Masahiro Ryo

Temporal Downscaling of Daily Gauged Precipitation by Application of a Satellite Product for Flood Simulation in a Poorly Gauged Basin and Its Evaluation with Multiple Regression Analysis

The study demonstrates that the temporal downscaling of rain gauge‐measured precipitation with satellite-based precipitation estimates enhances the accuracy of hydrological simulations, especially for flood duration. Multiple regression analysis was examined to predict which hydrometeorological parameters have a significant influence on accuracy. The approach was examined at the Hương River basin in Vietnam (1520km 2 ), which is a mountainous region subject to heavy rainfall. The multisensor algorithm Global Satellite Mapping of Precipitation, version Moving Vector with Kalman (MVK; GSMaP_MVK; Psat), was employedtodownscalethedailygauge precipitationmeasurements into6-htime steps.Discharge in the rainy season in 2006‐09 was simulated by a distributed hydrological model with 6-h time steps with four precipitation datasets: 6-h gauge (Pcontrol), daily uniform gauge (Puni), Psat, and the downscaled satellite product (Pds). Flood simulation with Pds performed better than that with Puni in 14 out of the 18 flood events, being close to the results with Pcontrol (median Nash‐Sutcliffe efficiencies of 0.776, 0.261, and 0.710, respectively). Multiple regression analysis showed that the effectiveness of the downscaling method was significantly related to the bias and random errors of the satellite product. In conclusion, satellite-based precipitation measurements have potential for temporal downscaling of discharge simulations from daily to subdaily resolutions in moderate-sized watersheds that lack subdaily rainfall records, and the degree of simulation improvement can be estimated by statistical analysis. The suggested method can be broadly applied to watersheds where the daily precipitation is measured and when satellite-based precipitation measurements are available.

Evaluation of Spatial Pattern of Altered Flow Regimes on a River Network Using a Distributed Hydrological Model

Alteration of the spatial variability of natural flow regimes has been less studied than that of the temporal variability, despite its ecological importance for river ecosystems. Here, we aimed to quantify the spatial patterns of flow regime alterations along a river network in the Sagami River, Japan, by estimating river discharge under natural and altered flow conditions. We used a distributed hydrological model, which simulates hydrological processes spatiotemporally, to estimate 20-year daily river discharge along the river network. Then, 33 hydrologic indices (i.e., Indicators of Hydrologic Alteration) were calculated from the simulated discharge to estimate the spatial patterns of their alterations. Some hydrologic indices were relatively well estimated such as the magnitude and timing of maximum flows, monthly median flows, and the frequency of low and high flow pulses. The accuracy was evaluated with correlation analysis (r > 0.4) and the Kolmogorov–Smirnov test (α = 0.05) by comparing these indices calculated from both observed and simulated discharge. The spatial patterns of the flow regime alterations varied depending on the hydrologic indices. For example, both the median flow in August and the frequency of high flow pulses were reduced by the maximum of approximately 70%, but these strongest alterations were detected at different locations (i.e., on the mainstream and the tributary, respectively). These results are likely caused by different operational purposes of multiple water control facilities. The results imply that the evaluation only at discharge gauges is insufficient to capture the alteration of the flow regime. Our findings clearly emphasize the importance of evaluating the spatial pattern of flow regime alteration on a river network where its discharge is affected by multiple water control facilities.

Monitoring land cover change of a river-floodplain system using high-resolution satellite images

In this study, a method was developed to monitor habitat structure in river-floodplain systems using high-resolution satellite images from 2010 to 2012 across a 30-km longitudinal section of the Tagliamento River, Northeast Italy. Three ortho-corrected RapidEye satellite images at 5-m spatial resolution and cloud cover of < 1%, with four spectral bands, namely, blue, green, red, and near-infrared, were used for land cover classification by converting pixel values into digital number (DN) distributions. The DN distributions for each band were clustered into separate classes based on correlations among all bands. The rate of unchanged habitat was further calculated as the intersection of all habitats divided by the area of the habitat of interest. The land cover categories were bare alluvium, river water, and vegetation. Bare alluvium was the dominant type, covering 55–75% of land. Vegetation and river water covered a relatively smaller area of the upper part and a larger area of the middle part of the Tagliamento River. The accuracy of this method was greater (> 89%) than that of the conventional unsupervised ISODATA method (> 83%) as river water and vegetation could be differentiated more accurately using this new method. The unchanged area was greater for river water than for vegetation and bare alluvium. These results indicated that habitat distribution changed spatially and temporally, especially for fluvial habitats, while the composition of habitat types was preserved in the middle reaches of the Tagliamento River. This method can be used to continuously and accurately monitor the large-scale spatiotemporal dynamics of habitat structures.