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Recently, deep learning (DL) models, especially those based on long short-term memory (LSTM), have demonstrated their superior ability in resolving sequential data problems. This study investigated the performance of six models that belong to the supervised learning category to evaluate the performance of DL models in terms of streamflow forecasting. They include a feed-forward neural network (FFNN), a convolutional neural network (CNN), and four LSTM-based models. Two standard models with just one hidden layer—LSTM and gated recurrent unit (GRU)—are used against two more complex models—the stacked LSTM (StackedLSTM) model and the Bidirectional LSTM (BiLSTM) model. The Red River basin—the largest river basin in the north of Vietnam—was adopted as a case study because of its geographic relevance since Hanoi city—the capital of Vietnam—is located downstream of the Red River. Besides, the input data of these models are the observed data at seven hydrological stations on the three main river branches of the Red River system. This study indicates that the four LSTM-based models exhibited considerably better performance and maintained stability than the FFNN and CNN models. However, the complexity of the StackedLSTM and BiLSTM models is not accompanied by performance improvement because the results of the comparison illustrate that their respective performance is not higher than the two standard models—LSTM and GRU. The findings of this study present that LSTM-based models can reach impressive forecasts even in the presence of upstream dams and reservoirs. For the streamflow-forecasting problem, the LSTM and GRU models with a simple architecture (one hidden layer) are sufficient to produce highly reliable forecasts while minimizing the computation time.

Climate change currently affects the resilience and aquatic ecosystem. Climate change alters rainfall patterns which have a great impact on river flow. Annual flooding is an important hydrological characteristic of the Mekong River Basin (MRB) and it drives the high productivity of the ecosystem and biodiversity in the Tonle Sap floodplain and the Mekong Delta. This study aims to assess the impacts of climate change on river flow in the MRB and flood inundation in the Lower Mekong Basin (LMB). The changing impacts were assessed by a two-dimensional rainfall-runoff and inundation model (RRI model). The present climate (1979–2003) and future projected climate (2075–2099) datasets from MRI-AGCM3.2H and MRI-AGCM3.2S models were applied with a linear scaling bias correction method before input into the RRI model. The results of climate change suggested that flood magnitude in the LMB will be severer than the present climate by the end of the twenty-first century. The increment of precipitation between 6.6 and 14.2% could lead to increase extreme flow (Q5) 13–30%, peak inundation area 19–43%, and peak inundation volume 24–55% in the LMB for ranging of Representative Concentration Pathways (RCP) and sea surface temperature (SST) scenarios while there is no significant change on peak flood timing.

Human activity is a major source of high-frequency seismic noise. Long-term ambient seismic noise levels and their influencing factors are investigated. The diurnal seismic noise level in 5–15 Hz display high correlation with human activities including traffic and industrial operations that are related to economic conditions. The temporal noise-level variations are consistent among three components. Analysis with seismic noises in three consecutive months of each year enables us to estimate the noise levels without seasonal effects. The daytime seismic noise-level changes in major cities of 11 countries are assessed using the 3 month records for decades. The annual seismic noise levels present strong correlations with gross domestic product (GDP), particularly with manufacturing and industrial GDP. The seismic noise levels increase quickly with GDP in low-GDP regions but slowly in high-GDP regions. This is because high-GDP regions already have large volumes of existing noise-inducing sources and because added sources contribute weakly. The seismic noise levels increased by 14%–111% for 5–23 yr depending on the economic conditions. The correlation between ambient seismic noise level and economy growth is a global feature. The high-frequency noise level may be a proxy to present the economic condition. Economic growth affects the Earth environment in a wide range of aspects.

