Climate risk analysis of low-altitude tea gardens in central Taiwan using a Bayesian network.

Tea is a vital agricultural product in Taiwan. Due to global warming, the increasing extreme weather events have disrupted tea garden conditions and caused economic losses in agriculture. To address these challenges, a comprehensive tea garden risk assessment model, a Bayesian network (BN), was developed by considering various factors, including meteorological data, disaster events, tea garden environment (location, altitude, tea tree age, and soil characteristics), farming practices, and farmer interviews, and constructed risk assessment indicators for tea gardens based on the climate change risk analysis concept from the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). The results demonstrated an accuracy of over 92% in both validating and testing the model for tea tree damage and yield reduction. Sensitivity analysis revealed that tea tree damage and yield reduction were mutually influential, with weather, fertilization, and irrigation also impacting tea garden risk. Risk analysis under climate change scenarios from various global climate models (GCMs) indicated that droughts may pose the highest risk with up to 41% and 40% of serious tea tree growth damage and tea yield reduction, respectively, followed by cold events that most tea gardens may have less than 20% chances of serious impacts on tea tree growth and tea yield reduction. The impacts of heavy rains get the least concern because all five tea gardens may not be affected in terms of tea tree growth and tea yield with large chances of 67 to 85%. Comparing farming methods, natural farming showed lower disaster risk than conventional and organic approaches. The tea plantation risk assessment model can serve as a valuable resource for analyzing and offering recommendations for tea garden disaster management and is used to assess the impact of meteorological disasters on tea plantations in the future.

Applicability of modified SWAT model (SWAT-Twn) on simulation of watershed sediment yields under different land use/cover scenarios in Taiwan.

Climate change leads to increasing intensity and frequency of extreme rainfalls, especially in Taiwan with steep slopes and rapid currents. Heavy rainfalls trigger serious erosion and landslides on hillslopes, which increase sand concentration in rivers, and thus affect the water quality of reservoirs and the ecohydrological functions of rivers. We take the Zhuoshui River basin as an example and applied the modified Soil Water Assessment Tool (SWAT) model, SWAT-Twn, to simulate sediment in the basin. In SWAT-Twn, estimation of sediment yield is carried out by integrating the Taiwan Universal Soil Loss Equation (TUSLE) and the landslide simulation. Results of daily streamflow simulation showed that the model performances were above the satisfactory level, while simulations of daily sediment loads showed that the SWAT-Twn model performed better than the official SWAT (SWAT664), in terms of PBIAS of - 46.6 to 16.0% (SWAT-Twn) and - 1.2 to - 107.0% (SWAT664). Two scenarios of land use/cover, scenario 1 with fixed land use/cover and scenario 2 with updated land use/cover in each year, were applied to simulate annual sediment in the river basin for investigating the effects of landslide area variation on sediments. Results of sediment simulation under the two scenarios showed that although updating landslide area may facilitate sediment yield simulation at the subbasin level, the sediment transport equation, Bagnold equation, does not reflect the variation in sediment loads in the watershed. With further modifications, SWAT-Twn is expected to be an effective tool for simulating the impacts of landslide on sediment loads in the watersheds with rainfall-induced landslide.

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.

A GIS-based policy support tool to determine national responsibilities and priorities for biodiversity conservation.

Efficient biodiversity conservation requires that limited resources be allocated in accordance with national responsibilities and priorities. Without appropriate computational tools, the process of determining these national responsibilities and conservation priorities is time intensive when considering many species across geographic scales. Here, we have developed a computational tool as a module for the ArcGIS geographic information system. The ArcGIS National Responsibility Assessment Tool (NRA-Tool) can be used to create hierarchical lists of national responsibilities and priorities for global species conservation. Our tool will allow conservationists to prioritize conservation efforts and to focus limited resources on relevant species and regions. We showcase our tool with data on 258 bird species and various biophysical regions, including Environmental Zones in 58 Asian countries and regions. Our tool provides a decision support system for conservation policy with attractive and easily interpretable visual outputs illustrating national responsibilities and priorities for species conservation. The graphical output allows for smooth integration into assessment reports, such as the European Article 17 report, the Living Planet Index report, or similar regional and global reports.

Detecting the land-cover changes induced by large-physical disturbances using landscape metrics, spatial sampling, simulation and spatial analysis.

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran'I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management.

Remote sensing data with the conditional latin hypercube sampling and geostatistical approach to delineate landscape changes induced by large chronological physical disturbances.

