more complex:
# Constant lake level
# Time variable boundary conditions (weakly coupled lake model)
# Sequential coupling (2 way with lake) (help from Gopal?)

Other properties for initial runs:
* 30 year simulation period (1979-2009)
* Use 40 m deep land surface. (Aquifer consists of 40 to 60 m of unconsolidated Pleistocene glacial deposits, mostly glacial outwash sands and gravel. Horizontal hydraulic conductivities are estimated to be ~10 m/day Pint et al 2003).
* Start with uniform soils
* Use single atmospheric data set for entire domain. Two options, hydroterre forcing data or NTL data.
* Define dirichlet boundary condition (fixed as constant head) for all lakes in the catchment. Start with median lake levels. Can then implement time variable from GLM.
* No lake ice for now
* Inlet boundary condition for riv file?



{{#set:
Owner=Hilary_Dugan|
Participants=Chris_Duffy|
Participants=Lele_Shu|
StartDate=2015-04-21|
SubTask=Construct_catchment_mesh|
TargetDate=2015-05-31|
Type=Medium}}

Predictions and projections for NTL

2015-04-22T15:57:27Z

Hilary: Hilary moved page Predictions and projections to test PIHM for NTL to Predictions and projections for NTL without leaving a redirect: Moving page

[[Category:Task]]

Implement PIHM for North Temperate Lakes

2015-04-22T15:57:27Z

Hilary: Deleted PropertyValue: SubTask = Predictions_and_projections_to_test_PIHM_for_NTL

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Participants=Hilary_Dugan|
Participants=Chris_Duffy|
Participants=Lele_Shu|
Participants=Gopal_Bhatt|
StartDate=2014-11-01|
SubTask=Run_the_PIHM_for_NTL|
SubTask=Calibrate_PIHM_for_NTL|
SubTask=Verify_and_validate_PIHM_for_NTL|
SubTask=NTL_site_information|
SubTask=Set_up_PIHM_for_NTL|
Type=High}}

Implement PIHM for North Temperate Lakes

2015-04-22T15:57:27Z

Hilary: Added PropertyValue: SubTask = Predictions_and_projections_for_NTL

Document the PIHM catchment model

2015-04-22T15:32:37Z

Hilary: Set PropertyValue: Type = low

[[Category:Task]]

= Overview =
:The Penn State Integrated Hydrologic Model (PIHM) is a multiprocess, multi-scale hydrologic model where the major hydrological processes are fully coupled using the semi-discrete finite volume method. PIHM represents our strategy for the synthesis of multi-state, multiscale distributed hydrologic models using the integral representation of the underlying physical process equations and state variables. Our interest is in devising a concise representation of watershed and/or river basin hydrodynamics, which allows interactions among major physical processes operating simultaneously, but with the flexibility to add or eliminate states/processes/constitutive relations depending on the objective of the numerical experiment or purpose of the scientific or operational application.

:The PIHM Modeling System was initially developed under research grants to The Pennsylvania State University (Penn State) from NSF (EAR 9876800, 1999-2007; EAR 03-10122, 2003-2007), NOAA (NA040AR4310085, 2003-2007), NASA (NAG5-12611, 2002-2005), with continuing grants from NSF (0725019) Critical Zone Observatory and EPA for community model development.

:Penn State University makes no proprietary claims, either statutory or otherwise, to this version and release of PIHM and considers PIHM to be in the public domain for use by any person or entity for any purpose without any fee or charge. We request that any PIHM user include a credit to Penn State in any publications that result from the use of PIHM. The names Penn State shall not be used or referenced in any advertising or publicity which endorses or promotes any products or commercial entity associated with or using PIHM, or any derivative works thereof, without the written authorization of Penn State University. 

:PIHM is provided on an "AS IS" basis and any warranties, either express or implied, including but not limited to implied warranties of noninfringement, originality, merchantability and fitness for a particular purpose, are disclaimed. Penn State will not be obligated to provide the user with any support, consulting, training or assistance of any kind with regard to the use, operation and performance of PIHM nor to provide the user with any updates, revisions, new versions, error corrections or "bug" fixes. In no event will Penn State be liable for any damages, whatsoever, whether direct, indirect, consequential or special, which may result from an action in contract, negligence or other claim that arises out of or in connection with the access, use or performance of PIHM, including infringement actions.