Precipitation is a crucial component of the water cycle and plays a key role in hydrological processes. Recently, satellite-based precipitation products (SPPs) have provided grid-based precipitation with spatiotemporal variability. However, SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution of these products is still relatively coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation based on a combination of rainfall observation data with multiple SPPs for the period of 2003–2017 across South Korea. A Random Forest (RF) machine-learning algorithm model was applied for producing a new merged precipitation product. In addition, several statistical linear merging methods have been adopted to compare with the results achieved from the RF model. To investigate the efficiency of RF, rainfall data from 64 observed Automated Synoptic Observation System (ASOS) installations were collected to analyze the accuracy of products through several continuous as well as categorical indicators. The new precipitation values produced by the merging procedure generally not only report higher accuracy than a single satellite rainfall product but also indicate that RF is more effective than the statistical merging method. Thus, the achievements from this study point out that the RF model might be applied for merging multiple satellite precipitation products, especially in sparse region areas.

Recently, machine learning (ML) is being applied to various fields, including hydrology and hydraulics. The numerical models based on ML algorithms have been widely used for forecasting water levels or flowrate in different timescales. Especially in estuary areas where the hydrodynamic regime becomes complicated, the water level forecast information in this area plays an essential role in the operation of tidal sluices. This study proposes an efficient approach using an ML model, long short-term memory (LSTM), to predict short-term water levels in tidal sluice gates from 6 to 48 hours ahead. The An Tho culvert located in the Bac Hung Hai irrigation system, the most extensive irrigation system in Vietnam, was selected as a case study station. The high accuracy of predictive results reveals LSTM models' effectiveness in different forecasting scenarios. In the first scenario using just water level data at the prediction station, the Kling-Gupta efficiency (KGE) coefficient ranges from nearly 0.89 to 0.96. Meanwhile, in the second scenario, the combination of observed data of three gauge stations exhibited better performance with KGE coefficients ranging from just under 0.93 to 0.98 for eight forecasted cases. The findings of this study highlight the performance of LSTM models in providing high-accuracy short-period water level forecasts for areas near estuaries. These obtained results can play a vital role in the management and operation of tidal sluices in the Bac Hung Hai irrigation system, as well as a reference for the operation of other irrigation systems around the world.

In Korea, approximately 70% of the country is mountainous, with steep slopes and heavy rainfall in summer from June to September. Korea is classified as a high-risk country for soil erosion, and the rate of soil erosion is rapidly increasing. In particular, the operation of Doam dam was suspended in 2001 because of water quality issues due to severe soil erosion from the upstream areas. In spite of serious dam sediment problems in this basin, in-depth studies on the origin of sedimentation using physic-based models have not been conducted. This study aims to analyze the spatial distribution of net erosion during typhoon events using a spatially distributed physics-based erosion model and to improve the model based on a field survey. The spatially uniform erodibility constants of the surface flow detachment equation in the original erosion model were replaced by land use erodibility constants based on benchmarking experimental values to reflect the effect of land use on net erosion. The results of the upgraded model considering spatial erodibility show a significant increase in soil erosion in crop fields and bare land, unlike the simulation results before model improvement. The total erosion and deposition for Typhoon Maemi in 2003 were 36,689.0 and 9893.3 m3, respectively, while the total erosion and deposition for Typhoon Rusa in 2002 were 142,476.6 and 44,806.8 m3, respectively, despite about twice as much rainfall and 1.2 times as high rainfall intensity. However, there is a limitation in quantifying the sources of erosion in the study watershed, since direct comparison of the simulated net erosion with observed spatial information from aerial images, etc., is impossible due to nonperiodic image photographing. Therefore, continuous monitoring of not only sediment yield but also periodic spatial detection on erosion and deposition is critical for reducing data uncertainty and improving simulation accuracy.

Debris flow triggered by shallow landslides in hillslope catchments is a main geological phenomenon driving landscape changes, and represents an important natural hazard. Numerous studies have assessed sediment transport and deposition by debris flows in hillslope catchments. Thus, the objective of this study is a development of two-dimensional debris flow model to estimate sediment transport and deposition in hillslope catchments. To simulate debris flow, we implemented a vertically integrated shallow-water governing equation based on the Voellmy rheological model and a simple entrainment model. In addition, we applied a quadtree grid structure to support adaptive mesh refinement, where the mesh for the simulation was automatically generated as the debris flow proceeded. Finally, a well-balanced numerical scheme for wet–dry transition treatment was implemented and implicit discretization of the general source terms, including the rheological term, was included for numerical stability. The developed model was verified based on a debris flow triggered by the 2011 Mt. Umyeon landslides in the Republic of Korea. Sediment transport was successfully generated and the sediment deposition area generally matched the field survey data well. Overall, the simulated sediment volume was in good agreement with the survey results, with an error below 1%.

