Watershed In Taiwan Research Articles

Evaluation of hydrological responses to climate change for a data-scarce mountainous watershed in Taiwan

Abstract Despite the mountainous watersheds being important for the ecosystem, water resources, and hydropower, little hydrological data has been collected. This data scarcity makes it difficult to evaluate their hydrologic response to climate change. This study integrated short-term hydrological data with physics-based meteorological and hydrological models to measure the impact of climate change on future water scarcity in the Wuling Mountain Watershed, Taiwan. Twenty-eight-month hydrological datasets from 2013 to 2015 were used for the hydrological characterization. Scenarios from CMIP5 were selected for the climate projection for the period 2021–2040 based on the inferred 1986–2005 baseline data. The results showed that precipitation, water percolation, and streamflow will decrease by about 10% and increase by about 20–25% in the dry and wet seasons, respectively. The evapotranspiration is lower than that of the baseline in January, March, and December, whereas it can be as high as 4% during the other months. The increase in the annual amount and change in the water distribution for all studied water components indicate the possible acceleration of the water cycle. Spectrum analysis showed that surface water tends toward becoming more irregular. Groundwater remains mildly persistent and thus may serve as a buffer for the impact of climate change on water resources.

Certainty Factor Analyses and Spatiotemporal Characteristics of Landslide Evolution: Case Studies in the Chishan River Watershed in Taiwan

The 1999 Chichi earthquake and Typhoon Morakot in 2009 caused two serious landslide events in the Chishan river watershed in southern Taiwan. In this study, certainty factor analysis was used to evaluate the effectiveness of landslide occurrence, and spatiotemporal hotspot analysis was used to explain the pattern and distribution of landslide hotspots. The Z-values from the Getis–Ord formula were used to assess the clustering strength of landslide evolution on different scales and with different landslide sizes in different time periods. The landslide-prone area had an elevation of 1000–1750 m, a slope of >40°, and hillslopes with N, NE, E, and SE aspects and was within 100 m of rivers. The main spatiotemporal hotspot patterns of landslide evolution during 1999–2017 were oscillating hotspots, intensifying hotspots, and persistent hotspots, and the three main hotspot patterns occupied 80.1–89.4% of all hotspot areas. The main spatiotemporal landslide hotspots were concentrated in the core landslide areas and the downslopes of riverbank landslide areas, especially in the upstream subwatersheds. The landslide clustered strength in the upstream watershed was 3.4 times larger than that in the Chishan river watershed, and that in large landslides was 2.4 and 6.6 times larger than those in medium and small landslides, respectively.

Resident Willingness to Pay for Ecosystem Services in Hillside Forests.

This study investigated the willingness of residents to pay for ecosystem services in a hillside forest in the Lanyang River Basin, which is among the most vulnerable watersheds in Taiwan. The economic value of provisioning, regulating, cultural, and supporting ecosystem services was evaluated. The Contingent Valuation Method (CVM) was applied for economic analysis of public welfare. The determinants of the economic values were identified. A total of 444 respondents completed the questionnaire. The results revealed that the four ecosystem services had high economic value, indicating that conserving hillside forests can ensure the welfare of nearby residents. The findings of this study can serve as reference for regional land planning and social and economic system development policies. In addition, this study addressed policy implementation from the perspective of ecological economics to contribute to an improved Anthropocene.

Comparison of Rainfall-Runoff Simulation between Support Vector Regression and HEC-HMS for a Rural Watershed in Taiwan

To better understand the effect and constraint of different data lengths on the data-driven model training for the rainfall-runoff simulation, the support vector regression (SVR) approach was applied to the data-driven model as the core algorithm in the present study. Various features selection strategies and different data lengths were employed in the training phase of the model. The validated results of the SVR were compared with the rainfall-runoff simulation derived from a physically based hydrologic model, the Hydrologic Modeling System (HEC-HMS). The HEC-HMS was considered a conventional approach and was also calibrated with a dataset period identical to the SVR. Our results showed that the SVR and HEC-HMS models could be adopted for short and long periods of rainfall-runoff simulation. However, the SVR model estimated the rainfall-runoff relationship reasonably well even if the observational data of one year or one typhoon event was used. In contrast, the HEC-HMS model needed more parameter optimization and inference processes to achieve the same performance level as the SVR model. Overall, the SVR model was superior to the HEC-HMS model in the performance of the rainfall-runoff simulation.

