Reservoirs In Taiwan Research Articles

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

Machine and deep learning-based trophic state classification of national freshwater reservoirs in Taiwan using Sentinel-2 data

Since the beginning of the industrial revolution in the nineteenth century water pollution has grown into a global transboundary problem that negatively impacts aquatic ecosystems a risk linked directly to sustainable economic development, human, aquatic, and wildlife health. However, one major impediment to monitoring lake water quality is a lack of data for the relevant monitoring parameters at the relevant temporal and spatial scales. Therefore, this study investigates sixteen major reservoirs on a national scale based on 2020 in-situ sampling and Sentinel-2 MSI satellite data. Machine and deep learning algorithms were employed to classify and predict various water quality classes using Sentinel-2 MSI pixel color values and spectral indices. Multivariate techniques such as PCA transformation and regularized MANOVA were used as input feature preprocessing for variable reconstruction to deal with collinearity. Consequently, this improved the model classification performances overall. The results highlight deep neural net (DNN) and XGBoost as the outstanding classifying algorithms yielding F1-score of 0.94 and 0.93; Recall of 0.93 and 0.92; ROC-AUC of 0.92 and 0.89 respectively. Nutrient pollution affects all of the investigated reservoirs as they are classified as either meso- or hypereutrophic, and the situation is deteriorating. This study does not only give a feasible solution for effective water quality observation, but also provides a unique data pipeline for basic creation of training data for biogeochemical parameters for studying lake water quality using an integrated Google Earth Engine (GEE) Python Application Programming Interface (API) run in Google Colaboratory notebook.

The impacts of water storage infrastructure on real property values and crop production

Water storage infrastructure is an indispensable part of many water supply systems, and its importance is experiencing a resurgence due to the increasing water irregularity induced by climate change and ever-growing water demand. Leveraging the commission of Hushan Reservoir in Taiwan as a quasi-experiment, this study provides some of the first causal evidence of the economic benefits of a reservoir from housing market and crop production for guiding sustainable water management. Using the administrative property transaction data and a spatial difference-in-differences framework, we find that the commission of Hushan Reservoir increases the values of residential property and farmlands by 4.1 and 8.9 %, respectively. We also find that enhanced irrigation water availability increases rice yield by over 4 % but has no impact on planted areas. Despite expectations of reduced groundwater use, our results show no evidence of a rebound in groundwater levels in the four years following the commission of the reservoir.

Eutrophication factor analysis using Carlson trophic state index (CTSI) towards non-algal impact reservoirs in Taiwan

Carlson trophic state index (CTSI) has been commonly adopted to assess the eutrophication potential of reservoirs or lakes in water quality management. This study aims to analyze the influential factors of CTSI-based eutrophication by using Pearson correlation analysis and principal component analysis with long-term data from 2008 to 2019 on 21 drinking water reservoirs in Taiwan. The trophic state index (TSI) deviation indicates that most drinking water reservoirs, around 45.5% of statistical data fall within non-algal turbidity with surplus phosphorus, especially in the spring and winter season. Besides, about 78% of total collected data show that TSI chlorophyll-a (Chl-a) is less than TSI Secchi depth (SD) due to the small particulate predominance. On the other hand, three TSI variables (SD, total phosphorus (TP) and Chl-a) of CTSI exhibits insignificant correlation to each other in most cases. Under such conditions, the probability of eutrophication (TSI > 50) based on TSI (SD) is 63%, while it is low as 20% based on TSI (TP) and TSI (Chl-a). The influencing factors of eutrophication variables by suspended solids (SS) composition and turbidity have shown that the SD is strongly influenced by non-algal SS. The deviations of three TSI have shown that the highest algae-induced eutrophication potential occurs in summer season. In addition, the TP is the most significant loading factor of algae-induced eutrophication for drinking water reservoirs. It is concluded that the CTSI has limited applicability to identify the trophic state of drinking water reservoirs in Taiwan in the presence of sustainable non-algal turbidity comparative Chl-a that specifically represents the potential of algal growth.

