Bias Correction Of Rainfall Research Articles

Impact assessment of climate change on compound flooding in a coastal city

Flood disasters in coastal cities are generally caused by the combined effect of typhoon rainfall, storm surge and other factors. Coastal cities will face more severe compound flooding under climate change. Although some studies have analyzed compound flooding under climate change, there is a lack of research considering the combined effect of sea level rise (SLR) and future rainfall changes, especially lack of the jacking effect of SLR on the volume and duration of compound flooding. This paper investigates the impact of future rainfall changes and SLR on compound flood risk, via a case study of a coastal city, Haikou, China. Future rainfall in Haikou is obtained by bias correction of rainfall from five global climate models (GCMs) in CMIP5. The SLR in 2050 and 2090 is predicted according to IPCC. Then, a 1D/2D hydrological-hydrodynamic model for dynamic simulation of surface runoff is applied to simulate compound flooding. Results show that both future rainfall changes and SLR will lead to the increase of flood extent, flood depth and flood duration. But future rainfall changes will have a greater impact on flood, and the combined effect of future rainfall changes and SLR on flood is greater than the sum of their individual effects. The individual effects of SLR and future rainfall changes would increase flood depth in 10.6 % and 32.1 % of the study area, respectively, while their combined effect would increase this proportion to 34.9 %. Also, the jacking effect of SLR on compound flooding will lead to an increase in flood volume, but it presents a nonlinear relationship that gradually slows down with the increase of SLR. A SLR of 0.3 m and 0.63 m will lead to a 2.34 % and 3.22 % increase in flood volume, respectively. And the jacking effect of SLR is more obvious when future rainfall changes are considered, which will lead to a sharp increase in the duration of compound flooding. Moreover, water storage facilities and tidal gates are effective ways to reduce the impact of climate change on compound flooding.

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Analysis of the streamflow extremes and long-term water balance in the Liguria region of Italy using a cloud-permitting grid spacing reanalysis dataset

Abstract. The characterization of the hydro-meteorological extremes, in terms of both rainfall and streamflow, and the estimation of long-term water balance indicators are essential issues for flood alert and water management services. In recent years, simulations carried out with meteorological models are becoming available at increasing spatial and temporal resolutions (both historical reanalysis and near-real-time hindcast studies); thus, these meteorological datasets can be used as input for distributed hydrological models to drive a long-period hydrological reanalysis. In this work we adopted a high-resolution (4 km spaced grid, 3-hourly) meteorological reanalysis dataset that covers Europe as a whole for the period between 1979 and 2008. This reanalysis dataset was used together with a rainfall downscaling algorithm and a rainfall bias correction (BC) technique in order to feed a continuous and distributed hydrological model. The resulting modeling chain allowed us to produce long time series of distributed hydrological variables for the Liguria region (northwestern Italy), which has been impacted by severe hydro-meteorological events. The available rain gauges were compared with the rainfall estimated by the dataset and then used to perform a bias correction in order to match the observed climatology. An analysis of the annual maxima discharges derived by simulated streamflow time series was carried out by comparing the latter with the observations (where available) or a regional statistical analysis (elsewhere). Eventually, an investigation of the long-term water balance was performed by comparing simulated runoff ratios (RRs) with the available observations. The study highlights the limits and the potential of the considered methodological approach in order to undertake a hydrological analysis in study areas mainly featured by small basins, thus allowing us to overcome the limits of observations which refer to specific locations and in some cases are not fully reliable.

Correcting bias in rainfall inputs to a semi-distributed hydrological model using downstream flow simulation errors

ABSTRACTThe focus of this study is on bias correcting semi-distributed rainfall inputs into a hydrological model applied in the Okavango River basin in southern Africa, where there are very few local observations and heavy reliance is placed on global rainfall datasets. While the hydrological model, before rainfall bias correction, is able to represent the broad characteristics of the sub-basin streamflow responses, as demonstrated by good agreement between observed and simulated flow duration curves, there are many years where the annual volumes are over- or underestimated. The long records of observed flow at downstream stations are successfully used to bias correct the rainfall inputs to the upstream sub-basins using an analysis of their individual contributions to downstream flow and their annual rainfall–runoff response ratios. The results show improved simulations for the relatively shorter observation periods at the upstream gauging stations.

Dynamic downscaling and bias correction of rainfall in the Pampanga River Basin, Philippines, for investigating flood risk changes due to global warming

Dynamic downscaling and bias correction of rainfall in the Pampanga River Basin, Philippines, for investigating flood risk changes due to global warming