The study aimed to assess the hydrological impacts of the basin development scenarios—which are BS (baseline scenario), DFS (definite future scenario), and FFS (foreseeable future scenario)—by the Mekong River Commission (MRC) on the hydrological regime between the Mekong River and Tonle Sap Lake. The proposed CAESAR-LISFLOOD system was applied to analyze flood inundation patterns of the Tonle Sap Lake using satellite images, as well as to compare the simulated inundation maps. The flow duration variability was also analyzed at the outlet point of the Mekong River basin based on the analysis results of CAESAR-LISFLOOD, according to the basin development. The results show that the CAESAR-LISFLOOD model produces acceptable flooding areas (8607 km2) compared with the satellite observations (9615 km2), and the flood inundation areas decrease gradually from 3.69 (103 km2) in the BS scenario to 3.54 (103 km2) in the FFS scenario. However, it was found that the annual average flow duration variability scenario at the Kratie point, presented in the MRC, does not significantly affect the flow duration downstream from the Kratie point.

At present, deep learning models have been widely applied in many studies related to the field of water resource management. In this study, several deep learning neural network models based on the Gated Recurrent Unit (GRU) architectures have been applied to the river water level prediction for a short-time period, from one hour to nine hours ahead. The input data of these models are hourly water levels which are observed at four hydrological stations on the Geum River, South Korea. Though the model does not require data such as topography, land cover, or precipitation data, the forecasted results indicate significant stability and performance. Compared to the observed water level data, the correlation coefficient NSE (Nash-Sutcliffe efficiency) is up to more than 99% in the case of a 1-hour forecast. The results of this study prove the potential of deep learning models in predicting water level and applicable to other river basins.

Spatiotemporal precipitation data is one of the essential components in modeling hydrological problems. Although the estimation of these data has achieved remarkable accuracy owning to the recent advances in remote-sensing technology, gaps remain between satellite-based precipitation and observed data due to the dependence of precipitation on the spatiotemporal distribution and the specific characteristics of the area. This paper presents an efficient approach based on a combination of the convolutional neural network and the autoencoder architecture, called the convolutional autoencoder (ConvAE) neural network, to correct the pixel-by-pixel bias for satellite-based products. The two daily gridded precipitation datasets with a spatial resolution of 0.25° employed are Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) as the observed data and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) as the satellite-based data. Furthermore, the Mekong River basin was selected as a case study, because it is one of the largest river basins, spanning six countries, most of which are developing countries. In addition to the ConvAE model, another bias correction method based on the standard deviation method was also introduced. The performance of the bias correction methods was evaluated in terms of the probability distribution, temporal correlation, and spatial correlation of precipitation. Compared with the standard deviation method, the ConvAE model demonstrated superior and stable performance in most comparisons conducted. Additionally, the ConvAE model also exhibited impressive performance in capturing extreme rainfall events, distribution trends, and described spatial relationships between adjacent grid cells well. The findings of this study highlight the potential of the ConvAE model to resolve the precipitation bias correction problem. Thus, the ConvAE model could be applied to other satellite-based products, higher-resolution precipitation data, or other issues related to gridded data.

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&ndash, 2005), verification (2006&ndash, 2007), and prediction (2008&ndash, 2100), while the LSTM model went through the process of training (1980&ndash, 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&ndash, 2007) was evaluated, the SWAT model showed a Nash&ndash, 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&ndash, 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.