Reservoir Sediment Management and Downstream River Impacts for Sustainable Water Resources—Case Study of Shihmen Reservoir

Sustainable water resources of reservoirs depend on preserving the valuable storage capacity. Sediment management is a crucial task in reservoir operations. Extreme floods caused by typhoon events have brought a massive amount of sediments from the watershed of Shihmen Reservoir in Taiwan. In the case study of Shihmen Reservoir, the primary purpose of the sediment management strategies is to minimize sediment deposition and recover reservoir capacity. Two assessment indexes, the capacity–inflow ratio (CIR) and the capacity–sediment ratio (CSR) are investigated to provide a feasible assessment of desilting techniques. Three desilting projects have been planned and implemented in progress, including one modified power plant penstock and two desilting tunnels. Without effective sediment management strategies, the projected storage capacity may drop to 32.9% of the initial storage capacity in the next fifty years. On the other hand, if sediment management is implemented as per the project schedule, 70.3% of the initial storage capacity may be retained, enabling the provision of sustainable water supplies to meet projected water demands. In investigating the impacts caused by the desilting operations, the floodwater level and riverbed variations along the downstream river of the dam are simulated by the hydromorphological model. From simulated results, the downstream river morphology may adjust toward the equilibrium state under the long-term desilting operations of Shihmen Reservoir. Although relatively high sediment concentration is released during the desilting operation, it decays gradually along the downstream river. In addition, riverbed deposition does not significantly affect the floodwater level caused by the increment of riverbed elevation in desilting operations.

Analysis of Factors Influencing the Trophic State of Drinking Water Reservoirs in Taiwan

Eutrophication is an environmental pollution problem that occurs in natural water bodies. Regression analyses with interaction terms are carried out to identify the factors influencing the Shimen, Mingde, and Fongshan Reservoirs in Taiwan. The results indicate that the main factor influencing these reservoirs is total phosphorus. In the Shimen and Mingde Reservoirs, the influence of total phosphorus, when interacting with other factors, on water quality trophic state is more serious than that of total phosphorus per se. This implies that the actual influence of total phosphorus on the eutrophic condition could be underestimated. Furthermore, there was no deterministic causality between climate and water quality variables. In addition, time lagged effects, or the influence of their interaction with other variables, were considered separately in this study to further determine the actual relationships between water trophic state and influencing factors. The influencing patterns for three reservoirs are different, because the type, size, and background environment of each reservoir are different. This is as expected, since it is difficult to predict eutrophication in reservoirs with a universal index or equation. However, the multiple linear regression model used in this study could be a suitable quick-to-use, case-by-case model option for this problem.

A Method of Multi-Stage Reservoir Water Level Forecasting Systems: A Case Study of Techi Hydropower in Taiwan

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

Establishment of the watershed health indicators and health check of reservoirs

This study analyzed big data collected from 95 public water supply, power generation, and irrigation reservoirs in Taiwan and its outlying islands to establish and quantify the standards for seven watershed health indicators. The water supply reservoirs have high forest coverage and a low soil erosion depth; however, these reservoirs have a low water quality due to a low point source pollution was treated (9.1%–17.7%) and 44–65.8 kg nonpoint source pollution per area (km2). Most irrigation reservoirs are located in the flat plains, which have low forest coverage (50.5%) and high sediment accumulation (36.7%). The power generation reservoirs are located on steep mountains with high forest coverage and only natural soil erosion. The 30 outlying reservoirs have a Carlson’s trophic state index ​​value higher than 50, which is primarily a function of transparency. The chemical oxygen demand achievement rate is a more suitable health indicator for the outlying reservoirs than the other aforementioned indicators. The forest coverage rate and point source pollution reduction were positively associated with watershed health, whereas sediment accumulation, nonpoint source pollution, and CTSI/RPI were negatively associated with watershed health. The results of this study suggest that management strategies need to be tailored to the type of watershed.

