Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

Kum, Donghyuk; Park, Younsik; Jung, Young Hun; Shin, Min Hwan; Ryu, Jichul; Park, Ji‐Hyung; Yang, Jae E.; Lim, Kyoung Jae

doi:10.15681/kswe.2015.31.3.241

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…However, the climate change scenario contains a certain amount of uncertainty due to the following limitations: incomplete physical understanding of various natural conditions, the selection of future virtual scenarios, and computational performance [21][22][23][24][25]. Thus, a bias-correction process using the observation data is needed to minimize the uncertainty in the climate change scenario.…”

Section: Climate Change Scenariomentioning

confidence: 99%

Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Lee

Kim

et al. 2020

Water

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In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

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Section: Climate Change Scenariomentioning

confidence: 99%

Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Lee

Kim

et al. 2020

Water

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A Study to Develop Monthly Cover Management Factor Database for Monthly Soil Loss Estimation

Sung¹,

Jung²,

Lim³

et al. 2016

Journal of The Korean Society of Agricultural Engineers

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Soil loss is an accompanying phenomenon of hydrologic cycle in watersheds. Both rainfall drops and runoff lead to soil particle detachment, the detached soil particles are transported into streams by runoff. Here, a sediment-laden water problem can be issued if soil particles are severely detached and transported into stream in the watershed. There is a need to estimate or simulate soil erosion in watersheds so that an adequate plan to manage soil erosion can be established. Universal Soil Loss Equation (USLE), therefore, was developed and modified by many researchers for their watersheds, moreover the simple model, USLE, has been employed in many hydrologic models for soil erosion simulations. While the USLE has been applied even in South-Korea, the model is often regarded as being limited in applications for the watersheds in South-Korea since monthly conditions against soil erosion on soil surface are not capable to represent. Thus, the monthly USLE factors against soil erosion, soil erodibility and crop management factors, were established for four major watersheds, which are Daecheong-dam, Soyang-dam, Juam-dam, and Imha-dam watersheds. The monthly factors were established by recent fifteen years from 2000 to 2015. Five crops were selected for the monthly crop management factor establishments. Soil loss estimations with the modified factors were compared to conventional approach that is average annual estimations. The differences ranged from 9.3 % (Juam-dam watershed) to 28.1 % (Daecheong-dam watershed), since the conventional approaches were not capable of seasonally and regionally different conditions.

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Analysis of Rainfall-Runoff Characteristics on Bias Correction Method of Climate Change Scenarios

Cited by 2 publications

References 33 publications

Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

A Study to Develop Monthly Cover Management Factor Database for Monthly Soil Loss Estimation

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