= Concept =

:The Penn State Integrated Hydrologic Model (PIHM) is a fully coupled multiprocess hydrologic model. Instead of coupling through artificial boundary conditions, major hydrological processes are fully coupled by the semi-discrete finite volume approach. For those processes whose governing equations are partial differential equations (PDE), we first discretize in space via the finite volume method. This results in a system of ordinary differential equations (ODE) representing those procesess within the control volume. Within the same control volume, combining other processes whose governing equations are ODE’s, (e.g. the snow accumulation and melt process), a local ODE system is formed for the complete dynamics of the finite volume. After assembling the local ODE system throughout the entire domain, the global ODE system is formed and solved by a state-of-art ODE solver.

:The approach is based on the semi-discrete finite-volume method (FVM) which represents a system of coupled partial differential equations (e.g. groundwater-surface water, overland flow-infiltration, etc.) in integral form, as a spatially-discrete system of ordinary differential equations. Domain discretization is fundamental to the approach and an unstructured triangular irregular network (e.g. Delaunay triangles) is generated with constraints (geometric, and parametric) using TRIANGLE. A local prismatic control volume is formed by vertical projection of the Delauney triangles forming each layer of the model. Given a set of constraints (e.g. river network support, watershed boundary, altitude zones, ecological regions, hydraulic properties, climate zones, etc), an “optimal” mesh is generated. River volume elements are also prismatic, with trapezoidal or rectangular cross-section, and are generated along edges of river triangles. The local control volume contains all equations to be solved and is referred to as the model kernel. The global ODE system is assembled by combining all local ODE systems throughout the domain and then solved by a state-of-the-art parallel ODE solver known as CVODE developed at the Lawrence- Livermore National Laboratory.

= Distributed Modeling with PIHM =

:PIHM has incorporated channel routing, surface overland flow, and subsurface flow together with interception, snow melt and evapotranspiration using the semi-discrete approach with FVM. Table 1 shows all these processes along with the original and reduced governing equations. For channel routing and overland flow which is governed by St. Venant equations, both kinematic wave and diffusion wave approximation are included. For saturated groundwater flow, the 2-D Dupuit approximation is applied. For unsaturated flow, either shallow groundwater assumption in which unsaturated soil moisture is dependent on groundwater level or 1-D vertical integrated form of Richards’s equation can be applied. From physical arguments, it is necessary to fully couple channel routing, overland flow and subsurface flow in the ODE solver. Snowmelt, vegetation and evapotranspiration are assumed to be weakly coupled. That is, these processes are calculated at end of each time step, which is automatically selected within a user specified range in the ODE solver.

PIHM_Processes

:The Penn State Integrated Hydrologic Model (PIHM) is a finite volume code that couples process-level equations for channel routing, surface overland flow, and subsurface flow together with interception storage and through fall, snow melt and evapotranspiration using the semi-discrete formulation and implicit solver. Table 1 shows all these processes along with the original and reduced governing equations. For channel routing and overland flow which is governed by St. Venant equations, both kinematic wave and diffusion wave approximation are included. For saturated groundwater flow, the 2-D Dupuit approximation is applied. For unsaturated flow, either shallow groundwater assumption in which unsaturated soil moisture is dependent on groundwater level or 1-D vertical integrated form of Richards’s equation can be applied. From physical arguments, it is necessary to fully couple channel routing, overland flow and subsurface flow in the ODE solver. Snowmelt, vegetation and evapotranspiration are assumed to be weakly coupled. That is, these processes are calculated at end of each time step, which is automatically selected within a user specified range in the ODE solver.

PIHMgis

:PIHMgis is an open source, “tightly-coupled” GIS interface to PIHM code. PIHMgis is platform independent and extensible. The tight coupling between GIS and the model is achieved by developing a shared data-model and hydrologic-model data structure for the deal-top. Details of PIHMgis are found by clicking on the link [[http://www.pihm.psu.edu]]

