Models and Modeling in FEWS Part I

Models and Modeling in FEWSPart I

Micha Werner

Deltares & UNESCO-IHE

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Objectives

• Discuss the approach to integration of models in FEWS (CHPS)

• General approach

• Limitations and considerations

• Discuss integrating models in FEWS

• Rainfall-Runoff, Snow, Groundwater

• Hydrodynamic routing

• Model and model adapter availability

• Aspects of integrating models with FEWS

• PC Raster

• Mixing models & model concepts

• Error correction

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Program: Models in FEWS I

Part I• Concepts of integrating models in FEWS (repeat)

• Distributed Hydrological Modeling

• Forcing, integration, model set-up, calibration, snow, groundwater

• Case studies: WASIM-ETH; PCRGLOB

• Integration of models using PCRaster

• Concepts of PC Raster

• Spatial data (pre/post) processing

• Linking PC Raster models (adapter, PCRaster-Python)

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Program: Models in FEWS II

Part II• Hydrodynamic routing models

• Model types, forcing, integration, tidal boundaries, internal boundaries, Inundation modeling, 1D & 2D modeling, regulation

• Case studies: Firth of Clyde, Scotland; Rhine

• Aspects relevant to model integration

• Approaches to bias correction

Integration models in FEWS(repeat)

In this section we will discuss some background to the running of models from FEWS. The objective is to establish an understanding of the concept of how this interaction works, without going in to the detail of how such interaction is considered. We will look at how data is exchanged, what data is exchanged and the different formats that data is exchanged in. This section will not outline how to configure FEWS to run models. This can be obtained in other classes.

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Integration of models in FEWS

• It is important to understand the principle on which FEWS has been built;

• Delft FEWS provides an interface to running models in a forecast environment

• There are in principle no inherent modeling capabilities

• All models linked FEWS follow the same approach

• Data is exported to the model in a defined format (Published Interface)

• Model runs using its own native formats

• Data is imported from the model in the same defined format (PI)

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Delft-FEWS (concept)

Delft-FEWS• import• validation• transformation / interpolation• data hierarchy• general adapter• export / report• administration (data, forecasts)• viewing (data, forecasts)• archiving• …

(forcing) data

models

export & dissemination

PI

imp

ort

external

simulated

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General Adapter Module

Running models – how does it work

local datastore

FEWS

model

native files(e.g. txt)

native files(e.g. txt)

xml files(PI)

export

xml files(PI)

import

pre-adapterrun

post-adapterrun

model

run

1: Export model inputs2: Run pre-adapter3: Run model4: Run post-adapter5 Import model results

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Models linked to FEWS

• All models follow the same principle – irrespective of model developer and/or concept

• “Complete” list of models integrated with Delft FEWS

http://public.deltares.nl/display/FEWSDOC/Models+linked+to+Delft-Fews

• Generally the “owner”of the model develops an adapter for that model to the FEWS interface

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Communicating data to models

FEWS database holds dynamic data (primarily) as well as static data

Dynamic data relevant to exchange with models

-Time series data (0D, 1D, 2D)

- States

0D – point time series data

1D – longitudinal time series data2D – longitudinal time series data

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• Most models applied in a hydrological forecast environment are initial state type models – i.e. require a known state to start from.

00:00 12:00 00:00 12:00

Start of forecast periodForecast period- Model requires inputs (forcing) across this period

Update run provides a state to start from (could also be default state by choice)

Model typically also “returns” state during forecast mode – but this will generally not be used

Model returns data for same run period

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• To FEWS inputs and outputs to the models can be in any of the three types

• Generally in the form of 0D time series data

• For distributed models, 2D data is common

• For hydrodynamic models, 1D data is sometimes used

• Mixing formats when running any particular model is not an issue

• States handled in “native” model format – tagged with a date/time

• Other data exported from FEWS database to model

• Model parameter sets (XML file that FEWS can read)

• Model parameter/dataset (binary file that FEWS just passes on)

• Run file with details on model run (start, end time, file paths/names)

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Limitations & Considerations• There is no model specific “knowledge” passed between FEWS &

Model and vice versa

• Advantage: guarantees an open system – model independent

• Advantage: FEWS has no necessary knowledge of what model is being run

• Disadvantage: Model is not “aware” of all data in database unless made aware – not all information can be passed.