Physically based approaches for the regional assessment of slope stability using DEM topography usually consist of one-dimensional descriptions and include many simplifying assumptions with respect to more realistic, three-dimensional analyses. We investigated a new application of the well-known, publicly available software TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Analysis) in combination with Scoops3D, to analyze three-dimensional slope stability throughout a digital landscape in a time-dependent fashion, typically non implemented in three-dimensional models. TRIGRS was used to simulate the dynamic hydraulic conditions within the slopes induced by a rainstorm. Scoops3D then used the resulting pore water pressure for three-dimensional stability assessment. We applied this approach to the July 2011 landslide event in Mt. Umyeon, South Korea, and results were compared with the landslide initiation locations reported for this rainfall event. Soil depth in the study area was described by three different simple models. Stability maps, obtained by the one-dimensional (TRIGRS only) and three-dimensional (TRIGRS and Scoops3D), time-dependent approaches, were compared to observations to assess the timing and locations of unstable sites by means of a synthetic index, previously specifically developed for dealing with point landslide locations. We highlight the performance of the three-dimensional approach with respect to the one-dimensional method represented by TRIGRS alone, and the consistency of the time-dependence of the results obtained using the combined approach with observations.

The Mekong River plays an extremely important role in Southeast Asia. Flowing through six countries, including China, Myanmar, Thailand, Laos PDR, Cambodia, and Vietnam, it is a site of great biological and ecological diversity and the habitat of numerous species of fish. It also supports a very large population that lives along the river basin. Therefore, much attention has been focused on the giant Mekong River Basin, particularly, its soil erosion and sedimentation problems. In fact, many methods have been used to calculate and simulate these problems. However, in the case of the Mekong River Basin, the available data is limited because of the extreme size of the area (about 795,000 ㎢) and lack of equipment systems in the countries through which the Mekong River flows. In this study, we applied the Universal Soil Loss Equation (USLE) model in a GIS (Geographic Information System) framework to calculate the amount of soil erosion and sediment load during the selected period, from 1951 to 2007. The result points out dangerous areas, such as the Upper Mekong River Basin and 3S Basin (containing the Sekong, Sesan, and Srepok Rivers) that are suffering the serious consequences of soil erosion problems. Moreover, the present model is also useful for supporting river basin management in the implementation of sustainable management practices in the Mekong River Basin and other basins.

A rainfall induced slope failure is a common natural hazard in mountainous areas worldwide. Sudden and rapid failures which have a high possibility of occurrence in a steep slope are always the most dangerous due to their suddenness and high velocities. Based on a series of experiments this study aimed to determine a critical angle which could be considered as an approximate threshold for a sudden failure. The experiments were performed using 0.42 mm mean grain size sand in a 200 cm long, 60 cm wide and 50 cm deep rectangular flume. A numerical model was created by integrating a 2D seepage flow model and a 2D slope stability analysis model to predict the failure surface and the time of occurrence. The results showed that, the failure mode for the entire material will be sudden for slopes greater than 67°; in contrast the failure mode becomes retrogressive. There is no clear link between the degree of saturation and the mode of failure. The simulation results in considering matric suction showed good matching with the results obtained from experiment. A subsequent discarding of the matric suction effect in calculating safety factors will result in a deeper predicted failure surface and an incorrect predicted time of occurrence.

Many researchers have evaluated the influence of vegetation cover on slope stability. However, due to the extensive variety of site conditions and vegetation types, different studies have often provided inconsistent results, especially when evaluating in different regions. Therefore, additional studies need to be conducted to identify the positive impacts of vegetation cover for slope stabilization. This study used the Transient Rainfall Infiltration and Grid-based Regional Slope-stability Model (TRIGRS) to predict the occurrence of landslides in a watershed in Jinbu-Myeon, Pyeongchang-gun, Korea. The influence of vegetation cover was assessed by spatially and temporally comparing the predicted landslides corresponding to multiple trials of cohesion values (which include the role of root cohesion) and real observed landslide scars to back-calculate the contribution of vegetation cover to slope stabilization. The lower bound of cohesion was defined based on the fact that there are no unstable cells in the raster stability map at initial conditions, and the modified success rate was used to evaluate the model performance. In the next step, the most reliable value representing the contribution of vegetation cover in the study area was applied for landslide assessment. The analyzed results showed that the role of vegetation cover could be replaced by increasing the soil cohesion by 3.8 kPa. Without considering the influence of vegetation cover, a large area of the studied watershed is unconditionally unstable in the initial condition. However, when tree root cohesion is taken into account, the model produces more realistic results with about 76.7% of observed unstable cells and 78.6% of observed stable cells being well predicted.

