Copyright: SDSM © R.L. Wilby, C.W.Dawson, UK


The main operations of SDSM 5.3 are:

Quality Control	Identifies data file size, missing data codes, and calculates the mean, maximum and minimum values in the file.
Transform Data	Transforms single or multi-column data files using standard functions (e.g., natural logs, squares, lags, binomials, etc.).  Also extracts ensemble members from a multi-column data file.
Screen Variables	Identifies significant correlations between specified local weather variables and regional-scale atmospheric predictor variables. Also provides scatterplots for seasonal sub-sets.
Calibrate Model	Constructs monthly, seasonal or annual statistical downscaling models given daily weather data and regional-scale atmospheric predictor variables.
Weather Generator	Generates ensembles of synthetic weather series from calibrated statistical downscaling models given daily atmospheric predictor variables (observed data).
Summary Statistics	Computes a range of statistics (e.g., mean, variance, peaks over threshold, spell lengths, percentiles, etc.) given synthetic daily weather series produced by the Weather Generator or Generate Scenario operations.
Frequency Analysis	Computes and displays in graphical form a number of statistics for both observed and modelled data.  The analysis includes the fitting of extreme value statistics, quantile-quantile plots, PDF plots and standard line plots.
Scenario Generator	Applies various treatments to weather data so that consequences and decisions can be evaluated.
Compare Results	Compares summary statistics produced by the Analyse operations using bar chart or line chart plots.
Time Series Analysis	Plots various times series data from modeled or observed data files.
Option from the all pages
Settings	Makes global changes to the system set-up, data handling, and model preferences.
Option from the home page
Literature	Comprehensive list of relevant literature.


Screen Variables

Identifying empirical relationships between gridded predictors (such as mean sea level pressure) and single site predictands (such as station precipitation) is central to all statistical downscaling methods.

The main purpose of the Screen Variables operation is to assist the user in the selection of appropriate downscaling predictor variables. This remains one of the most challenging stages in the development of any statistical downscaling model since the choice of predictors largely determines the character of the downscaled climate scenario. The decision process is also complicated by the fact that the explanatory power of individual predictor variables varies both spatially and temporally.

Screen Variables performs three supporting tasks: seasonal correlation analysis, partial correlation analysis, and scatterplots. Ultimately, however, the User must decide whether or not the identified relationships are physically sensible for the site(s) in question.

Select Predictand File

The first step in the Screen Variables operation is the selection of the predictand and predictor files. The predictand file (e.g., observed daily maximum temperature, daily precipitation totals, etc.) must be supplied by the User, in SDSM format (i.e., single column, ASCII). Click on the Select Predictand File button. An Open file window will appear - browse through until the appropriate directory has been located. Then click on the required predictand data file.

Data

The Data menu on the left-hand side of the Screen Variables window allows the start and end dates of the analysis period to be changed. The default dates are held in the Settings page. If the start and end dates lie outside the permissible range, the User will be prompted to enter new values. Enter the start and end date of the required analysis period (this should fall between the first two dates). Note: the software assumes that the predictor and predictand files have the same start date and length.

Select analysis period

The User must choose a seasonal subset from the pull down window under Select analysis period. The available options are Annual (no seasonal sub-setting), Winter (December-February), Spring (March-May), Summer (June-August), Autumn (September-November), and individual months. The default analysis period is Annual.

Predictor Variables

Locate and select the desired predictor variables by choosing the correct drive from the pull down window in the centre of the screen. The directories available on this drive will then appear in the window directly above the drive window. Browse through again until the appropriate directory is located. All files in this window are then listed in the window above. To select a predictor, simply click on the file name - it will be highlighted in blue. To deselect a file, click on it again, and it will no longer be highlighted. The number of predictor variables chosen is shown beneath this window.

Predictand

The target variable e.g. daily rainfall or maximum daily temperatures.

Autoregression

If the analysis is to include an autoregressive term, the Autoregressive term box should be ticked.  This forces SDSM to include the lagged predictand (one-day) as a predictor in the analysis.

Process

If the predictand depends on an intermediate occurrence process (e.g., precipitation amounts depend on wet-/dry-day occurrence) then click on Conditional, otherwise select Unconditional (as in the case of maximum temperature).

