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Considerations for the blending of multiple precipitation datasets for hydrological applications Luigi Renzullo Research Scientist CSIRO Land & Water,

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Presentation on theme: "Considerations for the blending of multiple precipitation datasets for hydrological applications Luigi Renzullo Research Scientist CSIRO Land & Water,"— Presentation transcript:

1 Considerations for the blending of multiple precipitation datasets for hydrological applications Luigi Renzullo Research Scientist CSIRO Land & Water, Canberra, Australia 4 th IPWG Workshop, Beijing, China 13 October 2008

2 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Background: Water Information R & D Alliance - WIRADA Commonwealth Water Act 2007 Australian Bureau of Meteorology (BoM) Mandate: ”Manage Australia’s water resources information …”; new responsibilities; new BoM Water Division formed. Water Information Research and Development Alliance (WIRADA) An R & D initiative between the BoM and CSIRO; partnership of $50M over 5 years (started July 2008) 10 WIRADA Projects Research incl. Water Accounting & Assessment; Water Availability Forecasting (Short- & Mid- to Long-term); Sensor Networks & Water Informatics WIRADA Project 10: Precipitation & Actual Evapotranspiration Products Aim: Blend rainfall radar, rain gauge, satellite-based PPT and QPF’s to service the need of the hydrological modelling/monitoring and forecasting community Help BoM Water Division deliver on their mandate by the Federal Govt.

3 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Precipitation information “wish list” What is desired? Water Forecasting Obs: ≤ 1 km resolution (may be < 5 km); ≤ 1 hourly rates (e)QPF’s: ≤ 1 hourly rates; > 48 hrs lead time Water Accounting Obs: ≤ 5 km resolution; ≤ daily accumulations; continental coverage Rainfall intensity distribution: rainfall duration; area/ fraction of catchment wet What is available? Rain gauges ~1000 report < 1 hour of event; ~2000 report < 24 hrs; ~7000 report daily accumulation ~ 6 months after end-of- year; sparse coverage Rainfall radar ~1 km reflectivities; ~10 mins; coverage limited to populous areas Satellite-based estimates rates reported 0.5-3 hourly intervals; ~6- 25 km resolution; latency ~ several hrs; continental coverage QPFs (BoM) ~5 km (regional) to ~38 km (continental) resolution; lead times 12 hrs (meso- scale) – 72 (continental); ensembles available “Current & historical gridded rainfall products at a scale & quality useful for hydrological application.” WIRADA Science Plan, May 2008

4 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Blending multiple data sets is the key Idea is not new – e.g. rainfall radar Project aims to : Develop strategies for blending multiple PPT data sets to derive gridded precipitation for use in water accounting & assessment, and the short- & long-term water availability forecasting. Demonstrate use of PPT distribution info (e.g. intensity, duration) to improve estimation Issues & Project Aims Disparate spatial resolution & temporal frequency between data sets Areas of hydrological significance (e.g. headwater catchments) often inadequately represented No individual, definitive PPT data set that meets all requirements Humble first steps Quantify spatial & temporal difference between data sets

5 4th IPWG W/shop,Beijing, China, 13-17 October 2008 (c) Daily rain gauges (5-jan-05) Interpolated surfaces of daily rain gauge observations - Total rainfall in 24 hrs to 9am (local time) Gridded precipitation estimates in Australia (a) BoM AWAP --- BILO 0 mm d -1 25 mm d -1 5 January 2005 (b) QDNR & M --- SILO TRMM-derived Daily rainfall

6 4th IPWG W/shop,Beijing, China, 13-17 October 2008 UTC 6 Nov 2005 7 Nov 2005 210000000300060009001200150018002100 0000 EST 7 Nov 2005 8 Nov 2005 070010001300160019002200010004000700 1000 7 Nov 2005 – 1200 UTC 69.7 mm of rain in 24 hours to 9am EST on 8 Nov 2005 Deriving daily rainfall totals from TMPA 3B42 (post- real-time) product TRMM daily rainfall (mm day -1 ) Huffman et al (2007), J. Hydrometeor.,5, 38-55

7 4th IPWG W/shop,Beijing, China, 13-17 October 2008 UTC + 8 UTC + 9.5 UTC + 10 Daily TRMM Rainfall (mm d -1 ) > TRMM daily rainfall (mm day -1 )

8 4th IPWG W/shop,Beijing, China, 13-17 October 2008 500 mm yr -1 -500 mm yr -1 0 1500 mm yr -1 Average Annual Rainfall 1998-2007 (mm yr -1 ) 0 mm yr -1 BILO SILOTRMM BILO - SILO BILO - TRMMSILO - TRMM

9 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Closer look: Differences in orographic rainfall: BILO - TRMM 1300 Elevation (m) BILO – TRMM (mm yr -1 ) Snowy Mt – Victorian Alpine Region Darling Escarpment, WA 100 700 -500 500 0 West coast Tasmania Which one is correct?