Investigating the reliability of machine learning algorithms as a sustainable tool for total suspended solid prediction

This research studies the implementation of artificial neural networks (ANN) in predicting the concentration of total suspended solids (TSS) for the Fei Tsui reservoir in Taiwan. The prediction model developed in this study is designed to be used for monitoring the water quality in the Fei Tsui reservoir. High concentrations of total suspended solids (TSS) have been a crucial problem in the Fei Tsui reservoir for decades. As the Fei Tsui reservoir is a primary water source for Taipei City, this issue impacts the drinking water supply for the city due to etherification problems in the reservoir. 10-year average monthly records and 13-year average annual records have been collected for 26 parameters and correlated with the TSS concentrations to determine the parameters that have a strong relationship with the TSS concentrations. The parameters that were shown to have a strong correlation with the TSS concentration are the trophic state index (TSI), nitrate (NO3) concentration, total phosphorous (TP) concentration, iron concentration (IRON), and turbidity. Linear regression was used to develop the model that estimates the TSS concentration in the Fei Tsui Reservoir. The results show that model 3, a three-layer ANN model that uses three-input parameters namely NO3 concentration, TP concentration, and turbidity, with five neurons, to predict the output parameter which is TSS concentration, produces the highest coefficient of determination (R2) and Willmott Index (WI), which are 0.9589 and 0.9933 respectively, and the lowest root mean square error, which is 0.4753. Based on these performance criteria, model 3 is concluded as the best model to predict TSS concentrations in this study.

Efficiency of the Drawdown Flushing and Partition Desilting of a Reservoir in Taiwan

Sedimentation limits the benefits of storage reservoirs, especially in areas with higher sediment yields, such as Agongdian Reservoir in southern Taiwan. Although drawdown flushing is a known strategy that releases large amounts of fine sediment into a downstream channel, there is limited information on the long-term monitoring and multiple metrics being used to evaluate flushing efficiency. The objectives of this study were three-fold: (1) to continue collecting valuable long-term observed data, since Agongdian Reservoir is one of the few reservoirs currently conducting sediment flushing operations; (2) to evaluate and identify the hydrological parameters that are highly related to the flushing efficiency; (3) to execute numerical simulations of different reservoir flushing scenarios at multiple water levels to discuss potential strategies to improve the flushing efficiency. The findings of this study revealed that long-term monitoring data was valuable for identifying factors highly related to the flushing efficiency, which included the initial water level; average water level; average velocity. Based on simulations, compartmentalizing the reservoir is a proposed strategy that has demonstrated high levels of improvement in terms of the flushing efficiency, depending on particular scenarios involving partition desilting, empty flushing, or a combination of both. Recommendations to increase the flushing efficiency include lowering the initial water level, creating a narrower gorge-like geometry by partitioning, and further considering to modify the operation rules.

Influencing Factors Analysis of Taiwan Eutrophicated Reservoirs

Treatment cost and quality of domestic water are highly correlated with raw water quality in reservoirs. This study aims to identify the key factors that influence the trophic state levels and correlations among Carlson trophic state index (CTSI) levels, water quality parameters and weather factors in four major reservoirs in Taiwan from 2000 to 2017. Weather (e.g., air temperature, relative humidity, total precipitation, sunlight percentage and cloud cover) and water quality parameters (e.g., pH, chemical oxygen demand, suspended solids (SS), ammonia, total hardness, nitrate, nitrite and water temperature) were included in the principal component analysis and absolute principal component score models to evaluate the main governing factors of the trophic state levels (e.g., CTSI). SS were washed out by precipitation, thereby influencing the reservoir transparency tremendously and contributing over 50% to the CTSI level in eutrophicated reservoirs (e.g., the Shihmen and Chengchinghu Reservoirs). CTSI levels in the mesotrophic reservoir (e.g., Liyutan Reservoir) had strong correlation with chlorophyll-a and total phosphorus. Results show that rainfall/weather factors were the key driving factors that affected the CTSI levels in Taiwan eutrophicated reservoirs, indicating the need to consider basin management and the impacts of extreme precipitation in reservoir management and future policymaking.

Application of Empirical Mode Decomposition Method to Synthesize Flow Data: A Case Study of Hushan Reservoir in Taiwan