Significance Level

Specify the significance level used to test the strength of predictor-predictand correlations. Insignificant correlation scores are omitted from the Results screen. The default is p<0.05 (5%).

Analyse

The Analyse button is used to investigate the percentage of variance explained by specific predictand-predictor pairs.

Scatterplot

The Scatter button is used for visual inspections of inter-variable behaviour for specified sub-periods (annual, seasonal or monthly). The resultant scatterplot(s) indicate the nature of the association (linear, non-linear, etc.), whether or not data transformation(s) may be needed, and the importance of outliers.

Correlation

The Correlation button is used to investigate the strength of inter-variable correlations for specified sub-periods (annual, seasonal or monthly).


Calibrate Model

The Calibrate Model operation takes a user-specified predictand along with a set of predictor variables, and computes the parameters of multiple linear regression equations via the efficient dual simplex algorithm (forced entry method).

The User specifies the model structure: whether monthly, seasonal or annual sub-models are required; whether the process is unconditional or conditional. In Unconditional models a direct link is assumed between the predictors and predictand (e.g., local wind speeds may be a function of regional airflow indices). In Conditional models, there is an intermediate process between regional forcing and local weather (e.g., local precipitation amounts depend on wet-/dry-day occurrence, which in turn depend on regional-scale predictors such as humidity and atmospheric pressure). Furthermore, it is possible to apply standard transformations to the predictand in conditional models (see Advanced Settings), and/or to specific predictors (see Transform).

Select Predictand File

To begin model building, click on the Select Predictand File button in the top left-hand corner. An Open file window appears; browse through until the correct directory and file are reached, then click on the appropriate file name. The name of the file will then appear beneath the button. Note: predictand file names must have the extension *.DAT

Output File

Opens the parameter file (*.PAR) to receive meta-data associated with the model calibration, along with downscaling model parameters.

To specify the name of the output parameter (*.PAR) file, click on the Output File button in the upper right-hand corner. A Save As file window appears. For maximum convenience, make sure that the parameter file is saved in the same directory as the predictor and predictand files for observed data. Type in an appropriate file name in the File name box then click on the Save button. The name of the parameter file will then be displayed beneath the Output File button.

Data Period

The Data Period menu on the left-hand side of the Screen Variables window allows the start and end dates of the analysis period to be changed. The default dates are held in the Settings page. If the start and end dates lie outside the permissible range, the User will be prompted to enter new values. Enter the start and end date of the required analysis period (this should fall between the first two dates). Note: the software assumes that the predictor and predictand files have the same start date and length.

Predictor Variables

Locate and select the desired predictor variables by choosing the correct drive from the pull down window in the centre of the screen. The directories available on this drive will then appear in the window directly above the drive window. Browse through again until the appropriate directory is located. All files in this window are then listed in the window above. To select a predictor, simply click on the file name - it will be highlighted in blue. To deselect a file, click on it again, and it will no longer be highlighted. The number of predictor variables chosen is shown beneath this window.

Model Type

Specify whether individual downscaling models should be calibrated for each calendar month, climatological season or for the entire year. To determine the temporal resolution of the downscaling model check either Monthly, Seasonal or Annual under the Model Type box. The default is Monthly. Note: the calibration procedure can take significantly longer for large data sets whenever Seasonal or Annual model types are selected.

Process

If the predictand depends on an intermediate occurrence process (e.g., precipitation amounts depend on wet-/dry-day occurrence) then click on Conditional, otherwise select Unconditional (as in the case of maximum temperature). Note: if the process is Conditional it is further possible to specify standard transformations in Advanced Settings.

Autoregression

If the model is to include an autoregressive term as a predictor (the lagged predictand by one-day), the Include box should be ticked. This forces SDSM to include an autoregressive component in the model.  If the model is conditional, the autoregressive component is only used for the unconditional part of the process - the conditional part does not include an autoregressive term as the autoregressive component may stem from several days previously.

Residual Analysis

Allows the User to select a number of residual statistics to be plotted once the calibration process has completed.  The scatter plot produces a scatter diagram showing residual versus modeled predictand; the histogram option produces a histogram chart of residuals (the number of bars can be adjusted by altering the value in the Number of categories box.

