Incorporating Backward-forward Stochastic Particle Tracking Model into the EFDC model for Probable Sedimentation Source identification in Typhoon events

<p>The reservoir siltation has been of critical environmental concerns in recent years. The vulnerability and the overdevelopment in the reservoir watershed are the causes of the reservoir sedimentation. While typhoon events happen, in addition to the great amount of sediment volume transported from the upstream to the reservoir region, the density currents may evolve, which will steeply increase turbidity levels for the periods of time. In particular, the Shihmen Reservoir, one of essential hydraulic engineering projects in northern Taiwan, has been exposed to crisis that the sedimentation may fill up in the next few decades. Therefore, in order to maintain the reservoir capacity to an operational extent, modeling the sediment transport patterns in Shihmen Reservoir will utilize the three-dimensional Environmental Fluid Dynamics Code (EFDC) for quantifying sediment concentrations during the typhoon event. Calibration and validation of EFDC are performed by comparing two independent sets of event-based hydrodynamic and sediment concentration data with assistance of the parameter optimization algorithm. Next, the Backward-forward Stochastic Particle Tracking Model (BF-SPTM) is further incorporated into the EFDC hydrodynamic module to check the likelihood of the potential source of sediment particles. Results of simulations are expected to provide a more precise release timing for flow regulation to ensure the effective slag removal for density currents. Additionally, with probable sedimentation sources available for a reservoir, effective land use change and restrictions on overdevelopment of the risk prone areas can be enforced to decrease the sediment yields into the reservoir. It is expected that this incorporation of BF-SPTM into EFDC can be applied to simulate sediment transport in typhoon events, and to provide appropriate reservoir management alternatives.</p><p>Keywords: Environmental Fluid Dynamics Code (EFDC), suspended sediment concentration, Backward-forward Stochastic Particle Tracking Model, Probable sedimentation source</p>

Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed

The sediment delivery ratio (SDR) connects the weight of sediments eroded and transported from slopes of a watershed to the weight that eventually enters streams and rivers ending at the watershed outlet. For watershed management agencies, the estimation of annual sediment yield (SY) and the sediment delivery has been a top priority due to the influence that sedimentation has on the holding capacity of reservoirs and the annual economic cost of sediment-related disasters. This study establishes the SEdiment Delivery Distributed (SEDD) model for the Shihmen Reservoir watershed using watershed-wide SDRw and determines the geospatial distribution of individual SDRi and SY in its sub-watersheds. Furthermore, this research considers the statistical and geospatial distribution of SDRi across the two discretizations of sub-watersheds in the study area. It shows the probability density function (PDF) of the SDRi. The watershed-specific coefficient (β) of SDRi is 0.00515 for the Shihmen Reservoir watershed using the recursive method. The SY mean of the entire watershed was determined to be 42.08 t/ha/year. Moreover, maps of the mean SY by 25 and 93 sub-watersheds were proposed for watershed prioritization for future research and remedial works. The outcomes of this study can ameliorate future watershed remediation planning and sediment control by the implementation of geospatial SDRw/SDRi and the inclusion of the sub-watershed prioritization in decision-making. Finally, it is essential to note that the sediment yield modeling can be improved by increased on-site validation and the use of aerial photogrammetry to deliver more updated data to better understand the field situations.

Intelligent management of reservoir and photoelectric ponds under water-food-energy nexus perspective

