This paper examines the ethical issues of water environment in the context of river management in practical engineering and technological applications. In particular, three important issues are discussed in this paper referring to two actual engineering cases in ancient and modern China, that is, the construction of ancient Dujiangyan irrigation project in Sichuan, China, and the modern practice of integrated operation of flood control and pollution prevention in Huai River Basin. The three issues include how to consider the trade-offs between flood control and irrigation, how to balance flood control and contamination prevention related to sudden water pollution incident, and how to ensure the protection of water environments and ecology in rivers under the grand challenges of natural environmental changes and high-intensity human activities. Finally, this paper concludes by emphasizing the future development of water environmental ethics and its interdisciplinary integration with modern science & technology in smart river management in China.
The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities, leading to severe flooding and significant economic losses for civilians. This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians, which is often overlooked in traditional flood hazard analyses. This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods, with a specific focus on Cúcuta, Colombia. The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling. Advanced computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6, 2020, using drones for digital elevation model data collection. In Cúcuta, where flood risk is high and drainage infrastructure is limited, the PCSWMM (Computer-based Urban Stormwater Management Model) was calibrated and validated to simulate extreme flood events. The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta’s urban basin. Four return periods (5, 10, 50, 100), with extreme rainfall of 3 h, were used to estimate the variability of the risk map. The output of the model was analyzed, and an integrated and time-varying comparison of the results was done. Results show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme events. Also, from 5 to 100 years return period, the percentage of area at risk increased from 9.6% to 16.6%. The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually. This study identified medium to high-risk locations, which are dynamic in time. We can conclude dynamics are spatiotemporal, and the added information layer of pedestrians brings vulnerability information that is also dynamic. Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events. The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.
In this research, a modeling approach of rainfall generator coupled with high resolution rainfall products were proposed to generate designed rainfall events under multiple spatial and temporal distributions, which was then employed to analyze the impacts of spatial and temporal rainfall heterogeneities on peak runoff for watersheds. Three scenarios were developed under multiple degrees of impermeable underlying surface areas within an urban watershed in south China. Detailed runoff processes were analyzed through the adoption of a distributed hydrological model (GSSHA). A covariance analysis method combined with rainfall spatio-temporal heterogeneity characteristic were used to quantify heterogeneity effects on peak runoff. Results indicated that coupling short period (2008–2016) remotely rainfall data and RainyDay results could successfully reproduce designed rainfall events, spatio-temporal heterogeneity of rainfall contributed significantly to the peak runoff, which was greater than those by rainfall duration and capacity, and the increase in impermeable underlying surface enhanced the complexities of the effects. Over each rainfall duration with increasing rainfall return period, the indicator of rainfall peak coefficient (RWD) would decrease and then increase. Regarding the total rainfall center (tg), 25 mm/h threshold rainfall spatial coverage (A25) decreased with increasing imperviousness, 1-h maximum rainfall (Rmax) surged with increasing imperviousness at rainfall duration of 2 and 24 h. Innovations of this research lied in: combination of a rainfall generator model based on a stochastic storm transposition technique and remote-sensing rainfall data to generate designed rainfall events, a rainfall spatial and temporal heterogeneities index system was developed to reveal how the changing characteristics of rainfall distribution and the impacts on peak runoff, and in-depth analysis of the impacts on runoff peak under multiple urban development scenarios for increasing capability in flood control/prevention.
Soil organic carbon (C) sequestration and nitrogen (N) and phosphorus (P) burial were measured in two floodplain wetlands’ soils of the West Fork of the White River watershed (Indiana, United States) whose catchments differed in land use to better understand how land use practices affect wetland C and nutrient retention. The catchment of one floodplain, UpperWest Fork, is dominated by row crop agriculture (61%) whereas the second catchment, Beanblossom Creek, is mostly forested (85%). Soils (0–30cm) of the two floodplain wetlands had similar bulk density (1.23 g/cm3). Soil organic C and N were low in both floodplains but the percent organic C and N was two times greater (3.3% C, 0.22% N) in the agricultural floodplain than in the floodplain in the forested catchment (1.5% C, 0.14% N). Soil P was three times greater in the agricultural (1100 µg/g) than in the forested floodplain (350 µg/g). Recent soil accretion based on 137Cs which provides a historical record since 1964 (60 years), was two times greater in the agricultural floodplain (2.2mm/year) than in the forested catchment (1.0mm/year). Sediment deposition (2500 g/m2/year), C sequestration (90 g/m2/year), and N burial (7.5g/m2/year) were three times greater in the agricultural floodplain and P burial was seven times greater (3.0 vs. 0.41 g/m2/year). Long-term measurements (100 years) based on 210Pb did not show large differences in C sequestration and N burial between the two floodplains though soil accretion and sediment deposition were greater in the forested floodplain. We attribute these higher rates to greater erosion in the watershed before 1950 when the catchment had more agricultural land and before instruction on best management practices to reduce soil erosion. These findings confirm previously published studies that show that P enrichment and accumulation in floodplain soils represent legacy effects of agricultural land use in the catchment.
Landslide in reservoirs imposes challenges to reservoir operation and dam safety management practices. The understanding of the landside mechanism during reservoir operation is crucial to landslide-related risk migration. During the reservoir operation from 2018 to 2021, a massive landslide occurred with over 107m3 in total volume on the bank of the NE reservoir. The surface movement characteristics before and after the occurrence of landslides in the NE reservoir in the region scale were detected and interpreted by Sentinel-2 time series images. Experimental studies were conducted to investigate the geotechnical properties of the fine-grained soil. The slope stability was evaluated for a typical slope profile considering the rising water level using the extended Bishop’s simplified method, which was implemented in the code STAB-UNSAT. It can be found that the landslide in the fine-grained soil occurred simultaneously when the water level rose. The cumulative area of soil slope failure on the left bank of the NE reservoir increased continuously during the reservoir operation from 2018 to 2020, especially had a remarkable increment from August to October in 2019. The extended Bishop’s simplified method provides a more rational method to evaluate the soil slope stability. The slope failure mechanism of the studied soil, that is, collapse-erosion-slide upon the rising reservoir water has been proposed.
