Assessing reference evapotranspiration at regional scale based on remote sensing, weather forecast and GIS tools

Abstract Reference evapotranspiration ( ET o ) is a key component in efficient water management, especially in arid and semi-arid environments. However, accurate ET o assessment at the regional scale is complicated by the limited number of weather stations and the strict requirements in terms of their location and surrounding physical conditions for the collection of valid weather data. In an attempt to overcome this limitation, new approaches based on the use of remote sensing techniques and weather forecast tools have been proposed. Use of the Land Surface Analysis Satellite Application Facility (LSA SAF) tool and Geographic Information Systems (GIS) have allowed the design and development of innovative approaches for ET o assessment, which are especially useful for areas lacking available weather data from weather stations. Thus, by identifying the best-performing interpolation approaches (such as the Thin Plate Splines, TPS) and by developing new approaches (such as the use of data from the most similar weather station, TS, or spatially distributed correction factors, CITS), errors as low as 1.1% were achieved for ET o assessment. Spatial and temporal analyses reveal that the generated errors were smaller during spring and summer as well as in homogenous topographic areas. The proposed approaches not only enabled accurate calculations of seasonal and daily ET o values, but also contributed to the development of a useful methodology for evaluating the optimum number of weather stations to be integrated into a weather station network and the appropriateness of their locations. In addition to ET o , other variables included in weather forecast datasets (such as temperature or rainfall) could be evaluated using the same innovative methodology proposed in this study.

[1]  Salah Er-Raki,et al.  Citrus orchard evapotranspiration: Comparison between eddy covariance measurements and the FAO-56 approach estimates , 2009 .

[2]  Ayse Irmak,et al.  Spatial Interpolation of Climate Variables in Nebraska , 2010 .

[3]  V. J. Kollias,et al.  Comparison of Interpolation Methods for the Prediction of Reference Evapotranspiration—An Application in Greece , 2005 .

[4]  A. Lézine,et al.  Temperature variability over Africa during the last 2000 years , 2013 .

[5]  Donna Peuquet,et al.  An ensemble approach to space–time interpolation , 2010, Int. J. Geogr. Inf. Sci..

[6]  A. H. Thiessen.  PRECIPITATION AVERAGES FOR LARGE AREAS , 1911 .

[7]  B. Glahn,et al.  Error Estimation of Objective Analysis of Surface Observations , 2013 .

[8]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[9]  L. S. Pereira,et al.  A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO56 Penman-Monteith method , 2006 .

[10]  Isabel F. Trigo,et al.  Reference crop evapotranspiration derived from geo-stationary satellite imagery - a case study for the Fogera flood plain, NW-Ethiopia and the Jordan Valley, Jordan , 2010 .

[11]  G. Glass,et al.  High-resolution spatiotemporal weather models for climate studies , 2008, International journal of health geographics.

[12]  James E. Ayars,et al.  A processing method for weighing lysimeter data and comparison to micrometeorological ETo predictions , 2007 .

[13]  P. Gavilán,et al.  Reference Evapotranspiration Estimation in a Highly Advective Semiarid Environment , 2005 .

[14]  I. A. Nalder,et al.  Spatial interpolation of climatic Normals: test of a new method in the Canadian boreal forest , 1998 .

[15]  A. Martínez-cob,et al.  Multivariate geostatistical analysis of evapotranspiration and precipitation in mountainous terrain , 1996 .

[16]  M. Tasumi,et al.  Integrating satellite-based evapotranspiration with simulation models for irrigation management at the scheme level , 2008, Irrigation Science.

[17]  Mehdi Keblouti,et al.  Spatial Interpolation of Annual Precipitation in Annaba-Algeria - Comparison and Evaluation of Methods , 2012 .

[18]  Christopher A. Fiebrich,et al.  Quality Assurance Procedures in the Oklahoma Mesonetwork , 2000 .

[19]  Ignacio J. Lorite,et al.  Assessment of the Irrigation Advisory Services’ Recommendations and Farmers’ Irrigation Management: A Case Study in Southern Spain , 2012, Water Resources Management.

[20]  C. Santos,et al.  Assessment of reference evapotranspiration using remote sensing and forecasting tools under semi-arid conditions , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[21]  G. C. Sediyama,et al.  Geostatistical improvements of evapotranspiration spatial information using satellite land surface and weather stations data , 2013, Theoretical and Applied Climatology.

[22]  Estimation of Reference Evapotranspiration by an Energy Balance Approach , 2007 .

[23]  S. Ustin,et al.  Daily reference evapotranspiration for California using satellite imagery and weather station measurement interpolation , 2009 .

[24]  M. Tasumi,et al.  Performance assessment of an irrigation scheme using indicators determined with remote sensing techniques , 2010, Irrigation Science.

[25]  Baryohay Davidoff,et al.  Comparison of Some Reference Evapotranspiration Equations for California , 2005 .

[26]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[27]  A. Persson.  User Guide to ECMWF forecast products , 2001 .

[28]  I. Lorite,et al.  Uncertainty in estimating reference evapotranspiration using remotely sensed and forecasted weather data under the climatic conditions of Southern Spain , 2015 .

[29]  Terry A. Howell,et al.  Evaluating eddy covariance cotton ET measurements in an advective environment with large weighing lysimeters , 2009, Irrigation Science.

[30]  I. Lorite,et al.  Regional calibration of Hargreaves equation for estimating reference ET in a semiarid environment , 2006 .

[31]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[32]  E. Fereres,et al.  Deficit irrigation for reducing agricultural water use. , 2006, Journal of experimental botany.

[33]  Steven R. Evett,et al.  The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment , 2000 .

[34]  Jerry L. Hatfield,et al.  Data quality checking for single station meteorological databases , 1994 .

[35]  G. La Loggia,et al.  Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[36]  Arthur I. Zygielbaum,et al.  Network requirements for sensor accuracy and precision: a case study to assess atmospheric variability in simple terrain , 2008 .

[37]  Terry A. Howell.  Adjusting Temperature Parameters to Reflect Well-Watered Conditions , 2000 .

[38]  Jun Li,et al.  A GIS-based approach for estimating spatial distribution of seasonal temperature in Zhejiang Province, China , 2006 .

[39]  Richard G. Allen,et al.  Assessing Integrity of Weather Data for Reference Evapotranspiration Estimation , 1996 .

[40]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[41]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[42]  I. Lorite,et al.  Using weather forecast data for irrigation scheduling under semi-arid conditions , 2015, Irrigation Science.

[43]  Isabel F. Trigo,et al.  Reference Crop Evapotranspiration obtained from the geostationary satellite MSG (METEOSAT). , 2012 .

[44]  Ignacio J. Lorite,et al.  An innovative remote sensing based reference evapotranspiration method to support irrigation water management under semi-arid conditions , 2014 .

[45]  N. Stuart,et al.  A comparison among strategies for interpolating maximum and minimum daily air temperatures. Part II: The interaction between number of guiding variables and the type of interpolation method , 2001 .

[46]  M. Taylor,et al.  A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales , 2008 .