= Distributed Data System =

:The HydroTerre Data System [http://www.hydroterre.psu.edu] is data infrastructure that enables research on water model development on a national scale. It represents a robust, reusable, and extensible framework of data management building blocks, and demonstrate the utility of these infrastructure tools that scale over geo-spatial extent: rivers, river basins, and systems of rivers. HydroTerre aggregates and pre-processes essential terrestrial variable data from federal agencies at different geo-spatial resolutions and over varying temporal scales; it improve access to federal data; make community data resources available via federation; and can interface with other community activities (e.g CUAHSI Hydroshare) to provide registration of new community data sets and discovery and access. HTDS has specialized server architecture that utilizes 2U and 4U servers with 24-48 cpu’s and up to 100 TB of data per server. The configuration greater enhances model-data accessibility and scalability during larger river basin simulations. HydroTerre is a component of the Penn State Institute for CyberScience (ICS) and has been developed with support from ICS, the Penn State Institute for Energy and the Environment, the World Universities Network, NOAA, NASA and EPA. You can get to the HydroTerre site from here. [[http://www.hydroterre.psu.edu]]

= Model Applications=

The Shale Hills Critical Zone Observatory, PA 

Geography: The Shale Hills CZO is a small, forested, upland catchment in Central PA near the Penn State University Park Campus. The observatory is highly instrumented and serves real-time data to the National CZO Program. The observatory lies within the Valley and Ridge Physiographic Province of the central Appalachian Mountains in Huntingdon County, Pennsylvania (40º39’52. 39”N 77º54’24.23”W). It is a first order, V-shaped basin characterized by relatively steep slopes (25-35%) and narrow ridges. The stream is a tributary of Shavers Creek that eventually discharges into the Juniata River, a part of the Susquehanna River Basin. The SSHO basin is oriented in an east-west direction and the major side slopes have almost true north and south facing aspects. Elevation ranges from 256 meters at the outlet to 310 meters at the highest ridge. The relatively uniform side slopes are periodically interrupted by seven distinct topographic depressions.

Climate/Meteorology: Shale Hills is situated in a humid continental climate. Temperatures average 9.5°C with large seasonal differences: January temperature is –5.4°C, July is 19.0°C. The highest temperature recorded is 33.5°C (April 27, 2009) lowest –24.8°C (January 17, 2009). Annual average relative humidity is 70.2%.

Land Use: Historically, the region was logged for charcoal to support a 19th and 20th century iron industry. Today, Shale Hills is a relatively pristine forest and good wildlife habitat with little human impact. The basin is primarily available for recreation, education and research. The Penn State forest, of which the basin is a part, is managed for timber with set-asides for research. There are a number of active PSU research projects within the Penn State Forest.
[[File:CZO_Obs_12.png|thumb|Figure 1: Shale Hill CZO Field Observations, Sep 2012]]
Ecosystem Types: The Shale Hills forest ecosystem is dominated by oak (Quercus), hickory (Carya) and pine (Pinus) species. Hemlock (Tsuga canadensis), red maple (Acer rubrum), white oak (Quercus alba) and white pine (Pinus strobus) line the deep, moist soils of the stream banks, while on the drier, shallower north and south-facing slopes, red oak (Quercus rubra), chestnut oak (Quercus prinus), pignut hickory (Carya glabra) and mockernut hickory (Carya tomentosa) are dominant, with Virginia pine (Pinus virginiana) only appearing on the north-facing ridge tops.

Observations: The Shale Hills watershed has a comprehensive base of instrumentation for physical, chemical and biological characterization of water, energy, stable isotopes and geochemical conditions. This includes a dense network of soil moisture observations at multiple depths (120), a shallow observation well network (24 wells), soil lysimeters at multiple depths (+80), a COSMOS soil moisture instrument, a research weather station including eddy flux measurements for latent and sensible heat flux, CO2, and water vapor, radiation, barometric pressure, temperature, relative humidity, wind speed/direction, snow depth sensors, leaf wetness sensors, a load cell precipitation gauge. A laser precipitation monitor (LPM: rain, sleet, hail, snow, etc.) was installed in 2008, as were automated water samplers (daily) for precipitation, groundwater, and stream water for chemistry and stable isotopes with weekly sampling of lysimeters. Arrays of sapflow measurements are carried out over several years as a function of tree species (25 species in the watershed). A 25 node multi-hop wireless sensor network has been deployed for real-time observations of soil moisture, groundwater level, ground temperature. As of Sep 2012 the network is demonstrated in the figure.

Simulating the Water Balance: A model was calibrated for the Shale Hills Observatory and a simulation was carried out by Xuan Yu. The model was forced by National Land-Data-Assimilation [[File:reanalysisresponsetostorm.png|thumb|Figure 2: Shale Hills storm library from 1979-2012]]System hourly climate data (NLDAS-2) from NCEP-NOAA for the period Jan 1979-2012.

