Statistical Downscaling Of Daily Precipitation Research Articles

Statistical downscaling of precipitation in northwestern Iran using a hybrid model of discrete wavelet transform, artificial neural networks, and quantile mapping

Downscaling of daily precipitation from Global Circulation Models (GCMs)-predictors at a station level, especially in arid and semi-arid regions, has remained a formidable challenge yet. The current study aims at proposing a coupled model of Discrete Wavelet Transform (DWT), Artificial Neural Networks (ANNs), and Quantile Mapping (QM) for statistical downscaling of daily precipitation. Given the historic (1978–2005) and future (2006–2100) predictors of eight-selected GCMs under Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5, a viable DWT-ANN model was developed for each station. Subsequently, we linked QM to DWT-ANN for bias correction and drizzle effect postprocessing of the DWT-ANN-historic/future projected precipitation. The skill of DWT-ANN-QM was demonstrated using various evaluation metrics, including Taylor diagram, Quantile–Quantile plot, Empirical Cumulative Distribution Function, and wet/dry spell analysis. We appraise the efficacy of the coupled model at 12 weather stations over the Gharehsoo River Basin (GRB) in northwestern Iran. Compared to the observed wet/dry spells, the dry-spells were better simulated via DWT-ANN-QM rather than the wet-spells wherein length and exceedance probability of the spells were overestimated. Results indicated that the future precipitation across the GRB will rise, on average, from 10 to 17% depending on weather station. Seasonal spatial distribution of the middle future (2041–2070) precipitation illustrated that an increase for fall and winter, especially, is expected, whereas the amount of the future spring and summer precipitation is projected to be declined. Having been developed and tested in a semi-arid basin, the efficacy of the coupled model should be further assessed in more humid climates.

A multiscale approach to statistical downscaling of daily precipitation: Israel as a test case

AbstractRainfall in the Eastern Mediterranean is strongly modulated by complex topography and localized mesoscale processes. General circulation models (GCMs) struggle to capture daily precipitation variability in the region, both in time and in space. Rain in the Eastern Mediterranean occurs within a hierarchy of scales, as synoptic scale structures often drive local rainfall patterns. Daily rain prediction in the region can therefore benefit from analog downscaling—a nonlinear regression of a high‐resolution predictand from past synoptic‐scale predictors. We present a multiscaled downscaling algorithm of daily rain over Israel. The underlying goal is to create a mechanism‐based tool that will improve the analysis and prediction of precipitation on short time scales in models that cannot produce the field explicitly. We train the algorithm using coarse grid ERA5 reanalysis data and measurements from 21 rain gauges. The routine uses a k‐nearest neighbours algorithm to find the most similar past instances (i.e., analogs) for every predicted day. Analog selection is performed in two steps, based on scale (synoptic and local), as to not overshadow correlative but local predictors. The algorithm also includes several unique aspects tailored to Mediterranean climate: subdaily predictors of cyclone life cycles; representation of upper level cyclonic drivers; and the inclusion of rainfall potential using the Modified K‐Index (MKI). The proposed algorithm has better accuracy (66% correct predictions) compared to non‐downscaled reanalysis and climatological predictions. It better captures the spatial rainfall variance, mitigates the “drizzle bias,” and improves skill in extreme event prediction. However, it underestimates very rainy events and has trouble fully representing the spatial variance in the region. Nonetheless, our algorithm represents the potential for computationally inexpensive downscaling of daily precipitation in the Mediterranean with various possible applications, for example, characterization of droughts and storms, linking hydrological and synoptic scale processes and introducing uncertainty estimates using large ensembles.

A novel statistical downscaling approach for analyzing daily precipitation and extremes under the impact of climate change: Application to an arid region

Statistical downscaling of daily precipitation is important for assessing the impact of climate change. However, some issues (e.g., the underestimation of extremes) limit statistical downscaling methods to project daily precipitation accurately. This study develops an integrated hybrid statistical downscaling model combined with bias correction and Bayesian model averaging (named as SDBC-BMA) for improving the ability of downscaling methods. The performance of SDBC is compared with regression based statistical downscaling methods (RSDM) and hybrid statistical downscaling methods (HSDM) based on different performance metrics and extreme indices. SDBC-BMA is applied to the Amu Darya River Basin (ADRB) to demonstrate its feasibility and capability, where three GCMs and two CMIP6 scenarios are considered. Several findings can be summarized: (1) compared to RSDM and HSDM, SDBC is more robust with a higher computation ability of daily precipitation and a lower mean bias (i.e., +0.10 mm/day); (2) compared with the simple model average, the biases of extreme indices and mean daily precipitation obtained from SDBC-BMA could decrease by 49 % and 88 %, respectively; (3) the mean precipitation values would increase by 56 ± 41 mm/year (under SSP245) and 126 ± 55 mm/year (under SSP585) during 2076–2100, higher than mean precipitation in the historical period (1979–2005); (4) extreme indices under multi-GCMs (except for consecutive wet days) would increase by 20 % (under SSP245) and 22 % (under SSP585), implying that extreme events in ADRB are more frequent in the 21st century. The increasing risk of precipitation extremes would exacerbate threats to agricultural and ecological security.

Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events

AbstractThe performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

A hierarchical analysis of the impact of methodological decisions on statistical downscaling of daily precipitation and air temperatures

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for Tmin than Tmax. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias.

Statistical downscaling of daily precipitation and temperatures in southern La Plata Basin

La Plata Basin has a considerable socio‐economic value, being one of the most important agricultural and hydropower‐producing regions in the world. In this region, there is increasing evidence of a changing climate with more frequent and more intense extreme events. Despite the importance of empirical statistical downscaling for regional climate impact studies, few studies have addressed this issue for southern South American regions. In this work, the analogue method was calibrated and validated for simulating local daily precipitation and maximum and minimum temperatures in southern La Plata Basin. The model was trained for the 1979–2000 period and validated for the independent period 2001–2014. Daily fields from NCEP‐NCAR Reanalysis 2 were used as predictors and daily observed data from 25 meteorological stations were used as predictands. A variety of potential predictors (including circulation, temperature and humidity variables) and combinations of them over different domain sizes were tested, revealing that the method was more skilful when combined predictors were considered. However, depending on the local predictand and the season of the year different predictor sets may be more appropriate. The method was comprehensively evaluated by means of several skill measures concerning different properties such as mean values, day‐to‐day variance, daily correspondence, persistence, inter‐annual variability, probability distributions and extreme percentiles. The method showed an overall good performance. It tended to overestimate (underestimate) temperature values, especially during winter (summer); however, the day‐to‐day variance during these seasons was fairly well represented. The method was able to reproduce extreme percentiles and their spatial distributions for the three predictand variables as well as the probability of compound temperature and precipitation extreme events. The performance of the method was very good at estimating seasonal cycles of the different aspects explored. It showed some difficulties in representing the persistence in daily temperatures and the inter‐annual variability of seasonal precipitation.

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June–September) and winter (November–December and January–March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979–2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson’s correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54–0.57 in summer and 0.56–0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.

Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe

The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171km2 in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall-dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35–60% of the total variance.

Probabilistic Multisite Statistical Downscaling for Daily Precipitation Using a Bernoulli–Generalized Pareto Multivariate Autoregressive Model

Abstract A Bernoulli–generalized Pareto multivariate autoregressive (BMAR) model is proposed in this paper for multisite statistical downscaling of daily precipitation. The proposed model relies on a probabilistic framework to describe the conditional probability density function of precipitation at each station for a given day and handles multivariate dependence in both time and space using a multivariate autoregressive model. Within a probabilistic framework, BMAR employs a regression model whose outputs are parameters of the mixed Bernoulli–generalized Pareto distribution. As a stochastic component, the BMAR employs a latent multivariate autoregressive Gaussian field to preserve lag-0 and lag-1 cross correlations of precipitation at multiple sites. The proposed model is applied for the downscaling of AOGCM data to daily precipitation in the southern part of Québec, Canada. Reanalysis products are used in this study to assess the potential of the proposed method. Based on the mean errors (MEs), the root-mean-square errors (RMSEs), precipitation indices, and the ability to preserve lag-0 and lag-1 cross correlation, results of the study indicate the superiority of the proposed model over a multivariate multiple linear regression (MMLR) model and a multisite hybrid statistical downscaling procedure that combines MMLR and stochastic generator schemes.

Quantile regression for spatially correlated data: an empirical likelihood approach

Quantile regression is a useful approach to modeling various aspects of conditional distributions. The Bayesian approach provides a natural framework for incorporating spatial correlation in a quantile regression model. This paper con- siders Bayesian spatial quantile regression using empirical likelihood as a working likelihood. The proposed approach inherits the merits of quantile regression in the sense that we can work with linear conditional quantile functions without having to assume a parametric form of the conditional distributions, and we allow each covariate to have dierential impacts on dierent parts of the conditional distribu- tions. Put into a Bayesian framework, this approach can incorporate spatial priors to smooth the conditional quantile functions across locations and across quantiles. We demonstrate both theoretically and empirically how the proposed approach can take advantage of spatial correlation to improve eciency over the usual quan- tile regression estimators. An application to the statistical downscaling of daily precipitation in the Chicago area is given to illustrate the merit of our approach. Bayesian empirical likelihood approach, informative priors, nonparametric spatial regression, spatial data

