Estimation of Potential and Actual Evapotranspiration under Limited Climate Data

Author: Adeeba Ayaz
Date: 2022-04-29
Report no: IIIT/TH/2022/43
Advisor: Shaik Rehana


Evapotranspiration (ET) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. ET is a complex hydrological variable defined by various hydro climatological variables. The major forms of ET which are widely applicable in hydrological, water balance, drought assessments and other ecological assessments are Potential (PET) and Actual Evapotranspiration (AET). PET represents the atmospheric water demand with a focus on climatological variables. AET is influenced by climate, vegetation, soil moisture, and the amount of available water and presents the annual water balance between precipitation and latent heat exchange. Various empirical formulations have been developed to estimate PET and AET depending upon the availability of hydrometeorological variables. These empirical formulations are region-specific and developed for particular climatic conditions. Furthermore, the empirical family of models has a major limitation as they require a large number of hydrometeorological inputs, limiting their utility in data-scarce areas of ungauged basins. In this context, empirical and mathematical models have emerged as simple and readily implementable for estimating PET and AET with measured hydrometeorological parameters as independent variables. Such mathematical models can be valuable to predict PET and AET when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict PET and AET using various hydroclimatic variables. Four empirical methods, such as FAO-based Penman-Monteith method, temperature-based Hargreaves method, and radiation-based Turc, and Priestley-Taylor method, were used to estimate PET at a daily time scale. Five data-driven algorithms, such as Long short-term memory neural networks (LSTM), Artificial Neural Network (ANN), Gradient Boosting Regressor (GBR), Random Forest (RF) and Support Vector Regression (SVR), were implemented. Two empirical AET models, such as Budyko and Turc methods, were utilized in estimating AET. These models were evaluated over two different climatic regions, Hyderabad, the largest city of the Indian state, Telangana and aipara in New Zealand, both with semiarid climates. Dataset consists of daily meteorological data of maximum and minimum air temperature, relative humidity, solar radiation, and wind speed over a period of 51 years (1965 – 2015) for Hyderabad and for a period of 6 years (2010- 2016) for Waipara station.The Penman-Monteith method was considered as the standard method to compare the different models and various empirical models of PET. The models were trained and tested with climate variables as input variables and various empirical models as reference models. The most influencing climate variables on PET were found in the order of temperature, solar radiation, wind speed, and relative humidity, which formed as the basis to choose different datasets to train over models and compare the results to validate. Temperature and radiation-based models of Turc and Priestley-Taylor methods can be used to estimate PET when all other climate variables are not available as they are also promising with the Penman-Monteith method. The results indicated that 99% accuracy could be achieved with all climatic input, whereas accuracy drops to 86% with limited data. Both LSTM and ANN models have been noted as the most robust models for estimating PET with minimal climate data. Even though the excellent performance can be achievable when all input variables are used, the study, however, found that even a three-parameter combination (temperature, wind speed and relative humidity values) or two-parameter combination (temperature and relative humidity, temperature and wind speed) can also be promising in PET estimation for a semi-arid climate. The AET over semi-arid climatic conditions of Hyderabad, Telangana, India and Waipara (New Zealand) was estimated using modelled and different empirical methods-based PET using Budyko and Turc models. The proposed empirical-based AET models, Budyko and Turc, showed that the AET process has the potential to be estimated by structurally simple methods. Equation-based AET methods made it possible to extract useful information about the hydrological process. It was observed that the meteorological variables of temperature and solar radiation have more significant contributions than other variables in the estimation of AET. In addition, the effects of the meteorological variables were found to be essential and effective in the estimation of AET. The research findings of the study reveal that under limited data availability, the best input combinations were identified as temperature and wind speed for estimating PET; temperature, wind speed and precipitation for estimating AET for semiarid climatology. Overall, the research findings of the study stress on the use of limited data in understanding the complex hydrological processes such as PET and AET using data-driven and empirical-based approaches for diverse climatological conditions.

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