Li-Chi Chiang

Effectiveness of best management practices in improving water quality in a pasture-dominated watershed

The nonpoint source pollution problem can be controlled by implementing various best management practices (BMPs) in the watershed. However, before such practices are adopted, their effectiveness at various spatial and temporal scales must be evaluated. The objective of this research was to evaluate a suite of BMPs in a pasture-dominated watershed in their effectiveness at controlling nutrient losses. A total of 171 different BMP combinations incorporating grazing and pasture management, riparian and buffer zones, and poultry litter applications were evaluated for their effectiveness using the Soil and Water Assessment Tool (SWAT) model. The SWAT model was parameterized using detailed farm and watershed-scale data. The stochasticity in weather was captured by generating 250 various possible weather realizations for a 25-year period, using measured historical climate data for the watershed. Model results indicated that losses of both total nitrogen, mineral phosphorus, and total phosphorus increased with an increase in litter application rates. For the same application rates, greatest losses were predicted for fall application timings compared to spring and summer applications. Overgrazing resulted in greater nutrient losses compared to baseline conditions for all application rates, timings, and litter characteristics, indicating that overgrazing of pasture areas must be avoided if any improvement in the water quality is to be expected. Variability in weather conditions significantly affected BMP performance; under certain weather conditions, an increase in pollutant losses can be greater than reductions due to BMPs implemented in the watershed. Buffer strips and grazing management were two most important BMPs affecting the losses of total nitrogen and total phosphorus from the pasture areas.

Differentiating impacts of land use changes from pasture management in a CEAP watershed using the SWAT model.

Due to intensive farm practices, nonpoint-source (NPS) pollution has become one of the most challenging environmental problems in agricultural and mixed land use watersheds. Usually, various conservation practices are implemented in the watershed to control the NPS pollution problem. However, land use changes can mask the water quality improvements from the conservation practices implemented in the watershed. The objectives of this study were to evaluate the linkage between nutrient input from various pasture management practices and water quality, and to quantify the impacts of land use changes and pasture management on water quality in a pasture-dominated watershed. Land use data from 1992, 1994, 1996, 1999, 2001, and 2004 were evaluated for the land use changes in the watershed, and the corresponding implemented management practices were also incorporated into the Soil and Water Assessment Tool (SWAT) model. The individual impacts of land use change and pasture management were quantified by comparing the SWAT simulation results for different land use change and pasture management scenarios. The results indicated that land use changes resulted in greater total sediment (499 kg ha-1) and nitrogen losses (3.8 kg ha-1) in the Moores Creek subwatershed, whereas pasture management resulted in greater total nitrogen losses (4.3 kg ha-1) in the Beatty Branch subwatershed. Overall, the combined impacts of land use changes and pasture management resulted in greater total sediment (28 to 764 kg ha-1 of cumulative combined impacts between 1992 and 2007) and nitrogen losses (5.1 to 6.1 kg ha-1) and less total phosphorus losses (1.5 to 2.1 kg ha-1) in the Beatty Branch, Upper Moores Creek, and Moores Creek subwatersheds. By quantifying the individual impacts of land use changes and pasture management, we found that an increase in total nitrogen losses in the Beatty Branch subwatershed was mainly due to an increase in nutrient inputs in the pasture areas, and total sediment and nitrogen losses in the Moores Creek subwatershed were mainly due to an increase in urban lands. Therefore, the individual impacts of land use changes and conservation practices should be quantified to get a true picture of the success of CEAP programs in watersheds experiencing significant land use changes.

