Author: Sharath Chandra
Date: 2023-04-21
Report no: IIIT/TH/2023/26
Advisor:Shaik Rehana

Abstract

Drought is a natural hazard with a significant impact on the economy, agriculture, and environment. It is defined as a significant decrease in water availability in all its forms. Droughts are estimated using drought indices. Drought indices are numerical measurements that describe the severity of drought by combining data from one or more variables (indicators), such as precipitation and evapotranspiration (ET), into a single number. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, which take both precipitation, potential and actual evapotranspiration into account. Since ET is a critical factor in estimating drought, well-grounded ET estimations are required. The major forms of ET are Potential evapotranspiration (PET) and Actual evapotranspiration (AET). PET is described as the loss of water from a significant area that is equally covered with short, green crops that are actively growing. PET is regarded as the maximum amount of evapotranspiration. AET is defined as the total amount of water used in evaporation and transpiration by a crop during the entire growing season. Various methods have been developed to estimate PET and AET depending upon the availability of hydro meteorological variables. Various empirical based methods and hydrological model based simulations of PET and AET have been developed. Advancement of data-driven algorithms also have been extensively developed to estimate ET. In this context, many studies used empirical based estimates of ET for the calculation of drought indices. None of these studies analyzed the sensitivity of various ET methods to assess the drought characteristics. Thus, the present study aimed to include various PET and AET methods in the drought characterization. Various empirical methods, such as Penman-Monteith, Hargreaves, Turc, and Priestley-Taylor and data-driven method of Artificial Neural Network (ANN) has been used to estimate PET. The input variables used to estimate these methods are temperature, wind speed, solar radiation, and relative humidity which are obtained over the period of 1965 to 2015 for Hyderabad station. ANN model was trained and tested with climate variables as input variables and various empirical models as reference models to predict the best PET method. Penman-Monteith, Hargreaves, and Turc method performed better with ANN model estimates. Later, to study the impact of various empirical based PET estimates on drought estimation, SPEI is calculated using various PET estimates at different time scales. All other methods are compared with the Penman-Monteith method, which is considered the standard method because it considers the main meteorological factors. Hargreaves and Turc methods performed better with the standard method and these methods can be useful in estimating drought when minimum data is available. To assess the drought events accurately by various drought indices it is necessary to predict the hydro-meteorological variables (PET and AET) precisely. There are several challenges in estimating AET and PET at the fine spatial resolution. There are various empirical models (Budyko, Penman- Monteith, Hargreaves, and Turc) for estimating AET and PET. Still, these empirical methods does not account for the catchment characteristics, which may underestimates the actual amount of hydrological variables. Further, satellite-based remote sensing data are accessible for extracting evapotranspiration (ET) values. It provides global coverage and continuous observations of land surface variables affecting ET. Another conceptual based approach to estimate PET and AET at catchment scale is hydrological model such as Soil and Water Assessment Tool (SWAT). The present study aimed to include various approaches of empirical (Budyko, Penman-Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing in the drought characterization using SPEI and SPAEI. Remote sensing PET and AET are considered as standard methods to compare both empirical and modeled PET and AET estimates. The present methodology was tested on a dry-sub-humid river catchment of India, the Tungabhadra River catchment. It is recommended to use PET instead of AET when estimating drought indices as SPEI values performed relatively better than SPAEI. In the present study it is observed that although PET and AET estimates vary with different models, drought indices SPEI and SPAEI are not differing much at annual scales. Hargreaves and Penman-Monteith performed better results compared to remote sensing method in SPEI calculations. And for SPAEI, Budyko and Turc performed better results. Thus, the present study concludes that empirical models correlated better with the remote sensing data. The study will be prominent for ungauged river basins, where detailed hydrological data is limited and difficult to implement hydrological models, empirical based PET and AET estimates can be better choice for drought characterization.

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