Over the last five decades, there has been a decline of rural communities in Taiwan due to urbanization expansion. In the past 10 years, the central government has implemented the Rural Regeneration Project (RRP) aimed at revitalization and sustainable development in rural Taiwan. During the project’s implementation, communities have faced several disasters as a result of climate change-induced extreme rainfall events. Perceptions and adaptation practices of climate change-induced extreme events are critical to community sustainability and resilience. The gap between perceived and actual risks that communities experience creates challenges for policy-makers in achieving sustainability goals. This study aims to evaluate the perceived climate change-induced flooding hazard perceptions compared to the scientific projection and actual hazard events in 287 rural communities implementing the RRP. This study revealed consistency in risk perception, in that communities facing high potential exposure to extreme rainfall showed higher awareness of various impacts of climate change. However, when comparing climate actions, communities exposed to low-potential hazard areas had a relatively higher degree of recognition of the benefits of adaptation to climate change. Moreover, 59 rural communities with low awareness and exposed to high potentials of extreme events were widely distributed among hills of western, southern, and northern Taiwan, where compound disasters such as mudslides can occur. This research suggests that there is a need to integrate climate change planning and work with communities to bridge the gap between perceived and actual climate risks. In particular, capacity training, counseling services, and implementation of adaptation practices should be integrated into institutional planning and management for providing assistance in disaster prevention, relief, and post-event restoration; also, encouraging climate actions can directly improve community resilience toward climate change. While investing in the sustainable development of rural communities is largely based on revitalizing economic development, this study revealed the link to ensure resilience and social-ecological sustainability in rural communities under climate change impacts.

Systematic conservation planning (SCP) deals with a delicate interplay of competing interests and has far-reaching impacts for all stakeholders and systems involved. While SCP has traditionally attempted to conserve ecosystem services that benefit ecological systems, public perceptions of conservation initiatives influence their ultimate feasibility and sustainability. In an attempt to balance ecological integrity, social utility, and urban development, this study develops a framework that applies four popular models to represent these competing factors, including two ecosystem services models—InVEST (Integrated Valuation of Environmental Services and Tradeoffs) for biophysical services (BpS), and SolVES (Social Values for Ecosystem Services) for social values (SV); a land use and land cover (LULC) suitability model; and Zonation for delimiting high priority areas. We also analyze a number of conservation scenarios that consider varying levels of urban development. While BpS are distributed with considerable spatial variability, SV spatially overlap. Approximately 6% of the area was identified as having both high BpS and SV, whereas a further 24.5% of the area was identified as either high BpS low SV or vise-versa. Urban development scenarios affected the conservation area selection drastically. These results indicate tradeoffs and potential synergies between development, SV, and BpS. Our findings suggest that the information provided by the proposed framework can assist in finding solutions to social-ecological planning complexities that serve multiple stakeholders.

Topographies during the erosion process obtained from the single-stripe laser-scanning method may provide an accurate, but affordable, soil loss estimation based on high-precision digital elevation model (DEM) data. In this study, we used laboratory erosion experiments with a sloping flume, a rainfall simulator, and a stripe laser apparatus to evaluate topographic changes of soil surface and the erosion process. In the experiments, six slope gradients of the flume (5° to 30° with an increment of 5°) were used and the rainfall simulator generated a 30-min rainfall with the kinetic energy equivalent to 80 mm/h on average. The laser-scanned topography and sediment yield were collected every 5 min in each test. The difference between the DEMs from laser scans of different time steps was used to obtain the eroded soil volumes and the corresponding estimates of soil loss in mass. The results suggest that the collected sediment yield and eroded soil volume increased with rainfall duration and slope, and quantified equations are proposed for soil loss prediction using rainfall duration and slope. This study shows the applicability of the stripe laser-scanning method in soil loss prediction and erosion evaluation in a laboratory case study.

A forecasting model is developed using a hybrid approach of artificial neural network (ANN) and multiple regression analysis (MRA) to predict the total typhoon rainfall and groundwater-level change in the Zhuoshui River basin. We used information from the raingauge stations in eastern Taiwan and open source typhoon data to build the ANN model for forecasting the total rainfall and the groundwater level during a typhoon event; then we revised the predictive values using MRA. As a result, the average accuracy improved up to 80% when the hybrid model of ANN and MRA was applied, even where insufficient data were available for model training. The outcome of this research can be applied to forecasts of total rainfall and groundwater-level change before a typhoon event reaches the Zhuoshui River basin once the typhoon has made landfall on the east coast of Taiwan.