We analyzed the time series of changes in areas subject to frequent inland water flooding (hereinafter referred to as "frequent inland water flooding areas") and inland water flooding occurrence factor indices (Topographical, Meteorological and Urban factor indices) in Osaka and Wakayama cities in Japan, and analyzed the causes of the changes.The frequency of inland flooding was calculated using the flood area maps provided by the Ministry of Land, Infrastructure, Transport and Tourism. We calculated flooding risk from the frequency of inland water flooding, topographical factors from inundation analysis by two-dimensional unsteady flow model, meteorological factors from the Radar-AMeDAS rainfall provided Japan Meteorological Agency. Urban factors were calculated by inverse analysis from the relationship equations (Flood Risk = Topographical factor × Meteorological factor × Urban factor ).The period covered in this study was 25 years, from 1993 to 2017. The spatial resolution is 5km mesh for meteorological factors and 100m mesh for other indicators. We identified 171 areas flooded more than 4 times in Osaka City and 79 frequent areas flooded more than 3 times in Wakayama City as frequent inland water flooding areas. The mean values of each indicator in the frequent inland water flooding areas showed a decrease in urban factors in Osaka City and a decrease in topographical factors and an increase in urban factors in Wakayama City. These were confirmed from the field survey that the land use change from paddy fields to resident areas increased the flooding risk in Wakayama City, while the progress in the construction of drainage facilities decreased the flooding risk as the number of areas subject to frequent inland water floods decreased from 52 to 9 in Osaka City.

The purpose of this study is to propose an adaptation measure to promote early evacuation in areas where residents typically underestimate flood risks and experience fewer disasters. As one of the adaptation measures to the climate change, we examined whether it is possible to improve the presentation of hazard maps and to promote evacuations. This study proposes a new risk rank evaluation method using flood inundation simulation results. Two rivers, the Jinzu and the Jouganji River, were chosen, and Toyama City, which was inundated by the target rivers, was selected as the target area. A flood inundation model was applied to the target area to compute the flood inundation depth and velocity. Flood inundation simulations with several scenarios were analyzed in the computation. We simulated the worst-case flood inundation to demonstrate its effects to the local people. In order to develop a new flood hazard map, we proposed a risk-rank assessment for inundation that identifies safe and unsafe zones. In addition to the inundation depth and velocity, the duration of inundation and the increase rate of the inundation depth are also considered in the risk rank evaluation. The validity of the risk rank assessment was evaluated by including past disaster data in the unsafe zone. Based on the results of the flood simulations, the hazard map consisting of only two colors (safe zone and unsafe zone) were created based on the new risk rank proposed in this study. We believe that the hazard maps that uses only two colors can have a strong impact on the residents, who underestimate the risks, and can promote evacuation in the event of a flood disaster.

Hydrologic regime plays an important role in providing the sustainable ecosystem services. However, its alterations over the time in a watershed due to various climatic and anthropogenic (hydropower, dams) activities cause significant impacts to societies and the immediate environment at the downstream region. Hence in this paper we have analyzed the degree of hydrologic alterations in a snow fed Marshyangdi watershed, located in Central Nepal, which has a greater potential for hydropower development. Indicators of Hydrologic alterations tools based on RVA approach was used to analyse the 33 hydrologic indices representing the magnitude of mean flow, duration, timing, frequency and rate of change of extreme flow conditions. Pettit test was applied to identify the abrupt change point and to define preimpact (1987-1999) act and post impact year (2000-2015). The mean annual streamflow in the basin was  221.6 m³/s which is insignificantly increasing  with a trend of 1.12m³/s/y and the trend continue to increase in the post impact period (13.7%) too with a trend of 0.59m³/s/y. Flow duration curve shows deviation at less than 100m³/s at  75% exceedance of time in post impact period. Hydrologic alterations (HA) in the basin varies among the groups from low to moderate with an overall mean HA 27.9% among in the natural flow regime of the watershed. Increase in the median flow values during the period of March-August and consequent statistically significant increasing trend in the 30 and 90-day maximum values indicates the possibility of flood in the basin in future. Further, increases in anthropogenic influences may affect the natural flow regime of the Marshyangdi watershed which may exacerbate in future and the implications pointed out could have severe ecological consequences with the high degree of hydrologic alteration.

Woody debris, which can threaten our lives, is said to increase in the future in Japan because of climate change and forest devastation. Therefore, understanding the mechanism of woody debris export on the catchment scale is important.Because of the difficulties in estimation of woody debris generation from the forest, the mainstream of woody debris research was the analysis between the amount of exported woody debris and hydrometeorological factors. To further understand the mechanism of woody debris export, estimatimg the amount of generated woody debris in the forest and analyzing the amount of generated, accumulated, and exported woody debris are important. Therefore, the objectives of this study were: (1) to estimate the amount of generated woody debris in the forest using NDVI based on satellite images; (2) to analyze the amount of generated and exported woody debris at dam reservoirs in northern and southern Japan. As a result, we could estimate the amount of generated woody debris with the accuracy that had 72.1% recall and 67.8% precision by using NDVI differences between before and after the disaster in Shobara City, Hiroshima, where slope collapses with large woody debris happened in 2010. Also, we estimated the amount of generated woody debris at all dam reservoirs by using annual NDVI differences during 1996-2017. From the analysis of all dam reservoirs, the amount of generated woody debris was positively correlated with the direct catchment scale in all dam reservoirs, and the amount of exported woody debris was positively correlated with it in each dam reservoir in northern and southern Japan. Furthermore, it was revealed that the amount of exported woody debris in southern Japan tended to be higher. Therefore, it is considered that the amount of accumulated woody debris in the forest in southern Japan was smaller than that in northern Japan.