• Several layers of exchange – often file based

• Advantage: independent, easy to test, clear interfaces

• Disadvantage: many intermediate steps (though focus on options to make this more efficient)

Questions…

Running distributed models

In this section we will discuss the use of distributed models in FEWS. Similarities and differences with lumped models are briefly discussed. Considerations on integrating models with FEWS are discussed, as well as how models are combined with routing models. Examples of some distributed models integrated are discussed

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Distributed models versus lumped models

• Lumped models consider a watershed or basin as a single lumped entity

• Model inputs at the basin level: e.g. MAT & MAP

• Model parameters defined at the basin level

• Applied as a semi-distributed concept

• Basin divided into several sub-basins (horizontally / vertically)

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Distributed models versus lumped models

• Distributed models discretize a basins in small units

• Typically in the form of grids – or other geometric unit

• Model inputs required in same discretized form

• Model parameters typically defined similarly(in some cases associated to geo-morphological attributes – linked using distributed model layer of these attributes)

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Lumped model Semi-distributed model

Fully Distributed model

From lumped to distributed

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Physically based versus conceptual models

• Conceptual model: Conceptual representation of catchment processes: Fluxes and Stores

• Conservation of mass• Physically based model: Explicitly model processes in catchment

as described by the laws of physics

• Conservation of energy, momentum, mass

DHM is a distributed version of the SAC conceptual model

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Physically based models

43 REWs (Strahler

2nd order)

Representative Elementary Watershed (REW) ModelHydrological Response unit approach

Grid based distributed approach (e.g. Mike-SHE)

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Physically based vs Conceptual models

Physically based models

Pros;

• Physical processes modeled in the best possible manner

• Changes in catchment conditions can be incorporated in a plausible way

Cons;

• Models are data intensive – require detailed information of catchment properties (topography, soil, vegetation etc.)

• Scale issue – balance between detail of process response and lumping response into “units” of e.g. 1x1 km

• Reductionist approach – assumes that all processes fully understood and adequately described.

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Hydrological (Rainfall/Snow) Models linked to FEWS (Examples)

Lumped (or semi-distributed)

SAC-SMA

SNOW-17

HEC-HMS

PDM

PACK

HBV

MIKE-NAM

URBS

NWS

NWS

USACE

CEH-Wallingford

CEH-Wallingford

SMHI

DHI

Don Carrol

US

US

Po, Nile, etc

England & Wales, Scotland


Rhine (CH & DE)

England & Wales, Spain, Po

Mekong

Distributed Grid2Grid

WASIM-ETH

PREVAH

TOPKAPI

Vflo

REW*

WFLOW*

MODFLOW

CEH-Wallingford

ETH Zürich/Jürg Schulla

WSL

ProGea/Uni-Bologna

Baxter Vieux

Deltares

Deltares

Deltares & Adam Taylor


Switzerland

Switzerland

Italy, Spain

Taiwan

Research Applications

Research Applications

England & Wales (NGMS)

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Question/Poll

A. Physically based models will always provide better forecast results than conceptual models

• True

• False

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Running a distributed model in a workflow

Import workflow

Export workflow

Example workflow

Fill gaps in precip & temp

Interpolate to model grid

Merge Grids

Run Distributed model

Run Routing Model

Principle is exactly the same as when running a lumped model

However, data processing steps may differ

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Inputs & Outputs for a distributed model

• Required inputs will depend very much on the type of model being used

• Typical set of inputs (gridded at the same resolution as the model)

• Rainfall

• Temperature

• Evaporation/Humidity/Vapor Pressure/Temp (wet bulb)

• Incoming Radiation

• Set of outputs will equally depend on type of model being used

• Point (accumulated) & Gridded outputs

• Flow, runoff, soil moisture (layers), evaporation, SWE, etc.