Chow Test

Calculates the Chow statistic for the model (ie tests for stationarity of the model).

Cross Validation

Applies a k-fold cross validation to the model.  The data are split into n folds (n must be between 2 and 10).  A model is constructed on n-1 of these folds and then tested on the excluded fold.  This is repeated n times so that k-fold cross validation statistics can be calculated.  These are the R-Squared, SE, Durbin-Watson, Spearman Rank and Bias for an unconditional model, and R-Squared, SE and proportion of days correctly identified as wet/dry; and the R-Squared, SE and Spearman Rank of wet days in a conditional process.

Calibrate

Click on button to initiate model calibration algorithm. Hit Esc to abort the calibration process.


Weather Generator

The Weather Generator produces ensembles of synthetic daily weather series given observed (or re-analysis) atmospheric predictor variables. The procedure enables the verification of calibrated models (ideally using independent data) as well as the synthesis of artificial time series representing current climate conditions.

The User selects a calibrated model and SDSM automatically links all necessary predictor files to regression model weights. The User must also specify the period of record to be synthesized as well as the desired number of ensemble members. Synthetic time series are written to User defined output files for later statistical analysis and/or impacts modelling.

Select Parameter File

The first step in the synthesis process is the selection of the file containing model parameters and meta-data. Click on the Select Parameter File button, in the top left-hand corner. An Open file window appears; browse through until the correct directory and file are reached, then click on the appropriate file. The name of the file will then appear beneath the button. Note: parameter file names must have the extension *.PAR

Select Predictor Directory

Choose the appropriate directory where the predictors are located.

Ensemble Size

Decide how many ensembles of synthetic data are needed, up to a maximum of 100, and enter the appropriate value in the Ensemble Size box on the left-hand side of the window (the default is 20).

View Details

Lists the data files (*.DAT) containing the predictand (first line) and predictors (second line onwards) used to calibrate the statistical downscaling model. The number of predictor variables, Record Start date and Record Length (days) used for calibration are also displayed.

Synthesis Start

Enter the start date for simulation (this must be later than, or equal to, the predictor start date).

Synthesis Length

Enter the number of days for simulation (this must be less than, or equal to, the number of days in the predictor file(s)).

The default values for Synthesis Start and Sythesis Length are used to simulate the period of record used for model calibration. If, however, model verification is to be undertaken using a set of independent data withheld from the calibration process, then the two values should be amended accordingly. If simulation of observed data based on the complete predictor record is needed, then the Record Start and Record Length values should be used.

Select Output File

To save synthetic data to a results file, it is necessary to select an appropriate directory and file name. Click on the Select Output File button in the top right-hand corner. An Open file window appears; browse through until the correct directory is reached, then enter a suitable file name. The name of the file will then appear beneath the button.

Synthesize

Click on button to initiate the downscaling model simulation. Hit Esc to abort the process.


Scenario Generator

To access the new functions click on the Scenario Generator tab at the top of the master menu.

Select Input File: This is your text file containing the input daily weather series. This can be up to 200 years long and have up to 100 columns (i.e., ensemble members). The series may be observed data or direct output from the Weather Generator.

File Start Date: This is the date of the first data value in the Input File (entered by the User in dd/mm/yyyy format).  This information is needed so that SDSM can adjust settings and weights attached to individual months (see below). The default date is taken from the Standard Start Date in the master Settings.

Ensemble Size: This is the number of ensemble members (columns) in the Input File.  If the User enters, for example, 20 and there are 100 members, only the first 20 columns will be treated.  If the User enters, for example, 50 and there are only 20 members within the Input File, an error message will be displayed.

Output File: This is the User specified output file name and pathway for the generated daily weather series.

Conditional Process: The box should be checked if the process is conditional (as in the case of precipitation). This enables simultaneous manipulation of the unconditional process (for rainfall - Occurrence of wet-days above the specified Event Threshold below). When left unchecked, only the Mean, Variance and Trend Treatments (see below) are activated.

Event Threshold: This is the threshold that determines occurrence of the conditional process (see above).  For example, the threshold might be used to discriminate between wet- and dry-days using zero and non-zero-values.   In this case, any values greater than the threshold (zero) would be treated as a wet day. Larger thresholds might be used where measurement precision is lower, or if the modelling is focusing on extreme values.

