Method for Operating Drainage Pump Stations Considering Downstream Water Level and Reduction in Urban River Flooding

Korea experiences increasing annual torrential rains owing to climate change and river flooding. The government is expanding a new drainage pump station to minimize flood damage, but the river level has not been adjusted because of torrential rains. Therefore, the river level must be adjusted to operate the drainage pump station, and it can be adjusted through the reservoir of the drainage pump station. In this study, we developed a method for operating drainage pump stations to control the river level and verify the effectiveness of the proposed method. A stormwater management model (SWMM) was used to simulate the Suyeong River and Oncheon River in Busan, Korea. The rainfall data from 2011 to 2021 were investigated. The data were sorted into ten big floods that occurred in Busan. The model was calibrated with actual rainfall data. The water level of the Suyeong River and the Oncheon River was the highest in most simulations. The simulation results showed an average decrease of 3018.2 m3 in Suyeong River flooding, and the Oncheon River needed to be supplemented due to structural problems. As a result of the recombination by simply supplementing the structural problems of the Oncheon River, the average flooding of 194.5 m3 was reduced. The proposed method is economical and efficient for reducing urban stream flooding in areas susceptible to severe damage caused by climate change.

Realignments of the Colorado River by ~2 m.y. of rotational bedrock landsliding: The Surprise Valley landslide complex, Grand Canyon, Arizona

The Surprise Valley landslide complex is the name used here for a group of prominent river-damming landslides in Grand Canyon (Arizona, USA) that has shifted the path of the Colorado River several times in the past 2 m.y. We document a sequence of eight landslides. Three are Toreva-block landslides containing back-rotated but only mildly disrupted bedrock stratigraphy. The largest of these landslides, Surprise Valley landslide, is hypothesized to have dammed the Colorado River, cut off a meander loop through Surprise Valley, and rerouted the river 2.5 km south to near its present course at the Granite Narrows. Another bedrock landslide, Poncho’s runup, involved a mass detachment from the north side of the river that drove a kilometer-scale bedrock slab across the river and up the south canyon wall to a height of 823 m above the river. A lake behind this landslide is inferred from the presence of mainstem gravels atop the slide that represent the approximate spillway elevation. We postulate that this landslide lake facilitated the upriver 133 Mile slide detachment and Toreva block formation. The other five landslides are subsequent slides that consist of debris from the primary slides; these also partially blocked and diverted the Colorado River as well as the Deer Creek and Tapeats Creek tributaries into new bedrock gorges over the past 1 m.y. The sequence of landslides is reconstructed from inset relationships revealed by geologic mapping and restored cross-sections. Relative ages are estimated by measuring landslide base height above the modern river level in locations where landslides filled paleochannels of the Colorado River and its tributaries. We calculate an average bedrock incision rate of 138 m/m.y. as determined by a 0.674 ± 0.022 Ma detrital sanidine maximum depositional age of the paleoriver channel fill of the Piano slide, which has its base 70 m above the river level and ~93 m above bedrock level beneath the modern river channel. This date is within error of, and significantly refines, the prior cosmogenic burial date of 0.88 ± 0.44 Ma on paleochannel cobbles. Assuming steady incision at 138 m/m.y., the age of Surprise Valley landslide is estimated to be ca. 2.1 Ma; Poncho’s runup is estimated to be ca. 610 ka; and diversion of Deer Creek to form modern Deer Creek Falls is estimated to be ca. 400 ka. The age of the most recent slide, Backeddy slide, is estimated to be ca. 170 ka based on its near-river-level position. Our proposed triggering mechanism for Surprise Valley landslides involves groundwater saturation of a failure plane in the weak Bright Angel Formation resulting from large volumes of Grand Canyon north-rim groundwater recharge prior to establishment of the modern Deer, Thunder, and Tapeats springs. Poncho’s and Piano landslides may have been triggered by shale saturation caused by 600–650 ka lava dams that formed 45 river miles (73 river km; river miles are measured along the Colorado River downstream from Lees Ferry, with 1 river mile = 1.62 river kms) downstream near Lava Falls. We cannot rule out effects from seismic triggering along the nearby Sinyala fault. Each of the inferred landslide dams was quickly overtopped (tens of years), filled with sediment (hundreds of years), and removed (thousands of years) by the Colorado River, as is also the potential fate of modern dams.

