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The Optimal Alternative for Quantifying Reference Evapotranspiration in Climatic Sub-regions of Bangladesh

Roquia Salam
Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

Abu Reza Md. Towfiqul Islam
Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

Quoc Bao Pham
Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Majid Dehghani
Civil Engineering Department, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

Nadhir Al-Ansari
Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Lulea, Sweden

Nguyen Thi Thuy Linh
Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

Abstract/ Executive Summary:

Reference evapotranspiration (ET_o) is a basic element for hydrological designing and agricultural water resources management. The FAO56 recommended Penman–Monteith (FAO56-PM) formula recognized worldwide as the robust and standard model for calculating ET_o. However, the use of the FAO56-PM model is restricted in some data-scarce regions like Bangladesh. Therefore, it is imperative to find an optimal alternative for estimating ET_o against FAO56-PM model. This study comprehensively compared the performance of 13 empirical models (Hargreaves–Samani, HargreavesM1, Hargreaves M2, Berti, WMO, Abtew, Irmak 1, Irmak 2, Makkink, Priestley-Taylor, Jensen–Haise, Tabari and Turc) by using statistical criteria for 38-years dataset from 1980 to 2017 in Bangladesh. The radiation-based model proposed by Abtew (ET_o,6) was selected as an optimal alternative in all the sub-regions and whole Bangladesh against FAO56-PM model owing to its high accuracy, reliability in outlining substantial spatiotemporal variations of ET_o, with very well linearly correlation with the FAO56-PM and the least errors. The importance degree analysis of 13 models based on the random forest (RF) also depicted that Abtew (ET_o,6) is the most reliable and robust model for ET_o computation in different sub-regions. Validation of the optimal alternative produced the largest correlation coefficient of 0.989 between ET_o,s and ET_o,6 and confirmed that Abtew (ET_o,6) is the best suitable method for ET_o calculation in Bangladesh.

Keywords: Optimal alternative, Quantifying reference, Evapotranspiration, Climatic sub-regions, Bangladesh

Location: Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

Start Date:

End Date:

Program Area: Crop-Soil-Water Management

Commodity: Water management

Objectives:

The novelty of this research lies in employing 13 empirical models with a heuristic random forest model for the first time in Bangladesh that enables us to find an optimal alternative used for important environmental implication from the most reliable and outperformed equations for ET_o computation in different climatic sub-regions of Bangladesh.

Methodology:

Study area description and data sources

Bangladesh, situated in Southeast Asia, geographically it encompasses between 20° 30′ N and 26° 45′ N latitudes and 88° 0′ E to 92° 45′ E longitudes. Banglapedia divided Bangladesh into seven climatic sub-regions based on climatology and geography. The seven sub-regions are (1) south-eastern zone; (2) north-eastern zone; (3) northern part of the northern zone; (4) north-western zone; (5) western zone; (6) south-western zone and (7) south-central zone. Bangladesh experiences a sub-tropical humid monsoon climate with seasonal differences.

Western Bangladesh has usually become drier compared to other regions in Bangladesh. Here, climatic variability is a regular scenario. Long-term daily average relative humidity, minimum temperature, maximum temperature, wind speed (at 2 m height), net radiation, evapotranspiration across the country are, respectively, 80%, 21.39 °C, 29.94 °C 1.32 ms⁻¹, 10.44 MJm⁻²day⁻¹, and 3.72 mm day⁻¹.

Bangladesh Meteorological Department (BMD) runs only 43 meteorological stations across the country. The meteorological stations are unevenly distributed all over the country and most of the stations are located in the south-eastern regions. These meteorological stations are available for the climatic dataset, although some of the stations are newly established after the 1990s in Bangladesh and they do not have long-term data records (www.bmd.gov.bd). When more climatic variables are required, the dataset from a smaller number of stations was available. Due to these drawbacks, 20 stations were chosen for ET_o estimation over the 38 years from 1980 to 2017. These selected 20 stations embody the seven climatic sub-regions of the country. Daily minimum (T_min) and maximum temperature (T_max) (°C), mean relative humidity (Hr) (%), wind speed (Uz) (Knots) and sunshine hour (h day⁻¹) datasets of 20 stations were sourced from the BMD. Net radiation (Rn) and wind speed at 2 m height (U2) cannot directly be measured by weather stations. Daily Rn and U2 were estimated using the procedures recommended by Allen et al.¹ with the available meteorological datasets. A brief geographical and meteorological description of the selected stations is found in the Supplementary Material of Table S2. However, missing data in almost all the 20 stations was found. After the initial screening test, missing data of the 20 stations were less than 5% for the period of 1980–2017. Missing data for each station were filled by the existing records for the respective days from the adjacent neighbor stations. It is worthy to note that sunshine hour dataset in this study is continuous with no missing data. More details about the fill-up of missing meteorological datasets is given in the Supplementary Material (Table S3). The BMD follows the guideline of World Meteorological Organization (WMO) for weather dataset collection and record archiving. However, quality control of the dataset was primarily undertaken thoroughly by checking namely, positive values of parameters, for example, T_min is lower than T_max, and humidity is less than 100%. The homogeneity tests of the dataset were conducted to exhibit any anomaly in the dataset. All of the datasets were passed through the quality control by the staff of the BMD.

Source of Information: Scientific Reports volume 10, Article number: 20171 (2020)

Article Link (Online Journal):

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

In this study, daily meteorological datasets from 20 weather stations from seven sub-regions in Bangladesh for the period of 1980–2017 were used. A widespread comparison between ET_o,s (calculated by FAO56-PM) and ET_o,i (calculated by HS, HM1, HM2, BT, WMO, ABT, IR1, IR2, MAK, PT, JH, TAB, TR, respectively) has been carried out. The possible roles of these 13 empirical models against the FAO56-PM were also explored in this study. Out of 20 stations, 5 stations showed an increasing trend of ET_o,s; 11 stations showed a decreasing trend and 4 stations showed no trend of ET_o,s. Spatiotemporal distribution of ET_o,s, and ET_o,i revealed that the model proposed by Abtew model showing the closest distribution of ET_o,i to ET_o,s. RE, RMSE, MAE, MBE, and NSE were employed for evaluating the empirical models which were identified ET_o,6 as the outperformed model with the lowest errors for calculating ETo in different sub-regions and whole Bangladesh. By contrast, ET_o,5 (WMO) and ET_o,13 (Turc) models selected as the poorer alternative models with the higher statistical errors. RF model also confirmed the Abtew as the outperformed model. The linear regression model showed that a strong linear correlation was found between FAO56-PM and Abtew model. Validation by using Eq. (33) explored the similar outcomes that the ET_o,6 model outperformed than the other models. This study recommends the model proposed by the Abtew (ET_o,6) as the best alternative model with high accuracy, reliability and lowest errors for all the sub-regions and whole Bangladesh for calculating ETo when full climatic datasets for FAO56-PM model are unavailable. Future study should be focused on the evaluation of machine learning ensemble models for estimating daily ETo in Bangladesh. This research is a vital scientific contribution to ETo quantification and influential empirical models in Bangladesh where the large set of meteorological datasets could not be acquired. This study provides an important guidance for agricultural water practices, hydrological processes and irrigation management in Bangladesh, also useful as well as the similar subtropical climate region elsewhere in the world.

Knowledge Management: Journal