Evaluating the Landslide Stability and Vegetation Recovery: Case Studies in the Tsengwen Reservoir Watershed in Taiwan

The sediment yield from numerous landslides triggered in Taiwan’s mountainous regions by 2009 Typhoon Morakot have had substantial long-term impacts on the evolution of rivers. This study evaluated the long-term evolution of landslides induced by 2001 Typhoon Nari and 2009 Typhoon Morakot in the Tsengwen Reservoir Watershed by using multiannual landslide inventories and rainfall records for the 2001–2017 period. The landslide activity, vegetation recovery time, and the landslide spatiotemporal hotspot analyses were used in the study. Severe landslides most commonly occurred on 35–45° slopes at elevations of 1400–2000 m located within 500 m of the rivers. The average vegetation recovery time was 2.29 years, and landslides with vegetation recovery times exceeding 10 years were most frequently retrogressive landslide, riverbank landslides in sinuous reaches, and the core area of large landslides. The annual landslide area decline ratios after 2009 Typhoon Morakot in Southern Taiwan was 4.75% to 7.45%, and the time of landslide recovery in the Tsengwen reservoir watershed was predicted to be 28.48 years. Oscillating hotspots and coldspots occupied 95.8% of spatiotemporal patterns in the watershed area. The results indicate that landslides moved from hillslopes to rivers in the 2001–2017 period because the enormous amount of sediment deposited in rivers resulted in the change of river geomorphology and the riverbank landslides.

Cluster and Redundancy Analyses of Taiwan Upstream Watersheds Based on Monthly 30 Years AVHRR NDVI3g Data

The study uses 30 years of the third generation of Advanced Very-High-Resolution Radiometer (AVHRR) NDVI3g monthly data from 1982 to 2012 to identify the natural clusters and important driving factors of the upstream watersheds in Taiwan through hierarchical cluster analysis (HCA) and redundancy analysis (RDA), respectively. Subsequently, as a result of HCA, six clusters were identified based on the 30 years of monthly NDVI data, delineating unique NDVI characteristics of the upstream watersheds. Additionally, based on the RDA results, environmental factors, including precipitation, temperature, slope, and aspect, can explain approximately 52% of the NDVI variance over the entire time series. Among environmental factors, nine factors were identified significantly through RDA analysis for explaining NDVI variance: average slope, temperature, flat slope, northeast-facing slope, rainfall, east-facing slope, southeast-facing slope, west-facing slope, and northwest-facing slope, which reflect an intimate connection between climatic and orthographic factors with vegetation. Furthermore, the rainfall and temperature represent different variations in all scenarios and seasons. With consideration of the characteristics of the clusters and significant environmental factors, corresponding climate change adaptation strategies are proposed for each cluster under climate change scenarios. Thus, the results provide insight to assess the natural clustering of the upstream watersheds in Taiwan, benefitting future sustainable watershed management.

DEM- and GIS-Based Analysis of Soil Erosion Depth Using Machine Learning

Soil erosion is a form of land degradation. It is the process of moving surface soil with the action of external forces such as wind or water. Tillage also causes soil erosion. As outlined by the United Nations Sustainable Development Goal (UN SDG) #15, it is a global challenge to “combat desertification, and halt and reverse land degradation and halt biodiversity loss.” In order to advance this goal, we studied and modeled the soil erosion depth of a typical watershed in Taiwan using 26 morphometric factors derived from a digital elevation model (DEM) and 10 environmental factors. Feature selection was performed using the Boruta algorithm to determine 15 factors with confirmed importance and one tentative factor. Then, machine learning models, including the random forest (RF) and gradient boosting machine (GBM), were used to create prediction models validated by erosion pin measurements. The results show that GBM, coupled with 15 important factors (confirmed), achieved the best result in the context of root mean square error (RMSE) and Nash–Sutcliffe efficiency (NSE). Finally, we present the maps of soil erosion depth using the two machine learning models. The maps are useful for conservation planning and mitigating future soil erosion.

