Chapter 1 Overview

This file is a user guide or technical documentation of the SHUD modeling system. PDF version of the User Guide is available via :SHUD User Guide

The Simulator for Hydrologic Unstructured Domains (SHUD - pronounced “SHOULD”) is a multi-process, multi-scale hydrological model where major hydrological processes are fully coupled using the semi-discrete Finite Volume Method (FVM).

rSHUD is an open-source GIS and hydrological analysis toolbox designed for the SHUD modeling system. The rSHUD provides access to the digital data sets (terrain, forcing, and parameters) and tools necessary to drive the model, as well as a collection of GIS-based pre- and post-processing tools.

Collectively the system is referred to as the SHUD Modeling System.

The SHUD and rSHUD is an open-source software, freely available for download at SHUD website or Github Page along with installation and user guides.

1.1 Standing on the shoulders of giants

As a descendant of PIHM, SHUD inherits the fundamental idea of solving hydrological variables in CVODE. The code has been completely rewritten in a new programming language, with a new discretization and corresponding improvements to the underlying algorithms, adapting new mathematical schemes and a new user-friendly input/output data format. Although SHUD is forked from PIHM’s track, SHUD still inherits the use of CVODE for solving the ODEs but modernizes and extends PIHM’s technical and scientific capabilities. The SHUD is imcompatible to PIHM.

It is our intention (me and previous PIHM group)to begin a debate on the role of Community Models in the hydrologic sciences.

SHUD and PIHM represent our strategy for the synthesis of multi-state, multi-scale 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.

To satisfy the objectives, the SHUD…

  • is a distributed hydrologic model, based on the semi-discrete Finite Volume Method (FVM) in which domain discretization is an unstructured triangular irregular network (e.g. Delaunay triangles) generated with constraints (geometric, and parametric). A local prismatic control volume is formed by the vertical projection of the Delaunay 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 cells are also prismatic, with trapezoidal or rectangular cross-section, and are generated along or cross edges of Delaunay triangles. The local control volume contains all equations to be solved and is referred to as the model kernel.
  • is a physically-based model in which all equations used are describing the physics of the hydrological processes which control the catchment. The physical model is able to predict the water in the ungage water system, to estimate the sediment, pollutants, and vegetation, etc., such that it is practical to be coupled with biochemistry, geomorphology, limnology, and other water-related research. 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.
  • is a fully-coupled hydrologic model, where the state and flux variables in the hydrologic system are solved within the same time step and conserve the mass. The fluxes are infiltration, overland flow, groundwater recharge, lateral groundwater flow, exchange of river and soil/groundwater and river discharge.
  • is of an adaptable temporal and spatial resolution. The spatial resolution of the model varies from meters to kilometers based requirement of modeling and computing resources. The internal time step of the iteration step is adjustable; it is able to export the status of the catchment in less 1 second to days. Also, the time interval for exporting results is configured flexibly. The flexible spatial and temporal resolution is rather valuable for community model coupling.
  • is an open-source model; anyone can access the source code, use and submit their improvement.
  • is a long-term yield and single-event flood model.

1.2 Brief History of PIHM system

  • 2005 PIHM v1.0

Dr. Yizhong Qu (Qu and Duffy 2007) developed and verified the first version of PIHM in 2001-2005 during his Ph.D. in Pennsylvania State Unversity, following the blueprint of Freeze and Harlan (1969). This version of PIHM is the soul of the PIHM model.

  • 2009 PIHMgis

Dr. Gopal Bhartt (Bhatt 2012) developed the PIHMgis with support of C++, Qt GUI library, TRIANGLE library, and QGIS developing kit. The development of PIHMgis makes the learning curve of PIHM moderate and benefits the developing, modeling and coupling.

  • 2015 MM-PIHM

Dr. Yuninh Shi led and developed the MM-PIHM (Multi-Module PIHM), which embedded all modules from PIHM family, such as RT-PIHM, LE-PIHM, flux-PIHM, BGC-PIHM, etc. together. The sophisticated design and coupling of the MM-PIHM is the summit of the PIHM as a Community Model that combined all water-related modules together.

