Stochastic bias-correction of daily rainfall scenarios for hydrological applications (original) (raw)
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Advances in Geosciences, 2010
The accuracy of local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes because the presence of bias in downscaled precipitation may produce large bias in the assessment of soil moisture dynamics, river flows, and groundwater recharge. In this study, the output of a regional climate model (RCM) is downscaled using a stochastic modelling of the point rainfall process able to adequately reproduce the daily rainfall intermittency which is one of the crucial aspects for the hydrological processes characterizing Mediterranean environments. The historical time-series from a dense raingauge network were used for the analysis of the RCM bias in terms of dry and wet daily period and then to investigate the predicted alteration in the local rainfall regime. A Poisson Rectangular Pulse (PRP) model (Rodriguez-Iturbe et al., 1987) was finally adopted for the stochastic generation of local daily rainfall as a continuous-time point process with forcing parameters resulting from the bias correction of the RCM scenario.
International Journal of Climatology, 2014
ABSTRACT For climate impact assessment regarding hydrology, the availability of long precipitation time series with high temporal and spatial resolution is essential. A possible approach to obtain this data is the statistical downscaling of precipitation simulated by a global climate model (GCM) using a stochastic rainfall model with parameters conditioned on circulation patterns (CP). This approach requires: (1) the existence of a strong relationship between CP and precipitation, (2) the sufficient reproduction of CPs by the GCM, (3) the adequate simulation of precipitation by the rainfall model and (4) either stationarity of the relationship between precipitation and CPs or an approach to account for non-stationarity. The objective of this research is the careful evaluation and discussion of the above stated four hypotheses. For this purpose, a case study for the Aller–Leine river basin in Northern Germany has been created. It has been found that CPs can be defined which show significant differences in precipitation behaviour. The CPs derived from re-analysis data are well reproduced by the GCM simulations. In addition, the hourly stochastic rainfall model simulates the observed precipitation characteristics well, except for a certain overestimation of the extremes. However, the change in rainfall between past and future time periods as predicted by a regional climate model could not be explained by the change in CP frequency, due to the non-stationarity of the relationship between rainfall and CP. This can be best accounted for by re-estimating the parameters of the stochastic rainfall model for future conditions based on corrected observations using a delta change approach regarding simulated rainfall from a regional climate model.
2010
s u m m a r y A statistical bias correction methodology for global climate simulations is developed and applied to daily land precipitation and mean, minimum and maximum daily land temperatures. The bias correction is based on a fitted histogram equalization function. This function is defined daily, as opposed to earlier published versions in which they were derived yearly or seasonally at best, while conserving properties of robustness and eliminating unrealistic jumps at seasonal or monthly transitions. The methodology is tested using the newly available global dataset of observed hydrological forcing data of the last 50 years from the EU project WATCH (WATer and global CHange) and an initial conditions ensemble of simulations performed with the ECHAM5 global climate model for the same period. Bias corrections are derived from 1960 to 1969 observed and simulated data and then applied to 1990-1999 simulations. Results confirm the effectiveness of the methodology for all tested variables. Bias corrections are also derived using three other non-overlapping decades from 1970 to 1999 and all members of the ECHAM5 initial conditions ensemble. A methodology is proposed to use the resulting ''ensemble of bias corrections'' to quantify the error in simulated scenario projections of components of the hydrological cycle.
Rainfall downscaling and flood forecasting: a case study in the Mediterranean area
Natural Hazards and Earth System Science, 2006
The prediction of the small-scale spatial-temporal pattern of intense rainfall events is crucial for flood risk assessment in small catchments and urban areas. In the absence of a full deterministic modelling of small-scale rainfall, it is common practice to resort to the use of stochastic downscaling models to generate ensemble rainfall predictions to be used as inputs to rainfall-runoff models. In this work we present an application of a new spatial-temporal downscaling procedure, called RainFARM, to an intense precipitation event predicted by the limited-area meteorological model Lokal Model over north-west Italy. The uncertainty in flood prediction associated with the small unresolved scales of forecasted precipitation fields is evaluated by using an ensemble of downscaled fields to drive a semi-distributed rainfall-runoff model.