Deep learning for automated river-level monitoring through river-camera images: an approach based on water segmentation and transfer learning

River-level estimation is a critical task required for the understanding of flood events and is often complicated by the scarcity of available data. Recent studies have proposed to take advantage of large networks of river-camera images to estimate river levels but, currently, the utility of this approach remains limited as it requires a large amount of manual intervention (ground topographic surveys and water image annotation). We have developed an approach using an automated water semantic segmentation method to ease the process of river-level estimation from river-camera images. Our method is based on the application of a transfer learning methodology to deep semantic neural networks designed for water segmentation. Using datasets of image series extracted from four river cameras and manually annotated for the observation of a flood event on the rivers Severn and Avon, UK (21 November–5 December 2012), we show that this algorithm is able to automate the annotation process with an accuracy greater than 91 %. Then, we apply our approach to year-long image series from the same cameras observing the rivers Severn and Avon (from 1 June 2019 to 31 May 2020) and compare the results with nearby river-gauge measurements. Given the high correlation (Pearson's correlation coefficient >0.94) between these results and the river-gauge measurements, it is clear that our approach to automation of the water segmentation on river-camera images could allow for straightforward, inexpensive observation of flood events, especially at ungauged locations.

Amazonian run-of-river dam reservoir impacts underestimated: Evidence from a Before-After Control-Impact study of freshwater turtle nesting areas

1. Although the construction of hydropower dams is widely assumed to generate myriad negative impacts on biodiversity there remains a lack of robust scientific evidence documenting the magnitude of these impacts particularly across highly biodiverse tropical waterways. Hydropower expansion is an increasing threat to the Endangered yellow-spotted river turtle (Podocnemis unifilis) across its tropical South American range. 2. Turtle nesting-areas were monitored as an indicator of dry-season river level changes following run-of-river dam reservoir filling. A Before-After-Control-Impact (BACI) study design was established with multi-year field campaigns monitoring turtle nesting-areas along 66 km of river upstream of the dam, separated into 33 km control and impact zones. 3. The cause and extent of changes in nesting-areas was established using Generalized Additive Models. Nesting-area density was examined in relation to four variables: Before-After, Control-Impact, distance to the dam and precipitation. The extent of changes was examined by comparing the proportion of nesting-areas remaining in subzones during the four years after reservoir filling. 4. Comparison of the proportion of nesting-areas remaining after dam construction showed that a permanent dry season river level rise extended more than 20 km beyond impact assessment limits. On average the density of nesting-areas declined 69% (from 0.48 to 0.15 per km) across 33 km of river directly impacted by the dam. This loss was reflected in a significant BACI interaction. The variation in nesting-areas was not explained by seasonal precipitation, whilst nesting-area density increased significantly further away from the dam. 5. Standardized monitoring of freshwater turtle nesting-areas provided an effective means to quantify impacts of hydropower developments across biodiverse yet rapidly changing waterways. The negative impacts should be preventable by mitigation actions including habitat restoration and dry season flow regulation. Such measures are also likely to benefit multiple species in highly diverse Amazonian rivers increasingly impacted by run-of-river dams.

Stable Water Isotope Assessment of Tundra Wetland Hydrology as a Potential Source of Arctic Riverine Dissolved Organic Carbon in the Indigirka River Lowland, Northeastern Siberia

Arctic tundra wetlands may be an important source of dissolved organic carbon (DOC) in Arctic rivers and the Arctic Ocean under global warming. We investigated stable water isotopes and DOC concentration in wetlands, tributaries, and the mainstream at the lower reaches of the Indigirka River in northeastern Siberia during the summers of 2010–2014 to assess the complex hydrology and role of wetlands as sources of riverine DOC. The wetlands had higher values of δ18O and DOC concentration than the tributaries and mainstream of the Indigirka River. A relationship between the two parameters was observed in the wetlands, tributaries, and mainstream, suggesting the wetlands can be a source of DOC for the mainstream through the tributaries. The combined temporal variations in riverine δ18O and DOC concentration indicate the mainstream water flowed into the tributaries during relatively high river-level periods in summer, whereas high DOC water in the downstream wetlands could be discharged to the mainstream through the tributaries during the low river-level periods. A minor fraction (7–13%) of riverine and wetland DOC was degraded during 40 days of dark incubation. Overall, the downstream wetlands potentially provide relatively less biodegradable DOC to the Arctic river and costal ecosystem during the low river-level periods—from late summer to autumn.

Development of a Model for Evaluating the Disaster Risk Around a Pier due to Local Scouring Based on the Observed Weather Information

Heavy rainfall disasters frequently damage bridge piers due to scouring, which resulted in collapse of bridges in many areas in Japan. In this study, we developed a model for evaluating the tilting risk of bridge pier due to scouring around the pier, which fluctuates depending on rainfall conditions based on machine learning. For evaluating the risk potential of scouring, we developed a model based on past disaster data due to scouring around the pier using a neural network. Furthermore, a sensitivity analysis was conducted using the parameters of explanatory variables of the developed model, river level, and distance from the water edge to the pier. The results showed that the disaster risk around the pier due to scouring increased with the increase in river level and decrease in the distance from the water edge to the pier. Additionally, a river level prediction model was developed using support vector regression with the precipitation time measured 5 – 8 h beforehand and river level measured several hours earlier as an explanatory variable. Furthermore, this study shows that the two developed models can be combined with each other to assess the disaster risk around the jetty due to scouring, which varies with rainfall conditions, based on the observed meteorological information.