7 th -12 th December, 2006, St Moritz, Switzerland

1. Evapotranspiration Estimation Using NWP By Asnor Ishak and Dr. Dawei Han Water Environment Management Research Centre (WEMRC) Department of Civil Engineering, Faculty of Engineering, University of Bristol, University Walk, Clifton, Bristol 24 May 2010 7th -12th December, 2006, St Moritz, Switzerland

2. OUTLINE Methodology Motivation/Intro Result/ Discussion Q/A Conclusion Further research

3. 1) what are the accuracy of the downscaled data in comparison with the in-situ measurements? 2) is there an improvement between the downscaled data and the original global data? 3) what is the accuracy of the evapotranspiration estimated from the downscaled data? 4) what are the impact of individual weather variables on the evapotranspiration estimation? 5) how could the result be further improved? This study explores the first 4 questions and the last question is discussed with some suggestions. MOTIVATION

4. INTRODUCTION • Evapotranspiration plays a major role in the hydrological cycle, which has great importance in agricultural, hydrological, ecological and climatic systems • Downscaling techniques - the performance of the global data in ungauged catchments • Useful for water resources assessment or forecasting by using meteorological and hydrological model • Need to quantify/make a comparative assessment

5. Comparison of the modeled and measured data Methodology - What do we need • Numerical Weather Model – MM5 - Regional mesoscale model used for creating weather forecasts and climate projections. – - Maintained by Penn State University and the NCAR. (Since 1970.s) • FAO Penman equation The Penman-Monteith method refers to the use of an equation for computing water evaporation from vegetated surfaces. • ERA-40 reanalysis data • available from ECMWF website – 10x10 resolution • Variables? Rh, Wnd, Nr, Temp, Prs • 6 hourly data with 4 months data in 1994 (Jan, Mac, Jul, Oct 1994) • Reanalysis data sets have become one of the most important data sets for scientific and application communities i.e to generate “climate” data • Observation data from HYREX study in the Brue catchment

6. METHODOLOGY Components of MM5 • long = -2.47, lat = 51.11 • grid resolution 1-km for Domain 4 • 19 × 19 horizontal grids • Initial and boundary condition were • obtained from ECMWF • parameterization scheme didn’t take • into account

7. METHODOLOGY Components of ETo where : Rn = net radiation at the grass surface (MJ m-2 hour-1), G = soil heat flux density (MJ m-2 hour-1), Thr = mean hourly air temperature (oC), = saturation slope vapor pressure curve at Thr (kPa oC-1), eo(Thr) = saturation vapor pressure at air temperature Thr (kPa), ea = average hourly actual vapor pressure (kPa), and u2 = average hourly wind speed (m s-1). Rh = relative humidity = psychrometric constant (kPa oC-1), There are 21 equations need to be calculated for estimation of ETo

8. Cover an area of 135.2 sq km (in Somerset) Obtained by NERC - HYREX (The Hydrological Radar Experiment) May 93 – 97 ext to 2000 METHODOLOGY Brue Catchment

9. METHODOLOGY Hourly Evaluation : Statistical performance and Sensitivity Analysis • The SD and MBE are also expressed as percentage of mean value of • corresponding meteorological parameters. • Not meant as a pass/fail test but to put modeling results in the • proper perspective

10. RESULT The statistics of different meteorological variables derived from the mesoscale model (MM5)

11. RESULT The comparison between the ERA-40 reanalysis data and the MM5 data

12. RESULT Ten day time series of surface pressure between the reanalysis, MM5 and the observed Ten day time series of surface temperature between the reanalysis, MM5 and the observed (left: winter; right: summer)

13. RESULT Ten day time series of relative humidy between the reanalysis, MM5 and the observed Ten day time series of wind speed between the reanalysis, MM5 and the observed

14. RESULT The results of the ETo derived from MM5 MM5 downscaled data impact on ETo

15. CONCLUSIONS • Atmospheric pressure can be estimated very accurately from the downscaled data (with less than 0.2% error) • Wind speed is the worst weather variable to derive (with a huge discrepancy of around 300%) • Air temperature is quite reasonable (< 10%). The net radiation and relative humidity have about 10~20% error • In comparison with the original reanalysis data, the downscaled data are generally better except wind speed. • The ETo estimation from the downscaled data has about 30%~40% error compared with the estimation from the observed weather variables.

16. The sensitivity analysis has shown that the most important weather variables are net radiation and relative humidity The dominant weather variables are net radiation (during the warm period) and relative humidity (during the cold period) It is interesting to note that albeit the huge discrepancy in wind speed (around 300%), its impact to ETo is insignificant This study provides hydrologists with valuable information on downscaled weather variables and further exploration of this potentially valuable data source by the hydrological community should be encourage so that useful experience and knowledge could be accumulated for different geographical and climatical conditions CONCLUSIONS

17. Several approaches to improve evapotranspiration estimation from the downscaled data: 1) For example, wind speed from the MM5 downscaled data is generally overestimated with large bias; If a temporary weather station is set up at the investigation site to collect short term in-situ measurements; A mathematical model could be developed from the concurrent in-situ measurements and the downscaled data to correct the overestimated wind speed; If surrounding catchments have in-situ measurements, a correction model could be developed from those nearby sites and then applied to the investigation site using regionalisation principles; This model could be applied for long term water resources assessment. FUTURE STUDIES

18. 2) data assimilation using multiple sources could improve the downscaled data. Remote sensing using satellites is able to measure some weather variables such as solar radiation. Interpolation from the nearby weather stations could provide useful information for the investigation site. It may be useful to build a data assimilation model to assimilate various information sources so that the integrated data have the maximised utilisation of all the information available; FUTURE STUDIES • 3) modern artificial intelligence (AI) technology provides us with many useful tools to model complex physical processes. • If a short in-situ measurements are available at the investigation site, an AI model such as Artificial Neural Networks or Support Vector Machines could be developed to map the downscaled weather variables to ETo estimation. • Further on, the AI model may be used to model the catchment runoff directly if rainfall data are available; • 4) numerical weather models such as the MM5 have many parameterisation schemes and it may be useful to explore these schemes to find an overall optimal scheme for evapotranspiration estimations.

19. Thank you

20. DEFINITION • Evapotranspiration : - includes all processes by which water at the earth’s surface is converted to water vapor. - It includes evaporation from the plant canopy, transpiration, sublimation and evaporation from the soil. - roughly 62% of the precipitation that falls on the continents is evapotranspired. - Evapotranspiration exceeds runoff in most river basins and on all continents except Antarctica (Dingman, 1994)