Disaster prevention planning is affected in a significant way by a lack of in‐depth understanding of the numerous uncertainties involved with flood delineation and related estimations. Currently, flood inundation extent is represented as a deterministic map without in‐depth consideration of the inherent uncertainties associated with variables such as precipitation, streamflow, topographic representation, modelling parameters and techniques, and geospatial operations. The motivation of this study is to estimate uncertainties in flood inundation mapping based on a non‐parametric bootstrapping method. The uncertainty is addressed through the application of non‐parametric bootstrap sampling to the hydrodynamic modelling software, HEC‐RAS, integrated with Geographic Information System (GIS). This approach was used to simulate different water levels and flow rates corresponding to different return periods from the available database. The study area was the Langat River Basin in Malaysia. The results revealed that the inundated land and infrastructure are subject to a flooding hazard of high‐frequency events and that the flood damage potential is increasing significantly for residential areas and valuable land‐use classes with higher return periods. The proposed methodology, as well as the study outcomes, of this paper could be beneficial to policymakers, water resources managers, insurance companies and other flood‐related stakeholders. Copyright © 2017 John Wiley & Sons, Ltd.
This study is designed to consider the uncertainty in the kinematic runoff and erosion model named KINEROS2. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used for assessing the uncertainty associated with model predictions, which assumes that due to the limitations in model structure, data and calibration scheme, many different parameter sets can make acceptable simulations. GLUE is a Bayesian approach based on the Monte Carlo method for model calibration and uncertainty analysis. The assessment was performed in the Zayanderood River basin located in Central Iran. To make an accurate calibration, five runoff events were selected from three different gauging stations for the purpose. Statistical evaluations for streamflow prediction indicate that there is good agreement between the measured and simulated flows with NashSutcliffe values of efficiency of 0.85 and 0.79 for calibration and validation periods respectively.Uncertainty analysis was carried out on the new distribution of input parameters for model validation.
Abstract. The study of climate change impact on water resources has accelerated worldwide over the past two decades. An important component of such studies is the bias correction step, which accounts for spatiotemporal biases present in climate model outputs over a reference period, and which allows realistic streamflow simulations using future climate scenarios. Most of the literature on bias correction focuses on daily scale climate model temporal resolution. However, a large amount of regional and global climate simulations are becoming increasingly available at the sub-daily time step, and even extend to the hourly scale, with convection-permitting models exploring sub-hourly time resolution. Recent studies have shown that the diurnal cycle of variables simulated by climate models is also biased, which raises issues respecting the necessity (or not) of correcting such biases prior to generating streamflows at the sub-daily time scale. This paper investigates the impact of bias-correcting the diurnal cycle of climate model outputs on the computation of streamflow over 133 small to large North American catchments. A standard hydrological modeling chain was set up using the temperature and precipitation outputs from a high spatial (12-km) and temporal (1-hour) regional climate model large ensemble (ClimEx-LE). Two bias-corrected time series were generated using a multivariate quantile mapping method, with and without correction of the diurnal cycles of temperature and precipitation. The impact of this correction was evaluated on three small (< 500 km2), medium and large (> 1000 km2) surface area catchment size classes. Results show small but systematic improvements of streamflow simulations when bias-correcting the diurnal cycle of precipitation and temperature. The greatest improvements were seen on the small catchments, and least noticeable on the largest. The diurnal cycle correction allowed for hydrological simulations to accurately represent the diurnal cycle of summer streamflow on small catchments. Bias-correcting the diurnal cycle of precipitation and temperature is therefore recommended when conducting impact studies at the sub-daily time scale on small catchments.
Abstract. The study of climate change impact on water resources has accelerated worldwide over the past 2 decades. An important component of such studies is the bias-correction step, which accounts for spatiotemporal biases present in climate model outputs over a reference period, and which allows for realistic streamflow simulations using future climate scenarios. Most of the literature on bias correction focuses on daily scale climate model temporal resolution. However, a large amount of regional and global climate simulations are becoming increasingly available at the sub-daily time step, and even extend to the hourly scale, with convection-permitting models exploring sub-hourly time resolution. Recent studies have shown that the diurnal cycle of variables simulated by climate models is also biased, which raises issues respecting the necessity (or not) of correcting such biases prior to generating streamflows at the sub-daily timescale. This paper investigates the impact of bias-correcting the diurnal cycle of climate model outputs on the computation of streamflow over 133 small to large North American catchments. A standard hydrological modeling chain was set up using the temperature and precipitation outputs from a high spatial (0.11∘) and temporal (1 h) regional climate model large ensemble (ClimEx-LE). Two bias-corrected time series were generated using a multivariate quantile mapping method, with and without correction of the diurnal cycles of temperature and precipitation. The impact of this correction was evaluated on three small (< 500 km2), medium (between 500 and 1000 km2), and large (> 1000 km2) surface area catchment size classes. Results show relatively small (3 % to 5 %) but systematic decreases in the relative error of most simulated flow quantiles when bias-correcting the diurnal cycle of precipitation and temperature. There was a clear relationship with catchment size, with improvements being most noticeable for the small catchments. The diurnal cycle correction allowed for hydrological simulations to accurately represent the diurnal cycle of summer streamflow in small catchments. Bias-correcting the diurnal cycle of precipitation and temperature is therefore recommended when conducting impact studies at the sub-daily timescale on small catchments.
This work explores the relationship between catchment size, rainfall duration and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-hour) resolution Single Model Initial condition Large Ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 hours and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency and catchment size, with the shortest durations, longest return periods and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-year rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration-frequency-size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return period flows, both being conditions for which the amplification of future flow will be maximized.
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