Water-Trap Series and City Pond to Control The Destructive Power of Runoff Water from Mbay Hills

Weworuwet Hill, which is part of the Mbay hillside in Flores – NTT has sparse vegetation, only a stretch of grass that covers it, and is dry in the dry season like a barren teletabic hillside. This has the potential for surface water runoff, which has high destructive power, especially in the lowlands of Mbay City. To overcome this problem, a study to control the destructive force of water runoff was carried out by applying a water-trap series system, so that the potential for the destructive power of water can be reduced. Tertiary, secondary and primary runoff analysis studies are carried out to determine the location of the required watertraps. This study was conducted using a geographic information system-based program. Furthermore, the hydrological analysis of the area is carried out to determine which flood discharge can be controlled, and the volume of water that can be used for greening hills so that it can reduce the potential for damage to water runoff. The remaining water discharge in the downstream will be accommodated in the city pond, which functions as water conservation infrastructure. Finally, by applying a series of water traps on the tertiary, secondary and primary runoff from the Mbay hilly area, the destructive power of the runoff can be controlled, so that it does not impact and burden the residential plains of the town of Mbay.

Demonstration of Stormwater Management Technology by Short-Term Rainfall Prediction and Real-Time Runoff Analysis System Using Small X-Band Radar

We developed a system that combines urban area rainfall radar (small X-band, dual-polarization radar), short-term rainfall prediction model, and real-time runoff analysis technology, and the demonstration study was conducted on the drainage districts in Fukui City and Toyama City. We demonstrated the effectiveness of the flood damage, by providing the real-time information on rainfall prediction, water level in sewerage pipes, and inland flood prediction to the operators of drainage pump of stormwater storage pipes, and residents in flood-prone areas. During the study for about two years, it was confirmed that the accuracy of the radar rainfall observation was comparable to that of the X-band dual-polarization Doppler weather radar managed by the Ministry of Land, Infrastructure, Transport and Tourism. In the operation of the drainage pump for the Tsukimiminori Stormwater Storage Pipe in Fukui City, we were able to secure the storage capacity for the next rainfall based on the forecast information by maximizing the drainage capacity of the discharge destination. In addition, it was also confirmed that the residents themselves could secure the lead time for setting up water-stop sandbags and moving their vehicles to higher ground.

Floods in Siberia: geographical and statistical analysis for the period of climate change

In Siberia, floods are one of dangerous natural disaster. The danger of floods varies under the climatic and anthropogenic changes, as well as socio-economic development. The aim was to study the current position of problems associated with flood hazard. A key to understanding the flood situation is geographical and statistical analysis of the floods for the period of climate change (1985-2019). Such analyzes addresses the following aspects: study of flood genesis and recurrence, the severity of the impact for floods of different genesis; maxima runoff analysis as the principal cause of floods; analysis of the spatial distribution of settlements vulnerable to flooding; analysis of the ice jams and ice dams as a specific natural factor causing the floods in Siberia; assessment of the degree of danger, and identification of areas with the different integral flood danger. In Siberia, more than 1400 settlements are flooded at regular intervals. Most of them are concentrated in the southern, most developed territories in the Ob, Tom and Yeniseiy basins. In Siberia, rainfall, mixed (from snow melting with rainfall) and ice-dam floods are the most dangerous. They have the highest recurrence and severity of the impact. The greatest floods risk is in the most populated and economically developed southern regions within the Ob, Lena and Yenisey rivers and Lake Baikal basins. Territories with the highest risk of floods were determined. For the Baikal region, one of the most developed territories of Siberia, the flood hazard was determined for all administrative districts. Flood hazard maps for Siberian regions can be the basis for developing the flood adaptation strategies.

GIS-Based Watershed Analysis for Water Storage Facilities in Underdeveloped Areas

Digital elevation models (DEMs) are created to study the topography of the area by using point krigging method of gridding. The results show a significant level difference between the start and end points of the valley sloping naturally. A longitudinal profile shows an average slope of 2.6% in a stretch of 15 km inside the area under study. Later, the detailed watershed and runoff analysis have been performed by preparing various maps using IDF curves available for the area. The area is found to experience sufficient rainfall for a 50 to 100-year storm return period. The expected location and amount of runoff water accumulation have also been determined which may help for the construction of water storage or rainwater harvesting facilities. The chapter shows the role of GIS-based techniques for the hydrological analysis of difficult terrains which can be applied for planning and management of water storage facilities in underdeveloped areas.

Rainfall-runoff Analysis Method Considering the Effect of High-rise Buildings

In this study, a rainfall runoff process analysis method considering the effect of high-rise buildings was proposed. The proposed method was applied to the Yeoksam urban basin in Seoul. For rainfall-runoff analysis, a shot noise process based model was used to independently analyze the runoff from the wall and roof of a high-rise building. Thus, the Yeoksam urban basin was divided into 155 sub-basins for analysis. It was observed that the peak runoff increased by 22.0% in the 9-2 sub-basin. However, in a sub-basin in which the peak runoff increased by 10.0% or more due to high-rise buildings, there was no case where the increase rate of peak runoff was maintained greater than 5.0% until the next sub-basin outlet. Finally, by deriving the runoff hydrograph for the entire Yeoksam urban basin, it was observed that there was no significant difference in rainfall-runoff process, regardless of whether the building was considered. Therefore, it was concluded that the phenomenon of increase in peak runoff due to high-rise buildings occurs only in sub-basin units.

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

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.

Assessment of a Stream Gauge Network Using Upstream and Downstream Runoff Characteristics and Entropy

A method for constructing a stream gauge network that reflects upstream and downstream runoff characteristics is assessed. For the construction of an optimal stream gauge network, we develop representative unit hydrographs that reflect such characteristics based on actual rainfall–runoff analysis. Then, the unit hydrographs are converted to probability density functions for application to entropy theory. This allows a comparison between two cases: one that considers the upstream and downstream runoff characteristics of a core dam area in South Korea, and another that uses empirical formula, which is an approach that has been widely used for constructing the stream gauge network. The result suggests that the case of a stream gauge network that considers upstream and downstream runoff characteristics provides more information to deliver, although the number of selected stream gauge stations of this case is less than that of the case that uses the empirical formula. This is probably because the information delivered from the constructed stream gauge network well represents the runoff characteristics of the upstream and downstream stations. The study area, the Chungju Dam basin, requires 12 stream gauge stations out of the current total of 18 stations for an optimal network that reflects both upstream and downstream runoff characteristics.