[PIHM] [FIHM (/PIHM3D)] [PIHMgis] [FoRM/GaRM] [GeoTopSed] [BPT] [SPAC] [DEWS]
PIHM: Pennstate Integrated Hydrologic Model
PIHM is a physically-based, spatially distributed hydrologic model. The model uses full physical process coupling and parsimonious but accurate data representation for efficient and accurate simulation of distributed hydrologic states. PIHM simulates six hydrologic states (snow water equivalent, interception storage, soil moisture of the surface soil layer, unsaturated zone soil moisture, groundwater depth and stream stage), at each discretization unit in the watershed. It uses a semi-discrete, finite-volume approach to define the distributed process equations on discretized unit elements, thereby ensuring conservation of the solution property within each discretized element. Processes simulated include snow and rain interception, snowmelt with a temperature-index approach, transpiration, evaporation, overland flow, subsurface flow, streamflow, macropore-based infiltration, lateral stormflow, and flow through and over hydraulic structures such as weirs and dams. Physical process representation for streamflow is based on a depth-averaged 1-D diffusive wave equation, surface flow is based on a depth-averaged 2-D diffusive wave approximation of the Saint Venant equations, and subsurface flow is based on the depth-averaged, moving boundary approximation of Richard’s equation [Kumar, 2009]. The model incorporates full coupling between different physical process states based on the continuity of head and flux. An important feature of the PIHM formulation is that its data structure remains isolated and independent from the solver’s data structure. This approach allows the user to easily alter the system of equations in the kernel without having to manually change the numerical discretization, thereby providing flexibility in the choice of process equations used in different parts of the model domain.
Links: Sourceforge, PIHM website. Send an email to mkumar4_at_ua_dot_edu to obtain the latest version of PIHM code!
Relevant References:
Kumar, M., 2009. Towards a Hydrologic Modeling System, Ph.D Thesis, Pennsylvania State University.
Qu. Y and C. Duffy, 2007, A semidiscrete finite volume formulation for multiprocess watershed simulation. Water Resources Research.
FIHM (PIHM3D): Finite volume based Integrated Hydrologic Model
FIHM (or PIHM3D) is a second order accurate, physically distributed, fully coupled, upwind cell-centered Finite-Volume based model. The model simulates two-dimensional surface flow based on a depth-averaged diffusive wave approximation of the Saint Venant equations, while the subsurface flow simulation is based on the complete three-dimensional, nonlinear, variably saturated form of Richards’s equation. The model has been validated and verified against numerous controlled experiments and analytical solutions and is applicable in heterogeneous and anisotropic domains.
Links: Send an email to mkumar4_at_ua_dot_edu to obtain the FIHM (PIHM3D) code.
Relevant References:
Kumar, M., 2009. Towards a Hydrologic Modeling System, Ph.D Thesis, Pennsylvania State University.
M. Kumar, C.J. Duffy and K. Salvage, 2009. A Second Order Accurate, Finite Volume Based, Integrated Hydrologic Modeling (FIHM) Framework for Simulation of Surface and Subsurface Flow, Vadose Zone Journal.
PIHMgis: A GIS interface to PIHM
PIHMgis is a GIS interface to PIHM. Since the new generation of physics-based, distributed hydrologic models needs information regarding heterogeneity in climate, land use, topography and hydrogeology to simulate hydrologic state variables in space and time, a close linkage between GIS and modeling systems is needed. PIHMgis addresses this concern by allowing concurrent usage of data both by a GIS and a hydrologic model. It facilitates seamless and dynamic data flow across modeling, data analyses and visualization systems. The framework decreases model setup time and reduces data redundancy. PIHMgis can be used to prototype new model simulations in a matter of few hours.
Links: Sourceforge, PIHM website.
Relevant References:
Kumar, M., 2009. Towards a Hydrologic Modeling System, Ph.D Thesis, Pennsylvania State University.
G. Bhatt, M. Kumar and C. J. Duffy, 2014, A tightly coupled GIS and distributed hydrologic modeling framework, Environmental Modeling and Software.
M. Kumar, G. Bhatt and C. Duffy, 2010, An Object Oriented Shared Data Model for GIS and Distributed Hydrologic Models, International Journal of GIS.
M. Kumar, G. Bhatt and C. Duffy, 2009, An efficient domain decomposition framework for accurate representation of geodata in distributed hydrologic models, International Journal of GIS.
