The potential of water availability in Maros Watershed using Thornthwaite-Mather water balance method

Lias, Syamsul Arifin; Irmayani,; Laban, Sartika

doi:10.1088/1755-1315/486/1/012135

Cited by 5 publications

(2 citation statements)

References 1 publication

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“…The high the rainfall, the greater potential for water availability. On the other hand, the lower the rainfall, the higher the water deficit [30]. However, this can also show that in 2023, the level of vulnerability to flooding will be even greater.…”

Section: Rainfall Area In Maros Regencymentioning

confidence: 98%

Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency

Aslim

Jasruddin

Palloan

et al. 2023

SJI

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Purpose: This study aims to identify the right combination of network architecture, learning rate, and epoch in making predictions at each rainfall post in Maros Regency. In addition, this study also predicts the monthly rainfall profile in 2021-2025 in Maros Regency.Methods: The method in this study is the backpropagation neural network algorithm to learn and predict the data. BPNN is one of the most commonly used non-linear methods in making predictions recently. The data used in this study is monthly rainfall data from 2000-2020 as training and testing data at four rainfall stations including BPP Batubassi, Staklim Maros, Stamet Hasanuddin, and BPP Tanralili.Result: The results showed that the combination of network architecture, learning rate, and epoch obtained at each rainfall post was different. The highest level of prediction accuracy was obtained on 5 layers rather than 3 or 4 layers of network architecture with prediction accuracy at each rainfall post respectively 76.91%, 72.47%, 75.24%, and 76.53%. The predictions of rainfall from 2021-2025 are following the monsoon rain pattern with the highest rainfall in January 2025 of 964.1 mm, while the largest annual rainfall is obtained in 2023 with a total of 3359.6 mm.Novelty: In this study, various combinations of network architecture parameters consisting of learning rate, epoch, and architecture at each rainfall post obtained different results. Particularly in the Maros Regency, the combination that is most suitable for use in predicting monthly rainfall at the Batubassi BPP post is learning rate 0.7, epoch 50000, and network architecture 11-6-10-7-5, at Staklim Maros post is learning rate 0.5, epoch 50000, and network architecture 11-5-9-10-5, at Stamet Hasanuddin post is learning rate 0.8, epoch 20000, and network architecture 11-5-8-6-5, and at BPP Tanralili post is learning rate 0.5, epoch 10000, and 11-5-9-9-5 network architecture.

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Section: Rainfall Area In Maros Regencymentioning

confidence: 98%

Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency

Aslim

Jasruddin

Palloan

et al. 2023

SJI

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“…3). The water balance is an essential hydrological characterization tool (Al-Sudani 2018; Rodrigues et al 2018aRodrigues et al , 2018bLias and Laban 2020).…”

Section: Climatological Water Balancementioning

confidence: 99%

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Lima

Aparecido

Lorençone

et al. 2021

Theor Appl Climatol

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Climate Classification System (CCS) is an important tool for validating climate change models, subsidizing the characterization of new areas suitable or unfit for agricultural activity according to future climate change scenarios. This study aims to classify the climate of the Brazilian territory in the various climate change scenarios of the IPCC through the Thornthwaite system (1948). We used a 30-year historical series of climatic data of average air temperature (°C) and rainfall (mm), obtained from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources platform (NASA/POWER). Potential evapotranspiration (ETP) was estimated by the method of Camargo (1971); the climatological water balance (CWB) was calculated by the method of Thornthwaite and Mather (1955), using 100 mm of soil water storage capacity. CWB extracts were combined for classification by Thornthwaite (1948). The scenarios used were based on the IPCC (2014) projections and the study of Pirttioja et al. (2015). The Brazilian territory had an average air temperature of 22.20 °C (± 3.20) °C and annual precipitation of 1987 mm (± 725) mm. The climatic classification of Thornthwaite presented 108 climatic classes for the current scenario with a more significant predominance of the classes ArAʹaʹ, B4rAʹaʹ, and B3rAʹaʹ representing 20.54%, 15.62%, and 9.46% of the Brazilian territory, respectively. The climate class ArAʹaʹ had 39.20% in the North and 14.97% in the Midwest. The South region has a predominance of 24.31% for the class ArBʹ3aʹ. In the Southeast and Northeast, the climate classes B2rBʹ3aʹ and DdBʹ2aʹ represented 14.80% and 15.26% of the regions, respectively. The S5 scenario was considered more favorable to establishing crops, with 48.04% of Brazil represented by the climate class ArAʹaʹ. Furthermore, the most catastrophic scenarios for crops were S3 and S4, promoting Brazil a predominance of classes B3rAʹaʹ in 18.02% and B1rAʹaʹ in 21.04%, respectively, favoring the occurrence of arid and dry climates in large part of the Brazilian territory.

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Sequential climatic water balance applied in the hydrographic region of Paraguaçu, in Brazil

Souza

Nascimento

2022

Sustain. Water Resour. Manag.

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The potential of water availability in Maros Watershed using Thornthwaite-Mather water balance method

Cited by 5 publications

References 1 publication

Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency

Monthly Rainfall Prediction Using the Backpropagation Neural Network (BPNN) Algorithm in Maros Regency

Climate change in Brazil: future scenarios classified by Thornthwaite (1948)

Sequential climatic water balance applied in the hydrographic region of Paraguaçu, in Brazil

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