distributed hydrologic modelling for operational forecasting...flooded forest and desert creek:...

Distributed Hydrologic Modelling for Operational ForecastingAddressing challenges and meeting current and future needs

Floodplain Management Conference with ASFPM/Arid RegionsRancho Mirage, CA September 2015

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Introduction• Needs and Drivers

• Data Availability and Model Capabilities

• Approaches

• Case Stories• New Zealand Flood Mapping and Rapid Assessment• Murrumbidgee Real-time River Forecast System (Riparian ET + GW)• Chao Praya – Realtime 2D floodplain model• Big Cypress Basin, FL – Distributed hydrology and operations• Boulder, CO – Advanced floodplain model development

Distributed Hydrologic Modelling for Operational Forecasting

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More detailed, localized forecast information− Sub km, flash flooding response− Multi-resolution models, grids, scaling− Forecast tools that are built for Data Assimilation

Integrated hydrologic processes- Dynamics of surface water + groundwater + vegetation- Urbanized systems interaction with surface/groundwater processes- Cumulative impacts and scale issues

Operational tools− Systems/structure controls and feedback with model systems− Auto-calibration, uncertainty, quality of forecast assessment− Scenario analysis

Needs and Drivers

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Distributed Flood Hazard Assessment Rain on Grid, with losses (infiltration rate)

Screening for detailed studies10m grid, low relief Rural/urban landuseLevee failure assessment

Waimakarere, South Island NZ

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• Wide variety of methods and models available and in development− Deterministic/stochastic, lumped/distributed, partial process

models/integrated model/”full” physics, sequential approaches− Full 2D / Quasi 2D / Indexed Flood Mapping

• Modern capabilities in IMS, model/data availability and interoperability means that:− These approaches are not mutually exclusive and lead to

opportunities for hybrid solutions − The ability to run more models, parameters sets, and scenarios− The need to manage the flow of data, results, and decisions

Operational Hydrology in Practice

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Model Approaches

• Multi-scaled and nested models, unstructured, flexible

• Real-time distributed hydrologic modeling

• Linked and Integrated models (hybrid H&H, and integrated process)

• Rapid Screening, approximate methods in Hydrology and Hydraulics

• Not new but now possible at distributed model scale and speed

• Hydraulic modeling with embedded hydrology (ie; Rain on Grid/Mesh)


Big Cypress Basin, FLDistributed Hydrologic Forecast

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Based on Everglades Restoration Plan (CERP)and Picayune Strand Restoration Project (PSRP)• Channel hydraulics including complexstructure operations (MIKE11 HD/SO)

• Close interaction between surface waterand groundwater (MIKE SHE)

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Flood Hydraulics and Urban Drainage

• Quadrangular mesh for known flow directions• Triangular mesh for unknown flow directions

Designed for Multiple Parallelization Approaches

MIKE FLOOD - 2D surface model

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Flexible Mesh: Mixed triangular and quadrangularCoupled to 1D and Pipe Networks

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Flood Hydraulics and Urban Drainage Dynamically Coupled Flood Model and Storm System with Rain on Grid

• Basic Concept- Domain decomposition concept

(physical sub-domains)- Each processor integrates the equations

in the assigned sub-domain- Data exchange between sub-domains

is based on halo layer/elements concept

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Parallelization – Distributed memory approach

• Combines GPU technology with the MPI technology (a cluster of GPU’s)

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Hybrid Parallelization – A new frontier

IT4Innovation’s AnselmCluster at Ostrava University (eastern Czech Republic CZ)

• 3344 compute nodes• each node has 2 x Intel

E5-2665 2.4GHz (16 cores)• 23 GPU accelerated nodes• 15 TB RAM

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Advanced Flood Modelling:Hybrid Parallelization

Christchurch, New Zealand2D model domain: 4.2 million elements 10 m x 10 m resolution flexible mesh (rectangular elements) Distributed rainfall-runoff with no losses (rain-on-grid)

- 1% AEP event- 21 hour storm

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Information Systems Approach:

• Continuous data collection and real-time operation

• Numerical modelling with physically based model packages (mechanistic)

• Platform / Information Management system: Open / Extendable Architecture

• Goals: Provision of accurate forecasts and lead time

Targeted & feature-rich dissemination of forecasts

Efficient operation and forecast management

Technical Sustainability


Integrated Platform for Real-time Flood ForecastingFlood Warning System, Environment Agency, Slovenia

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Extending the Platform: Model Forecast Evaluation Tools and CapabilitiesForecast Uncertainty Estimation – Probabilistic Forecasting

Currently implemented:• Uncertainty post-processor• Uncertainty pre-processor• Hydro-meteorological ensemble prediction• Weather generator

DHI Platform for Real-time IMS and Model Management

Hydro-meteorological Ensemble Prediction System

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MIKE Operations Real Time

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• Open Architecture: Plug-in Model and Data connections, published API, GUI development, Python scripting, R, and GIS

Extending the Platform: Model Forecast Evaluation Tools and Capabilities

Currently implemented:Skills scores: automated, spatial mappingUsing R with MC Platform / HydroGOFNCAR Toolset for Forecast EvaluationError modelProbabilistic Forecasting

DHI Platform for Real-time IMS and Model Management

HOME RAINFALL RUNOFF

Spatially distributed rainfall-runoff modelling &Integrated groundwater and surface water modelling

Distributed Integrated Hydrology MIKE SHE

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A flexible process description• Processes can be used as required• Processes run on different spatial scales• Processes run on different time scales

Physically based process descriptions• conservation of mass and momentum• physically meaningful model parameters• Runoff calculated physically through explicit