Treatments

The following Treatments adjust daily input values by User-defined factors and functions, including separate handling of Unconditional and Conditional Processes. Treatments may be applied in isolation or combination to create more complex transformations of the input series.

Occurrence

Frequency change: This is the percentage by which the occurrence of the conditional process (e.g., rain days) should be changed. For example, 5 means increase the incidence of the process by 5% more days; -10 means reduce the incidence by 10%.  When Stochastic is selected (see below) the days are randomly added or removed from each month relative to the initial frequency profile.

Stochastic: Adds or removes rain days randomly based on existing likelihood of events in each month.  For example, if a month is particularly wet, it has a greater chance of a rain day being added.  Conversely, if a month is relatively dry it has a greater chance of a rain day being removed (if the User inputs a negative value to the Frequency change box).

Forced: This overrides the Frequency change value (see above) by forcing the creation or removal of rain days according to the percentages entered for each month in Advanced Settings (Wet Day Percentage Profile).  For example, if the user enters 50 (percent) for January in the Advanced Settings and the observed record has only 30% wet days in January,  SDSM generates wet days (randomly) for January until 50% of days are wet.  Conversely, if January has 60% wet days, SDSM removes wet days (randomly) until 50% of days in January remain wet. SDSM randomly selects wet day amounts from the month when generating new wet days in that month.  If there are no wet-days to sample in a given month, SDSM searches for wet-day amounts from neighbouring months.

Preserve Total: When checked, SDSM preserves the original annual mean rainfall total by proportionately scaling all wet-day amounts.  Note that if the User reduces the frequency of wet days, the remaining wet day amounts are automatically increased to preserve the annual total, and so too is the standard deviation of wet day amounts.  However, if the User also selects a Mean change (see below) this becomes the new annual total that is preserved.

Mean

Factor: This is the percentage by which data are changed.  For example, 10 increases all values by 10%; conversely -40 decreases values by 40%. In both cases, the factor is applied only to values larger than the Event Threshold when a Conditional Process is selected.

Addition: This is the absolute amount by which data are changed.  For example, 10 adds 10 to each day; -5 removes 5 from each day. In both cases, the factor is applied only to values larger than the Event Threshold when a Conditional Process is selected.

Variance

Factor: This is the percentage by which the variance is changed. For example, 50 increases the variance by 50%.  The algorithm subtracts the mean of the series from each value, then multiplies by the factor (e.g., 1.50 in this example), and then adds the mean.  The factor is applied only to those values greater than the Event Threshold when a Conditional Process is selected. All Conditional variables are transformed to approximate a normal distribution using the Box-Cox method (Sakia, 1992; Hinkley, 1977).

Note that in order to change the variance but preserve the coefficient of variation (mean divided by standard deviation) the User needs only to apply the factor to the Mean (see above); the Variance will change by the same amount.

Trend

Linear: The entered value is added (or subtracted) cumulatively each year.  For example, 10 adds values from 0 to 10 in the first year, 10 to 20 in the second year, 20 to 30 the following year, etc.  In this case, each day in the calendar year is adjusted 10/365.25 x the day number (1 to 365 or 366). For a Conditional Process only values exceeding the Event Threshold are adjusted.  For example, if the User enters 5, wet-day amounts in the first year are increased from 0 to 5% linearly (for days 1 to 365); amounts in the second year are increased by 5% to 10% linearly; and so forth.  This means that in the first year, the first day will be slightly changed; a value in the middle of the year will be increased by around 2.5%; and a value at the end of the year by 5%.

Exponential: The entered value is added (or subtracted) following an exponential function across the entire span of the data.  For example, entering +5 will add between 0 (for the first value) to +5 (for the final value) and points in between exponentially.  A value of zero cannot be used. For a Conditional Process the treatment is multiplicative rather than additive. For example, +10 causes wet day amounts to be multiplied by 1 to 1.10 from start to end with values in between changing by 1 to 1.10 in an exponential manner.

