Estimating Potential Evapotranspiration

Hargreaves, George H.; Samani, Zohrab

doi:10.1061/jrcea4.0001390

Cited by 1,195 publications

(269 citation statements)

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“…Among them, three well-established statistical indicators: Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and ratio of the root mean square error to the standard deviation of measured data (RSR) were used in this study for model evaluation [ Surface runoff was estimated by the SCS curve number (SCS-CN) [75] method. The potential evapotranspiration was computed using Hargreaves method [76]. The computed runoff from each sub-basin was routed through the river network to the main basin outlet by using variable storage method.…”

Section: Performance Evaluation Criteriamentioning

confidence: 99%

“…Based on the modeling experience in Nepalese basins and judgement of the study team, manual method was used for calibrating the model.Surface runoff was estimated by the SCS curve number (SCS-CN)[75] method. The potential evapotranspiration was computed using Hargreaves method[76]. The computed runoff from each sub-basin was routed through the river network to the main basin outlet by using variable storage method.…”

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Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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The soil and water assessment tool (SWAT) hydrological model has been used extensively by the scientific community to simulate varying hydro-climatic conditions and geo-physical environment. This study used SWAT to characterize the rainfall-runoff behaviour of a complex mountainous basin, the Budhigandaki River Basin (BRB), in central Nepal. The specific objectives of this research were to: (i) assess the applicability of SWAT model in data scarce and complex mountainous river basin using well-established performance indicators; and (ii) generate spatially distributed flows and evaluate the water balance at the sub-basin level. The BRB was discretised into 16 sub-basins and 344 hydrological response units (HRUs) and calibration and validation was carried out at Arughat using daily flow data of 20 years and 10 years, respectively. Moreover, this study carried out additional validation at three supplementary points at which the study team collected primary river flow data. Four statistical indicators: Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), ratio of the root mean square error to the standard deviation of measured data (RSR) and Kling Gupta efficiency (KGE) have been used for the model evaluation. Calibration and validation results rank the model performance as “very good”. This study estimated the mean annual flow at BRB outlet to be 240 m3/s and annual precipitation 1528 mm with distinct seasonal variability. Snowmelt contributes 20% of the total flow at the basin outlet during the pre-monsoon and 8% in the post monsoon period. The 90%, 40% and 10% exceedance flows were calculated to be 39, 126 and 453 m3/s respectively. This study provides additional evidence to the SWAT diaspora of its applicability to simulate the rainfall-runoff characteristics of such a complex mountainous catchment. The findings will be useful for hydrologists and planners in general to utilize the available water rationally in the times to come and particularly, to harness the hydroelectric potential of the basin.

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Section: Performance Evaluation Criteriamentioning

confidence: 99%

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confidence: 99%

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Marahatta

Devkota

Aryal

2021

Water

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“…We selected 17 grid cells covering the hazelnut growing regions in the seven selected municipalities. Daily global solar radiation (MJ m –2 d –1 ) as input to HAZEL was estimated from air temperature according to Hargreaves and Samani (1982) .…”

Section: Methodsmentioning

confidence: 99%

The HADES Yield Prediction System – A Case Study on the Turkish Hazelnut Sector

et al. 2021

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Crop yield forecasting activities are essential to support decision making of farmers, private companies and public entities. While standard systems use georeferenced agro-climatic data as input to process-based simulation models, new trends entail the application of machine learning for yield prediction. In this paper we present HADES (HAzelnut yielD forEcaSt), a hazelnut yield prediction system, in which process-based modeling and machine learning techniques are hybridized and applied in Turkey. Official yields in the top hazelnut producing municipalities in 2004–2019 are used as reference data, whereas ground observations of phenology and weather data represent the main HADES inputs. A statistical analysis allows inferring the occurrence and magnitude of biennial bearing in official yields and is used to aid the calibration of a process-based hazelnut simulation model. Then, a Random Forest algorithm is deployed in regression mode using the outputs of the process-based model as predictors, together with information on hazelnut varieties, the presence of alternate bearing in the yield series, and agro-meteorological indicators. HADES predictive ability in calibration and validation was balanced, with relative root mean square error below 20%, and R2 and Nash-Sutcliffe modeling efficiency above 0.7 considering all municipalities together. HADES paves the way for a next-generation yield prediction system, to deliver timely and robust information and enhance the sustainability of the hazelnut sector across the globe.

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“…Daily PET (mmÁday À1 ) was estimated using the Hargreaves method (Hargreaves and Samani, 1982;1985) which makes use of T min ( C) and T max ( C) such that…”

Section: Potential Evapotranspiration Petmentioning

confidence: 99%

Water availability trends across water management zones in Uganda

Onyutha

Asiimwe

Muhwezi

et al. 2021

Atmospheric Science Letters

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This study assessed trends in gridded (0.25 Â 0.25 ) Climate Forecast System Reanalysis (CFSR) precipitation, potential evapotranspiration (PET), and precipitation minus PET (PMP) across the four water management zones (WMZs) in Uganda including Kyoga, Victoria, Albert, and Upper Nile. The period considered was 1979-2013. Validation of CFSR datasets was conducted using precipitation observed at eight meteorological stations across the country.Observed precipitation trend direction was satisfactorily reproduced by CFSR data extracted at five out of eight stations. Negative (positive) values of longterm PMP mean were considered to indicate areas characterized by water scarcity (surplus). Areas with large positive PMP were confined to Lake Victoria and mountains such as Rwenzori and Elgon. The largest negative PMP values were in the arid and semi-arid areas of north and northeastern Uganda. The null hypothesis H 0 (no trend) was rejected (p < 0.05) for increasing annual precipitation trends across the various WMZs except in the extreme eastern parts of the Upper Nile, Kyoga, and Victoria WMZs (or areas along the boundary of Uganda and Kenya). The H 0 (no trend) was rejected (p < 0.05) for decreasing trends in annual PET over West Nile region of the Upper Nile, western parts of Victoria, and the Albert WMZs. For increasing trend in PMP, the H 0 (no trend) was rejected (p < 0.05) across the various WMZs except around the Mount Elgon area. The study findings are relevant for planning of water resources management across the different WMZs in the country.

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Estimating Potential Evapotranspiration

Cited by 1,195 publications

References 0 publications

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part I: Model Development)

The HADES Yield Prediction System – A Case Study on the Turkish Hazelnut Sector

Water availability trends across water management zones in Uganda

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