Mapping the local impacts of water consumption with regionalized three-dimensional arrays

This study aims to develop a regionalized characterization model of water consumption that can delineate what the potential damage receptors are and where they will be. In order to improve local water resources management, assessment results with more accurate spatial information are needed. This model could be used to derive regionalized life cycle assessment (LCA) results, especially for evaluations within a country. We developed a regionalized characterization model using a watershed-affected matrix and related factors. To construct the watershed-affected matrix, a watershed-affected area (WAA) describing the geographical boundary that will be affected by insufficient water supply from the watershed was proposed as a spatial scale to evaluate the impacts of local water consumption. The model could be performed with input water consumption inventory data carrying watershed scale attributes, and with output damage results carrying local administrative scale attributes. The area of concern (AoC) concept was applied to the model so that water consumption users, such as domestic, agricultural, and industrial sectors, could be used as damage receptor to reveal societal concern about which sector will be influenced due to water depletion. This regionalized characterization model was tested on the three main watersheds in Taiwan, each representing typical, but different, water-usage features. Sector-wise WAAs of each watershed were constructed, and endpoint characterization factors (CFs) of local water consumption were calculated. The visualization results show the damage distribution of water consumption among the damage receptors, which used to be a single average value at the watershed or national scale. The model with the AoC concept is sufficiently flexible for choosing representative affected users and outcome indicators according to stakeholder preferences. Spatial differentiation and comparable results can also support LCA interpretation with detailed information. This model is expected to be further applied to the development of damage prevention strategies.

Influence of landscape mosaic structure on nitrate and phosphate discharges: An island-wide assessment in subtropical mountainous Taiwan

Landscape composition and configuration, which runoff flows through, have substantial effects on nutrient export of humid mountainous watersheds. The relationships between landscape metrics and annual and seasonal NO3-N and PO4-P fluxes are developed for 43 watersheds distributed island-wide in Taiwan during 2015–2016. The results show that the watersheds with higher concentrations of NO3-N and PO4-P mainly distributed on western Taiwan whereas watersheds with lower concentrations of NO3-N and PO4-P scattered across mountain regions and eastern Taiwan. Multiple stepwise regression models are used to realize the effects of landscape pattern on NO3-N and PO4-P exports at watershed level and riparian buffers of various width (100 m, 200 m, 500 m, 1000 m, 1500 m, and 2000 m). Our findings show that the average annual exports of NO3-N and PO4-P across various watersheds are 3.8–50.8 and 0.04–7.7 kg ha−1 yr−1, respectively, much higher than reported values worldwide. The class-level metrics IJI (interspersion and juxtaposition) of agricultural land and LPI (largest patch index) of buildup area obtained from 200 m buffer explain 85% of variation of NO3-N flux, and ED (edge of density) of buildup area and LPI of water body or percentage of water body at 200 m buffer combined contribute 90% of variation in PO4-P exports. The highly mixed development of urban and agriculture distributed along with the limited extent of riparian buffers within watersheds in Taiwan make the watershed susceptible to loss of a large quantity of nutrients and sediments into streams. Management practices such as diminishing fragmented agriculture and buildup area are recommended as they should help to reduce water pollution, and better designs of developed area and water body can further improve water quality.

Drought analysis framework based on copula and Poisson process with nonstationarity