10 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Average differences for each basin BILO – SILO generally between ±20 mm yr -1 Surfaces – TRMM generally between ±70 mm yr -1 Rainfall intensity distribution: rainfall duration; area/ fraction of catchment wet Overall exception is Tasmania BILO > SILO ~ 70 mm yr -1 Surfaces > TRMM ~800 mm yr -1 Closer look: Average annual rainfall for major Drainage Divisions Max Min

11 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Trends in average annual rainfall (selected basins) Murray BILO SILO TRMM Tasmania Changes in Terrestrial Water Storage from GRACE Tapley et al (2004), Science.,305, 503-505 Rodell et al. (2006) Hydrogeol. J., 15, 159-166 Swenson et al (2008), Water Resour. Res., 44.

12 4th IPWG W/shop,Beijing, China, 13-17 October 2008 SW Coast Average monthly rainfall (selected basins) Murray West Plateau (N) Gulf of Carpentaria PPT (mm month -1 ) Max Min

13 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Gridded precipitation estimates in Australia NWP comparisons with TRMM data BoM’s Limited Area Prediction System (LAPS) mm/hr 0 0000 Z 0100 Z0200 Z0300 Z 0400 Z0500 Z0600 Z 0700 Z0800 Z0900 Z 1000 Z 1100 Z 1200 Z 1300 Z1400 Z1500 Z 1600 Z1700 Z1800 Z 1900 Z2000 Z2100 Z 2200 Z2300 Z Data and forecast from 0000 – 2300 UTC on 8 June 2007 * Hunter Valley Floods June 2007 TRMM 3B42RT TRMM 3B42 LAPS

14 4th IPWG W/shop,Beijing, China, 13-17 October 2008 1 mm d -1 ( a ) LAPS-0.375º ( b ) mesoLAPS-0.05º ( c ) TRMM 3B42 ( d ) SILO Hunter Valley Floods, NSW June 2007 >$500M insurance (2 nd largest deployment in SES history) 24 hr accumulations to 9am on 9 June 2007 Areal means for ROI: (a) 88.3 mm d -1 (b) 85.3 mm d -1 (c) 82.1 mm d -1 (d) SILO = 96.6 mm d -1 Comparison of daily accumulations (single event)

15 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Monthly accumulations based on daily data

16 4th IPWG W/shop,Beijing, China, 13-17 October 2008 Final comments & future directions Still early days in WIRADA PPT & AET Products Project Impact of different PPT on Hydrological model = f(scale) Impact of difference on estimation (whole range of hydrological applications) needs to be investigated  further refine the requirements Future research tasks include: Blending radar rainfall with rain gauge observations Assess impact of using BoM’s best-practice corrected radar rainfall data on lumped-catchment stream flow estimation by: quantifying rainfall duration (i.e. sub-daily rainfall intensity distribution); and quantifying rainfall spatial extent over a catchment. Disaggregating daily rainfall using rainfall intensity distributions Examine pluviometer observations (6 minutely observations) to define rainfall intensity distribution (RID) functions and assess: various interpolation schemes for estimating RID parameter values at non-pluviometer locations (incl. locations with only daily rainfall gauges); and the errors/biases in RID estimates and impact on disaggregation results. Blending near real-time satellite-based precipitation rates with real-time rain gauge observations Calibrate near real-time satellite-based precipitation products using the available real-time rain gauge data to produce continental-scale near real-time maps of precipitation. Statistical downscaling of quantitative precipitation forecasts Explore statistical approaches for downscaling QPFs in near real-time, exploiting satellite- and real-time gauge observations when/where available for uptake in stream flow forecasting. Using satellite-based precipitation observations to aid interpolation of archived daily rain gauge data Use satellite-based precipitation estimates as a covariate (along with e.g. elevation, distance-from-coast, …) in the interpolation of rain gauge observations, thus assessing: the utility of the satellite observations to give useful information between gauge locations; and the suitability of the 0.5-3 hrly sampling frequency to provide useful information on rainfall duration rates at 0.5–3 hrly intervals to capture spatial information between gauges locations

17 Thank you CSIRO Land and Water Dr Luigi J Renzullo Research Scientist Phone: +61 2 6246 5758 Email: Luigi.Renzullo@csiro.au Contact Us Phone: 1300 363 400 or +61 3 9545 2176 Email: Enquiries@csiro.au Web: www.csiro.au

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