# ASM 2008 Presentation

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SDL FORMALIZATION OF A HYDROLOGIC MODEL

Jeaneth López C., Pau Fonseca i Casas, Josep Casanovas

Outline

§ § § § § § Hydrologic Models Simulation Formalisms GIS data Cellular automaton SDL Specification of the model. Discussion

Hydrologic Models

IPCC estimates that there are four places in the earth with high risk to reduce the availability of hydrologic resources and present abrupt climate changes. Spain (south of Europe), California (EEUU), South Africa, North east of

Greenhouse effects

Hydrologic resource

Rise Sea level Natural disastres

•Cyclones •Droughts •Floods •Frost •Etc…

Human Migration

Rise Temperature

Water Availability

Increase of population Precipi tation Contami nation Waste water

Human Consume Agricul ture

Available Water

Industry Ecosystems

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

Watershed modeling, or hydrologic model (rainfall-runoff modeling) began in the 1950s and 1960s with the evolution of the digital computer. The Stanford Watershed Model (SWM) was one of the first watershed hydrologic model. (Crawford and Linsey 1966). LSSMs (Large-scale Spatial Models)(Watson et al., 1999) DHSVM (Distributed Hydrology-Soil-Vegetation Model) (Wigmosta 1997, 2002) LSHWM (Large-Scale Hybrid Watershed Modeling)(Aral and Gunduz, 2003), MHM (Macroscale Hydrological Model using Variable Infiltration Capacity VIC 3-layer)(Srinivasan2003) The VIC-3L model, is based on a 3-layer Soil Vegetation Atmosphere Transfer scheme to model different surface conditions. L-THIA (Long Term Hydrologic Impacts Assessment) (Lim et al., 2006) Hydrological model SHALL3 (Zimmermann, 2002) (Haan et al.,1982, Singh, 1988, Singh and Frevert, 2006)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

The SWAT (Soil and Water Assessment Tool) was developed by modifying the models specified below. All of this models use the SCS Curve Number Method, an empirical formula for predicting runoff from daily rainfall.

GLEAMS

pesticide compone nt daily rainfall hydrology component Crop Growth compone nt

QUAL2 E

Instream kinetics

Example SWAT adaptations

ESWAT SWATG SWIM

SWRRB

(multiple subbasins, subbasins, other other

CREAMS

SWRRB (multiple

SWAT

routing structur e

components)

EPIC

ROTO

Schematic of SWAT developmental history, including selected SWAT

1970s

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Nowada ys

Simulation Formalisms

§ Petri Nets

§ Introduced by Carl Adam Petri (1960). Also known as a Place/Transition

§ DEVS (Discreet Event System Specification)

§ In the 70s Bernard Zeigler proposed a mathematical formalism.

§ SDL (Specification and Description Language)

§ The semantics of SDL was defined formally in 1988.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Petri Nets

RdP=(P,T,A,W,M0)

P = Finiteset of nodes Places T = Finiteset of nodes Transitions A C(PxT) and T. (TxP): subset of cartesianproduct between nodes P

W = a {1,2,3,..}: weigh of arcs M0 = Pi {1,2,3,..} node Pi: number ok tokens initials in each place

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

DEVS Formalism

M=<X, S, Y, δint, δext, λ, ta> where: X: set of input values. S: set of states values. Y: set of output values. δint: internal transition function.

δint :S

S

δext: external transition function. δext Q x X S , on

Q={(s,e)|sS, 0≤e≤ta(s)} set of states. e elapsed time from the last transition.

λ : output function: λ : S Y ta : time advance function.

ta: S

R+0∞

DEVS Processor example

M=<X, S, Y, δint, δext, λ, ta> on: X={job1, job2, .., jobn} S={job1, job2,.., jobn}U[Ø]xR+0∞ Y={y(job1), y(job2),.. Y(jobn)} δint(job,σ)=(Ø, ∞) δext(job,σ,e,x)= if job= Ø (x,tp,(x)) else (job, σ-e) λ(job, σ)=y(job) Ta(job, σ)= σ

SDL formalism

Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

SDL Formalism

The semantics of SDL was defined formally in 1988.