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Inputs & Outputs for a distributed model

Pre-processing of model inputs likely to be different in forecast and in update period• This may introduce bias in (distributed) inputs – prep-processing?• Some distributed models provide capabilities to interpolate (observed)

meteorological data. Preferably this should be done outside the model or in two steps to allow merging (update-forecast period; backup time series)

Interpolation

Observed meteo. variables

Distributed Model(simulation)

Interpolated (observed)

meteorological grids

Downscaling

Meteorological forecast grids

Distributed Model(simulation)

Downscaled meteorological grids

State

Update period Forecast period

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Case Study

Distributed modeling in Switzerland

Motivation• Currently lumped model used for all

catchments: HBV Conceptual model• Experience showed that model does

not quite capture dynamic response of (higher elevation) catchments

• Modeling distributed processes such as Snow

Two models piloted in smaller sub-basins• PREVAH – Sihl & Linth Basins• WASIM – Emme basin• Outputs of Dist. Model routed into

HBV model chain

Elevation model for the Emme basinAs used in WASIM (500m resolution)

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Case studies

Integration of WASIM-ETH

Model developed at ETH-Zurich• Fully distributed grid based model• Models main hydrological

processes

• Interception

• Infiltration

• Unsaturated zone (Richard’s/Topmodel)

• Glacier & Snowmelt

Processes modelled (in German!)

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Case Studies


Adapter developed 2010 to run WASIM from FEWS. Pilot implemented for Emme catchment

Model Inputs (all gridded)

Temperature

Precipitation

Vapour Pressure

Wind Speed

Global Radiation

Sunshine Duration

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Case Studies


WASIM (interpolation)

Observed meteorological

variables

WASIM(simulation)

Interpolated (observed)

meteorological grids

Output grids & discharge (selected locations)

Downscaling

Meteorological forecast grids

WASIM(simulation)

Downscaled meteorological grids

Output grids & discharge (selected locations)

State

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Case Studies


Workflow• Relatively simple structure of workflow

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Case Studies

WASIM-ETH –Outputs returned to FEWS (currently)

Variable Parameter identifier Unit Description

Precipitation (snow) grid and scalar P.uh.snow mm Precipitation on each grid cell in solid form

Precipitation (rain) grid and scalar P.uh.rain mm Precipitation on each grid cell in fluid form

Runoff (direct) grid and scalar q.uh.dir mm Direct runoff from each cell

Runoff (Interflow) grid and scalar q.uh.ifl mm Runoff as interflow from each cell

Runoff (baseflow) grid and scalar q.uh.bas mm Runoff as baseflow from each cell

Snow water Equivalent grid and scalar SWE.uh mm Snow water equivalent in each grid cell

Evapotranspiration grid and scalar E.uh.etr mm Evaportranspiration from each grid cell

Root Zone Moisture content

grid and scalar RZM.uh mm Soil moisture content in the root zone for each grid cell

Runoff (total) scalar only q.uh.bas mm Total runoff

Discharge (total) scalar only Q.uh m3/s Total discharge at each point (includes all runoff from upstream of that grid cell point)

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Case Studies

Snow water equivalent

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Case Studies

Direct runoff

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Case Studies

Interflow (unsaturated zone)

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Case Studies

Base flow

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Considerations on integrating distributed models

• Runtime for distributed models can be considerably larger• Example: Emme catchment: 936 km 2

WASIM: Model grid resolution 500m (106x96 cells)UpdateStates run: length: 9 days

Run time (preprocessing): 46 secRun time (model run): 1 min 38 sec

HBV: 3 sub-basinsRun time (model run): 3 sec

• Database sizes can be considerable largerWASIM:

Input data processing: 3.5MBModel results: 6.5 MB (of which 10.1 KB scalar time series)