 The purpose of this study is to evaluate runoff on land-use change and vegetation change in small river basins using a distributed hydrological model for taking into account the runoff change by climate change and depopulation.
 We modified a distributed runoff model to improve the accuracy of the model, optimizing for the parameters of infiltration capacity, storage function method, and roughness. We used the Nash-Sutcliffe (NS) coefficient to evaluate the error between the observed and estimated values. The evaluation was carried out for three basins, the Sukawa river, the Koshizawa, and the Ohtani river, whose areas are 72.6 km2, 3.1 km2, and 48.6 km2, respectively. The NS coefficients were obtained as 0.84 in the Koshizawa basin and 0.62 in the Sukawa river basin, which observed were available.
 We compared the current land-use with future rand-use, which has the river channels covering with vegetation and the forest except for the river channels. When the future land-use are changed the infiltration rate for each land-use, the annual maximum discharge decreased by 2.89% in the Sukawa river basin and 2.58% in the Ohtani river basin. The vegetation change to broadleaf trees increased 16% to 51% the annual maximum discharge in all basins. On the contrary, the vegetation change to needle-leaved trees decreased 23% to 36% the annual maximum discharge in all basins. The results indicate that a decrease in the maximum annual discharge brings a decrease in flood risk downstream of the basin.

SNS data is used to understand when and how people use rivers. Image detector with deep learning software is applied to SNS images in rivers to estimate number of people. Single Shot Multibox Detector (SSD) was used for counting the number of people on a picture. VOC2007 data was used for deep learning of SSD for people detection. However, VOC2007 does not prepare data with people in rivers. Therefore, we made image data with people in rivers randomly from Instagram images with #river. These images with people in rivers were used for validation by IoU value under learning from 2000 to 50000 times iteration for 10 images for each 6 type. We apply the model after 50000 iteration to estimate the number of people for Instagram images with #river in 2015. As the comparison between this application and actual survey for river visitors, both numbers of river use for week and weekend days have a good agreement in spring and summer. However, it does not show high correlation in autumn and winter. The reason is that we can see many activities such playing with water and walk by rivers in summer and spring.References
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C Berg, 2016, SSD: Single Shot MultiBox Detector, European Conference on Computer Vision, pp.21-37.
Mark Everingham, Luc Van Gool, Chris Williams, John Winn, Andrew Zisserman, The Pascal Visual Object Classes Challenge 2007 (VOC2007) Results, http://www.pascal-network.org/challenges/VOC/voc2007/workshop/index.html (ref:25Jan, 2019)
MLIT, Census of rivers, 2009.
MLIT, Census of rivers, 2015.
http://www.nilim.go.jp/lab/fbg/ksnkankyo/mizukokuweb/kuukan/index.htm
(ref:14Jan, 2020)

Recently, torrential rains have been occurred more frequently due to abnormal climate variations, causing severe economic damages and casualty including infrastructures along the river. Most of cases it should be necessary to forecast water levels precisely in riversides, which would help to prevent such catastrophe. In this study, therefore, a water level prediction algorithm was developed using the LSTM (Long Short-term Memory) specialized for time series data during machine learning to prevent flood damage to the facilities. And, the cost-effectiveness was analyzed when managing systematically such disasters based on the precise flood forecasting conducted with this algorithm. The study area was selected as the Jamsu bridge in Seoul, Korea for overall period of 10 years (2011-2020). The input data set was constructed with complementary data composed of structured data, which were mainly used for prior research on the water level of Jamsu Bridge(EL.m), the amount of discharge from Paldang Dam(m3/s) , the tide level of Ganghwa Bridge(cm) and unstructured data that the number of tweets in Seoul. Forward 6 hours prediction for water levels was derived through this algorithm and economical analysis was performed for each flood forecasting stage. The benefits and damage by this disaster management were calculated with the establishment of the flood forecast system that subdivided into four stages (interest, pedestrian control, vehicle control, and vigilance). As a result, disaster costs were reduced by more than 5% and the investment effect of preventing natural disasters was increased by about four times compared to the previous ones. Major purpose of this economic analysis was to estimate the monetary value of the water level prediction and to refine the investment efficiency. It is expected to develop more general economic models, such as presenting feasibility of implementation, to achieve maximum disaster management effectiveness in the near future.