Logistic: The entered value is added (or subtracted) following a logistic (S-curve) function across the entire span of the data. The entered value is applied additively (unless a Conditional Process is chosen).  For example, when 1 is entered the addition ranges between 0 at the start to +1 at the end following the S-curve. Negative values can also be used to deplete the series following an S-curve. For a Conditional Process the treatment is applied multiplicatively.  For example, if 5 is entered, values exceeding the Event Threshold are scaled logistically between 1 to 1.05 across the length of the series.

Generate

Click on button to apply the treatment. Hit Esc to abort the process.


Frequency Analysis

The Frequency Analysis optional allows the User to plot various distribution diagnostics for both modelled (ensembles) and observed data.

File Selection

The first stage in the process is to select an observed data file and/or modelled data file to analyse. By clicking on the appropriate selection button, an input dialogue window appears in which the desired files can be selected.

Analysis Period

The second stage of the process is to enter the analysis period by entering the appropriate start and end dates in the Analysis Period box.  Note that observed data are assumed to follow natural calendar years (366 days every fourth year), while observed data take their year length from the standard SDSM Settings page.

Data Period

Select the appropriate time period for the analysis: all the data, individual months, or seasons.

Ensembles

If modelled (ensemble) data are being analysed, the User can select which part of the ensemble to include in the analysis by selecting either All Ensembles, Ensemble Mean, Ensemble Member, or All + Ensemble Member, in the Ensemble box.

Threshold

If the Apply Threshold? Box is ticked, only data which are above the global threshold (see Settings) are included in the analysis.

PDF Categories

If the User wishes to plot a PDF chart as part of the analysis, the number of categories for the PDF plot can be entered in the PDF Categories box:  the default is 20.

Q-Q Plot

A Quantile-Quantile plot that is used to compare a modelled data set with an observed data file.  The procedure works by sorting each of the data files into order and calculating the percentiles (1 to 99).  These are then plotted against one another on a scatter chart with observed data on the y-axis and modelled data on the x-axis.  Note, it is assumed that observed data are always based on calendar years (i.e. 365 days normally, 366 days in a leap year), while the length of the modelled data year is set using the Settings screen.

PDF Plot

The PDF plot provides a Probability Density Function plot of the selected data files.  The data are first sorted into order, then a number of categories are defined (the number of which is set by the User: the default being 20) ranging from the smallest to the largest data point in the file.  The data are then sorted into these categories and a count is made of the number of data points in each category.  The resultant density is plotted on a line chart.

Line Plot

This plots a simple line chart of the selected data.  Note that only a maximum of ten year’s of data can be plotted using this option.

Frequency Analysis

Four statistical distributions can be fitted to the selected data by clicking on the appropriate selection in the Frequency Analysis box.   These distributions can be fitted to the entire data range or to isolated time periods such as specific seasons or months according to the selection in the Data Period box.

The four distributions are: GEVs, stretched exponential, Empirical and Gumbel.  Results can be viewed in either tabular form by selecting FA Tabular or as line charts by selecting FA Graphical from the menu buttons at the top of the screen.  Results of modelled FA Tabular and FA Graphical are automatically saved to the main drive of the default directory for further analysis (for example, C: drive).  This can cause an error if the default drive cannot be written to (on a shared machine, for example, where write access can be limited).

Confidence (%)

When plotting extreme value distributions for modelled data with a number of ensembles, it is possible to plot confidence limits (lower and upper percentiles) around the fitted line.  For example, entering 5% in the Confidence (%) box provides a 95% confidence boundary on the fit by plotting the upper 97.5 percentile and lower 2.5 percentile around the line.  Enter the required confidence percentage in the box.  These percentiles are also calculated if FA Tabular is chosen.

Empirical

This option fits a simple empirical distribution to the data by sorting the annual maximums into ascending order and plotting these according to the return period.

GEV

This fits a Generalised Extreme Value distribution to the data of the form:

Gumbel

Fits a Gumbel distribution to the data using the annual maximum series.

Stretched Exponential

Fits the data to a Stretched Exponential distribution of the form:

It is used to calculated the probability that an event is greater than a threshold, r.  R0 is the mean of all events while c is determined from the data fitting.  The data are truncated to the User entered threshold value.

