Utilization and uncertainties of satellite precipitation data in flash flood hydrological analysis in ungauged watersheds

Abstract

<p>Aim of the study is to examine the potential utilization of satellite precipitation data to estimate the peak discharges of flash floods in ungauged Mediterranean watersheds. Cumulative precipitation heights from local rain gauge and the GPM-IMERG were correlated in a scatter plot. The calculated linear equations were used to adjust the uncalibrated GPM-IMERG precipitation data in Thasos island (Northern Greece), to investigate the mechanisms of the flash floods recorded in November 2019 and to evaluate the significance of satellite precipitation data in hydrological modeling. The uncalibrated GPM-IMERG precipitation failed to explain the flash floods phenomena. The rain gauge data are reliable to accurately predict the peak discharges only in cases, where the rain gauges are within the study area. The strong correlation between ground rainfall data and satellite spatiotemporal precipitation data (R2 > 0.65), provides linear regression equations that, through their extrapolation and appliance to the rest of the flooded area, could adjust and correct the satellite data, optimizing the efficiency and accuracy of flash flood analysis, especially in ungauged watersheds. The proposed methodology could highly contribute to the optimization of flood mitigation measures establishment, flood risk assessment, hydrological and hydraulic simulation of flash flood events in ungauged watersheds.</p>

Investigation of Flood Management and Mitigation Measures in Ungauged NATURA Protected Watersheds

“…The marginal cases are marked in orange, while the cases, in which the technical works will discharge the expected peak flows are presented in green color. Accepting the known uncertainties of the hydrological models in ungauged watersheds, the marginal values were based on a reasonable range between ±20%, which could be characterized as acceptable in hydrological modeling [10,56,71,72]. The results revealed that the specific peak flow ranged between 2.5 and 7.8 m 3 /s/km 2 , values which internationally, and in Greece, are considered relatively low for flood danger potential.…”

“…The hydrological modeling was applied for the seven watersheds of the study area, using the rainfall-runoff model of Soil Conservation Service-Curve Number (SCS-CN) [47]. SCS-CN hydrological model is well-known and widely used in many countries [49][50][51][52][53] and also in Greece [54][55][56][57][58][59][60]. The Curve Number (CN) is a dimensionless empirical parameter used for the estimation of runoff and infiltration from rainfall excess, and ranges from 30 to 100, with the highest values indicating higher runoff potential.…”

Impact of Forest Conversion to Agriculture on Hydrologic Regime in the Large Basin in Vietnam

“…Yet, Van Dijk et al, (2009) [13] reanalyzed the data used in [12] and suggested that the removal of trees does not affect large flood events. It is also known that forests present finite capabilities to retain large amounts of precipitation, especially during extreme rainfall events, even if the forest cover percentage is significantly high [14,15]. Defining a threshold above which forest cover is no longer effective in reducing a flood is a challenge in forest hydrology [16].…”

Identification of High-Impact Uncertainty Sources for Urban Flood Models in Hillside Peri-Urban Catchments

“…In urban catchments, flooding is a complex process that is difficult to describe. The increased damage potential is a result of the high building density [19], the often-occurring combination of fluvial and pluvial flooding and the high spatial and temporal variability of extreme storm events [17,20,21]. Additionally, several not-predictable conditions such as blockage scenarios in sewers, streams, or culverts can also have a significant impact on the flooding process [22].…”

“…While the focus of the past decade was on building a model with the highest accuracy, the current focus is on model application, such as real-time prediction [45][46][47] or the quantification of model uncertainties, sensitivities and calibration techniques [48]. However, model calibration in particular remains a challenge due to the spatially and temporally distributed properties of flooding and the often-poor data basis in terms of measurements [21,49].…”