Standard Formal Graphical

and symbol-based Object-oriented (OO)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

GIS Data

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Celular automaton

Each cell xi(t) represent a state of the cell i in the instant t. k = {Empty, Static and Dynamic}

xi

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Game of life

The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Method

§ Cellular Automaton Structure

§ Evolution Rules

§ Precipitation phase § Settlement phase

• Data layers

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Soil (Ground)

•Return Flow •Kind of soil •Saturate •Kind of soil use •Non-Saturate •Percolation

•Runoff •Humidity •Evapo-trans piration •Temperature •Rainfall •Precipitation

Hydrologic resource

Meteorology

Factors/variables which affects hydrologic resource

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Where:

SWt is the total quantity of water in the soil (basin) (mm), SWi is the initial quantity of water in the soil (mm) Ri (rainfall) is the quantity of rain in the period of analysis(mm) Qi is the quantity of runoff (mm) ETi is the evapotranspiration (mm) Pi is the percolation (mm) QRi return flow(mm) t is the time expressed in days i is the index of cell

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

This expression gives us the water volume existing in each cell of our cellular automaton in a specific time t. In the expression, one position of the cellular automaton matrix [xi,xj] is represented by the cell xi.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

System diagram

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

States diagram

Update Receive

Send

Dynamic

Receiv e

Static

d en S te/ ei ec R ve

Update Receive

Receive

Send

Up

da

Send Update

Empty

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Static state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Empty state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Dynamic state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Discussion and future work

This slides depicts the first and more important step to construct a discrete hydrologic simulator based in a cellular automaton. It shows the specification of hydrologic model with the aim of simulate the behaviour of water in a river basin. The use of the cellular simplifies the mathematical expressions and helps us to maintain a structured view of the geographical data needed to represent the model. The use of SDL formalism simplifies the understanding of the behaviour of model thanks its graphical structure. The model is based in the hydrologic balance equation which permits calculate the quantity exist in each cell of the cellular automaton

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

References

[1] L. Ma, L.R. Ahuja, and R.W. Malone. Systems Modeling for Soil and Water Research and Management: Current Status and Needs for the 21st Century. ASABE, 1907-2007. [2] IPCC Intergovernamental Panel on Climate Change. Report IPCC. 2007. [3] J. Samper, M.A. Garcia, B. Pisani, D. Alvares, A. Varela, and J.A. Losada. Modelos Hidrologicos y Sistemas de Informacion Geografica para la Estimacion de Recursos Hidricos:. Confederacin Hidrologica del Ebro, VII, 2005. [4] E. Guzman, J. Bonini, and D. Matamoros. Modelo Hidrologico SWAT (Soil Water Assessment Tool) Prediccin de Caudales y Sedimentos en una Cuenca. Revista Tecnologica, 17:152–161, 2004. [5] Erik D. Zimmermann. Modelo Hidrologico Superficial y Subterraneo. Centro Universitario rosario de Investigaciones Hidroambientales. [6] A. López, D. Sauri, and Jose M. Galán. Urban Water

References

[8] P. Pea and J. Alirio. Sobre las redes de petri r-difusas. Divulgaciones Matemticas, 7:87–99, 1999. [9] Gabriel Wainer. Applying cell-devs methodology for modeling the environment. SIMULATION, 82:635, 2006. [10] Liu Qi and Gabriel Wainer. Parallel environment for devs and cell-devs models. SIMULATION, 83:449, 2007. [11] Fonseca P. and Casanovas J. 2005. ESS205, Simplifying GIS data use inside discrete event simulation model through m_n-AC cellular automaton; Proceedings ESS 2005. [12] ITU-T Telecommunication Standardization Sector of ITU. Languages and general software aspects for telecommunication systems. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU, 2007. [13] Zeigler, B. P.; H. Praehofer; D. Kim. 2000. Theory of Modeling and Simulation. Academic Press. [14] Petri, Carl A. 1962. "Kommunikation mit Automaten". Ph. D. Thesis. University of Bonn. [15] Recalde, L.; Teruel, E.; Silva, M. 1999. Autonomous

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773

SDL FORMALIZATION OF A HYDROLOGIC MODEL

Jeaneth López C., Pau Fonseca i Casas, Josep Casanovas

Outline

§ § § § § § Hydrologic Models Simulation Formalisms GIS data Cellular automaton SDL Specification of the model. Discussion