Save Results To

The results of the Frequency Analysis can be saved to a *.TXT for further analysis or presentation by clicking the Save Results To button at the bottom of the page and completing the Save Dialogue box that opens.  This works with FA Tabular only.


Summary Statistics

Provides basic descriptive statistics for either SDSM derived scenarios or observed data. The Analyse Data screen can take as input downscaled climate data produced by either the Weather Generator, Scenario Generator options or observed data. In each case, the User must specify the required sub-period, and output file name (i.e. where the results will be saved to). For model output, the ensemble member or mean, must also be specified. In return, SDSM displays a suite of diagnostics (selected by the User from the Statistics page). The default statistics are monthly/seasonal/annual means, maxima, minima, sums and variances.  Note, sum is averaged by the number of years in the data set providing the monthly/seasonal/annual mean sum.

You can select the statistics you wish to generate by clicking on the Statistics button at top of the screen. Note - in order to calculate monthly, seasonal and annual statistics, the system begins by allocating the data to a number of ‘bins’ for each time period.  These bins are then analysed to derive the selected statistics.

Data Source

Select whether modelled data or observed data are to be analysed - the default is modelled data.

Select Input File

The next step in the analysis process is the selection of an appropriate input file to analyse.  This can be either an appropriate downscaled data  (*.OUT) file or a data file containing observed data (for example, this file could be a *.TXT file). Click on the Select File button, on the left-hand side. An Open file window appears; browse through until the correct directory and file are reached, then click on the appropriate file name. The name of the file will appear beneath the button.

Select Output File

Select an appropriate file to store the results of the analyses to.

Analysis start date

Enter the start date for analysis (this must be later than, or equal to, the simulation start date when working with modelled data). The default is for the full simulation period.

Analysis end date

Enter the end date for analysis (this must be less than, or equal to, the end date of the modelled data if appropriate). The default is the full simulation period.

Modelled Scenario

When analyzing modelled data there are some additional selections that can be made:

View details

Click the View Details button, and details of the simulation experiment are displayed (the number of predictors, the season and year length codes, the scenario start date, number of days in the simulation, and the ensemble size).

Ensemble Size

Check the Use Ensemble Mean? box if average statistics are required from all ensemble members, or enter an individual ensemble member number (integers 1 to 100), up to the Ensemble Size specified in the simulation, in the Ensemble Member box. The default computes the ensemble mean of each statistic.

Analyse

Click on the Analyse button to initiate the statistical analysis of the data. Hit Esc to abort the process.

Delta Statistics

Click on the Delta Stats button to calculate Delta Statistics. Delta statistics take the form:

if Percentage Difference is selected in Statistics, or:

if Absolute Difference is selected.

Vbase is the mean of all ensembles (or a specific ensemble if selected) for each statistic for the base period.  Likewise, V2020s is the mean of all ensembles (or a specific ensemble) for each statistic for  period A, and so on for V2050s and V2080s.


Settings (standard)

Before downscaling, the User should check the date ranges, type and integrity of all input data. To establish the working environment click on the spanner symbol at the top of the main menu (or at the top of any other page) to access the first Settings page.

Data

Specify the Standard Data Start and End Date for all input data. These dates will appear throughout the operation of SDSM, but may be updated from any page.

Miscellaneous

The default setting of Allow Negative Values enables simulation of negative values by the downscaling model (e.g., for minimum temperature); deselection truncates values at zero (e.g., for sunshine hours).

For some variables it is necessary to specify an Event Threshold. For example, when calibrating daily precipitation models, the parameter might be set to 0.3 mm/day to treat trace rain days as dry days. Similarly, the threshold for sunny versus cloudy days might be set at 1.0 hours/day to discriminate between overcast and sunny conditions.

The Missing Data Identifier is a universal code assigned to missing data in all input series. Whenever SDSM encounters this code the value will be skipped (e.g., during model calibration, or calculation of summary statistics).

Random Number Seed should be set if you wish the random generated sequences (in Scenario Generator and Weather Generator) to be different each time.  If you wish to replicate experiments, SDSM can regenerate the same sequence of randomly generated scenarios if you deselect this check box.

Default File Directory allows the User to select a default file where SDSM will default to on each of the main screens.  For example, the User may wish to set the default directory to one in which their GCM data are stored.  This will save time looking through directories when selecting files in all the SDSM file input windows.