FoRM: Forest Radiation Model
FORM is used to predict spatially distributed radiation components within heterogeneous forest patches. FoRM can be run at relatively fine temporal scales (e.g. < 1 hr.) and can therefore be used to assess the temporal variability of all radiative energy components ranging from daily to the entire snow seasonal scales, and across a range of spatial scales ranging from the tree (e.g. <1 m) to the stand (e.g. >10 m) level. FoRM accounts for modification of radiation at the forest floor due to a number of tree morphometric characteristics including height, shape, crown diameter and density, and crown base height.
Relevant References:
B. Seyednasrollah, M. Kumar, and T.Link, On the role of vegetation density on net snow cover radiation at the forest floor, Journal of Geophysical Research-Atmospheres, 2013.
B. Seyednasrollah and M. Kumar, Effects of Tree Morphometry on Net Snowcover Radiation on Forest Floor for Varying Vegetation Densities, Journal of Geophysical Research-Atmospheres, 2013.
GaRM: Gap Radiation Model
GaRM is used to simulate spatially distributed all-wave radiation in circular clearings, surrounded by a homogeneous dense forest
Relevant References:
B. Seyednasrollah and M. Kumar, Net radiation in a snow-covered discontinuous forest gap for a range of gap sizes and topographic configurations, Journal of Geophysical Research-Atmospheres, 2014.
GeotopSed: Spatially-explicit sediment module for GeoTop model
GeoTopSed is an open-source, spatially-explicit, sediment dynamics model that has been validated at both plot and catchment scales. The model simulates spatio-temporal dynamics of soil erosion, deposition, and yield; and can be used to study the role of hydrologic feedbacks on sediment dynamics.
Relevant References:
T. Zi, M. Kumar, G. Kiely, C. Lewis, and J. Albertson, Simulating the spatio-temporal dynamics of soil erosion, deposition, and yield using a coupled sediment dynamics and 3D distributed hydrologic modelEnvironmental Modeling and Software, 2016.
BPT: Barrier Prioritization Tool
BPT is a GIS tool for prioritizing removal of dams, based on eco-hydrologic and social metrics. The tool uses a hierarchical decision-support framework to rank dams for removal based on criteria such as good habitat and water quality connectivity, larger streamflow at the dam, improved dam safety and longer stream mile connectivity.This tool should be used in conjunction with the expert knowledge of resource managers to further investigate site-specific factors, thereby determining the final priority of projects.
Relevant References:
K. Hoenke, M. Kumar, and L. Batt, A GIS Based Approach for Prioritizing Dams for Potential Removal, Ecological Engineering, 2014.
SPAC: Soil-Plant-Atmosphere Continuum Model
The SPAC model consists of three process components: a soil-water balance; a plant water transport that is based on cohesion-tension theory and associated hydraulic properties; and an atmospheric boundary layer (ABL) development model that permits evapotranspiration to alter the height, temperature, and specific humidity of the boundary layer. Water transport within plants is modeled as a resistance system with no capacitance. Water vapor and CO2 exchange at the leaf level are modeled by combining Fickian diffusion of gases and the Farquahar photosynthesis model, where the stomatal kinetics are determined by optimizing carbon gain while minimizing water losses. The stomatal conductance is affected by both atmospheric conditions and plant water status. This evapotranspiration-ABL coupling allows SPAC to consider the co-occurrence of extreme drought and heat stress, which has been pointed out as the main environmental trigger of tree mortality. The coupled SPAC model simulates ecohydrologic states at an hourly interval.
Links: GitHub
Relevant References:
Y. Liu, A. Parolari, M. Kumar, C.W. Huang, G. Katul, and A. Porporato, Increasing atmospheric humidity and CO2 concentration alleviate forest mortality risk, Proceedings of National Academy of Sciences, 2017. Full Manuscript + Supplementary Document.
DEWS: Detection of Early Warning Signal
DEWS is a a Bayesian Dynamic Linear Model based early warning detection system to predict tree mortality much before it occurs. DEWS uses a Kalman filter to evaluate time-varying autocorrelation of the time series under consideration. It also accounts for temporal variation of other components, including intrinsic stochastic noise, long-term trend, and seasonality inherent in both observed time series and causal forcings. Accounting for the variation in causal forcing can provide critical information for EWS detection thereby improving its accuracy, especially by avoiding false alarms that arise from increasingly auto-correlated climate conditions
Links: GitHub
Relevant References:
Y. Liu, M. Kumar, G. Katul, and A. Porporato, Reduced resilience as an early warning signal of forest mortality, Nature Climate Change, 2019. Full Manuscript in PDF + Supplementary PDF