Implementation of BMP Strategies for Adaptation to Climate Change and Land Use Change in a Pasture-Dominated Watershed

Implementing a suite of best management practices (BMPs) can reduce non-point source (NPS) pollutants from various land use activities. Watershed models are generally used to evaluate the effectiveness of BMP performance in improving water quality as the basis for watershed management recommendations. This study evaluates 171 management practice combinations that incorporate nutrient management, vegetated filter strips (VFS) and grazing management for their performances in improving water quality in a pasture-dominated watershed with dynamic land use changes during 1992–2007 by using the Soil and Water Assessment Tool (SWAT). These selected BMPs were further examined with future climate conditions (2010–2069) downscaled from three general circulation models (GCMs) for understanding how climate change may impact BMP performance. Simulation results indicate that total nitrogen (TN) and total phosphorus (TP) losses increase with increasing litter application rates. Alum-treated litter applications resulted in greater TN losses, and fewer TP losses than the losses from untreated poultry litter applications. For the same litter application rates, sediment and TP losses are greater for summer applications than fall and spring applications, while TN losses are greater for fall applications. Overgrazing management resulted in the greatest sediment and phosphorus losses, and VFS is the most influential management practice in reducing pollutant losses. Simulations also indicate that climate change impacts TSS losses the most, resulting in a larger magnitude of TSS losses. However, the performance of selected BMPs in reducing TN and TP losses was more stable in future climate change conditions than in the BMP performance in the historical climate condition. We recommend that selection of BMPs to reduce TSS losses should be a priority concern when multiple uses of BMPs that benefit nutrient reductions are considered in a watershed. Therefore, the BMP combination of spring litter application, optimum grazing management and filter strip with a VFS ratio of 42 could be a promising alternative for use in mitigating future climate change.

Comparing the selection and placement of best management practices in improving water quality using a multiobjective optimization and targeting method.

Suites of Best Management Practices (BMPs) are usually selected to be economically and environmentally efficient in reducing nonpoint source (NPS) pollutants from agricultural areas in a watershed. The objective of this research was to compare the selection and placement of BMPs in a pasture-dominated watershed using multiobjective optimization and targeting methods. Two objective functions were used in the optimization process, which minimize pollutant losses and the BMP placement areas. The optimization tool was an integration of a multi-objective genetic algorithm (GA) and a watershed model (Soil and Water Assessment Tool—SWAT). For the targeting method, an optimum BMP option was implemented in critical areas in the watershed that contribute the greatest pollutant losses. A total of 171 BMP combinations, which consist of grazing management, vegetated filter strips (VFS), and poultry litter applications were considered. The results showed that the optimization is less effective when vegetated filter strips (VFS) are not considered, and it requires much longer computation times than the targeting method to search for optimum BMPs. Although the targeting method is effective in selecting and placing an optimum BMP, larger areas are needed for BMP implementation to achieve the same pollutant reductions as the optimization method.

Assessing and mapping spatial associations among oral cancer mortality rates, concentrations of heavy metals in soil, and land use types based on multiple scale data.

In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate data for Taiwan from the district scale to a 1 km × 1 km grid scale. Local cluster analysis (LCA) of OC mortality rates was then performed to identify OC mortality rate hot spots based on the downscaled and the p-field-simulated OC mortality maps. The relationship between OC mortality and land use was studied by overlapping the maps of the downscaled OC mortality, the LCA results, and the land uses. One thousand simulations were performed to quantify local and spatial uncertainties in the LCA to identify OC mortality hot spots. The scatter plots and Spearman’s rank correlation yielded the relationship between OC mortality and concentrations of the seven metals in the 1 km cell grid. The correlation analysis results for the 1 km scale revealed a weak correlation between OC mortality rate and concentrations of the seven studied heavy metals in soil. Accordingly, the heavy metal concentrations in soil are not major determinants of OC mortality rates at the 1 km scale at which soils were sampled. The LCA statistical results for local indicator of spatial association (LISA) revealed that the sites with high probability of high-high (high value surrounded by high values) OC mortality at the 1 km grid scale were clustered in southern, eastern, and mid-western Taiwan. The number of such sites was also significantly higher on agricultural land and in urban regions than on land with other uses. The proposed approach can be used to downscale and evaluate uncertainty in mortality data from a coarse scale to a fine scale at which useful additional information can be obtained for assessing and managing land use and risk.

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management.