This study suggests an approach to obtain flood extent boundaries using spatial analysis based on Landsat-5 Thematic Mapper imageries and the digital elevation model. The suggested approach firstly extracts the flood inundation areas using the ISODATA image-processing algorithm from four Landsat 5TM imageries. Then, the ground elevations at the intersections of the extracted flood extent boundaries and the specified river cross sections are read from the digital elevation to estimate the elevation-discharge relationship. Lastly, the flood extent is generated based on the estimated elevation-discharge relationship. The methodology was tested over two river reaches in Indiana, United States. The estimated elevation-discharge relationship showed a good match with the correlation coefficients varying between 0.82 and 0.99. In addition, self-validation was also performed for the estimated spatial extent of the flood by comparing it to the waterbody extracted from the Landsat images used to develop the elevation-discharge relationship. The result indicated that the match between the estimated and the extracted flood extents was better with higher flood magnitude. We expect that the suggested methodology will help under-developed and developing countries to obtain flood maps, which have difficulties getting flood maps through traditional approaches based on computer modeling.

We performed bias correction in future climate change scenarios to provide better accuracy of models through adaptation to future climate change. The proposed combination of the change factor (CF) and quantile mapping (QM) methods combines the individual advantages of both methods for adjusting the bias in global circulation models (GCMs) and regional circulation models (RCMs). We selected a study site in Songwol-dong, Seoul, Republic of Korea, to test and assess our proposed method. Our results show that the combined CF + QM method delivers better performance in terms of correcting the bias in GCMs/RCMs than when both methods are applied individually. In particular, our proposed method considerably improved the bias-corrected precipitation by capturing both the high peaks and amounts of precipitation as compared to that from the CF-only and QM-only methods. Thus, our proposed method can provide high-accuracy bias-corrected precipitation data, which could prove to be highly useful in interdisciplinary studies across the world.

Collection and investigation of flood information are essential to understand the nature of floods, but this has proved difficult in data-poor environments, or in developing or under-developed countries due to economic and technological limitations. The development of remote sensing data, GIS, and modeling techniques have, therefore, proved to be useful tools in the analysis of the nature of floods. Accordingly, this study attempts to estimate a flood discharge using the generalized likelihood uncertainty estimation (GLUE) methodology and a 1D hydraulic model, with remote sensing data and topographic data, under the assumed condition that there is no gauge station in the Missouri river, Nebraska, and Wabash River, Indiana, in the United States. The results show that the use of Landsat leads to a better discharge approximation on a large-scale reach than on a small-scale. Discharge approximation using the GLUE depended on the selection of likelihood measures. Consideration of physical conditions in study reaches could, therefore, contribute to an appropriate selection of informal likely measurements. The river discharge assessed by using Landsat image and the GLUE Methodology could be useful in supplementing flood information for flood risk management at a planning level in ungauged basins. However, it should be noted that this approach to the real-time application might be difficult due to the GLUE procedure.

Groundwater has received increasing attention as an important strategic water resource for adaptation to climate change. In this regard, the separation of baseflow from streamflow and the analysis of recession curves make a significant contribution to integrated river basin management. The United States Geological Survey (USGS) RECESS model adopting the master-recession curve (MRC) method can enhance the accuracy with which baseflow may be separated from streamflow, compared to other baseflow-separation schemes that are more limited in their ability to reflect various watershed/aquifer characteristics. The RECESS model has been widely used for the analysis of hydrographs, but the applications using RECESS were only available through Microsoft-Disk Operating System (MS-DOS). Thus, this study aims to develop a web-based RECESS model for easy separation of baseflow from streamflow, with easy applications for ungauged regions. RECESS on the web derived the alpha factor, which is a baseflow recession constant in the Soil Water Assessment Tool (SWAT), and this variable was provided to SWAT as the input. The results showed that the alpha factor estimated from the web-based RECESS model improved the predictions of streamflow and recession. Furthermore, these findings showed that the baseflow characteristics of the ungauged watersheds were influenced by the land use and slope angle of watersheds, as well as by precipitation and streamflow.