Although empirical mode decomposition (EMD) was developed to analyze nonlinear and non-stationary data in the beginning, the purpose of this study is to propose a new method—based on EMD—to synthesize and generate data which be interfered with the non-stationary problems. While using EMD to decompose flow record, the intrinsic mode functions and residue of a given record can be re-arranged and re-combined to generate synthetic time series with the same period. Next, the new synthetic and historical flow data will be used to simulate the water supply system of Hushan reservoir, and explore the difference between the newly synthetic and historical flow data for each goal in the water supply system of Hushan reservoir. Compared the historical flow with the synthetic data generated by EMD, the synthetic data is similar to the historical flow distribution overall. The flow during dry season changes in significantly (±0.78 m3/s); however, the flow distribution during wet season varies significantly (±0.63 m3/s). There are two analytic scenarios for demand. For Scenario I, without supporting industrial demand, the simulation results of the generation data of Method I and II show that both are more severe than the current condition, the shortage index of each method is between 0.67–1.96 but are acceptable. For Scenario II, no matter in which way the synthesis flow is simulated, supporting industrial demand will seriously affect the equity of domestic demand, the shortage index of each method is between 1.203 and 2.12.

Surface properties and microstructures of ceramic glaze made from reservoir sediment

AbstractThe 96 reservoirs in Taiwan have a total reservoir capacity of 2.862 billion cubic meters, and a deposition rate of 28.95%. Although the reservoir sediment has similar main constituents to traditional Taiwanese glaze, it cannot independently replace Taiwanese glaze. The reservoir sediment is used as the main raw material to improve the settleability and viscosity of the reservoir sediment glaze by adjusting the proportions of raw materials, adding adhesion aid (Taiwan red clay), and the suspending agent (bentonite). The surface characteristics of the glaze are improved by finding the optimum soaking time. The recycled glass powder is blended to reduce the firing temperature of the reservoir sludge glaze. Experimental results indicate that red clay and bentonite enhance the consistency of reservoir sediment glaze paste, while sodium silicate and American fairy water reduce it. Prolonging the soaking time improves the glaze thickness. The roughness of the reservoir sediment glaze is reduced after firing, and the firing temperature is negatively correlated with the roughness of reservoir sediment glaze. The glass powder (5–10%) can reduce the firing temperature of reservoir sediment glaze by 100–150°C, but does not decrease the roughness. Therefore, this investigation recommends the optimum liquid–solid ratio (L/S) of reservoir sediment glaze paste as 1.7–1.8, the optimum blending ratio of red clay as 10–15%, the optimum blending ratio of bentonite as 5%, and the soaking time as 8 s.

Pseudomethylobacillus aquaticus gen. nov., sp. nov., a new member of the family Methylophilaceae isolated from an artificial reservoir.

A bacterial strain, designated H-5T, was isolated from an artificial reservoir in Taiwan and characterized using a polyphasic taxonomic approach. Cells of strain H-5T were Gram-stain-negative, aerobic, motile by means of a single polar flagellum, rod-shaped, covered by large capsules and formed white colonies. Growth occurred at 15-30 °C (optimum, 25 °C), at pH 6-8 (optimum, pH 7) and with 0-0.5 % NaCl (optimum, 0 %). Phylogenetic analyses based on the 16S rRNA gene, the methanol dehydrogenase gene and the coding sequences of 92 protein clusters indicated that strain H-5T was affiliated with genera in the family Methylophilaceae in the class Betaproteobacteria. Strain H-5T was most closely related to Methylobacillus methanolivorans ZT with a 95.0 % 16S rRNA gene sequence similarity. Strain H-5T showed less than 73.7 % average nucleotide identity and less than 23.6 % digital DNA-DNA hybridization identity compared to the strains of related genera within the family Methylophilaceae. The predominant fatty acids were summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c) and C16 : 0. The major isoprenoid quinone was Q-8 and the DNA G+C content was 58.3 mol%. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol, one uncharacterized aminophospholipid, one uncharacterized phospholipid and one uncharacterized lipid. On the basis of the genotypic and phenotypic data presented here, strain H-5T represents a novel species of a new genus in the family Methylophilaceae, for which the name Pseudomethylobacillus aquaticus gen. nov., sp. nov. is proposed. The type strain is H-5T (=BCRC 81154T=KCTC 62865T).