  • 2019 SHUD

    Based on the accumulated contribution of PIHM modeling and coupling with related researches, it is necessary to solve the known bugs and limitations, improve the performance of the model with parallel methods, and adopt new updates from SUNDIALS solver and programming strategy.

Several publications that may helps:

  • (Qu 2004)
  • (Qu and Duffy 2007)
  • (Li 2008)
  • (Kumar, Duffy, and Reed 2004)
  • (Kumar, Bhatt, and Duffy 2009)
  • (Yu et al. 2015)
  • (Yu et al. 2014)
  • (Li and Duffy 2011)
  • (Shi, Baldwin, et al. 2015)
  • (Shi, Davis, et al. 2015)
  • (Bhatt, Kumar, and Duffy 2014)

References

Bhatt, Gopal. 2012. “A distributed hydrologic modeling system: Framework for discovery and management of water resources.” PhD thesis, Pennsylvania State University.

Bhatt, Gopal, Mukesh Kumar, and Christopher J. Duffy. 2014. “A tightly coupled GIS and distributed hydrologic modeling framework.” Environmental Modelling and Software 62. Elsevier Ltd: 70–84. https://doi.org/10.1016/j.envsoft.2014.08.003.

Kumar, M, C J Duffy, and P M Reed. 2004. “Enhancing the performance of feature selection algorithms for classifying hyperspectral imagery.” In Geoscience and Remote Sensing Symposium, 2004. IGARSS ’04. Proceedings. 2004 Ieee International, 5:3264–7 vol.5. C. https://doi.org/10.1109/IGARSS.2004.1370398.

Kumar, Mukesh, Gopal Bhatt, and Christopher J. Duffy. 2009. “An efficient domain decomposition framework for accurate representation of geodata in distributed hydrologic models.” International Journal of Geographical Information Science 23 (12). Taylor & Francis Ltd: 1569–96. https://doi.org/10.1080/13658810802344143.

Li, Shuangcai. 2008. “INTEGRATED MODELING OF MULTI-SCALE HYDRODYNAMICS, SEDIMENT AND POLLUTANT TRANSPORT.” PhD thesis.

Li, Shuangcai, and Christopher J. Duffy. 2011. “Fully coupled approach to modeling shallow water flow, sediment transport, and bed evolution in rivers.” Water Resources Research 47 (3): 1–20. https://doi.org/10.1029/2010WR009751.

Qu, Yizhong. 2004. “An integrated hydrologic model for multi-process simulation using semi-discrete finite volume approach.” PhD thesis. http://gradworks.umi.com/31/72/3172997.html.

Qu, Yizhong, and Christopher J. Duffy. 2007. “A semidiscrete finite volume formulation for multiprocess watershed simulation.” Water Resources Research 43 (8): 1–18. https://doi.org/10.1029/2006WR005752.

Shi, Yuning, Douglas C. Baldwin, Kenneth J. Davis, Xuan Yu, Christopher J. Duffy, and Henry Lin. 2015. “Simulating high-resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model.” Hydrological Processes 29 (21): 4624–37. https://doi.org/10.1002/hyp.10593.

Shi, Yuning, Kenneth J. Davis, Fuqing Zhang, Christopher J. Duffy, and Xuan Yu. 2015. “Parameter estimation of a physically-based land surface hydrologic model using an ensemble Kalman filter: A multivariate real-data experiment.” Advances in Water Resources 83. Elsevier Ltd.: 421–27. https://doi.org/10.1016/j.advwatres.2015.06.009.

Yu, Xuan, Christopher Duffy, Doug C. Baldwin, and Henry Lin. 2014. “The role of macropores and multi-resolution soil survey datasets for distributed surface-subsurface flow modeling.” Journal of Hydrology 516. Elsevier B.V.: 97–106. https://doi.org/10.1016/j.jhydrol.2014.02.055.

Yu, Xuan, Anna Lamačová, Christopher Duffy, Pavel Krám, and Jakub Hruška. 2015. “Hydrological model uncertainty due to spatial evapotranspiration estimation methods.” Computers & Geosciences 90: 90–101. https://doi.org/10.1016/j.cageo.2015.05.006.