Hydrologic Models

IPCC estimates that there are four places in the earth with high risk to reduce the availability of hydrologic resources and present abrupt climate changes. Spain (south of Europe), California (EEUU), South Africa, North east of

Greenhouse effects

Hydrologic resource

Rise Sea level Natural disastres

•Cyclones •Droughts •Floods •Frost •Etc…

Human Migration

Rise Temperature

Water Availability

Increase of population Precipi tation Contami nation Waste water

Human Consume Agricul ture

Available Water

Industry Ecosystems

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

Watershed modeling, or hydrologic model (rainfall-runoff modeling) began in the 1950s and 1960s with the evolution of the digital computer. The Stanford Watershed Model (SWM) was one of the first watershed hydrologic model. (Crawford and Linsey 1966). LSSMs (Large-scale Spatial Models)(Watson et al., 1999) DHSVM (Distributed Hydrology-Soil-Vegetation Model) (Wigmosta 1997, 2002) LSHWM (Large-Scale Hybrid Watershed Modeling)(Aral and Gunduz, 2003), MHM (Macroscale Hydrological Model using Variable Infiltration Capacity VIC 3-layer)(Srinivasan2003) The VIC-3L model, is based on a 3-layer Soil Vegetation Atmosphere Transfer scheme to model different surface conditions. L-THIA (Long Term Hydrologic Impacts Assessment) (Lim et al., 2006) Hydrological model SHALL3 (Zimmermann, 2002) (Haan et al.,1982, Singh, 1988, Singh and Frevert, 2006)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Hydrologic Models

The SWAT (Soil and Water Assessment Tool) was developed by modifying the models specified below. All of this models use the SCS Curve Number Method, an empirical formula for predicting runoff from daily rainfall.

GLEAMS

pesticide compone nt daily rainfall hydrology component Crop Growth compone nt

QUAL2 E

Instream kinetics

Example SWAT adaptations

ESWAT SWATG SWIM

SWRRB

(multiple subbasins, subbasins, other other

CREAMS

SWRRB (multiple

SWAT

routing structur e

components)

EPIC

ROTO

Schematic of SWAT developmental history, including selected SWAT

1970s

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Nowada ys

Simulation Formalisms

§ Petri Nets

§ Introduced by Carl Adam Petri (1960). Also known as a Place/Transition

§ DEVS (Discreet Event System Specification)

§ In the 70s Bernard Zeigler proposed a mathematical formalism.

§ SDL (Specification and Description Language)

§ The semantics of SDL was defined formally in 1988.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Petri Nets

RdP=(P,T,A,W,M0)

P = Finiteset of nodes Places T = Finiteset of nodes Transitions A C(PxT) and T. (TxP): subset of cartesianproduct between nodes P

W = a {1,2,3,..}: weigh of arcs M0 = Pi {1,2,3,..} node Pi: number ok tokens initials in each place

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

DEVS Formalism

M=<X, S, Y, δint, δext, λ, ta> where: X: set of input values. S: set of states values. Y: set of output values. δint: internal transition function.

δint :S

S

δext: external transition function. δext Q x X S , on

Q={(s,e)|sS, 0≤e≤ta(s)} set of states. e elapsed time from the last transition.

λ : output function: λ : S Y ta : time advance function.

ta: S

R+0∞

DEVS Processor example

M=<X, S, Y, δint, δext, λ, ta> on: X={job1, job2, .., jobn} S={job1, job2,.., jobn}U[Ø]xR+0∞ Y={y(job1), y(job2),.. Y(jobn)} δint(job,σ)=(Ø, ∞) δext(job,σ,e,x)= if job= Ø (x,tp,(x)) else (job, σ-e) λ(job, σ)=y(job) Ta(job, σ)= σ

SDL formalism

Object-oriented, formal language defined by The International Telecommunications Union as recommendation Z.100. Intended for the specification of complex, event-driven, real-time, and interactive applications involving many concurrent activities that communicate using discrete signals.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

SDL Formalism

The semantics of SDL was defined formally in 1988.