HBV:Model results: 7.1 KB

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Comparison

• General impression: WASIM gives a better representation of the dynamic response of the catchment – but often oversimulates

Emme at Eggewil

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80

observed flow (m3/s)

sim

ula

ted

flo

w (

m3/

s)

HBV

WASIM

y=x

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Comparison of results from HBV and from WASIM at Eggewil and at Wiler

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2.95

3.75

0.99

2.05

3.08

1.23

2160saribroc

2179senstoer

2215saanlaup

2085aarehagn

2019aarebrie

2109luetgest

2469kandhond

2151simmober

2135aarebern

2457AareringBrienzersee

2030AarethunThunersee

2409emmeeggi

2070emmeemme

2155emmewile

2471murgmurg

2063aaremurg

2016aarebrug

2450wiggzofi

2434duenolt

2091rheirhei

2378orbeorb

2480areuboud

2034broypaye

2447CanasugiMurtensee

2446ZihlgampNeuenburgersee

2029AarebrueBielersee

2.6

2.59

0.3

2.07

3.67

4.77

3.67

FEWS-CH: Schematisation and time lags of AARE

?

?

P,T

P,T

P,T

P,T Emme catchment• ARMA error correction at Emmemat & Wiler• Input correction ofr Emmemat & Egge sub-basins

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HBVEmme-Egge

ARMA

Rou

ting

HBVEmme-Emme

HBVEmme-Wiler

ARMA

Semi-distributed model fully-distributed model

Forecast @ Emmematt

Forecast @ Wiler

Forecast @ Emmematt

Forecast @ Wiler

Issue: distributed model does not make use of observed data in internal gauges

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Mixing models to utilize both advantages

Simulation @ Emmematt

Incremental flow @ Wiler

Distributed model requires option to output incremental flow

ARMA

Rou

ting

ARMA

Semi-distributed model

Forecast @ Emmematt

Forecast @ Wiler

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Distributed models & interaction

• Interaction between forecaster & distributed model less obvious than with lumbed model

• Example: for Sacramento it is common to change contents of different stores - this is not a realistic proposition with a distributed model

• Difficult is that error in simulated flow cannot be easily be attributed to a part of the model

• Options• Influencing forcings (distributed)• Selected parameters (e.g. meltrate)• Changing areas of model with similar characteristics

All these will introducing some form of lumping!

• Opportunities• Using other data to update model – e.g. snow cover, soil moisture• Active research area

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Other distributed models: TOPKAPI

TOPKAPI: TOPographic Kinematic APproximationandIntegration• Developed by University of Bologna (Italy)• Applied in operational forecasting system for the Po in Italy, as

well as in Spain• Can be applied both in lumped form and in distributed form• Physically based model

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• TOPKAPI linked to FEWS using standard adapter approach

• In application in FEWS-Po (Italy) inputs are only rainfall and temperature.

• TOPKAPI started life as a research model

• Version used in FEWS with FEWS Adapter developed by ProGea

http://www.progea.net/prodotti.php?p=TOPKAPI&c=Software&lin=inglese

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Other distributed models: MODFLOW

MODFLOW: • Three dimensional Groundwater modelling system

• Linked to FEWS using adapter approach: developed for use in National Groundwater Modelling System (NGMS, UK)

http://en.wikipedia.org/wiki/MODFLOW

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National Groundwater Modelling System

Rolf Farrell (EA-UK): How to make groundwater models useful and accessible for regulatory staff

Thanks to Peter Gijsbers for the slide

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NHI (National Hydrological Instrument) The Netherlands

Build a high resolution integrated hydrological model:

→ NHI (National Hydrological Instrument)

Incorporate this in a real time operational forecasting system:

→ FEWS-Water management

Support the National Co-ordination Committee for Water Allocation in its decision process under drought conditions

→ Information on current status of the system, deficits, deviations from climatology, damage

→ Input for official drought publications: “Droogtebericht”


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→ NHI (National Hydrological Instrument)