Droughts have been occurring with increased frequency and bringing with them considerable losses. Due to its nature, the best approach to monitoring and assessing droughts is in terms of stochastic theories. As a consequence, this study applied SPI in the hydrological drought detection, and examine/interpret drought-related phenomena. The 3-month SPI is used to decide the drought development and terminate phases based on the coincidence of hydrological drought and anomalies in precipitation.Four primary characteristics were determined to clarify the descriptions of drought magnitude and occurrence: duration, deficit, occurrence time and recurrence year. A stochastic process of drought is established to take account these characteristics using the copula function and a Poisson process as well as non-stationarity. Case study on the Shihmen and Zengwen reservoir watersheds in Taiwan revealed that hydrological drought events often coincide with meteorological drought, but some inconsistencies due to unusual hydrological hysteresis in rainfall and runoff. Our analysis raised several issues, including the choice of drought identification parameters and SPI time scale/ threshold, which should take into account anthropogenic activity and hydrological characteristics. A positive correlation between drought duration and drought deficit and a negative correlation between them and occurrence time were identified. It was also observed that subsequent drought event may be more severe due to the partial recovery of hydrological condition. In terms of non-stationarity, our results do not show strong evidence of long-term trend for drought characteristics. Some limitations could be due to the percentile-to-percentile basis of copula. This issue may need to be examined using non-stationary copula analysis or time-variant correlation-covariance models. In the end, we demonstrated that this framework can apply in forecasting drought conditions and generating synthetic droughts for use in the formulation of water resources management strategies and the development of drought preparedness plans.

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.

Acinetobacter baumannii and methicillin-resistant Staphylococcus aureus in long-term care facilities in eastern Taiwan.

Objective:The prevention of infections is crucial in long-term care programs. Investigations of the occurrence and sources of pathogens in long-term care facilities (LTCFs) are still lacking, especially in eastern Taiwan. In this study, we conducted a surveillance of two common pathogens, Acinetobacter baumannii (AB) and methicillin-resistant Staphylococcus aureus (MRSA), in LTCFs in Hualien.Materials and Methods:Pathogenic assays including isolation, identification, and antimicrobial susceptibility tests were conducted for AB and MRSA at LTCFs in Eastern Taiwan. Staphylococcal cassette chromosome mec typing assays were done to understand the relatedness of clonal strains of MRSA.Results:All AB-positive samples in the LTCFs were mainly from water-rich samples and were drug susceptible. Our data indicated that the AB strains from LTCFs were similar to those from Puzi River watersheds in Taiwan, which were not drug resistant to commonly used antibiotics. On the other hand, the drug resistance analysis of MRSA indicated that the genotypes from the LTCFs were similar to those from nearby hospitals. Eight strains of MRSA were isolated from four LTCFs, of which five were identified as hospital-acquired strains according to SSCmed typing assays.Conclusion:These findings suggest that MRSA in LTCFs might propagate from hospitals and could be transmitted between hospitals and LTCFs. Health authorities should be aware of this risk. The long-term follow-up of MRSA is recommended in local medical institutions as well as in LTCFs for correlative analysis.

Potential impact of climate change and extreme events on slope land hazard – a case study of Xindian watershed in Taiwan

Abstract. The production and transportation of sediment in mountainous areas caused by extreme rainfall events that are triggered by climate change is a challenging problem, especially in watersheds. To investigate this issue, the present study adopted the scenario approach coupled with simulations using various models. Upon careful model selection, the simulation of projected rainfall, landslide, debris flow, and loss assessment was integrated by connecting the models' input and output. The Xindian watershed upstream from Taipei, Taiwan, was identified and two extreme rainfall scenarios from the late 20th and 21st centuries were selected to compare the effects of climate change. Using sequence simulations, the chain reaction and compounded disaster were analysed. Moreover, the potential effects of slope land hazards were compared for the present and future, and the likely impacts in the selected watershed areas were discussed with respect to extreme climate. The results established that the unstable sediment volume would increase by 28.81 % in terms of the projected extreme event. The total economic losses caused by the chain impacts of slope land disasters under climate change would be increased to USD 358.25 million. Owing to the geographical environment of the Taipei metropolitan area, the indirect losses of a water supply shortage caused by slope land disasters would be more serious than direct losses. In particular, avenues to ensure the availability of the water supply will be the most critical disaster prevention topic in the event of a future slope land disaster. The results obtained from this study are expected to be beneficial because they provide critical information for devising long-term strategies to combat the impacts of slope land disasters.