Model accuracy and runtime are two key issues for flood warnings in rivers. Traditional hydrodynamic models, which have a rigorous physical mechanism for flood routine, have been widely adopted for water level prediction in river, lake, and urban areas. However, these models require various types of data, in-depth domain knowledge, experience with modeling, and intensive computational time, which hinders short-term or real-time prediction. In this paper, we propose a new framework based on machine learning methods to alleviate the aforementioned limitation. We develop a wide range of machine learning models such as linear regression (LR), support vector regression (SVR), random forest regression (RFR), multilayer perceptron regression (MLPR), and light gradient boosting machine regression (LGBMR) to predict the hourly water level at Le Thuy and Kien Giang stations of the Kien Giang river based on collected data of 2010, 2012, and 2020. Four evaluation metrics, that is, R2, Nash-Sutcliffe efficiency, mean absolute error, and root mean square error, are employed to examine the reliability of the proposed models. The results show that the LR model outperforms the SVR, RFR, MLPR, and LGBMR models.
Flow and sediment problem is one of the key factors which affect the dispatching operation and life of the Three Gorges Project (TGP). Many approaches have been employed to research the flow and sediment problems of the TGP during its demonstration, planning, design, construction and operation, and many important results have been obtained. To understand the progress of flow and sediment measurement in China’s representative projects and the experience of sediment observation in super large reservoirs, the flow and sediment measurement of the TGP is mainly introduced in this paper. It includes the general situation of the TGP, the distribution of the hydrological station network, the measurement factors, the new measurement technology, and the sediment changes in the reservoir and downstream after the impoundment of the TGP. The sediment measurement results show that the basic situation of sediment problems is good, and these sediment problems may probably accumulate, develop, and transform over time, so they should be paid continuous attention.
Nyabugogo Stream receives sediment loads from practiced economic activities along its path, these sediment loads affect the composition of the water by changing its natural state, and lead to its deterioration and riverine wetland ecosystem. In this study the main sources of sediments are delineated, while corresponding loads are also quantified. After the analysis of those sediments in different periods, the relationship between economic development activities and sediment loads in Nyabugogo Stream were also determined. The findings revealed that the top most economic activities impacting the quantity of sediment load in Nyabugogo Stream were found to be mining followed by poor agricultural practices, deforestation, untreated sewages, and clay mining/fabrication of bricks respectively. Analysed samples showed in laboratory that at point A situated in Rutare sector have the lowest value of sediment loads of 3.29 × 106 tons/year while at point C situated in Kigali sector have the highest value of 141.35 × 106 tons/year, these results showed to be increased as the stream flows from Lake Muhazi to Nyabarongo River as the Stream continue to be experiencing the increase of economic activities practiced in the its catchment which also have been delineated using ArcMap, this showed the relationship between economic activities and sediment loads generated in the stream. The researchers recommend to impose the enforcement of regulations, policies and guidelines for different economic activities so that they cannot pollute natural water bodies and disturb aquatic ecosystem.
The historical background of a watercourse, whether it be a natural river or an artificial canal, holds great importance in understanding hydrological heritage, landscape evolution, planning processes, and making informed predictions for the future. This study focuses on the case study of the Churni River, which has been regarded as an artificial canal in the local literature, history, and geography research papers. The objective of this study is to investigate the authenticity of the claims and explore whether the Churni River is a natural river or an artificially constructed canal. Through an examination of existing literature and the present channel morphology, it becomes apparent that the alleged myths and rumors surrounding the origin of the Churni River, proposing its deliberate construction as a man-made canal by Maharaja Krishnachandra, lack substantial evidence. Conversely, a more plausible scenario emerges, suggesting that the Maharaja’s endeavors were likely focused on rejuvenating the degraded and silt-laden course of an already existing river. Additionally, no evidence is found to support the claim that the name “Churni” was assigned by Maharaja Krishnachandra, and the alternative name “Kangkana” lacks substantiation as a name for the river. This research contributes to a comprehensive understanding of the historical and geographical context of the Churni River, shedding light on its origin and nature while emphasizing the significance of historical analysis in distinguishing natural river channels from artificial canals and will widen the avenue of future research on river heritage of like situation.
Flood forecasting is critical for mitigating flood damage and ensuring a safe operation of hydroelectric power plants and reservoirs. This paper presents a new hybrid hydrological model based on the combination of the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model and an Encoder- Decoder-Long Short-Term Memory network to enhance the accuracy of real-time flood forecasting. The proposed hybrid model has been applied to the Krong H’nang hydropower reservoir. The observed data from 33 floods monitored between 2016 and 2021 are used to calibrate, validate, and test the hybrid model. Results show that the HEC-HMS-artificial neural network hybrid model significantly improves the forecast quality, especially for results at a longer forecasting time. In detail, the Kling–Gupta efficiency (KGE) index, for example, increased from ΔKGE=16% at time t+1h to ΔKGE = 69% at time t + 6 h. Similar results were obtained for other indicators including peak error and volume error. The computer program developed for this study is being used in practice at the Krong H’nang hydropower to aid in reservoir planning, flood control, and water resource efficiency.