A molecular-based method to estimate the risk associated with cyanotoxins and odor compounds in drinking water sources

A biomolecular-based monitoring approach for the assessment of water quality hazards and risks associated with cyanobacteria was developed and validated in drinking reservoirs in Taiwan and the Philippines. The approach was based upon the measurement of gene abundances of toxigenic Microcystis and Cylindrospermopsis; for cyanotoxins; and for aesthetically offensive earthy-musty odor compounds. This was compared to conventional monitoring approaches, which included cell enumeration by microscopy, and toxin and odor compound analysis by instrumental analytical methods and immunoassays as appropriate for the metabolites. The validation involved samples from ten major reservoirs on Taiwan's main island, nineteen reservoirs on the offshore islands, and Laguna de Bay in the Philippines. The gene-based approach was successfully validated statistically and compared to conventional widely utilized risk assessment schemes which have employed ‘Alert Levels’ for toxic cyanobacteria. In this case a new integrated scheme of ‘Response Levels’ is proposed which incorporates odor metabolite hazards in addition to cyanotoxins and is based upon gene copy numbers to derive quantitative triggers. The comprehensive scheme evaluated from these locations is considered to be more precise and efficient for both monitoring and as a risk assessment diagnostic tool, given that it offers the capacity for analysis of the abundance of genes for cyanobacterial metabolites in large numbers of natural water samples in a significantly reduced period of time compared to the approaches of cell enumeration by microscopy or metabolite analytical techniques. This approach is the first time both the hazard and risk for both odors and cyanotoxins from cyanobacteria have been considered together in a monitoring scheme and offers an improved means for determining the Response Levels in the risk assessment process for cyanobacteria and their metabolites in drinking water sources.

Flavobacterium stagni sp. nov., isolated from a freshwater reservoir.

Strain WWJ-16T was isolated from a freshwater reservoir in Taiwan. Phylogenetic analyses based on 16S rRNA gene sequences and an up-to-date bacterial core gene set (92 protein clusters) indicated that strain WWJ-16T is affiliated with the species in the genus Flavobacterium. Strain WWJ-16T was most closely related to Flavobacterium fontis MIC3010T (97.9 % 16S rRNA gene sequence identity) and Flavobacterium squillarum CMJ-5T (97.6 %). Strain WWJ-16T showed 77.4 % average nucleotide identity and 20.6 % digital DNA-DNA hybridization identity with F. fontis MIC3010T. Cells of strain WWJ-16T were Gram-stain-negative, strictly aerobic, non-motile, rod-shaped and formed yellow colonies. Optimal growth occurred at 25 °C, pH 7 and in 0.5-1 % NaCl (w/v). Strain WWJ-16T contained iso-C15 : 1 G and iso-C15 : 0 as predominant fatty acids. The major hydroxyl fatty acids were iso-C15 : 0 3-OH, iso-C17 : 0 3-OH and iso-C16 : 0 3-OH. The polar lipid profile consisted of phosphatidylethanolamine, four uncharacterized aminophospholipids and four uncharacterized phospholipids. The major polyamine was homospermidine. The major isoprenoid quinone was MK-6. The genomic DNA G+C content of strain WWJ-16T was 39.4 mol%, as determined by genome sequencing. The genotypic and phenotypic characteristics indicate that strain WWJ-16T represents a novel species of the genus Flavobacterium, for which the name Flavobacterium stagni sp. nov. is proposed. The type strain is WWJ-16T (=BCRC 81125T =LMG 30600T=KCTC 62515T).

Development of probabilistic inundation mapping for dam failure induced floods

A primary function of flood inundation mapping is to forecast flood hazards and assess potential losses. However, uncertainties limit the reliability of inundation hazard assessments. Major sources of uncertainty should be taken into consideration by an optimal flood management strategy. This study focuses on the 20 km reach downstream of the Shihmen Reservoir in Taiwan. In the modified dam failure model proposed in this study, the surface area of the water in the reservoir and water elevation are allowed to vary with the volume of the reservoir. A dam failure induced flood herein provides the upstream boundary conditions of flood routing. The two major sources of uncertainty that are considered in the hydraulic model and the flood inundation mapping herein are uncertainties in the dam break model and uncertainty of the roughness coefficient. The perturbance moments method is applied to a dam break model and the hydrosystem model to develop probabilistic flood inundation mapping. Uncertain variables such as roughness coefficient, breach width and reservoir surface area can be considered in these models and the variability of outputs can be quantified. The probabilistic flood inundation mapping for dam break induced floods can be developed with consideration of the variability of output using a hydraulic model. Two different probabilistic flood inundation mappings are discussed and compared. Probabilistic flood inundation mappings are hoped to provide improved insights in support of the evaluation of concerning reservoir flooded areas.