Flood impacts threaten the socioeconomic conditions of peoples’ lives in the Mekong River Basin. In this study, the rainfall-runoff-inundation (RRI) model, capable of simulating rainfall ru...

Recent advances in computer with geographic information system (GIS) technologies have allowed modelers to develop physics-based models for modeling soil erosion processes in time and space. However, it has been widely recognized that the effect of uncertainties on model predictions may be more significant when modelers apply such models for their own modeling purposes. Sources of uncertainty involved in modeling include data, model structural, and parameter uncertainty. To deal with the uncertain parameters of a catchment-scale soil erosion model (CSEM) and assess simulation uncertainties in soil erosion, particle filtering modeling (PF) is introduced in the CSEM. The proposed method, CSEM-PF, estimates parameters of non-linear and non-Gaussian systems, such as a physics-based soil erosion model by assimilating observation data such as discharge and sediment discharge sequences at outlets. PF provides time-varying feasible parameter sets as well as uncertainty bounds of outputs while traditional automatic calibration techniques result in a time-invariant global optimal parameter set. CSEM-PF was applied to a small mountainous catchment of the Yongdam dam in Korea for soil erosion modeling and uncertainty assessment for three historical typhoon events. Finally, the most optimal parameter sets and uncertainty bounds of simulation of both discharge and sediment discharge at each time step of the study events are provided.

This paper proposes an extended model evaluation method that considers not only the model performance but also the model structure and parameter uncertainties in hydrologic modeling. A simple reservoir model (SFM) and distributed kinematic wave models (KWMSS1 and KWMSS2 using topography from 250-m, 500-m, and 1-km digital elevation models) were developed and assessed by three evaluative criteria for model performance, model structural stability, and parameter identifiability. All the models provided acceptable performance in terms of a global response, but the simpler SFM and KWMSS1 could not accurately represent the local behaviors of hydrographs. Moreover, SFM and KWMSS1 were structurally unstable; their performance was sensitive to the applied objective functions. On the other hand, the most sophisticated model, KWMSS2, performed well, satisfying both global and local behaviors. KMSS2 also showed good structural stability, reproducing hydrographs regardless of the applied objective functions; however, superior parameter identifiability was not guaranteed. A number of parameter sets could result in indistinguishable hydrographs. This result indicates that while making hydrologic models complex increases its performance accuracy and reduces its structural uncertainty, the model is likely to suffer from parameter uncertainty.

This paper addresses the effects of extreme rainfall on the stability of cut slopes in Yen Bai city, Northern Viet Nam. In this area, natural slopes are excavated to create places for infrastructures and buildings. Cut slopes are usually made without proper site investigations; the design is mostly based on experience. In recent years, many slope failures have occurred along these cuts especially in rainy seasons, resulting in properties damaged and loss of lives. To explain the reason that slope failure often happens during rainy seasons, this research analyzed the influence of extreme rainfalls, initial ground conditions, and soil permeability on the changes of pore water pressure within the typical slope, thereafter determining the impact of these changes on the slope stability factor of safety. The extreme rainfalls were selected based on all of the rainfalls triggering landslide events that have occurred over the period from 1960 to 2009. The factor of safety (FS) was calculated using Bishop's simplified method. The results show that when the maximum infiltration capacity of the slope top soil is less than the rainfall intensity, slope failures may occur 14 hours after the rain starts. And when this happens, the rainfall duration is the deciding factor that affects the slope FS values. In short, cut slopes in Yen Bai may be stable in normal conditions after the excavation, but under the influence of tropical rain storms, their stability is always questionable.

