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This study explored the impacts of various rainfall input types on short-term runoff simulations using the Cell2Flood model in the Waryong Reservoir Basin, South Korea. Six types of rainfall data were assessed: on-site gauge measurements, spatially interpolated data from 39 Automated Synoptic Observing System (ASOS) and 117 Automatic Weather System (AWS) stations using inverse distance weighting (IDW), and Hybrid Surface Rainfall (HSR) data from the Korea Meteorological Administration. The choice of rainfall input significantly affected model accuracy across the three rainfall events. The point-gauged ASOS (P-ASOS) data demonstrated the highest reliability in capturing the observed rainfall patterns, with Pearson’s r values of up to 0.84, whereas the radar-derived HSR data had the lowest correlations (Pearson’s r below 0.2), highlighting substantial discrepancies. For runoff simulation, the P-ASOS and ASOS-AWS combined interpolated dataset (R-AWS) achieved relatively accurate predictions, with P-ASOS and R-AWS exhibiting Normalized Peak Error (NPE) values of approximately 0.03 and Peak Time Error (PTE) within 20 min. In contrast, the HSR data produced large errors, with NPE up to 4.66 and PTE deviations exceeding 200 min, indicating poor temporal accuracy. Although input-specific calibration improved performance, significant errors persisted because of the inherent uncertainty of rainfall data. These findings underscore the importance of selecting and calibrating appropriate rainfall inputs to enhance the reliability of short-term flood modeling, particularly in ungauged and data-sparse basins.

The all-weather high-resolution air temperature data is crucial for understanding the urban thermal conditions with their spatio-temporal characteristics, driving factors, socio-economic and environmental consequences. In this study, we developed a novel 5-layer Deep Belief Network (DBN) deep learning model to fuse multi-source data and then generated air temperature data with 3H characteristics: High resolution, High spatio-temporal continuity (spatially seamless and temporally continuous), and High accuracy simultaneously. The DBN model was developed and applied for two different urban regions: Wuhan Metropolitan Area (WMA) in China, and Austin, Texas, USA. The model has a excellent ability to fit the complex nonlinear relationship between temperature and different predictive variables. After various adjustments to the model structure and different combinations of input variables, the daily 500-m air temperature in Wuhan Metropolitan Area (WMA) was initially generated by fusing remote sensing, reanalysis and in situ measurement products. The ten-fold cross-validation results indicated that the DBN model achieved promising results with the RMSE of 1.086 °C, MAE of 0.839 °C, and R2 of 0.986. Compared with conventional data fusion algorithms, the DBN model also exhibited better performance. In addition, the detailed evaluation of the model on spatial and temporal scales proved the advantages of using DBN model to generate 3H temperature data. The spatial transferability of the model was tested by conducting a validation experiment for Austin, USA. In general, the results and fine-scale analyses show that the employed framework is effective to generate 3H temperature, which is valuable for urban climate and urban heat island research.

Drought is a natural disaster affecting agriculture worldwide. Drought mitigation and proactive response require a comprehensive vulnerability mapping approach considering various factors. This study investigates the vulnerability to agricultural drought in South Korea based on exposure, sensitivity, and adaptability. The evaluation of agricultural drought factors yielded 14 items, which are categorized into meteorological, agricultural reservoir, social, and adaptability factors. Each item is assigned a weight using the analytical hierarchy process (AHP). We analyzed vulnerability to drought disaster in agricultural reservoirs, and generated vulnerability maps by applying the vulnerability framework for climate change. The generated map was divided into four categories based on drought vulnerability: A (Very high), B (High), C (Moderate), and D (Low). The weights for the meteorological (0.498), agricultural reservoir (0.286), social (0.166), and adaptability (0.05) factors were obtained using AHP. The rating frequencies were 41.91%, 19.76%, 9.58%, and 5.39% for A, B, C, and D, respectively. The western region is extremely vulnerable to meteorological and agricultural reservoir factors, whereas the eastern region is more vulnerable to adaptability. The results of this study visually represent agricultural drought and can be used for evaluating regional drought vulnerability for assisting preemptive drought responses to identify and support drought-prone areas.

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.

Understanding long-term changes in precipitation and temperature patterns is important in the detection and characterization of climate change, as is understanding the implications of climate change when performing impact assessments. This study uses a statistically robust methodology to quantify long-, medium- and short-term changes for evaluating the degree to which climate change and urbanization have caused temporal changes in precipitation and temperature in South Korea. We sought to identify a fingerprint of changes in precipitation and temperature based on statistically significant differences at multiple-timescales. This study evaluates historical weather data during a 40-year period (1973–2012) and from 54 weather stations. Our results demonstrate that between 1993–2012, minimum and maximum temperature trends in the vicinity of urban and agricultural areas are significantly different from the two previous decades (1973–1992). The results for precipitation amounts show significant differences in urban areas. These results indicate that the climate in urbanized areas has been affected by both the heat island effect and global warming-caused climate change. The increase in the number of rainfall events in agricultural areas is highly significant, although the temporal trends for precipitation amounts showed no significant differences. Overall, the impacts of climate change and urbanization in South Korea have not been continuous over time and have been expressed locally and regionally in terms of precipitation and temperature changes.