Performance assessment of different data mining methods in statistical downscaling of daily precipitation

Summary In this paper, nonlinear Data-Mining (DM) methods have been used to extend the most cited statistical downscaling model, SDSM, for downscaling of daily precipitation. The proposed model is Nonlinear Data-Mining Downscaling Model (NDMDM). The four nonlinear and semi-nonlinear DM methods which are included in NDMDM model are cubic-order Multivariate Adaptive Regression Splines (MARS), Model Tree (MT), k -Nearest Neighbor ( k NN) and Genetic Algorithm-optimized Support Vector Machine (GA-SVM). The daily records of 12 rain gauge stations scattered in basins with various climates in Iran are used to compare the performance of NDMDM model with statistical downscaling method. Comparison between statistical downscaling and NDMDM results in the selected stations indicates that combination of MT and MARS methods can provide daily rain estimations with less mean absolute error and closer monthly standard deviation and skewness values to the historical records for both calibration and validation periods. The results of the future projections of precipitation in the selected rain gauge stations using A2 and B2 SRES scenarios show significant uncertainty of the NDMDM and statistical downscaling models.

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.

Improved statistical downscaling of daily precipitation using SDSM platform and data‐mining methods

ABSTRACTIn this paper, an extension of the statistical downscaling model (SDSM), namely data‐mining downscaling model (DMDM), has been developed. DMDM has the same platform as the most cited statistical downscaling models, namely SDSM and ASD. Multiple linear regression (MLR), ridge regression (RR), multivariate adaptive regression splines (MARS) and model tree (MT) constitute the mathematical core of DMDM. DMDM uses linear basis functions in MARS and linear regression rules in MT to keep the linear structure of SDSM; therefore, all of the SDSM assumptions are also valid in DMDM. These methods highlight the effect of data partitioning for meteorological predictors in the downscaling procedure. Inputs and output of the presented approaches are the same as SDSM and ASD. In the case study of this research, NCEP/NCAR databases have been used for calibration and validation. According to the inherent linearity of the methods, suitable predictor selection has been done with stepwise regression as a preprocessing stage. The results of DMDM have been compared with observed precipitation in 12 rain gauge stations that are scattered in different basins in Iran and represent different climate regimes. Comparison between the results of SDSM and DMDM has indicated that the presented approach can highly improve downscaling efficiency in terms of reproducing monthly standard deviation and skewness for both calibration and validation datasets. Among the proposed methods in DMDM, the results of the case study have shown that MT has provided better performances both in modelling occurrence and amount of precipitation. Also, MT is potentially recognized as a powerful diagnostic tool that could extract information in key atmospheric drivers affecting local weather. It also has fewer parameters during dry seasons, in which the number of historical precipitation events might not be enough for calibrating SDSM model in many arid and semi‐arid regions.

Estimation of High Conditional Quantiles for Heavy-Tailed Distributions

Estimation of conditional quantiles at very high or low tails is of interest in numerous applications. Quantile regression provides a convenient and natural way of quantifying the impact of covariates at different quantiles of a response distribution. However, high tails are often associated with data sparsity, so quantile regression estimation can suffer from high variability at tails especially for heavy-tailed distributions. In this article, we develop new estimation methods for high conditional quantiles by first estimating the intermediate conditional quantiles in a conventional quantile regression framework and then extrapolating these estimates to the high tails based on reasonable assumptions on tail behaviors. We establish the asymptotic properties of the proposed estimators and demonstrate through simulation studies that the proposed methods enjoy higher accuracy than the conventional quantile regression estimates. In a real application involving statistical downscaling of daily precipitation in the Chicago area, the proposed methods provide more stable results quantifying the chance of heavy precipitation in the area. Supplementary materials for this article are available online.

Statistical downscaling of daily precipitation using support vector machines and multivariate analysis

Summary Downscaling local daily precipitation from large-scale weather variables is often necessary when studying how climate change impacts hydrology. This study proposes a two-step statistical downscaling method for projection of daily precipitation. The first step is classification to determine whether the day is dry or wet, and the second is regression to estimate the amount of precipitation conditional on the occurrence of a wet day. Predictors of classification and regression models are selected from large-scale weather variables in NECP reanalysis data based on statistical tests. The proposed statistical downscaling method is developed according to two methodologies. One methodology is support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR), and the other is multivariate analysis, including discriminant analysis (for classification) and multiple regression. The popular statistical downscaling model (SDSM) is analyzed for comparison. A comparison of downscaling results in the Shih-Men Reservoir basin in Taiwan reveals that overall, the SVM reproduces most reasonable daily precipitation properties, although the SDMS performs better than other models in small daily precipitation (less than about 10 mm). Finally, projection of local daily precipitation is performed, and future work to advance the downscaling method is proposed.