In Korea, landslide damage areas have increased significantly from the 1990s to the 2000s owing to increases in rainfall intensity and the number of rainy days in addition to indiscriminate land development. This study was conducted to predict shallow landslide-prone areas by using the transient rainfall infiltration and grid-based regional slope-stability model (TRIGRS). A landslide event that occurred on July 27, 2011, in Mt. Umyeon, Seoul, was modeled, and the stability results were compared with 140 observed landslide points in both time and location to evaluate the performance of TRIGRS. Simulated outcomes from five different raster cells of 5, 10, 15, 20, and 25 m with the same background information were compared to identify the ultimate grid scale. The results revealed that in location estimation, smaller grid size resulted in more accurate results. However, the 10 and 15 m grid sizes gave better results in the timing assessment When the best grid size was considered TRIGRS overestimated t...

This study compares the performance of transient rainfall infiltration and grid-based regional slope stability (TRIGRS) model and time-variant slope stability (TiVaSS) model in the prediction of rainfall-induced shallow landslides. TRIGRS employs one-dimensional (1-D) subsurface flow to simulate the infiltration rate, whereas a three-dimensional (3-D) model is utilized in TiVaSS. The former has been widely used in landslide modeling, while the latter was developed only recently. Both programs are used for the spatiotemporal prediction of shallow landslides caused by rainfall. This study uses the July 2011 landslide event that occurred in Mt. Umyeon, Seoul, Korea, for validation. The performance of the two programs is evaluated by comparison with data of the actual landslides in both location and timing by using a landslide ratio for each factor of safety class ( $${\text{LR}}_{\text{class}}$$ index), which was developed for addressing point-like landslide locations. Moreover, the influence of surface flow on landslide initiation is assessed. The results show that the shallow landslides predicted by the two models are highly consistent with those of the observed sliding sites, although the performance of TiVaSS is slightly better. Overland flow affects the buildup of the pressure head and reduces the slope stability, although this influence was not significant in this case. A slight increase in the predicted unstable area from 19.30 to 19.93% was recorded when the overland flow was considered. It is concluded that both models are suitable for application in the study area. However, although it is a well-established model requiring less input data and shorter run times, TRIGRS produces less accurate results.

This study aims to assess the slope stability variation of Jeonbuk drainage areas by RCM model outputs based on A1B climate change scenario and infinite slope stability model based on the previous research by Choi et al.(2013). For a large-scale slope stability analysis, we developed a GIS-based database regarding topographic, geologic and forestry parameters and also calculated daily maximum rainfall for the study period(1971~2100). Then, we assess slope stability variation of the 20 sub-catchments of Jeonbuk under the climate change scenario. The results show that the areal-average value of safety factor was estimated at 1.36(moderately stable) in spite of annual rainfall increase in the future. In addition, 7 sub-catchments became worse and 5 sub-catchments became better than the present period(1971~2000) in terms of safety factor in the future.

Catchments soil erosion, one of the most serious problems in the mountainous environment of the world, consists of a complex phenomenon involving the detachment of individual soil particles from the soil mass and their transport, storage and overland flow of rainfall, and infiltration. Sediment size distribution during erosion processes appear to depend on many factors such as rainfall characteristics, vegetation cover, hydraulic flow, soil properties and slope. This study involved laboratory flume experiments carried out under simulated rainfall in a 3.0 m long × 0.8 m wide × 0.7 m deep flume, set at 17° slope. Five experimental cases, consisting of twelve experiments using three different sediments with two different rainfall conditions, are reported. The experiments consisted of detailed observations of particle size distribution of the out-flow sediment. Sediment water mixture out-flow hydrograph and sediment mass out-flow rate over time, moisture profiles at different points within the soil domain, and seepage outflow were also reported. Moisture profiles, seepage outflow, and movement of overland flow were clearly found to be controlled by water retention function and hydraulic function of the soil. The difference of grain size distribution of original soil bed and the out-flow sediment was found to be insignificant in the cases of uniform sediment used experiments. However, in the cases of non-uniform sediment used experiments the outflow sediment was found to be coarser than the original soil domain. The results indicated that the sediment transport mechanism is the combination of particle segregation, suspension/saltation and rolling along the travel distance.