Distribution Model(national surfacewater

Δt=10d)

Mozart(regionalsurf.wat.Δt=10d)

Modflow(national ground water model, Δt=1d, 250x250m)

Meta-SWAP(sub-surface

Δt=1d)demand/allocate

demand/allocate

Demand/allocate


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Real time data feeds:

→ observations– meteo, sw, gw

→ forecasts– weather, river inflow



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FEWS-Water management output: ground water levels vs. climatology



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FEWS-Water management output: drought damage (fraction)



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FEWS-Water management output: surface water deficit



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National drought publication



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MODFLOW & FEWS

Current versions of MODFLOW supported: Modflow 96 & 88

Inputs• NGMS: Recharge, Abstractions (wells)• NHI: Recharge calculated in coupled Modflow – MetaSWAP model

(unsaturated zone)

Outputs (gridded, or sampled at a point)• heads, flows, streamflow accumulations

Size and runtime is an issue!• Model set-up typically hosted outside of FEWS database• Runs may take days to complete – not for real time forecasting!

Questions…

PCRaster and distributed models

In this section we will discuss the PCRaster package, how this has been integrated within Delft FEWS. A brief background to the package is given, and the two methods with which it has been used in FEWS are explained. Case studies are used to illustrate each of the two methods of use.

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PCRaster and DelftFEWS

Key concepts:• Script language for gridded data • Many hydrological functions (e.g. kinematic wave, catchment delineation

etc)• Extensively used within the hydrological research community

• Integrated into Delft-Fews using in-memory XML link (pcrTransformation module)

• Can be used by everybody with a DelftFEWS license

• Also available as external (command line) model that can run in DelftFEWS via a General Adapter

• Requires license from PCRaster supplier

• Free for personal use (download)

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PCRaster

From the pcraster web-site (http://pcraster.geo.uu.nl/)

“The PCRaster Environmental Modeling language is a computer language for construction of iterative spatio-temporal environmental models. It runs in the PCRaster interactive raster GIS environment that supports immediate pre- or post-modeling visualization of spatio-temporal data.”

“The PCRaster Environmental Modeling language is a high level computer language: it uses spatial-temporal operators with intrinsic functionality especially meant for construction of spatial-temporal models. “

Go to web page …. http://pcraster.geo.uu.nl/

Download page: http://pcraster.geo.uu.nl/downloads/

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PCRaster

PCRaster provides a simple environment with which dymanic spatial models can be build => Dynamic GIS environment

Short demo (from PCRaster documentation)

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PCRaster Demo

Calculate runoff over an area using a simple water balance model(explained fully on http://pcraster.geo.uu.nl/documentation/Demo/DynamicModellingDemo.html

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PCRaster Demo

Precipitation at 3 rainstations, mm/6 hours

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PCRaster Demo

Create Thyssen net from available rainfall stations

initial # coverage of meteorological stations for the whole area report RainZones=spreadzone(RainStations,0,1);

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PCRaster Demo

Variable infiltration map given soil properties

1 2.12 8.33 19.0

initial# create an infiltration capacity map (mm/6 hours), based on the soil map InfiltrationCapacity=lookupscalar(SoilInfiltrationTable,SoilType);

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PC Raster Demo

Create runoff direction map: local drainage direction (ldd)(Detail)

initial# generate the local drain direction map on basis of the elevation map Ldd=lddcreate(Dem,1e31,1e31,1e31,1e31);

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PC Raster Demo

Ready to run!!!

dynamic # calculate and report maps with rainfall at each timestep (mm/6 hours) SurfaceWater=timeinputscalar(RainTimeSeries,RainZones); # compute both runoff and actual infiltration RunoffPerTimestep,Infiltration= accuthresholdflux, accuthresholdstate(Ldd,SurfaceWater,InfiltrationCapacity); # output runoff, converted to m3/s, at each timestep report RunOff=RunoffPerTimestep/ConvConst;

See• Run the model for 28 timesteps 21.bat• Time loop of rainfall input per zone 9.bat• Time loop of runoff 22.bat

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PC Raster Demo

Sample runoff at points of interest

dynamic# output runoff (converted to m3/s) at each timestep for selected locations report RunoffTimeSeries=timeoutput(SamplePlaces,RunOff);

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Examples of useful PCRaster commands..