You can also select more Advanced Settings from here.


Advanced Settings

The Advanced Settings page allows the User to change and save further downscaling model preferences.

Model Transformation

Specifies the transformation applied to the predictand in Conditional models. The default (None) is used whenever the predictand is normally distributed (this is often the case for daily temperature). Two of the alternatives (Fourth root and Natural log) are used whenever data are highly skewed (as in the case of daily precipitation). The Inverse Normal transformation converts any data distribution into a normal distribution and then applies a resampling procedure to the data. This means that the maximum simulated event can never exceed the observed maximum in the calibration data. The Box Cox transformation calculates the optimal lamda (ë) value for the data before transforming the data based on this value.  The algorithm uses a trial-and-error approach to ‘home in’ on the optimal value – the search space being [-2, 2].  The optimal value is determined by the minimum of Hinkley’s (1977) equation:

The Box Cox transformation applied is defined as:

The transformation type is recorded in *.PAR and *.SIM files to ensure that data are consistently handled during subsequent scenario and data analysis operations.

Variance Inflation

Controls the magnitude of variance inflation in downscaled daily weather variables. This parameter changes the variance by adding/reducing the amount of “white noise” applied to regression model estimates of the local process. The default value produces approximately normal variance inflation (prior to any transformation). Variance inflation is de-activated by setting the parameter to zero. Note: for Fourth root and Natural log Model Transformations (see above), this parameter also affects changes in the mean estimate.

Bias Correction

Compensates for any tendency to over- or under-estimate the mean of conditional processes by the downscaling model (e.g., mean daily rainfall totals). The default value is 1.0, indicating no bias correction.

Optimisation Algorithm

SDSM 4.2 provides two means of optimising the model: Dual Simplex (as in earlier versions of SDSM) and Ordinary Least Squares.  Although both approaches give comparable results, Ordinary Least Squares is much faster.

 Stepwise regression works by progressively adding all parameters into the model and selecting the model which models the predictand most strongly according to one of two criteria: either AIC or BIC.

Conditional Selection

Adjusts the way in which the conditional process models a conditional event.  The default (stochastic) setting ensures that events (eg wet days) are only modelled when a generated pseudo random number  is less than the modelled threshold.  Fixed threshold (which can be set by the user) ensures an event is modelled when the conditional process models a value greater than this threshold (ie the random nature of the stochastic process is removed).

Wet Day Percentage Profile

Sets the percentages for each month the User wants to force the conditional process to during Scenario Generator.  For example, if the User enters 50 in January it will force January to have 50% wet days.  If January currently has, say 30% wet days, it will add wet days randomly; if January currently has 60% wet days SDSM will delete wet days randomly until only 50% of days remain wet.

Settings File

You can save your settings (Advanced and Standard) to different files and load these in from the Settings and Advanced Settings windows.  Note that SDSM will always try to load from C:/SDSM.INI when it is booted up:  if it cannot load data from this file it uses the default SDSM settings.

Save

A new or updated settings file is created whenever the Save button is clicked at the top of the page.

Load

Loads in previously saved settings.


Statistics Selection

By checking appropriate boxes, the User selects up to eight statistics for analysis (see below). The defaults are the mean, maximum, minimum, sum, and variance. Once all the necessary selections have been completed, click on the Back button to return to the Analyse Data screen.

Generic Tests

Lists statistics that may be applied to any variable (mean, maximum, minimum, sum, variance, median, peaks over/below threshold, percentile, inter quartile range, autocorrelation, skewness, Maximum N day Total). If selected, enter value(s) to count the frequency of events exceeding/ falling below thresholds. To extract a particular percentile, enter the required value; the default is the 95th percentile (i.e., the event magnitude that is exceeded 5% of the time).  Maximum N day total calculates the maximum sum of events across N days throughout the time period.  The default is 5 days.  Beware that large events falling at the end of a month or season may not be identified as SDSM does not calculate N day totals across period boundaries.

Precipitation Only

Lists statistics that are only applicable to daily precipitation series (percentage of days that are wet, mean dry /wet spell length, maximum dry /wet spell length, standard deviation of dry /wet spell length, Peaks over threshold based on a percentile threshold [default 90%], peaks over threshold as percentage of total rainfall).

