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Hailegnaw NS, Bayabil HK, Berihun ML, Teshome FT, Shelia V, Getachew F. Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management. Sci Total Environ 2024; 912:169403. [PMID: 38110092 DOI: 10.1016/j.scitotenv.2023.169403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/20/2023]
Abstract
The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.
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Affiliation(s)
- Niguss Solomon Hailegnaw
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA
| | - Haimanote K Bayabil
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA.
| | - Mulatu Liyew Berihun
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA; Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, Bahir Dar University, P.O. Box 26, Bahir Dar, Ethiopia
| | - Fitsum Tilahun Teshome
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA
| | - Vakhtang Shelia
- Agricultural and Biological Engineering Department, University of Florida, Gainesville, FL, USA
| | - Fikadu Getachew
- Agricultural and Biological Engineering Department, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, USA; Division of Basin Management and Modeling, St. Johns River Water Management District, Palatka, FL, USA
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Hagos YG, Andualem TG, Sebhat MY, Bedaso ZK, Teshome FT, Bayabil HK, Kebede EA, Demeke GG, Mitiku AB, Ayele WT, Alamayo DN, Demissie EA, Mengie MA. Soil erosion estimation and erosion risk area prioritization using GIS-based RUSLE model and identification of conservation strategies in Jejebe watershed, Southwestern Ethiopia. Environ Monit Assess 2023; 195:1501. [PMID: 37985507 DOI: 10.1007/s10661-023-12136-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 11/10/2023] [Indexed: 11/22/2023]
Abstract
Erosion of soil refers to the process of detaching and transporting topsoil from the land surface by natural forces such as water, wind, and other factors. As a result of this process, soil fertility is lost, water bodies' depth is reduced, water turbidity rises, and flood hazard problems, etc. Using a numerical model of erosion rates and erosion risks in the Jejebe watershed of the Baro Akobo basin in western Ethiopia, this study mapped erosion risks to prioritize conservation measures. In this study, the Revised Universal Soil Loss Equation (RUSLE) model was used, which was adapted to Ethiopian conditions. To estimate soil loss with RUSLE, the rainfall erosivity (R) factor was generated by interpolating rainfall data, the soil erodibility (K) factor was derived from the soil map, the topography (LS) factor was determined from the digital elevation model (DEM), cover and management (C) factor derived from the land use/cover data, and conservation practices (P) factor generated from digital elevation model (DEM) and land use/cover data were integrated with remote sensing data and the GIS 10.5 environment. The findings indicated that the watershed annual soil loss varies from nearly 0 on a gentle slope of forest lands to 265.8 t ha-1 year-1 in the very steep slope upper part of the watershed, with a mean annual soil loss of 36.2 t ha-1 year-1. The total annual soil loss in the watershed is estimated to be around 919,886.5 tons per year. To minimize the amount of soil erosion in the watershed that had been most severely affected, we identified eight conservation strategies that could be implemented. These strategies were based on the participatory watershed development (PWD) principles established by the Ethiopian government and the severity of the erosion in the watershed. The study's findings showed that a GIS-based RUSLE soil erosion assessment model can provide a realistic prediction of the amount of soil loss that will occur in the watershed. This tool can also help identify the priority areas for implementing effective erosion control measures.
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Affiliation(s)
- Yonas Gebreslasie Hagos
- Department of Geology and Environmental Geosciences, University of Dayton, 300 College Park, Dayton, OH, 45469-2364, USA.
- Ethiopian Construction Design and Supervision Works Corporation, 2561, Addis Ababa, Ethiopia.
| | - Tesfa Gebrie Andualem
- Department of Hydraulic and Water Resources Engineering, Debre Tabor University, 272, Debre Tabor, Ethiopia
- UniSA STEM, University of South Australia, Adelaide, SA, 5095, Australia
| | - Mesenbet Yibeltal Sebhat
- Faculty of Civil and Water Resources Engineering, Bahir Dar Institute of Technology, 73, Bahir Dar, Ethiopia
| | - Zelalem K Bedaso
- Department of Geology and Environmental Geosciences, University of Dayton, 300 College Park, Dayton, OH, 45469-2364, USA
| | - Fitsum Tilahun Teshome
- Department of Agricultural and Biological Engineering, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, 33031, USA
| | - Haimanote Kebede Bayabil
- Department of Agricultural and Biological Engineering, Tropical Research and Education Center, IFAS, University of Florida, Homestead, FL, 33031, USA
| | - Endalkachew Abebe Kebede
- Guna Tana Integrated Field Research and Development Center, Debre Tabor University, 272, Debre Tabor, Ethiopia
- College of Earth, Ocean, and Environment, University of Delaware, Newark, DE, 19716, USA
| | - Girum Getachew Demeke
- Department of Geography, National Taiwan University, Taipei, 10617, Taiwan
- Earth System Science, Taiwan International Graduate Program (TIGP), Academia Sinica, Taipei, 115, Taiwan
- Department of Natural Resources Management, Debre Tabor University, 272, Debre Tabor, Ethiopia
| | - Addisalem Bitew Mitiku
- Ethiopian Construction Design and Supervision Works Corporation, 2561, Addis Ababa, Ethiopia
| | - Workineh Tadesse Ayele
- Department of Hydraulic and Water Resources Engineering, Debre Tabor University, 272, Debre Tabor, Ethiopia
| | - Dinkisa Nagash Alamayo
- Ethiopian Construction Design and Supervision Works Corporation, 2561, Addis Ababa, Ethiopia
| | - Ermias Alemu Demissie
- Ethiopian Construction Design and Supervision Works Corporation, 2561, Addis Ababa, Ethiopia
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