Sediment Management in Taiwan’s Reservoirs and Barriers to Implementation

Reservoirs play a critically important role in supplying water for human uses. However, sedimentation limits storage capabilities and increases risk for aging infrastructure. The objectives of this paper are to synthesize both general sediment management strategies and past sediment management efforts in Taiwan in order to identify the barriers to more effective sediment management in reservoirs globally. A review of the broader literature and six Taiwan case studies was conducted to examine the characteristics, limitations, costs, and effectiveness of different sediment management strategies. Results highlight how social barriers play an important role in limiting reservoir sustainability, particularly the crisis-response approach to addressing sedimentation and the low priority for sediment management relative to competing objectives, such as tourism. Technical barriers are driven primarily by the engineering and costs of retrofitting existing dams and site conditions that may inhibit particular practices at any given site. Results also highlight tradeoffs in the effectiveness, costs, and time efficiency of various sediment management strategies in restoring storage capacity. The high sediment loads and rapid filling of reservoirs in Taiwan provide early insight into the management issues that are emerging worldwide, and these results emphasize the need for proactive engineering and management of sediment in reservoirs globally.

Effect of flying altitude and pulse repetition frequency on laser scanner penetration rate for digital elevation model generation in a tropical forest

In tropical forests, the penetration ability of airborne laser scanning (ALS) may be limited because of highly dense vegetation cover. However, in the typical planning of ALS surveys, the ability of laser pulses to penetrate forests is not considered. Nine round-trip flight lines covering the area of a tropical forest on the northeast side of the Tsengwen Reservoir in Taiwan were designed in this study. Five flight lines flew at altitudes of 1.525, 1.830, 2.135, 2.440, and 2.745 km, and the other four had pulse repetition frequencies (PRFs) of 100, 150, 200, and 250 kHz. The laser penetration index (LPI) is a quantitative index measuring the penetration ability of the ALS and consists of the ratio of the number of laser pulses reaching the forest floor to the total number of laser pulses. The LPI was used to represent the laser penetration rate and investigate the influence of flying altitude and PRF on the LPI. The results showed that as the flying altitude decreased by 1 km, the average LPI increased by 10%, and as the PRF decreased by 50 kHz, the average LPI increased by 2%. The effect of the LPI on digital elevation models (DEMs) was confirmed by visual images obtained by DEMs at five altitudes. The DEM obtained at an altitude of 2.745 km was coarsely textured, whereas that obtained at an altitude of 1.525 km was finely textured. The in-situ height data obtained from the electronic Global Navigation Satellite System (eGNSS) were compared with the data of the ALS-generated DEMs. The results indicated that when the LPI ≥60%, the height difference between the in situ data and DEM data was not prominent. However, when the LPI <60%, the ALS-derived DEM could be higher or lower than the in-situ height; the largest difference between the two was 1.7 m. The LPI of a forest should be considered for ALS survey planning, especially when consistent DEM precision for large tropical forest areas is paramount.

Physical Model-Based Investigation of Reservoir Sedimentation Processes

Sedimentation is a serious problem in the operations of reservoirs. In Taiwan, the situation became worse after the Chi-Chi Earthquake recorded on 21 September 1999. The sediment trap efficiency in several regional reservoirs has been sharply increased, adversely affecting the operations on water supplies. According to the field record, the average annual sediment deposition observed in several regional reservoirs in Taiwan has been increased. For instance, the typhoon event recorded in 2008 at the Wushe Reservoir, Taiwan, produced a 3 m sediment deposit upstream of the dam. The remaining storage capacity in the Wushe Reservoir was reduced to 35.9% or a volume of 53.79 million m3 for flood water detention in 2010. It is urgent that research should be conducted to understand the sediment movement in the Wushe Reservoir. In this study, a scale physical model was built to reproduce the flood flow through the reservoir, investigate the long-term depositional pattern, and evaluate sediment trap efficiency. This allows us to estimate the residual life of the reservoir by proposing a modification of Brune’s method. It can be presented to predict the lifespan of Taiwan reservoirs due to higher applicability in both the physical model and the observed data.