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000000250 020__ $$a   90-5808-331-4
000000250 041__ $$aEnglish
000000250 100__ $$aFarah, H. O.
000000250 245__ $$aEstimation of regional evaporation under different weather conditions from satellite and meteorological data: a case study in the Naivasha basin, Kenya
000000250 260__ $$c2001
000000250 260__ $$bInternational Inst. for Aerospace Survey and Earth Sciences - ITC, Enschede
000000250 300__ $$a170
000000250 440__ $$a   ITC dissertation 
000000250 440__ $$n  80
000000250 502__ $$aThesis (Ph. D.) - Wageningen University, The Netherlands
000000250 520__ $$a Existing remote sensing algorithms used to estimate evaporation from remotely sensed data differ in the way they describe the spatial variations of input parameters. An evaluation of the impact of spatially varying input parameters on distributed surface fluxes showed that the vertical near surface air temperature difference and frictional velocity were the most critical parameters. Most remote sensing algorithms treat air temperature as spatially constant indicating that they are less suitable for the calculation of distributed evaporation in heterogeneous catchments.  The temporal variability of the evaporative fractionΛat the daily and seasonal time frames was investigated with field data obtained at two experimental sites. For general weather conditions the values of the midday (12.00 to 13.00 hrs) evaporative fractionΛ mid compared well with the averaged day time evaporative fractionΛ day . A good relationship was obtained between daytime evaporation estimated fromΛ mid and evaporation measured by the Bowen ratio surface energy balance method. Less satisfactory evaporation results were obtained using morning (9.00 to 10.00 hrs) evaporation fractionΛ mor . The seasonal evolution ofΛ day was observed to be gradual. To capture the seasonal evolution ofΛ day it would be sufficient to measureΛ day approximately every 10 days. Moreover, it was shown that the inter-annual variability of the 10-day averageΛcould be reliably estimated from standard weather data.  To monitor the temporal evolution of daily evaporation over a season, evaporation has to be estimated between consecutive clear days with satellite images being available. Two methods to predict daily evaporation on days without satellite images due to cloud cover are presented. Field data acquired at two sites were used to test these methods. The first method consists of the application of the Penman-Monteith equation and Jarvis-Stewart model with standard weather data and the assumption of gradual soil moisture changes between consecutive clear days. With this method evaporation could be accurately predicted for up-to 5 continuous days with no satellite images. The second method is a simplified approach involving the use of a constantΛbetween cloud free days with measured evaporation. This approach did not give satisfactory results in predicting evaporation on individual days. However, the total evaporation of a 7-day time span was equally good for both methods.  Five NOAA AVHRR satellite images were used to produce daily evaporation maps of the Naivasha basin for 15 continuous days with intermittent cloud cover by using the Penman-Monteith equation coupled with the Jarvis-Stewart model as well as the evaporative fraction method. The evaporation maps were validated with field data and overall good agreement was obtained. This demonstrated that remote sensing methods can be extended for practical use under all weather conditions to map both the spatial patterns as well as the temporal evolution of evaporation in catchments and river basins. The methods of predicting evaporation can be applied at different time scales. Users can select the appropriate time scales depending on their needs. The implementation of the Penman-Monteith equation and Jarvis-Stewart model requires a land cover classification of the catchment to assign land cover dependent coefficients in the Jarvis-Stewart model. At each land cover type standard weather data has to be measured.
000000250 653__ $$aMeteorology and climatology
000000250 653__ $$aKenya
000000250 653__ $$aClimatic change
000000250 653__ $$aMeteorological observations
000000250 653__ $$aEvaporation
000000250 653__ $$aSatellite data
000000250 8564_ $$uhttp://edepot.wur.nl/194754$$yDownload
000000250 980__ $$aTHESIS