Analysis on Fluvial Geomorphological Characteristics based on Past and Present Data for River Restoration: An Application to the Miho River and the Naesung River

Abstract

As a basic work for river restoration, analysis on fluvial geomorphological characteristics is made using past and present data to understand close-to-nature geomorphic status. The Miho and the Naesung Rivers are targets of this study. Fluvial geomorphic variables including valley-floor width, sinuosity, bankfull width, channel gradient, bed material size, bankfull discharge and unit stream power are evaluated with dominant processes. Though common sand-bed rivers with similar catchment area, the Miho and the Naesung Rivers are different in terms of valley-floor width, channel shape variables and dominant processes related with longitudinal location. In addition, analyses on interrelationship among the geomorphological variables are carried. Bankfull width is shown to be proportional to bankfull discharge, as is in a rough agreement with the previous studies. Relationship of bankfull discharge and channel gradient shows meandering and braiding are prevalent in the Miho River, whereas the most of the sub-reaches of the Naesung River fall to braiding. Relationship of channel gradient with width-depth ratio indicates dune-ripple processes are dominant in the Miho River, while the Naesung River shows longitudinal diversity from braiding in the downstream sub-reaches to riffle-pool and plane-bed along the upper ones. Analyses based on the past data on a river in a close-to-nature status are thought to be rather reasonable in comparison with those on the same river in a engineered condition.

Water Level Prediction and Forecasting Using a Long Short-Term Memory Model for Nam Ngum River Basin in Lao PDR

“…The coefficient of determination (R 2 ), root mean square error (RMSE), and Nash Sutcliffe Efficiency (NSE) have been adopted in this study as the indicators to verify the accuracy of the predicted results. As mentioned previously, each performance metric is described in Equations ( 8), ( 9), (10), and (11), respectively.…”

“…Kim et al [10] developed an LSTM model to predict the water level using the historical water level and rainfall data at the same point, and the applicability of the model was proven by evaluating the prediction accuracy of the model according to the lead time. Lee et al [11] simulated the outflow at the lower Mekong River using both physical models and data-driven models, and the performance levels of these two models were evaluated. The authors concluded that the accuracy of the data-driven model was better, demonstrating that the data-driven model in fact outperformed the conventional physical model.…”

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

“…Furthermore, due to the local economic structures being dependent on primary industries (e.g., agriculture, fishing, and forestry), although the sensitivity to climate change and exposure to extreme weather events are high, the budgets and response policies for preventing natural disasters are highly limited. Therefore, predicting future water resource variability due to long-term and short-term variations in precipitation and establishing response policies is a key issue in terms of reducing socio-economic damage and securing the safety of people [2]. In order to solve this problem and promote efficient basin development, a rainfall-runoff analysis must be carried out incorporating the effects of climate change.…”

“…The Mekong River is an international river shared by six countries: China, Myanmar, Laos, Thailand, Cambodia, and Vietnam. It has a length of 4800 km, a total basin area of 795,000 km 2…”

Response of Terrestrial Insect Community to the Vegetation Invasion at a Sand-Bed Stream

“…1). 내성천은 우리나라의 대표적인 모래 하 상 하천으로서 보전가치가 높지만 현재 내성천의 중류 에 영주댐이 건설되어 앞으로 급격한 환경 변화가 예상 되는 하천이다 (Lee et al 2015).…”