2010
1] The coarse resolution of general circulation models (GCMs) necessitates use of downscaling approaches for transfer of GCM output to finer spatial resolutions for climate change impact assessment studies. This paper presents a stochastic downscaling framework for simulation of multisite daily rainfall occurrences and amounts that strive to maintain persistence attributes that are consistent with the observed record. At site, rainfall occurrences are modeled using a modified Markov model that modifies the transition probabilities of an assumed Markov order 1 rainfall occurrence process using exogenous atmospheric variables and aggregated rainfall attributes designed to provide longer-term persistence. At site rainfall amounts on wet days are modeled using a nonparametric kernel density simulator conditional on previous time step rainfall and selected atmospheric variables. The spatial dependence across the rainfall occurrence and amounts is maintained through spatially correlated random numbers and atmospheric variables that are common across the stations used. The proposed framework is developed using the current climate (years 1960-2002) reanalysis data and rainfall records at a network of 45 rain gauges near Sydney, Australia, while atmospheric variable simulations of the CSIRO Mk3.0 GCM (corresponding to Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) B1, A1B and A2 emission scenarios) are used for downscaling of rainfall for the current and future (year 2070) climate conditions. Results of the study indicate wetter autumn and summer and drier spring and winter conditions over the region in a warmer climate. The best estimates of annual rainfall project little change in the number of wet days and slight increase (2% in 2070) in the rainfall amount. An increase (about 4%) in daily rainfall intensity (rain per wet day) is estimated in year 2070. Changes in rainfall intensity, wet and dry spells, and rainfall amount in wet spells suggest that the future rainfall regime will have longer dry spells interrupted by heavier rainfall events.
Atmosphere
The response of Mediterranean small catchments hydrology to climate change is still relatively unexplored. Regional Climate Models (RCMs) are an established tool for evaluating the expected climate change impact on hydrology. Due to the relatively low resolution and systematic errors, RCM outputs are routinely and statistically post-processed before being used in impact studies. Nevertheless, these techniques can impact the original simulated trends and then impact model results. In this work, we characterize future changes of a small Apennines (Central Italy) catchment hydrology, according to two radiative forcing scenarios (Representative Concentration Pathways, RCPs, 4.5 and 8.5). We also investigate the impact of a widely used bias correction technique, the empirical Quantile Mapping (QM) on the original Climate Change Signal (CCS), and the subsequent alteration of the original Hydrological Change Signal (HCS). Original and bias-corrected simulations of five RCMs from Euro-CORDE...
Journal of Water and Climate Change, 2021
We propose to evaluate the impact of rainfall–runoff model (RRM) structural uncertainty on climate model evaluation, performed within a process-oriented framework using the RRM. Structural uncertainty is assessed with an ensemble approach using three conceptual RRMs (HBV, IHACRES and GR4J). We evaluate daily precipitation and temperature from 11 regional climate models forced by five general circulation models (GCM–RCMs), issued from EURO-CORDEX. The assessment was performed over the reference period (1970–2000) for five catchments situated in northern Tunisia. Seventeen discharge performance indexes were used to explore the representation of hydrological processes. The three RRMs performed well over the reference period, with Nash–Sutcliffe efficiency values ranging from 0.70 to 0.90 and bias close to 0%. The ranking of GCM–RCMs according to hydrological performance indexes is more meaningful before the bias correction, which considerably reduces the differences between GCM- and RC...
Journal of Hydrology, 1996
The relationship between the spatial variability of rainfall and catchment response is investigated by conducting experiments with a stochastic rainfall field model and a physically based distributed modelling system, the Systeme Hydrologique Europeen (SHE), both of which are calibrated for a small upland catchment. The development and calibration of the rainfall field model is described in Part 1 of this paper, and the experiments with simulated rainfall fields and the SHE catchment model are described in Part 2. The rainfall field model is based on the use of the Turning Bands Method (TBM) incorporating a fractionally differenced line process to generate Gaussian random fields with a specified space-time correlation structure which can be isotropic or anisotropic. A transformation is then applied to the Gaussian field to reproduce the non-stationary temporal structure and skewed marginal distribution of observed rainfall. The transformed field is then propagated in space with the required velocity. The model is calibrated using hourly data for ten storms observed at three sites in the upper Wye catchment (area 10.55 km*) at Plynlimon, Wales. As rainfall over the catchment exhibits significant variation with altitude (and other factors), an altitude correction factor is applied to the simulated rainfall fields. Comparisons of the means, variances, skewnesses, cross-and autocorrelation functions of observed and simulated storms at the sampling points show good agreement, and realistic spatial patterns are observed in the simulated fields. A procedure is developed and applied for generating conditional simulations whereby the historical storm rainfall at the sampling points can be reproduced exactly in simulated fields, thus allowing several realizations of the unobserved spatial variability of rainfall at all other points in the catchment to be generated for any historical storm.
Water Resources Research, 2017
Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.