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash&ndash, Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.

This study aims to assess the slope stability variation of Jeollabuk-do drainage areas by RCM model outputs based on A1B climate change scenario and infinite slope stability model based on the specific catchment area concept. For this objective, we downscaled RCM data in time and space: from watershed scale to rain gauge scale in space and from monthly data to daily data in time and also developed the GIS-based infinite slope stability model based on the concept of specific catchment area to calculate spatially-distributed wetness index. For model parameterization, topographic, geologic, forestry digital map were used and model parameters were set up in format of grid cells(). Finally, we applied the future daily rainfall data to the infinite slope stability model and then assess slope stability variation under the climate change scenario. This research consists of two papers: the first paper focuses on the methodologies of climate change scenario preparation and infinite slope stability model development.

A three-dimensional (3D) seepage-flow numerical simulation model was developed for seepage analysis of a landslide dam. A 3D seepage-flow numerical simulation model coupled with a two-dimensional (2D) surface flow and erosion/deposition model was developed for seepage analysis of a slope due to a rainfall event. The conventional water-phase (one-phase) seepage-flow model assumed only water-phase flow in seepage analysis, which was inadequate for unsaturated soil domains. A water-air two-phase seepage-flow model that considers both the water and air phases in the seepage-flow process was also used for the seepage analysis. The pore-water pressure and moisture-content data obtained from the seepage-flow model were used to analyze the stability. Janbu’s simplified method and the extended Spencer method were used for the stability analysis. The numerical simulation results almost compared well with laboratory experimental measurements.

Increasing rainfall intensity and frequency due to extreme climate change and haphazard land development are aggravating soil erosion problems in Korea. A quantitative estimate of the amount of sediment from the catchment is essential for soil and water conservation planning and management. Essential to catchment-scale soil erosion modeling is the ability to represent the fluvial transport system associated with the processes of detachment, transport, and deposition of soil particles due to rainfall and surface flow. This study applied a spatially distributed hydrologic model of rainfall–runoff–sediment yield simulation for flood events due to typhoons and then assessed the impact of topographic and climatic factors on erosion and deposition at a catchment scale. Measured versus predicted values of runoff and sediment discharge were acceptable in terms of applied model performance measures despite underestimation of simulated sediment loads near peak concentrations. Erosion occurred widely throughout the catchment, whereas deposition appeared near the channel network grid cells with a short hillslope flow path distance and gentle slope; the critical values of both topographic factors, providing only deposition, were observed at 3.5 (km) (hillslope flow path distance) and 0.2 (m/m) (local slope), respectively. In addition, spatially heterogeneous rainfall intensity, dependent on Thiessen polygons, led to spatially distinct net-erosion patterns; erosion increased gradually as rainfall amount increased, whereas deposition responded irregularly to variations in rainfall.

There has been an increase for the landslide areas and restoration expenses due, in large part, to the increased locally heavy rains caused by recent climate change as well as the reckless development. This study carried out a slope stability analysis by the application of distributed wetness index, using the GIS-based infinite slope stability model, which took the root cohesion effect into consideration, for part of Mt. Umyeon in Seoul, where landslide occurred in July 2011, in order to compensate the defects of existing analysis method, and subsequently compared its result with the case on the exploitation of lumped wetness index. In addition, this study estimated the distributed wetness index by methodology, applying three methods of specific catchment area calculation: single flow direction (SFD), multiple flow direction (MFD), and infinity flow direction (IFD), for catchment area, one of the variables of distributed wetness indices, and finally implemented a series of comparative analysis for slope stability by methodology. The simulation results showed that most unstable areas within the study site were dominantly located in cutting-area surroundings along with the residential area and the mountaintop and unstable areas of IFD and lumped wetness index method were similar while SFD and MFD provided smaller unstable areas than the two former methods.