The results are presented in the following link as daily time series for the catchment water balance: [http://www.pihm.psu.edu/Shalehillsreanalysis/versionII/budget.html]. The data can be manipulated by selecting and dragging to zoom in on short term events such as the impact of tropical storms on the soil moisture or groundwater storage for example. The figure illustrates some of the extra-tropical storms that produced large rainfall in late summer and early fall [https://dx.doi.org/10.1002/9781118872086.ch31].

Lysina Catchment, Czech Republic 

Developing the Lysina catchment model required an extensive data mining strategy to extract geospatial temporal data from paper documents, spreadsheets, agency archives and existing records from the Lysina research station [http://www.pihm.psu.edu/lysina/forest.html]. The model required geospatial- geotemporal data sufficient to support the physics-based numerical watershed simulator [http://www.tandfonline.com/doi/abs/10.1080/02626667.2014.897406]. The catchment model is now in a mature state and will be used for testing additional scenarios of climate change, and landuse change via soil degradation.

Through model scenario simulations we were able to show that sustainable tree harvesting practices can be compatible with sustainable water supply in a watershed where a forest of multiple-age trees are selectively harvested in small patches. The clearing of small patches of uniform age trees does not significantly change the overall water budget of the watershed or the potential for increased flooding or drought. Using the model to simulate the impact of removing the forest and changing the landuse to agricultural crops or pasture, indicates we should expect an increase in flooding potential in the spring but with a modest increased streamflow during the summer drought period.

==IEEE Paper Catchment Reanalysis==



{{#set:
Model software=PIHM_Software|
Owner=Chris_Duffy|
Participants=Xuan_Yu|
Participants=Gopal_Bhatt|
Progress=80|
StartDate=2014-11-01|
SubTask=IEEE_paper_application_of_catchment_reanalysis|
Type=Low}}

2015-04-22T15:17:40Z

Hilary: Deleted PropertyValue: SubTask = Set_up_the_PIHM_for_NTL

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Participants=Hilary_Dugan|
Participants=Chris_Duffy|
Participants=Lele_Shu|
Participants=Gopal_Bhatt|
StartDate=2014-11-01|
SubTask=Run_the_PIHM_for_NTL|
SubTask=Calibrate_PIHM_for_NTL|
SubTask=Verify_and_validate_PIHM_for_NTL|
SubTask=Predictions_and_projections_to_test_PIHM_for_NTL|
SubTask=NTL_site_information|
Type=High}}

Implement PIHM for North Temperate Lakes

2015-04-22T15:17:40Z

Hilary: Added PropertyValue: SubTask = Set_up_PIHM_for_NTL

[[Category:Task]]

== Context ==

We have selected [[Document_the_PIHM_catchment_model|PIHM]] as our catchment model.

Implementing a catchment model using the PIHM software has three basic steps that utilize specific tools:

1) Collect the geospatial and time series data for that covers the site: The site [www.hydroterre.psu.edu] includes the necessary geospatial data and climate forcing to carry out this task for HUC12 level catchments anywhere in the continental US.

2) Define the domain of the catchment boundaries and the stream network: PIHM_gis tool is a desktop tool for delineating the catchment domain, defining the stream network (user defined support) and delineating the stream network from a digital elevation model.

3) Create a numerical grid that satisfies the project goals or hypotheses and make initial estimates of the model parameters: PIHM_gis tool also has tools to make initial estimates of the model parameters for the soil, groundwater, surface flow and vegetation, These parameters are automatically assigned to each mesh element, and can be refined later by calibration constrained by stream gauging, groundwater levels, energy and soil observations.

In this research we will concentrate on 3 field sites. The north temperate lakes region in Wisconsin, the Shaver Creek watershed and Lake Perez in central PA and......tbd.



{{#set:
Participants=Hilary_Dugan|
Participants=Chris_Duffy|
Participants=Lele_Shu|
Participants=Gopal_Bhatt|
StartDate=2014-11-01|
SubTask=Run_the_PIHM_for_NTL|
SubTask=Calibrate_PIHM_for_NTL|
SubTask=Verify_and_validate_PIHM_for_NTL|
SubTask=Predictions_and_projections_to_test_PIHM_for_NTL|
SubTask=NTL_site_information|
SubTask=Set_up_PIHM_for_NTL|
Type=High}}