A simple method for water balance estimation based on the empirical method and remotely sensed evapotranspiration estimates

Abstract

Developing a methodology for water balance estimation is a significant challenge, especially in areas with little or no gauging. This is because direct measurements of all the water balance components are not feasible. To overcome this issue, we propose a simple methodology based on the predefined empirical relationship between remotely sensed evapotranspiration (ET), i.e. Moderate Resolution Imaging Spectroradiometer (MODIS) ET and groundwater recharge (GR), and readily available precipitation data at the monthly time step. The developed methodology was applied in seven catchments in NE Greece using time series of precipitation and remotely sensed ET from 2009 to 2019. The potential of the proposed method to accurately estimate the water balance was assessed by the comparison of the individual water balance components against modeled values. Three performance metrics were examined and indicated that the methodology produces a satisfactory outcome. Our results indicated mean ET accounting for approximately 54% of precipitation, mean GR of 24% and mean surface runoff approximately 22% of precipitation in the study area. The proposed approach was implemented using freely available remotely sensed products and the free R software for statistical computing and graphics, offering thus a convenient and inexpensive alternative for water balance estimation, even for basins with limited data availability.

Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

“…In other words, MLR aims to find the linear function that minimizes the sum of the squares of errors (SSE) between the observed and the predicted data. An advantage of this method is the easy interpretation of the coefficients, which are generated in the model with low computational effort, in comparison to more complex techniques, such as energy balance methods and artificial intelligence algorithms [13][14][15][16][17][18][19][20][21][24][25][26][27][28][29][30][37][38][39][40][41][42][43][67][68][69][70][71][72][73][74][75]. For the MLR model, the response (dependent) variable y is assumed to be a function of k independent variables x i .…”

“…Therefore, several methods for estimating ETo have been developed, ranging from simple empirical or physically based models [13,14] to complex algorithms and techniques, such as fuzzy logic and machine learning (ML) [15][16][17][18][19][20][21]. These methods employ data from meteorological stations, or retrieved data via remote sensors [22][23][24][25][26][27][28][29][30][31][32]. The FAO-56 Penman-Monteith (FAO PM) equation (Equation ( 1)) is the most established method used to compute ETo worldwide.…”

Artificial Neural Networks for the Prediction of the Reference Evapotranspiration of the Peloponnese Peninsula, Greece

“…The necessity of acquiring ETo values led to the development of several methods of estimation, ranging from simple empirical or physically based models [9,10] to complex neuro-fuzzy and machine learning algorithms . The methods incorporate data from meteorological stations or, due to the scarcity of the former, remotely sensed data [48][49][50][51][52][53][54][55][56][57][58]. The FAO-56 Penman Monteith (FAO PM) equation requires numerous meteorological variables for effective application [59,60].…”

Groundwater recharge estimation using water table fluctuation and empirical methods

“…The methods for estimating groundwater recharge range from simple to complicated. Recharge has been determined using a water table fluctuation (WTF) method (Delottier et al 2018), empirical methods (Falalakis & Gemitzi 2020;Andualem et al 2021), an integrated surface water-groundwater modeling approach (Chemingui et al 2015), baseflow separation (Coes et al 2007), soil moisture budget (Noorduijn et al 2018), water balance method (Dhungel & Fiedler 2016), lysimeter (Zhang et al 2020;Gong et al 2021), seepage meter (Michael et al 2003), Darcy's method (Yin et al 2011), chloride mass balance (Yin et al 2011;Crosbie et al 2018), stable isotopes (Jesiya et al 2021), modeling approach (Ebrahimi et al 2016;Mogaji & Lim 2020), GIS-based approach and satellite imageries (Batelaan & De Smedt 2007), etc. According to Healy & Cook (2002), the application of multiple recharge estimation methods increases the accuracy of recharge estimates.…”