NEAR REAL-TIM E WATER RESOURCE M ODELLING David Fuller, Principal - PowerPoint PPT Presentation

NEAR REAL-TIM E WATER RESOURCE M ODELLING David Fuller, Principal Water M anagement & Technology Angus Swindon, National Director Power and Water

OVERVIEW • Entura >10 years experience in near real-time modelling • Drivers - M ature system - Optimisation (water, energy, CO 2 ) - Operational decisions - Dam safety • Lessons learned illustrated by four examples:  Hydro-Tasmania Operations > power generation  Rowallan Dam Upgrade > flood forecasts  M eander Dam Optimisation > irrigation & power  M odular Software Systems > water management • Challenges and Future Directions

HYDRO-TASM AN IA SYSTEM S • 30 power stations • 45 major lakes • 13,500 M m 3 / yr Inflow Forecasts: • Dynamic models ( Sharma et al 2005 ) • 7 day forecasts • 61 locations • 2 hourly updates • Input: Rainfall • Input: Streamflow • Input: SCADA • Weather forecasts

W HY, W HAT & RESU LTS? • Increased value of data and information • Needs  Reliable data capture  Rapid transfer from field  Automated quality checking  Integration with modelling systems • Led to:  Electronic data capture  M ultiple communication systems  Remote monitoring and alarms  Ajenti Data M anagement System (ADM S)  Robust data storage  Improved modelling with dynamic data interfaces

EXAM PLE 1: HYDRO TAS FORECAST U PGRADES • Small, steep, wet & responsive catchments • Key limitations of past weather forecasts  Over-reliance on forecasters  Semi-quantitative, district scale  Limited coverage (only some HT catchments) • Emergence of Australian Digital Forecast Database :  Gridded data  Two forecasts per day  Statewide coverage  Biased – downscaling & observed to forecast  Still influenced by forecaster “experience”

RAIN FALL FORECAST BIAS CORRECTION Day 0 Day 1 Day 2 Day 3 Day 4 100 Daily 25% POE 0 Daily 50% POE 100 100 0 0 100 3 hour Observed M ean 0 0 100 Forecast

RESU LTS / CON CLUSION S • Gridded rainfall data is emerging technology. • Significant improvement on previous forecasts. • Good reliability and consistency of forecasts. • Needs careful bias correction. • Not a substitute for local observations. • Careful adoption and adaptation of data is appropriate. • Recent studies suggest forecasters may be over-riding large modelled rainfall forecasts! • What opportunities to remove the forecaster for operational model purposes?

EXAM PLE 2: ROW ALLAN REFURBISHM EN T

LAKE ROW ALLAN U PGRADE Need: Solution: • • 43m clay-core dam Sheetpile wall • • High hazard Lake drawdown • • Piping failure at interface Emergency backfill right spillway wall & • Customised flood warning embankment system • Replace 2 sections of dam + trigger or review actions. – core & filters. • M odel performance critical: • Reconstruct upper portion + reliability/ redundancy. of dam crest + accuracy. + avoid false alarms • M inimise risks during + avoid costs. construction – safety & cost.

ROW ALLAN – CU STOM FLOOD FORECASTS • Development of bespoke flood warning model using Hydstra M odelling. • Rainfall network and sensitivity testing, alarms and replacement as necessary (Ajenti). • M odel constructed based on back calculation of recorded storage levels and discharges plus rainfall records (20 years). • Shuffled complex evolution algorithm for parameter optimisation. • Commercial contract with BoM for numerical weather forecasts and briefings on weather conditions. • Customised bias correction.

ROW ALLAN – M ODEL OU TCOM ES • 10 day probabilistic forecasts with variable trigger levels depending on stage of construction. • Tolerances within –0.22m and +0.28m storage level. • Disseminated by SCADA and SM S to operators, dam safety engineers and site personnel. • End-to-end test of model prior to start of work. • Excellent performance. • Rain gauge replacement triggered.

EXAM PLE 3: M EAN DER DAM Need: Solution: • • Reliable irrigation delivery Ajenti system • • M inimise releases Links to SCADA, BoM , • Demonstrate efficiency DPIWE, HT gauges & water • M aximise hydropower meters • • M onitor water quality Optimise within constraints • • M aintain environment OPSIM model • • Report to regulators, etc. Customised interface(s) • • Invoicing Transparent operation • • Flexible / adaptable solution IE Aust National Award

M EAN DER DAM – IRRIGATION & POW ER Irrigation Operations M eander Dam M eander Dam Irrigation Network SCADA S ystem Ajenti TRX Ajenti TRX Alarms SM S, email Private 3G Hosted Ajenti Bureau of Forecast Network ADM S Data M eteorology OPSIM Ajenti Entura: M odel Databridge M anage “ The Cloud” M onitor Respond

EXAM PLE 4: AFRICAN W ATER M AN AGEM EN T Need: Solution: • • Integrated data capture Use existing tools • • M ultiple data sources Careful design stage • • Robust data management Fit for purpose • • Integrated with modelling Open, modular approach • • Near-term forecasts Redundancy built-in • • Long-term forecasts Customisable interfaces • • Invoicing Adaptable to latest research • • Flexible / adaptable solution Consider long term support

Satellite Data Products T elemetry Water M eters African Water M anagement System M obile Field Data M et. Forecasts Loggers SCADA Data Aquarius Time Ajenti DM S & Data Sources? AFDM Transformation Series Ajenti DataBridge Aquarius eWater Forecast etc. Source etc. Aquarius Solution? WebPortal Requirements: Browser Access Water M anagement Dashboards M obile Drought Flood Data & Graphics Plans & Invoicing Apps Forecasts Forecasts

W HAT HAVE W E LEARN ED? • Increasing value of data & information. • Data collection – from regular to reactive – from silos to sharing. • Robust systems increase data access, reliability and use. • Numerical weather predictions & satellite data  Increasing availability.  Enhance local data collection.  Not alternatives or replacements. • M odular software and modelling  Don’t reinvent the wheel!  Ajenti Data M anagement System (ADM S)

CHALLEN GES AHEAD • Focus on tools for water managers and operators  What do they need?  How will they use it?  Don’t forget the need to review history! • Ensemble forecasts  data, speed, processing power. • What role for forecasters? • M odular software and modelling  Open systems using appropriate models.  Secure data transfer  Software maintenance and support.

THAN KYOU David Fuller – Principal Water M anagement & Technology E: david.fuller@ent ura.com .au P: +61 438 559 763 Angus Swindon – Director Power and Water E: angus.sw indon@ent ura.com .au P: +61 6245 4335