Dynamic Modeling of Sediment Budget in Shihmen Reservoir Watershed in Taiwan

Qualifying sediment dynamic in a reservoir watershed is essential for water resource management. This study proposed an integrated model of Grid-based Sediment Production and Transport Model (GSPTM) at watershed scale to evaluate the dynamic of sediment production and transport in the Shihmen Reservoir watershed in Taiwan. The GSPTM integrates several models, revealing landslide susceptibility and processes of rainfall–runoff, sediment production from landslide and soil erosion, debris flow and mass movement, and sediment transport. For modeling rainfall–runoff process, the tanks model gives surface runoff volume and soil water index as a hydrological parameter for a logistic regression-based landslide susceptibility model. Then, applying landslide model with a scaling relation of volume and area predicts landslide occurrence. The Universal Soil Loss Equation is then used for calculating soil erosion volume. Finally, incorporating runoff-routing algorithm and the Hunt’s model achieves the dynamical modeling of sediment transport. The landslide module was calibrated using a well-documented inventory during 10 heavy rainfall or typhoon events since 2004. A simulation of Typhoon Morakot event was performed to evaluate model’s performance. The results show the simulation agrees with the tendency of runoff and sediment discharge evolution with an acceptable overestimation of peak runoff, and predicts more precise sediment discharge than rating methods do. In addition, with clear distribution of sediment mass trapped in the mountainous area, the GSPTM also showed a sediment delivery ratio of 30% to quantify how much mass produced by landslide and soil erosion is still trapped in mountainous area. The GSPTM is verified to be useful and capable of modeling the dynamic of sediment production and transport at watershed level, and can provide useful information for sustainable development of Shihmen Reservoir watershed.

Comparing watershed soil erosion of Taiwan and Thailand

Tropical watersheds in Taiwan and Thailand face the same severe soil erosion problem that is increasing at an alarming rate. In order to evaluate the severity of soil erosion, we quantitatively investigate the issue using a common soil erosion model (Universal Soil Loss Equation, USLE) on the Shihmen reservoir watershed of Taiwan and the Lam Phra Ploeng basin of Thailand, and compare their respective erosion factors. The results show an interesting contrast between the two watersheds. Some of the factors (rainfall factor, slope-steepness factor) are higher in the Shihmen reservoir watershed, while others (soil erodibility factor, cover and management factor) are higher in the Lam Phra Ploeng basin. The net result is that these factors cancel each other out, and the amount of soil erosion of the two watersheds are very similar at 68.03 t/ha/yr and 67.57 t/ha/yr, respectively.

Comparison and Evolution of Extreme Rainfall-Induced Landslides in Taiwan

This study analyzed the characteristics of, and locations prone to, extreme rainfall-induced landslides in three watersheds in Taiwan, as well as the long-term evolution of landslides in the Laonong River watershed (LRW), based on multiannual landslide inventories during 2003–2014. Extreme rainfall-induced landslides were centralized beside sinuous or meandering reaches, especially those with large sediment deposition. Landslide-prone strata during extreme rainfall events were sandstone and siltstone. Large-scale landslides were likely to occur when the maximum 6-h accumulated rainfall exceeded 420 mm. All of the large-scale landslides induced by short-duration and high-intensity rainfall developed from historical small-scale landslides beside the sinuous or meandering reaches or in the source area of rivers. However, most of the large-scale landslides induced by long-duration and high-intensity rainfall were new but were still located beside sinuous or meandering reaches or near the source. The frequency density of landslides under long-duration and high-intensity rainfall was larger by one order than those under short-duration rainfall, and the β values in the landslide frequency density-area analysis ranged from 1.22 to 1.348. The number of downslope landslides was three times larger than those of midslope and upslope landslides. The extreme rainfall-induced landslides occurred in the erosion gullies upstream of the watersheds, whereas those beside rivers were downstream. Analysis of the long-term evolution of landslides in the LRW showed that the geological setting, sinuousness of reaches, and sediment yield volume determined their location and evolution. Small-scale landslides constituted 71.9–96.2% of the total cases from 2003 to 2014, and were more easily induced after Typhoon Morakot (2009). The frequency density of landslides after Morakot was greater by one order than before, with 61% to 68% of total landslides located in the downslope. Small-scale landslides not beside the rivers disappeared within four years, whereas those beside rivers or located in the source areas either developed into large-scale landslides or slowly disappeared. Large-scale landslides caused by Morakot were either combined from several historical small-scale landslides in the river source areas or located beside the sinuous or meandering reaches. The probabilities of landslide recurrence in the LRW during the next 5, 10, and 20 years were determined to be 7.26%, 9.16%, and 10.48%, respectively, and those beside the rivers were 10.47%, 13.33%, and 15.41%, respectively.

