A downscaling method for the assessment of local climate change

2014

This paper proposes a spatial-temporal downscaling approach to construction of the Intensity-Duration-Frequency (IDF) relations at a local site in the context of climate change. More specifically, the proposed approach is based on a combination of a spatial downscaling method to link large-scale climate variables given by General Circulation Model (GCM) simulations with daily extreme precipitations at a site and a temporal downscaling procedure to describe the relationships between daily and sub-daily extreme precipitations based on the scaling General Extreme Value (GEV) distribution. The feasibility and accuracy of the suggested method were assessed using rainfall data available eight stations in Quebec (Canada) and five stations in South Korea for the 1961-2000 period and climate simulations under four different climate change scenarios provided by the Canadian (CGCM3) and the UK (HadCM3) GCM models. Results of this application using data from two completely different climatic regions have indicated that it is feasible to link sub-daily extreme rainfalls a local site with large-scale GCMbased daily climate predictors for the construction of the IDF relations for the present 1961-1990 period as well as for future periods (2020s, 2050s, and 2080s) under different climate change scenarios. The proposed downscaling method provided therefore an essential tool for estimating extreme rainfalls for various climate-related impact assessment studies for a given region.

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.

Journal of Earth System Science, 2014

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash-Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

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.

2016

Climate change is one of the greatest challenges for water resources management. Intensity and frequency of extreme rainfalls are increasing due to enhanced greenhouse gas effect caused by climate change. A lot of research has been done in developing innovative methods for assessing the impacts of climate change on rainfall extremes. Climate change strongly depends on General Circulation Model (GCM) outputs since they play a pivotal role in the understanding of climate change. However due to their coarse resolution, statistical downscaling is widely applied to match the scale between the GCM and the station scale. This research proposed to establish statistical downscaling model that was able to generate hourly rainfall data for future projection of hourly extreme rainfall in Peninsular Malaysia. An Advanced Weather Generator (AWE-GEN) built on stochastic downscaling principles was applied for simulating hourly rainfall data. The model construction involved 40 stations over Peninsul...

Future climate projections of Global Climate Models (GCMs) under different scenarios are usually used to develop climate change mitigation and adaptation strategies. However, present GCMs have limited skills to simulate the complex and local climate features and to provide reliable information on precipitation which is a principal input to hydrologic impact assessment models. Furthermore, the outputs provided by GCMs are too coarse to be used by such hydrologic models, as they require information at much finer scales. Downscaling of GCM outputs is usually employed to provide fine-resolution or point-scale information required for impact models. The downscaling methodologies developed to date can be broadly categorized as statistical and dynamical. Statistical downscaling tools have three main classes: 1) regression based, 2) weather generators, and 3) weather typing. The weather generator is one of the popular downscaling techniques. It is based on statistical principles and considered to be computationally less demanding than other downscaling techniques. In the present study, LARS-WG (a weather generator) and the outputs from HadCM3 (a climate change model) for present climate as well as future time slice of 2070-2099 (2080s) based on A2 scenario of Special Report on Emission Scenarios (SRES) are used to evaluate LARS-WG as a tool for the assessment of climate change impacts on extreme characteristics of daily rainfall at Owairaka station located in the Auckland region in New Zealand. The results obtained in this study illustrate that LARS-WG has reasonable skill to simulate the extreme rainfall events and can be adopted as an effective tool for incorporating climate change impacts into sustainable development.

Natural Hazards and Earth System Science, 2011

The accuracy of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on hydrological processes. In fact, 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, a comparison between statistical properties of rainfall observations and model control simulations from a Regional Climate Model (RCM) was performed through a robust and meaningful representation of the precipitation process. The output of the adopted RCM was analysed and re-scaled exploiting the structure of a stochastic model of the point rainfall process. In particular, the stochastic model is able to adequately reproduce the rainfall intermittency at the synoptic scale, which is one of the crucial aspects for the Mediterranean environments. Possible alteration in the local rainfall regime was investigated by means of the historical daily time-series from a dense rain-gauge network, which were also used for the analysis of the RCM bias in terms of dry and wet periods and storm intensity. The result is a stochastic scheme for bias-correction at the RCM-cell scale, which produces a realistic representation of the daily rainfall intermittency and precipitation depths, though a residual bias in the storm intensity of longer storm events persists.