Statistical downscaling of daily precipitation over Sweden using GCM output

A classification of Swedish weather patterns (SWP) was developed by applying a multi-objective fuzzy-rule-based classification method (MOFRBC) to large-scale-circulation predictors in the context of statistical downscaling of daily precipitation at the station level. The predictor data was mean sea level pressure (MSLP) and geopotential heights at 850 (H850) and 700 hPa (H700) from the NCEP/NCAR reanalysis and from the HadAM3 GCM. The MOFRBC was used to evaluate effects of two future climate scenarios (A2 and B2) on precipitation patterns on two regions in south-central and northern Sweden. The precipitation series were generated with a stochastic, autoregressive model conditioned on SWP. H850 was found to be the optimum predictor for SWP, and SWP could be used instead of local classifications with little information lost. The results in the climate projection indicated an increase in maximum 5-day precipitation and precipitation amount on a wet day for the scenarios A2 and B2 for the period 2070-2100 compared to 1961-1990. The relative increase was largest in the northern region and could be attributed to an increase in the specific humidity rather than to changes in the circulation patterns.

GCM grid-box choice and predictor selection associated with statistical downscaling of daily precipitation over Northern Ireland

Statistical downscaling bridges the gap between General Circulation Model (GCM) out- put and climate-impacts modellers' requirements. Recent development of user-friendly software has facilitated use of statistical downscaling methods by the wider climate-impacts community. Simplic- ity of use, however, does not imply underlying conceptual simplicity. Ostensibly innocuous choices made when parameterizing such downscaling tools can have major implications for output and the impact study. Issues of predictor variable selection and GCM grid-box choice in downscaling daily precipitation occurrence and amount are investigated using the Statistical DownScaling Model (SDSM) package version 3.1. Choices are discussed within the context of Northern Ireland (NIR), designated an oceanic grid box in GCMs' land -sea mask. Use of the Republic of Ireland (IR) grid box, or combination of IR and Scottish Borders (SB) grid boxes to represent the NIR grid box, are evalu- ated by identifying site-specific optimum predictor sets for a NIR precipitation network. Realism of contrasts evident in dominance and spatial pattern of predictors are discussed with reference to spatial and temporal characteristics of local climate and large-scale atmospheric processes. For example, NIR grid-box predictor output displays weaker spatial contrasts in spring and summer than its IR counterpart. Explained variance associated with grid box/predictor set combinations is ~25%, similar to other precipitation downscaling studies. Differences of explained variance between grid boxes are ~5%, an attractive gain when modelling daily precipitation using statistical downscaling (SD), where 'improvement' of explained variance is at a premium. Future projections indicate more pronounced precipitation intensity using the NIR grid box. Overall, IR grid-box output appears most appropriate for downscaling of NIR daily precipitation. The NIR case study highlights the impact of predictor choice and grid-box selection upon SD output, particularly when the land area is repre- sented by an oceanic grid box in GCM experiments.

Statistical downscaling of daily precipitation over Greece

AbstractThe capability of a multi‐site non‐homogeneous hidden Markov model (NHMM) to downscale winter daily precipitation over Greece is explored for the period 1971–2000. The input variables were selected from NCEP Reanalysis Data following an optimization procedure and include the precipitation rate as predictor. The most parsimonious NHMM identified five weather states. Successively, the forecast skill of the model was tested against sequentially withdrawn data. The NHMM reproduced successfully both the station‐scale statistics like the occurrence and amount of precipitation as well as the spatial pattern of precipitation such as the correlations between stations and the Log‐Odds ratios. Also, the temporal correlation in the data at most of the rain gauges was successfully captured at seasonal timescales. Finally, the model was capable of reproducing extreme precipitation events. It modelled successfully the dry spells as well as most of the indices corresponding to ‘very wet events’, including the maximum 5‐day precipitation amount, the number of extreme precipitation events and their corresponding fraction of the total precipitation amount, better to the stations where the very wet events are not associated with local scale features that could not be captured by the large‐scale predictors. The weakest reproduced indices correspond to the ‘mean wet day precipitation’ and the ‘90th percentile of precipitation on wet days’ where the model captured their magnitude but not their interannual variability. Copyright © 2007 Royal Meteorological Society

CENTER OF MASS AND ANKLE INCLINATION ANGLES: AN ALTERNATIVE DETECTION OF GAIT INSTABILITY

The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km2 in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall-dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35–60% of the total variance.