Precipitation controls on nutrient budgets in subtropical and tropical forests and the implications under changing climate

Biological, geological and hydrological drivers collectively control forest biogeochemical cycling. However, based on a close examination of recent literature, we argue that the role of hydrological control particularly precipitation on nutrient budgets is significantly underestimated in subtropical and tropical forests, hindering our predictions of future forest nutrient status under a changing climate in these systems. To test this hypothesis, we analyzed two decades of monthly nutrient input and output data in precipitation and streamwater from a subtropical forested watershed in Taiwan, one of the few sites that has long-term nutrient input-output data in the tropics and subtropics. The results showed that monthly input and output of all ions and budgets (output – input) of most ions were positively correlated with precipitation quantity and there was a surprisingly greater net ion export during the wet growing season, indicating strong precipitation control on the nutrient budget. The strong precipitation control is also supported by the divergence of acidic precipitation and near neutral acidity of streamwater, with the former being independent from precipitation quantity but the latter being positively related to precipitation quantity. An additional synthesis of annual precipitation quantity and nutrient budgets of 32 forests across the globe showed a strong correlation between precipitation quantity and nutrient output-input budget, indicating that strong precipitation control is ubiquitous at the global scale and is particularly important in the humid tropical and subtropical forests. Our results imply that climate change could directly affect ecosystem nutrient cycling in the tropics through changes in precipitation pattern and amount.

Influence of storm magnitude and watershed size on runoff nonlinearity

The inherent nonlinear characteristics of the watershed runoff process related to storm magnitude and watershed size are discussed in detail in this study. The first type of nonlinearity is referred to rainfall-runoff dynamic process and the second type is with respect to a Power-law relation between peak discharge and upstream drainage area. The dynamic nonlinearity induced by storm magnitude was first demonstrated by inspecting rainfall-runoff records at three watersheds in Taiwan. Then the derivation of the watershed unit hydrograph (UH) using two linear hydrological models shows that the peak discharge and time to peak discharge that characterize the shape of UH vary event-to-event. Hence, the intention of deriving a unique and universal UH for all rainfall-runoff simulation cases is questionable. In contrast, the UHs by the other two adopted nonlinear hydrological models were responsive to rainfall intensity without relying on linear proportion principle, and are excellent in presenting dynamic nonlinearity. Based on the two-segment regression, the scaling nonlinearity between peak discharge and drainage area was investigated by analyzing the variation of Power-law exponent. The results demonstrate that the scaling nonlinearity is particularly significant for a watershed having larger area and subjecting to a small-size of storm. For three study watersheds, a large tributary that contributes relatively great drainage area or inflow is found to cause a transition break in scaling relationship and convert the scaling relationship from linearity to nonlinearity.

Reconstructing nutrient criteria for source water areas using reference conditions

Abstract Nutrient concentrations are of concern in most watershed areas that serve as source water for public drinking water due to the high cost of nutrient removal treatment. Yet all nutrients criteria for source watersheds in Taiwan are identical for quality standards and the specific conditions of each source watershed were not considered. Nutrient RCs estimated by statistical methods and land disturbance modeling were applied to the Tamsui River in Taiwan. The results were then used to evaluate the nutrient status of Taipei Water Source Domain, which is located near the upstream portion of Tamsui River. The estimated RCs of NH 3 -N, TP, and Total N were 0.03–0.04, 0.019 to 0.036, and 1.22–1.50 mg L −1 , respectively. Comparing with current criteria of NH 3 -N (0.1 mg L −1 ), the estimated reference conditions are lower and the criteria could be stricter to maintain sufficient water quality. However, the reference condition of TP estimated by the disturbance modeling (0.03 mg L −1 ) is higher than current criteria (0.02 mg L −1 ), leading to a possible loosening adjustment.