Bias correction of high resolution regional climate model data

380

174

Abstract. Water resources are essential to the ecosystem and social economy in the desert and oasis of the arid Tarim River basin, northwestern China, and expected to be vulnerable to climate change. It has been demonstrated that regional climate models (RCMs) provide more reliable results for a regional impact study of climate change (e.g., on water resources) than general circulation models (GCMs). However, due to their considerable bias it is still necessary to apply bias correction before they are used for water resources research. In this paper, after a sensitivity analysis on input meteorological variables based on the Sobol' method, we compared five precipitation correction methods and three temperature correction methods in downscaling RCM simulations applied over the Kaidu River basin, one of the headwaters of the Tarim River basin. Precipitation correction methods applied include linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), distribution mapping (DM) and quantile mapping (QM), while temperature correction methods are LS, variance scaling (VARI) and DM. The corrected precipitation and temperature were compared to the observed meteorological data, prior to being used as meteorological inputs of a distributed hydrologic model to study their impacts on streamflow. The results show (1) streamflows are sensitive to precipitation, temperature and solar radiation but not to relative humidity and wind speed; (2) raw RCM simulations are heavily biased from observed meteorological data, and its use for streamflow simulations results in large biases from observed streamflow, and all bias correction methods effectively improved these simulations; (3) for precipitation, PT and QM methods performed equally best in correcting the frequency-based indices (e.g., standard deviation, percentile values) while the LOCI method performed best in terms of the time-series-based indices (e.g., Nash-Sutcliffe coefficient, R 2 ); (4) for temperature, all correction methods performed equally well in correcting raw temperature; and (5) for simulated streamflow, precipitation correction methods have more significant influence than temperature correction methods and the performances of streamflow simulations are consistent with those of corrected precipitation; i.e., the PT and QM methods performed equally best in correcting flow duration curve and peak flow while the LOCI method performed best in terms of the time-series-based indices. The case study is for an arid area in China based on a specific RCM and hydrologic model, but the methodology and some results can be applied to other areas and models.

Section: Evaluation Of Corrected Precipitation and Temperaturesupporting

confidence: 86%

Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China

Fang

Jian

Chen

et al. 2015

380

174

“…Over the last decade, most of the developed -and therefore applied -bias correction (BC) methods focused on the adjustment of the mean (e.g., Delta method, Xu, 1999), the variance (e.g., simple scaling adjustment, Berg et al, 2012) or more generally on the adjustment of the distribution (e.g., "quantile-mapping", Haddad and Rosenfeld, 1997). Bias adjustments of the whole distribution through quantilemapping techniques have been quite popular since it allows for adjusting not only the mean and variance but also any quantile of the variable of interest.…”

Section: Introductionmentioning

confidence: 99%

Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R<sup>2</sup>D<sup>2</sup>) bias correction

Vrac¹

2018

135

Abstract. Climate simulations often suffer from statistical biases with respect to observations or reanalyses. It is therefore common to correct (or adjust) those simulations before using them as inputs into impact models. However, most bias correction (BC) methods are univariate and so do not account for the statistical dependences linking the different locations and/or physical variables of interest. In addition, they are often deterministic, and stochasticity is frequently needed to investigate climate uncertainty and to add constrained randomness to climate simulations that do not possess a realistic variability. This study presents a multivariate method of rank resampling for distributions and dependences (R 2 D 2 ) bias correction allowing one to adjust not only the univariate distributions but also their inter-variable and inter-site dependence structures. Moreover, the proposed R 2 D 2 method provides some stochasticity since it can generate as many multivariate corrected outputs as the number of statistical dimensions (i.e., number of grid cell × number of climate variables) of the simulations to be corrected. It is based on an assumption of stability in time of the dependence structure -making it possible to deal with a high number of statistical dimensions -that lets the climate model drive the temporal properties and their changes in time. R 2 D 2 is applied on temperature and precipitation reanalysis time series with respect to high-resolution reference data over the southeast of France (1506 grid cell). Bivariate, 1506-dimensional and 3012-dimensional versions of R 2 D 2 are tested over a historical period and compared to a univariate BC. How the different BC methods behave in a climate change context is also illustrated with an application to regional climate simulations over the 2071-2100 period. The results indicate that the 1d-BC basically reproduces the climate model multivariate properties, 2d-R 2 D 2 is only satisfying in the inter-variable context, 1506d-R 2 D 2 strongly improves inter-site properties and 3012d-R 2 D 2 is able to account for both. Applications of the proposed R 2 D 2 method to various climate datasets are relevant for many impact studies. The perspectives of improvements are numerous, such as introducing stochasticity in the dependence itself, questioning its stability assumption, and accounting for temporal properties adjustment while including more physics in the adjustment procedures.

“…We chose the simplest form of bias correction for air temperature. The mean bias is added to the model data after calculating the bias for each month of the climatological year (Berg et al, 2012).…”

Section: Datamentioning

confidence: 99%

Infrastructure sufficiency in meeting water demand under climate-induced socio-hydrological transition in the urbanizing Capibaribe River basin – Brazil

Neto

Scott

Lima

et al. 2014

Abstract.Water availability for a range of human uses will increasingly be affected by climate change, especially in the arid and semiarid tropics. The main objective of this study is to evaluate the infrastructure sufficiency in meeting water demand under climate-induced socio-hydrological transition in the Capibaribe River basin (CRB). The basin has experienced spatial and sectoral (agriculture-to-urban) reconfiguration of water demands. Human settlements that were once dispersed, relying on intermittent sources of surface water, are now larger and more spatially concentrated, which increases water-scarcity effects. Based on the application of linked hydrologic and water-resources models using precipitation and temperature projections of the IPCC SRES (Special Report: Emissions Scenarios) A1B scenario, a reduction in rainfall of 26.0 % translated to streamflow reduction of 60.0 %. We used simulations from four members of the HadCM3 (UK Met Office Hadley Centre) perturbed physics ensemble, in which a single model structure is used and perturbations are introduced to the physical parameterization schemes in the model (Chou et al., 2012). We considered that the change of the water availability in the basin in the future scenarios must drive the water management and the development of adaptation strategies that will manage the water demand. Several adaptive responses are considered, including water-loss reductions, wastewater collection and reuse, and rainwater collection cisterns, which together have potential to reduce future water demand by 23.0 %. This study demonstrates the vulnerabilities of the infrastructure system during socio-hydrological transition in response to hydroclimatic and demand variabilities in the CRB and also indicates the differential spatial impacts and vulnerability of multiple uses of water to changes over time. The simulations showed that the measures proposed and the water from interbasin transfer project of the São Francisco River had a positive impact over the water supply in the basin, mainly for human use. Industry and irrigation will suffer impact unless other measures are implemented for demand control.