Standard Formal Graphical

and symbol-based Object-oriented (OO)

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

GIS Data

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Celular automaton

Each cell xi(t) represent a state of the cell i in the instant t. k = {Empty, Static and Dynamic}

xi

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Game of life

The Game of Life is a cellular automaton devised by the British mathematician John Horton Conway in 1970. It is the best-known example of a cellular

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Method

§ Cellular Automaton Structure

§ Evolution Rules

§ Precipitation phase § Settlement phase

• Data layers

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Soil (Ground)

•Return Flow •Kind of soil •Saturate •Kind of soil use •Non-Saturate •Percolation

•Runoff •Humidity •Evapo-trans piration •Temperature •Rainfall •Precipitation

Hydrologic resource

Meteorology

Factors/variables which affects hydrologic resource

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

Where:

SWt is the total quantity of water in the soil (basin) (mm), SWi is the initial quantity of water in the soil (mm) Ri (rainfall) is the quantity of rain in the period of analysis(mm) Qi is the quantity of runoff (mm) ETi is the evapotranspiration (mm) Pi is the percolation (mm) QRi return flow(mm) t is the time expressed in days i is the index of cell

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Model

This expression gives us the water volume existing in each cell of our cellular automaton in a specific time t. In the expression, one position of the cellular automaton matrix [xi,xj] is represented by the cell xi.

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

System diagram

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

States diagram

Update Receive

Send

Dynamic

Receiv e

Static

d en S te/ ei ec R ve

Update Receive

Receive

Send

Up

da

Send Update

Empty

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Static state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Empty state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Process diagram: Dynamic state

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

Discussion and future work

This slides depicts the first and more important step to construct a discrete hydrologic simulator based in a cellular automaton. It shows the specification of hydrologic model with the aim of simulate the behaviour of water in a river basin. The use of the cellular simplifies the mathematical expressions and helps us to maintain a structured view of the geographical data needed to represent the model. The use of SDL formalism simplifies the understanding of the behaviour of model thanks its graphical structure. The model is based in the hydrologic balance equation which permits calculate the quantity exist in each cell of the cellular automaton

Hydro. model – Sim. Formalisms – GIS Data – Cel. Automaton - SDL

References

[1] L. Ma, L.R. Ahuja, and R.W. Malone. Systems Modeling for Soil and Water Research and Management: Current Status and Needs for the 21st Century. ASABE, 1907-2007. [2] IPCC Intergovernamental Panel on Climate Change. Report IPCC. 2007. [3] J. Samper, M.A. Garcia, B. Pisani, D. Alvares, A. Varela, and J.A. Losada. Modelos Hidrologicos y Sistemas de Informacion Geografica para la Estimacion de Recursos Hidricos:. Confederacin Hidrologica del Ebro, VII, 2005. [4] E. Guzman, J. Bonini, and D. Matamoros. Modelo Hidrologico SWAT (Soil Water Assessment Tool) Prediccin de Caudales y Sedimentos en una Cuenca. Revista Tecnologica, 17:152–161, 2004. [5] Erik D. Zimmermann. Modelo Hidrologico Superficial y Subterraneo. Centro Universitario rosario de Investigaciones Hidroambientales. [6] A. López, D. Sauri, and Jose M. Galán. Urban Water

References

[8] P. Pea and J. Alirio. Sobre las redes de petri r-difusas. Divulgaciones Matemticas, 7:87–99, 1999. [9] Gabriel Wainer. Applying cell-devs methodology for modeling the environment. SIMULATION, 82:635, 2006. [10] Liu Qi and Gabriel Wainer. Parallel environment for devs and cell-devs models. SIMULATION, 83:449, 2007. [11] Fonseca P. and Casanovas J. 2005. ESS205, Simplifying GIS data use inside discrete event simulation model through m_n-AC cellular automaton; Proceedings ESS 2005. [12] ITU-T Telecommunication Standardization Sector of ITU. Languages and general software aspects for telecommunication systems. TELECOMMUNICATION STANDARDIZATION SECTOR OF ITU, 2007. [13] Zeigler, B. P.; H. Praehofer; D. Kim. 2000. Theory of Modeling and Simulation. Academic Press. [14] Petri, Carl A. 1962. "Kommunikation mit Automaten". Ph. D. Thesis. University of Bonn. [15] Recalde, L.; Teruel, E.; Silva, M. 1999. Autonomous

Thanks!

Pau Fonseca i Casas pau@fib.upc.edu

Technical University of Catalonia Barcelona School of Informatics Computing laboratory Barcelona Jordi Girona 1-3 (+34)93401773