<p>The world is in a crucial era of energy transition, and green energy will serve as a new engine that drives sustainable development in the future. Renewable energy becomes the core energy to cultivate green energy industries and promote energy self-sufficiency in Taiwan. In recent years, water, food and energy nexus (WFE Nexus) has gained global attention. Therefore, a multi-objective optimization framework is proposed in this study to explore the optimal solution to the WFE Nexus for improving the synergistic benefits of water, food, and energy (hydropower, small hydropower and solar power). The joint multi-objective operation of the Shihmen Reservoir and irrigation ponds in the northern Taiwan constitutes the case study. This study aims at achieving the optimal water supply to fulfill basic demands from different sectors as well as increasing green energy output by utilizing reservoir spilled water to lift up hydropower output, installing small hydropower in river channels, and setting up solar panels over irrigation ponds. The results support the high potential of photoelectric ponds because the installation of solar panels over irrigation ponds can 1) reduce evaporation amount and water temperature and 2) provide water quality conditions suitable for growing fish while increasing solar power output. The results also indicate that the optimal joint operation of the Shihmen Reservoir and irrigation ponds can promote reservoir hydropower output and the small hydropower output in river channels while increasing water supply and food production. This study demonstrates that the intelligent management of the reservoir and photoelectric ponds not only can increase green energy production, water supply and food production but also can enhance the synergistic benefits of the WFE Nexus, which provides long/short term policies for sustainable urban development.</p><p> </p><p>Keywords: Multi-objective reservoir operation; Optimization; Water, food and energy nexus (WFE Nexus); Green energy; Greenhouse</p>

Application of Multiple-Temporal Satellite Imagery with UAV Technology to evaluate Post-typhoon Event Remediation Efficiency in Shihmen Reservoir Watershed

<p>This study utilized multiple-temporal satellite imagery with UAV and IoT technology to evaluate and monitor the post-typhoon event remediation effectiveness of soil and water conservation of Shihmen Reservoir Watershed from 2015 to 2018.</p><p>A combination of the historical event-based landslide inventory and a collection and recent satellite imagery with coverage of the area pre- and post-typhoon MANGKHUT in 2018 were applied to evaluate landslide process, evolution and sediment environment change. In addition, two UAV operations were completed and captured over 160km2 in the 5 sub-watersheds to validate the remediation effectiveness and environmental change.</p><p>The results show that the landslide area within Shihmen Reservoir is less than that of the 1994 typhoon Aere and has no increased tendency. Effective conservation and remediation work can effectively reduce the sediment discharge of meteorological events and decrease the turbidity of the water at the storage point. In addition, the vegetation coverage rate of the entire Shihmen Reservoir watershed is close to 90%. Except for the occasional localized deforestation, the vegetation coverage has gradually stabilized.</p><p>Keywords: Shihmen reservoir, Remediation Efficiency, UAV and IoT Technology</p>

Scouring of Replenished Sediment through Reservoir Flood Discharge Affects Suspended Sediment Concentrations at Downstream River Water Intake

Dredging is a commonly used sedimentation management strategy to remove mechanically deposited sediment from reservoirs. However, dredged sediment disposal is costly. Dredged sediment can be considered a beneficial resource and used for riverbed replenishment to prevent downstream riverbed degradation and improve aquatic habitats. This study investigated the feasibility of using dredged deposits with cohesive sediment for replenishment at the Shihmen Reservoir. Using the criterion of critical scour velocity, we conducted hydraulic assessments and identified the feasible replenishment area as the experimental domain. A physical model was developed to mimic the scouring process in the replenishment area. By applying dynamic similarity for scouring fine replenished sediment, we derived the regression relationship between flow-critical velocity and sediment-dry density, and used it for model ratio scaling of the grain size, dry density, and concentration in the physical model. Scoured sediment concentrations were measured to study the scour ratio at various flood discharges. Experimental results indicated that the scour ratio was related to factors such as flood discharge, flood duration, and water content of the replenished sediment. The reduction ratio of the concentration of sediment scoured from the replenishment area to the concentration of sediment at the downstream water intake was approximately 90% in the present study.

Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning

Shihmen Reservoir watershed is vital to the water supply in Northern Taiwan but the reservoir has been heavily impacted by sedimentation and soil erosion since 1964. The purpose of this study was to explore the capability of machine learning algorithms, such as decision tree and random forest, to predict soil erosion (sheet and rill erosion) depths in the Shihmen reservoir watershed. The accuracy of the models was evaluated using the RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and R2. Moreover, the models were verified against the multiple regression analysis, which is commonly used in statistical analysis. The predictors of these models were 14 environmental factors which influence soil erosion, whereas the target was 550 erosion pins installed at 55 locations (on 55 slopes) and monitored over a period of approximately three years. The data sets for the models were separated into 70% for the training data and 30% for the testing data, using the simple random sampling and stratified random sampling methods. The results show that the random forest algorithm performed the best of the three methods. Moreover, the stratified random sampling method had better results among the two sampling methods, as anticipated. The average error (RMSE relative to 1:1 line) of the stratified random sampling method of the random forest algorithm is 0.93 mm/yr in the training data and 1.75 mm/yr in the testing data, respectively. Finally, the random forest algorithm predicted that type of slope, slope direction, and sub-watershed are the three most important factors of the 14 environmental factors collected and used in this study for splits in the trees and thus they are the three most important factors affecting the depth of sheet and rill erosion in the Shihmen Reservoir watershed. The results of this study can be employed by decision-makers to improve soil conservation planning and watershed remediation.

Modeling Sediment Yields and Stream Stability Due to Sediment-Related Disaster in Shihmen Reservoir Watershed in Taiwan

Accurate and reliable estimates of sediment yields from a watershed and identification of unstable stream reaches due to sediment-related disaster are crucial for watershed management, disaster prevention, and hazard mitigation purposes. In this study, we added hydrodynamic and sediment transport modules in a recently developed model to estimate sediment yields and identify the unstable stream reaches in a large-scale watershed (> 100km2). The calibrated and verified models can well reproduce the flow discharge and sediment discharge at the study site, the Shihmen Reservoir Watershed in Taiwan, during several typhoon events. For the scenario applications, the results revealed that the contribution (> 96%) of landslides on sediment supply is much more significant than compared to soil erosion (< 4%). The sediment contribution from the upstream of the hydrological station-Yufeng is approximately 36–55% of the total sediment supply for the rainfall events of 25, 50, 100, and 200 years return period. It also indicates that 22–52% of sediment still remain at foot of the slope and the streams, which become a potential source for sediment hazards in the future. Combining with the bed erosion and deposition depths, flow-induced shear stress from the SRH-2D model, and probability of slope failure within 250 m of stream reaches, the relatively stability of stream reaches can be identified. The results could provide the water resource authorities for reference to take precautionary measures in advance on the stream reaches with high-degree instability.

Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed

The estimation of soil erosion in Taiwan and many countries of the world is based on the widely used universal soil loss equation (USLE), which includes the factor of soil erodibility (K-factor). In Taiwan, K-factor values are referenced from past research compiled in the Taiwan Soil and Water Conservation Manual, but there is limited data for the downstream area of the Shihmen reservoir watershed. The designated K-factor from the manual cannot be directly applied to large-scale regional levels and also cannot distinguish and clarify the difference of soil erosion between small field plots or subdivisions. In view of the above, this study establishes additional values of K-factor by utilizing the double rings infiltration test and measures of soil physical–chemical properties and increases the spatial resolution of K-factor map for Shihmen reservoir watershed. Furthermore, the established values of K-factors were validated with the designated value set at Fuxing Sanmin from the manual for verifying the correctness of estimates. It is found that the comparative results agree well with established estimates within an allowable error range. Thus, the K-factors established by this study update the previous K-factor system and can be spatially estimated for any area of interest within the Shihmen reservoir watershed and improving upon past limitations.

Urban Metabolic Analysis of a Food-Water-Energy System for Sustainable Resources Management

Urban metabolism analyzes the supply and consumption of nutrition, material, energy, and other resources within cities. Food, water, and energy are critical resources for the human society and have complicated cooperative/competitive influences on each other. The management of interactive resources is essential for supply chain analysis. This research analyzes the food-water-energy system of urban metabolism for sustainable resources management. A system dynamics model is established to investigate the urban metabolism of food, water, and energy resources. This study conducts a case study of Shihmen Reservoir system, hydropower generation, paddy rice irrigation of Taoyuan and Shihmen Irrigation Associations, and water consumption in Taoyuan, New Taipei, and Hsinchu cities. The interactive influence of the food-water-energy nexus is quantified in this study; the uncertainty analysis of food, water, and energy nexus is presented.