This study applies variogram analyses of normalized difference vegetation index (NDVI) images derived from SPOT HRV images obtained before and after the ChiChi earthquake in the Chenyulan watershed, Taiwan, as well as images after four large typhoons, to delineate the spatial patterns, spatial structures and spatial variability of landscapes caused by these large disturbances. The conditional Latin hypercube sampling approach was applied to select samples from multiple NDVI images. Kriging and sequential Gaussian simulation with sufficient samples were then used to generate maps of NDVI images. The variography of NDVI image results demonstrate that spatial patterns of disturbed landscapes were successfully delineated by variogram analysis in study areas. The high-magnitude Chi-Chi earthquake created spatial landscape variations in the study area. After the earthquake, the cumulative impacts of typhoons on landscape patterns depended on the magnitudes and paths of typhoons, but were not always evident in the spatiotemporal variability of landscapes in the study area. The statistics and spatial structures of multiple NDVI images were captured by 3,000 samples from 62,500 grids in the NDVI images. Kriging and sequential Gaussian simulation with the 3,000 samples effectively reproduced spatial patterns of NDVI images. However, the proposed approach, which integrates the conditional Latin hypercube sampling approach, variogram, kriging and sequential Gaussian simulation in remotely sensed images, efficiently monitors, samples and maps the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial variability and heterogeneity.

Spatiotemporal Variation of Sediment Export from Multiple Taiwan Watersheds.

Soil erosion and landslide triggered by heavy rainfall are serious problems that have threatened water resources in Taiwan watersheds. This study investigated the relationship among streamflow, sediment load, sediment concentration and typhoon characteristics (path and rainfall amount) during 2000-2017 for nine gauging stations in five basins (Tamshui River basin, Zhuoshui River basin, Zengwen River basin, Gaoping River basin, and Hualien River basin) representing the diverse geomorphologic conditions in Taiwan. The results showed that streamflow and sediment load were positively correlated, and the correlation was improved when the sediment load data were grouped by sediment concentration. Among these basins, the Zhuoshui River basin has the highest unit-discharge sediment load and unit-area sediment load. The soil in the upstream was more erodible than the downstream soil during the normal discharge conditions, indicating its unique geological characteristics and how typhoons magnified sediment export. The spatiotemporal variation in sediment loads from different watersheds was further categorized by typhoons of different paths. Although typhoon path types matter, the Zhuoshui and Hualien River basin were usually impacted by typhoons of any path type. The results indicated that sediment concentration, the watershed soil characteristics, and typhoons paths were the key factors for sediment loads. This study can be useful for developing strategies of soil and water conservation implementation for sustainable watershed management.

A decision-making approach for delineating sites which are potentially contaminated by heavy metals via joint simulation.

This work develops a new approach for delineating sites that are contaminated by multiple soil heavy metals and applies it to a case study. First a number of contaminant sample data are transformed into multiple spatially un-correlated factors using Uniformly Weighted Exhaustive Diagonalization with Gauss iterations (U-WEDGE). Sequential Gaussian simulation (sGs) is then used to generate sets of realizations of each resultant factor. These are then transformed into sets of sGs contaminant distribution realizations, which are then used to analyze the local and spatial (global) uncertainties in the distribution and concentration of contaminants via joint simulation. Finally, Info-Gap Decision Theory (IGDT) is used to consider different monitoring and or remediation regimes based on the analysis of contaminant realization spatial uncertainty. In our case study each heavy metal contaminant was considered individually and together with all other heavy metals; as the number of heavy metals considered increased, higher critical proportion values of local probability were chosen to obtain a low global uncertainty (to provide high reliability). Info-Gap Decision Theory (IGDT) yielded the most appropriate critical proportion values which minimized information loss in terms of specific goals. When the false negative rate is set to zero, meaning that it is necessary to monitor all potentially polluted areas, the corresponding false positive rates are at least 63%, 65%, 66%, 68%, 70%, and 78% to yield robustness levels of 0.50, 0.60, 0.70, 0.80, 0.90, and 1.00 respectively. However, when the false negative rate tolerance threshold is raised to 50%, the false positive rate tolerance which yields robustness levels of 0.50, 0.60, 0.70, 0.80, 0.90 and 1.00 drop to 12%, 14%, 15%, 18%, 20%, and 39%. The case study demonstrates the effectiveness of the developed approach at making robust decisions concerning the delineation of sites contaminated by multiple heavy metals.

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.