Forest hydrology modeling tools for watershed management: A review

Sun, Ge; Wei, Xiaohua; Hao, Lu; Sanchis, María González; Hou, Yiping; Yousefpour, Rasoul; Tang, Run; Zhang, Zhiqiang

doi:10.1016/j.foreco.2022.120755

Cited by 18 publications

(16 citation statements)

References 217 publications

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“…However, decision-makers who study hydrology, crop production, and species distribution need data at scales between 10 and 50 kilometers. Therefore, numerous techniques have been devised to bridge the gap between GCM data and fine-scale climate data [7,8]. According to literature, downscaling can also be performed on spatial and temporal aspects [2,9].…”

Section: Introductionmentioning

confidence: 99%

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Koswaththa,

Ranasinghe,

Ekanayake

et al. 2024

Engineer

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Studying future precipitation behaviour in river basins is essential for proper water resources and land-use planning within them, as this will help to reduce the risk and mitigate disasters that can occur in the future. General Circulation Models (GCMs) are used to study future precipitation fluctuations, which simulate large-scale climate variations under the effect of greenhouse gas changes. The GCM runs at a coarse spatial resolution which cannot be directly used for climate impact studies. Therefore, downscaling is required to extract the sub-grid and local scale information. This study examines the use of the Long Short-Term Memory (LSTM) neural network for climate downscaling to the Mi-Oya river basin in Sri Lanka using CNRM-CM5 and HadCM3 GCMs and observed annual data for 35 years. The precipitation data were extracted to cover Sri Lanka. Current downscaling models mostly use Convolutional Neural Networks (CNNs) to downscale GCMs. Out of 42 GCMs, two appropriate GCMs were chosen using the data analysis tool Data Integration and Analysis System (DIAS). The best predictor variables were chosen using the LASSO regression method. In this research, Machine Learning models were implemented using the Google TensorFlow platform. The Nash–Sutcliffe coefficient, Pearson correlation coefficient, and root-mean-square error performance indices were used to evaluate the performances of different downscaling models. Statistical downscaling was performed on the data at RCP 2.6, 4.5, and 8.5 using a LSTM. Subsequently, the changes that would take place by the year 2100 were analysed. The results show that precipitation will be reduced in the 2nd and 3rd decades of the 21st century, and precipitation will increase toward the 22nd century.

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Section: Introductionmentioning

confidence: 99%

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Koswaththa,

Ranasinghe,

Ekanayake

et al. 2024

Engineer

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“…1. Modern techniques are required to help manage forests under numerous demands on water resources, as there are still difficulties in managing forests for water internationally [2]. A topographically determined area that drains into an outlet through a stream system is called a watershed.…”

Section: Introductionmentioning

confidence: 99%

Integrating forest management and Watershed health for Sustainable Water sources

Sumalatha,

Sugunan

et al. 2024

E3S Web of Conf.

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This paper explores the complex relationship among forest management practices and watershed health, emphasizing the crucial function of forests in sustaining freshwater sources. It critiques various factors impacting watershed fitness, including land use changes, deforestation, and urbanization. The study conducted here highlights the importance of sustainable forest management practices, such as environment-based techniques and carbon sequestration, in retaining water quality and quantity. Additionally, it discusses the impact of forest composition on watershed health and the subsequent results on soil approaches, water quality, and biodiversity. The paper additionally addresses the challenges and strategies in handling watersheds, considering the socio-economic, environmental, and political aspects. The findings recommend that an incorporated technique regarding community participation, cross-sectorial coverage integration, and advanced management strategies can improve watershed health and make a contribution to global water security.

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“…The temporal variability of PET is important especially for rainfed forest environments [36][37][38]. Exploring the performance of PET methods in forest ecosystems is particularly important for understanding water interactions and balance in forest ecosystems, as well as for assessing the water requirements and growth of forest vegetation [39,40]. Moreover, it becomes essential to assess model performance at high altitudes, where PET values exhibit distinctively higher levels, and models might display varying uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of 50 Temperature-Based Models for Estimating Potential Evapotranspiration (PET) in a Mediterranean Mountainous Forest Environment

Proutsos,

Fotelli,

Stefanidis

et al. 2024

Atmosphere

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Potential evapotranspiration (PET) is a crucial parameter for forest development, having an important role in ecological, biometeorological, and hydrological assessments. Accurate estimations of PET using the FAO–56 Penman–Monteith (FAO–56 PM) benchmark method require a wide range of data parameters, which are not typically available at meteorological stations installed in forest environments. The aim of this study is to investigate the accuracy of various methods with low data requirements for assessing PET in a Mediterranean forest environment and propose appropriate alternatives for accurate PET estimation. Specifically, 50 temperature-based methods were evaluated against the FAO–56 PM method in a sub-humid forest in northern Greece, using high-quality daily meteorological data. The outcomes indicate that temperature-based methods offer a viable alternative for PET estimation when data availability is limited, with a considerable number of methods (22) presenting low deviations (up to 10%) compared to the benchmark method. Temperature-based models outperformed those incorporating water-related parameters (as relative humidity or precipitation) in Mediterranean forest environments. The top performing methods in the study site, based on several statistical indices, were the equations of Ravazzani et al., proposed in 2012, followed by Hargreaves–Samani in 1985 and Heydari and Heydari in 2014.

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Forest hydrology modeling tools for watershed management: A review

Cited by 18 publications

References 217 publications

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Downscaling Future Precipitation over Mi Oya River Basin using Artificial Neural Networks

Integrating forest management and Watershed health for Sustainable Water sources

Assessing the Accuracy of 50 Temperature-Based Models for Estimating Potential Evapotranspiration (PET) in a Mediterranean Mountainous Forest Environment

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