COVER

Result = cover( expression 1, expression 2,... expression n)

Can be used as data hierarchy but unlike FEWS it does this on a per pixel base.

Example: Result1.map = cover(Expr1.map,sqrt(9))

Result1.map = cover(Expr1.map,sqrt(9))

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WINDOWTOTAL/AVERAGE/MAX/MIN

Result = windowaverage( expression, windowlength )

Moving window calculations. Smoothing etc…

Example: Result1.map = windowaverage( Expr.map, 6) ))

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if then else

Result = if( condition then expression1 else expression2 )

If then else is eveluated on a per pixel base. Not for model control but to assign values based on conditions per pixel.

Example: Result.map = if(Cond.map,Expr1.map,Expr2.map)

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• Key concept in environmental modelling, the LDD (Local Drainage Network)

• Used for:

1. Catchment deliniating

2. Downstream routing of material

3. Calculating upstream area

4. etc…

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PCRaster and DelftFEWS

• Can be used for simple operations or to build (very) complex distributed hydrological models

• Many useful functions, see pcraster web-site

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Hydrological modelling

A simple distributed hydrological model (demo from PCRaster) – 1/2

# model for simulation of rainfall and evapotranspiration# one timeslice represents one month

binding RainTimeSeries=rain12.tss; # timeseries with rainfall (mm) per month

# for two rain areas Precip=rain; # reported maps with precipitation,

# rain is suffix of filenames RainAreas=rainarea.map; # map with two rain areas VolumePrecip=volrain.tss; # reported timeseries with volume rain per

# month (cubic metres per second) CropCoeffTable=crcoefa.tbl; # column table with crop coefficients for

# classes on LandUse LandUse=landuse.map; # map with nominal landuse classes 1,2,3 EvapRefTimeSeries=evaref12.tss; # timeseries with reference

# evapotranspiration (mm) per month PrecipSurplus=rainsur; # maps with precipitation surplus (mm/month) InitSoilwater=initsw.map; # map with initial soilwater content Soilwater=soilwate; # reported maps with soilwater content (mm) SoilwaterSurplus=soilsurp; # reported maps with soilwater surplus (mm) Ldd=ldd.map; # local drain direction map Discharge=dis; # runoff discharge (metres3/second)

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Hydrological modellingareamap clone.map;

timer 1 12 1;

initial # crop coefficients (k) K=lookupscalar(CropCoeffTable,LandUse);

# initial soilwater content (mm) Soilwater=InitSoilwater; # maximum soilwater content (mm) MaxSoilwater=scalar(400);

dynamic report Precip=timeinputscalar(RainTimeSeries,RainAreas); report VolumePrecip=maptotal(Precip)*(cellarea()/2628);

EvapRef=timeinputscalar(EvapRefTimeSeries,1); report Evap=K*EvapRef; report PrecipSurplus=Precip-Evap;

Soilwater=Soilwater+PrecipSurplus; report SoilwaterSurplus=max(Soilwater-MaxSoilwater,0); report Soilwater=min(Soilwater,MaxSoilwater);

DischargeMM=accuflux(Ldd,SoilwaterSurplus); report Discharge=DischargeMM*(cellarea()/2628);

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Hydrological modelling – real world examples

Demo you have just seen• Not really a very useful model – but simple!