Unsaturated zone and ET process descriptions

Integrated Hydrologic Model1D channel flow 3D Groundwater flowsw-gw exchange

Model Grid (300m cell)Streams and Canals

Land Use

Soils

TopographyHigh

Low

Thickness of Confined AquiferHigh

Low

2D surface water flowDynamically linked 1D/2D domains

Parallelized Unsaturated zone - Richards Eq 1D solution

MIKE SHE has been In practical use for water management for 18+ years

HOME RAINFALL RUNOFF

MIKE SHE – rainfall – runoff oriented

Flexible process descriptionsAdditional conceptual based process descriptions

= lumped parameter approaches= fewer parameters to calibrate= less data required = faster simulation times

Ability to represent a subset of spatial processes

Useful for:• regional basin-wide models• models where single processes dominate• models with sparse or no calibration data© DHI

Hybrid Distributed + Lumped

MIKE SHE RR

…while maintaining lumped, conceptual

approach where we lack data

e.g. groundwater

Distributed, physically based where we have detailed info

e.g. climate, vegetation and soils

MIKE 11 Providing detailed channel

structure and reservoir operations

Auto-calibration

Rapid Flood Hazard Assessment

• Removes the 1D channel component• Utilizes available LiDAR data directly• Limited schematization - fast development• Hydraulically accurate flood extents• Floodplain volumes accurately captured• Results include flood level, depth, extent,

velocity & hazard• Method is amenable to automation • Detailed assessment: can be extended to

include elements: structures, channels, pipes

DEM / DTM LiDAR Development

RFHM Data Requirements• Event Rainfall• DEM / DTM• Surface Roughness• Water Level Boundaries

DEM including major cross drainage structures

Rainfall on LiDAR Approach• Land levels from LiDAR transformed to fine grid (1-10m)• If needed, roads and buildings “burnt in”• Design rainfall either net rain after all losses or on-grid abstraction/infiltration/ET• If necessary rainfall can be spatially distributed (DHI utility generates 2D time varying

grid of net rainfall)• Rainfall applied directly to MIKE 21 2D model.

Catchment delineation for RR methodCatchment tools streamlines the GIS processing steps:• Filling DEM• Generate Flow Direction and Flow Accumulation Grids• Generate Pour Points and Flow Paths• Calculate SC Slope using Equal Area Method• Automate

model setupIt is possible to limit sub-catchment size

and define slope sampling interval

Outputs – flow field and inundation depthsdepth-velocity products and derived indices

Outputs – hazard zones

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Mt Maunganui South Catchment

• Mt Maunganui is located in the Tauranga Region• Widespread flooding occurred in April 2013 from a

rainfall event with a depth of 7 5 inches over 3 days

Model Hydrology

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• Traditional lumped sub catchment approach

• Rain on Grid Methodology • Infiltration and Leakage

module to apply dynamic distributed, losses

• Modelling initial loss with a continuing rate dependant infiltration

River operations

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“The CARM project will make control of water flows more responsive and more precise.”State Water Corporation

New South Wales, Australia

CARM hydrological and hydraulic modelling components

• Optimisation: optimise dam releases and weir management; also helps plan reaching environmental flow targets in the river

• Catchment inflows: tributary forecasting for dry weather and flood

• River dynamics: hydrodynamic model of river channel

• River losses/gains: river bank ET and groundwater percolation

• Data assimilation: real-time updating of the model

River Losses and Gains• MIKE SHE Integrated hydrology• Accounts for near bank ET, bank storage and groundwater inflows/outflows• Real-time implementation within operational environment

29 September, 2015© DHI #55

Drought-adapted Ecalypt (Eucalyptus camaldulnesis)Lifespan 500-1000 yr, 10m sinker root depths, up to 30mFlooded Forest and Desert Creek: Ecology and History of the River Red Gum, By Matthew Colloff

Red Gum Riparian

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Losses and Gains by River Section


Comparison to Unaccounted Differences


• Not a “calibration”• MSHE is able to account

for some of the historical AUD

• Better validation can occur once abstraction metering is in place (currently in progress).

Thank you.

Visit us this week at the DHI booth in the exhibit area.

For more information, please contactStephen Blake [email protected]

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mailto:[email protected]

Distributed Hydrologic Modelling �for Operational ForecastingDistributed Hydrologic Modelling for Operational ForecastingNeeds and DriversSlide Number 4Operational Hydrology in PracticeSlide Number 7Big Cypress Basin, FL�Distributed Hydrologic ForecastSlide Number 9MIKE FLOOD - 2D surface model Slide Number 11Flood Hydraulics and Urban Drainage Parallelization – Distributed memory approachSlide Number 15Slide Number 16Distributed Hydrologic Modelling �for Operational ForecastingIntegrated Platform for Real-time Flood Forecasting�Flood Warning System, Environment Agency, SloveniaDHI Platform for Real-time IMS and Model Management Hydro-meteorological Ensemble Prediction SystemMIKE Operations Real TimeDHI Platform for Real-time IMS and Model Management Distributed Integrated HydrologyMIKE SHEIntegrated Hydrologic ModelMIKE SHE – rainfall – runoff orientedSlide Number 29Rapid Flood Hazard AssessmentDEM / DTM LiDAR DevelopmentDEM including major cross drainage structuresRainfall on LiDAR ApproachCatchment delineation for RR methodOutputs – flow field and inundation depths�depth-velocity products and derived indicesOutputs – hazard zonesSlide Number 42Slide Number 43Model HydrologySlide Number 46Rapid Flood Hazard AssessmentRiver operationsCARM hydrological and hydraulic modelling componentsRiver Losses and GainsLosses and Gains by River SectionComparison to Unaccounted DifferencesThank you.��Visit us this week at the DHI booth in the exhibit area.��For more information, please contact

distributed hydrologic modelling for operational forecasting...flooded forest and desert creek:...

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