Spatial-Temporal Interpolation of Reference Evapotranspiration for Pakistan

The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irriga...

Full description

Saved in:
Bibliographic Details
Published inMathematical problems in engineering Vol. 2022; pp. 1 - 18
Main Authors Saeed Shah, Syed Muhammad, El-Morshedy, M., Mansoor, Wahidullah
Format Journal Article
LanguageEnglish
Published New York Hindawi 28.04.2022
Hindawi Limited
Subjects
Agriculture
Annual rainfall
Climate change
Engineering
Evapotranspiration
Heat
Humidity
Hydrology
Irrigation scheduling
Methods
Performance evaluation
Radiation
Rain
Relative humidity
Resource scheduling
Solar radiation
Spacetime
Spatial data
Trends
Vegetation
Water distribution
Water engineering
Water resources management
Water shortages
Weather stations
Wind speed
Pakistan
Online AccessGet full text

Cover

More Information
Summary:The unequal water distribution in the universe has resulted in more than 2 billion people living in water-stressed areas. Globally, withdrawals of water resources are still below the critical level. Pakistan is also affected by this serious problem. In the context of environmental evaluation, irrigation scheduling, and water resource management, evapotranspiration predictions are crucial. The evapotranspiration estimation methods may need to be evaluated daily or monthly to understand the climate change effects in the local areas. This paper investigates spatial-temporal interpolation of evapotranspiration data from 41 Pakistani meteorological stations. To estimate evapotranspiration, we have used the average time series data between 2006 and 2015 on temperature, relative humidity, wind speed, and solar radiation. We developed a new modified Hargreaves equation to estimate evapotranspiration and evaluate the performance of different models. We found that our modified Hargreaves model could perform better than the original Hargreaves model. To analyze trends in different seasons, such as rainy, dry, and annual, FAO-56 Penman-Monteith, Blaney-Criddle, and Hargreaves-Samani models are used. We used Ordinary Kriging for Functional Data to map and predict evapotranspiration spatially and temporally in various locations.
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/5488725