SAC-SMA• Distributed version of SAC-SMA concept• Linked to FEWS using General Adapter and PC Script

see sacramento.mod

PCRGLOB• Distributed hydrological model at global scale, used for climate impact

research• Dept. Physical Geography, Utrecht University

• Linked to FEWS using General Adapter and PC Scriptsee pcrglob_full_fews.mod

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Linking PC Raster with FEWS

PCRaster has been linked to FEWS through two ways

Standard model adapter approach• PCRaster Model adapter• Applied for running models developed in PCRaster• Uses all standard model adapter functionality• Models can also be run “stand alone”outside FEWS

Integrated into Delft-Fews using in-memory XML link (pcrTransformation module)

• Runs as a standard FEWS data transformation module• Applied for “complex” spatial data transformations• Can be used by everybody with a DelftFEWS license

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Embedded link with FEWS

• In memory XML based interface

• Script “embedded” in FEWS

pcraster engine

pcrTransformation

fews database

pcrTransformation

fews database

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Examples

Lapsing temperature to zero

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Examples

PCRaster script

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Filter radar data

#! --unitcell

dynamic RADARunit = if(Radar > 0.0 then 1.0);RF = windowtotal(RADARunit,2);RFL = windowtotal(RADARunit,6);RADARFILT = if(RF > 2 or RFL > 14 , Radar);

Input from FEWS – Radar gridded time series

Return to FEWS – Filtered Radar gridded time series

Notes•This is a very simple filter! Better filters may be made using e.g. the clump operator•Unitcell means that the windowlength is defined in number of cells, otherwise use unittrue (default)

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Filter radar data – raw data

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Filter radar data – filtered data

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Real world example: PREVAH Model, Switzerland

• A semi-distributed conceptual model (written in FORTRAN) linked to FEWS by GA

• Post/Preoprocessing steps done using combination of PCRaster module and other transformations

• Model concept based on hydrological response units

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Gridded data handling problem• Model domain discretised as Hydrological Response Units,

combined with elevation zones: referred to as MeteoZones• Temperature input data from NWP model

• Different resolution to model resolution

• Orography in NWP model differs from orography in hydrological model as a result

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Emme Catchment to Wiler(all forecasts 17-01-2010 00:00 UTC)

Comparison of model orography to NWP orography

NWP Temperature profiles compared to observed interpolated profiles

0 200 400 600 800 1000400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

Area (km2)

Ele

vatio

n (m

)

Area-Elevation

modelcosmo2

cosmo7

cosmo leps

ecmwfmax elevation

-15 -10 -5 0 5 10 15400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

Temperature (degc)

Temperature-Elevation

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Processing of NWP Forecast temperatures.

Step 1: Lapse temperature to mean sea level using NWP elevation model

Step 2: Downscale lapsed temperatures from NWP grid resolution to Model resolution using bi-linear interpolation

Step 3: Lapse downscaled temperatures to PREVAH model elevation

Step 4: Sample temperature values per meteo-zone

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Lapsing forecast temperature to mean sea level

NWP Forecast gridForecast T0 05-05-2010 06:00TimeSlice: 06-05-2010 14:00

Lapsed NWP Forecast gridForecast T0 05-05-2010 06:00TimeSlice: 06-05-2010 14:00

Mean = 6.49 oCstd = 4.89 oC Mean = 15.05 oC

std = 1.09 oC

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Resampling and lapsing to PREVAH Model Elevation

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Average temperature per meteo-zone

Meteo Zones gridAveraged per meteozone

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Sample temperature time series per meteo-zone

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PCRaster adapter

Standard PCRaster adapter• Data passed to PCRaster through adapter (note that PCRaster grid format

one of the three standard grid exchange formats)• Model run as in command line mode

Using PCRaster through Python• Recent development: PCRaster available as a Python Package• Model can be developed as in Python• Python scripts run through General adapter

• requires Python libraries to read FEWS formatted I/O).

• Some “research” adapters developed

• PREVAH model adapter developed in Python• Offers many opportunities for rapid model development• Python-FEWS Package?

See http://pcraster.geo.uu.nl/documentation/PCRasterPython/index.html

Example: WFLOW Model (research model at Deltares – Jaap Schellekens)

Questions…

Models and Modeling in FEWS Part I

Documents

Transcript of Models and Modeling in FEWS Part I