2012

Many climate studies in the recent past have revealed an obvious variation in climate compared to the past. Recent extreme events such as flash flooding, bushfires and drought provide ample evidence of these variations. In addition to the natural cycle of the climate, the anthropogenic effects of human development are no longer negligible. Emission of greenhouse gases and other aerosols into the atmosphere have led to warmer temperatures and consequently to more extreme events. Urban stormwater systems will particularly be influenced by climate change. Conventionally, to design and develop the stormwater collection systems, it has been assumed that events that occurred in the past would happen in the future. The change in climatic patterns has led to a new approach of considering the future variation in climate for the assessment of stormwater systems. In this study statistical and stochastic approaches to downscaling climate variables from Global Climate Models (GCMs) are discussed and a convenient approach to downscale daily rainfall data into sub-hourly timescales is presented. The Statistical Downscaling Model (SDSM) has been selected to downscale GCMs spatially at the site location. SDSM provides results in a daily time base. A disaggregation approach has been modified and simplified to generate sub hourly time scale rainfalls from daily rainfalls. Availability of these data is essential for the assessment of stormwater system functionality against future variability. This research is an ongoing attempt to develop a new statistic stochastic approach to increase the accuracy of the model in re-sampling of the observed data especially at the sub-hourly time scale.

Water Resources Research, 2011

1] The aim of this paper is to define a method for determining reasonable estimates of rainfall modeled by global circulation models (GCMs) coupled with regional climate models (RCMs). The paper describes and uses two new procedures designed to give confidence in the interpretation of such rainfall estimates. The first of these procedures is the use of circulation patterns (CPs) to define quantile-quantile (Q-Q) transforms between observed and RCM-estimated rainfall (the CPs were derived from sea level pressure (SLP) fields obtained from reanalysis of historical daily weather in a previous study). The Q-Q transforms are derived using two downscaling techniques during a 20 year calibration period and were validated during a 10 year period of observations. The second novel procedure is the use of a double Q-Q transform to estimate the rainfall patterns and amounts from GCM-RCM predictions of SLP and rainfall fields during a future period. This procedure is essential because we find that the CP-dependent rainfall frequency distributions on each block are unexpectedly different from the corresponding historical distributions. The daily rainfall fields compared are recorded on a 25 km grid over the Rhine basin in Germany; the observed daily data are averaged over the grid blocks, and the RCM values have been estimated over the same grid. Annual extremes, recorded on each block during the validation period, of (1) maximum daily rainfall and (2) the lowest 5% of filtered rainfall were calculated to determine the ability of RCMs to capture rainfall characteristics which are important for hydrological applications. The conclusions are that (1) RCM outputs used here are good at capturing the patterns and rankings of CP-dependent rainfall; (2) CP-dependent downscaling, coupled with the double Q-Q transform, gives good estimates of the rainfall during the validation period; (3) because the RCMs offer future CP-dependent rainfall distributions that are different from the observed distributions, it is judged that these predictions, once modified by the double Q-Q transforms, are hydrologically reasonable; and (4) the climate in the Rhine basin in the future, as modeled by the RCMs, is likely to be wetter than in the past. The results suggest that such future projections may be used with cautious confidence.

EPiC series in engineering, 2018

This paper proposes an efficient spatio-temporal statistical downscaling approach for estimating IDF relations at an ungauged site using daily rainfalls downscaled from global climate model (GCM) outputs. More specifically, the proposed approach involves two steps: (1) a spatial downscaling using scaling factors to transfer the daily downscaled GCM extreme rainfall projections at a regional scale to a given ungauged site and (2) a temporal downscaling using the scale-invariance GEV model to derive the distribution of sub-daily extreme rainfalls from downscaled daily rainfalls at the same location. The feasibility and accuracy of the proposed approach were evaluated based on the climate simulation outputs from 21 GCMs that have been downscaled by NASA to a regional 25-km scale for two different RCP 4.5 and 8.5 scenarios and the observed extreme rainfall data available from a network of 15 raingauges located in Ontario, Canada. The jackknife technique was used to represent the ungauged site conditions. Results based on different statistical criteria have indicated the feasibility and accuracy of the proposed approach.