Note, the definition of a wet day can be adjusted using the Event Threshold under Settings. The default is zero (i.e., wet days are defined as all days with non zero precipitation totals).

Note, if any precipitation variables are selected this will affect how certain generic tests are performed:  i.e. the system assumes the data represent precipitation data and adjusts its analyses accordingly.  For example, the generic mean will be calculated as the mean of only those events that are above the threshold if a precipitation statistic is selected.  Only Autocorrelation and Maximum N day total are unaffected by selecting precipitation only statistics.  All other generic statistics will be calculated on events over the threshold only.

Delta Statistics

Enter appropriate periods so that delta statistics can be calculated.  Delta statistics take the form:

if Percentage Difference is selected, or:

if Absolute Difference is selected.

Vbase is the mean of all ensembles (or a specific ensemble if selected) for each statistic for the base period.  Likewise, V2020s is the mean of all ensembles (or a specific ensemble) for each statistic for  period A, and so on for V2050s and V2080s.


Chart

Draws a chart to compare data. The Y axis is set to twice the maximum data value to begin with.  You can adjust this value using settings..

Double click on bars or lines to adjust their colour.

Double click on the title and Yaxis title to change the text font.

Select the axis labels or chart title (by clicking on them) and hit delete or backspace to remove.

To incorporate the Chart  in a Word document, first use the Copy button at the top of the window, then in Word use Paste Special (Picture).

Make adjustments to the chart in Chart Settings.


Time Series Chart

Draws a chart to view individual data sets or to compare data selected from the Time Series Analysis screen. The Y axis is set to twice the maximum data value to begin with.  You can adjust this value by clicking on Settings.

Double click on the lines to adjust their colour.  You can adjust the width of the lines in Settings.

Double click on the title and axis labels to change the text font.

Select the axis labels, chart title or legend (by clicking on them) and hit delete or backspace to remove them

To incorporate the Chart in a Word document, first use the Copy button at the top of the window, then, in Word, use Paste Special (Picture).

Make adjustments to the chart in Settings.


Calibration Results

This screen provides a summary of the calibration process.

You can print out the results using the icon.


Predictor Variables

Regression based downscaling methods rely on empirical relationships between local scale predictands and regional scale predictor variable(s). Individual downscaling schemes differ according to the choice of mathematical transfer function, predictor variables or statistical fitting procedure. To date, linear and non linear regression, artificial neural networks, canonical correlation and principal components analyses have all been used to derive predictor predictand relationships.

The main strength of regression downscaling is the relative ease of application, coupled with their use of observable trans scale relationships. Hybrid statistical models that incorporate stochastic (random) processes or time varying model parameters, also facilitate the generation of regional climate ensembles.

The main weakness of regression based methods is that the models often explain only a fraction of the observed climate variability (especially in precipitation series). Regression methods also assume validity of the model parameters under future climate conditions, and regression based downscaling is known to be sensitive to the choice of predictor variables and statistical transfer function. Furthermore, downscaling future extreme events using regression methods can be problematic since these phenomena, by definition, lie at the limits, or beyond the range, of the calibration data set.

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Transform Data

In many instances it may be appropriate to transform predictors and/or predictands prior to model calibration. The Transform facility takes specified data files and applies selected transformations (e.g., logarithm, power, inverse, lag, binomial, etc).  It can also extract an individual ensemble member from a multi column file (no transformation is applied to the extracted ensemble).

Select Input File

Click the Select File button. An Open file window will appear:  browse through until you have located the directory and file to be checked. Click on the required data file.

Columns in Input File

If the data file that is being transformed contains more than one column (for example, a *.OUT ensemble file generated using Weather Generator or Scenario Generator) the value must be entered here.

Create SIM File

Click this option if a SIM file should be created for the transformed data file.

Extract Ensemble Member

If you wish to extract a single ensemble member from a multi column data file check the Extract box here.  Enter the number of the ensemble member you wish to extract and this will be extracted to the selected Save File.  A single column file will be created containing the required ensemble member.  Note, no transformation is applied to this extracted member.

Transformation

Select the transformation by checking the appropriate button. Available transformations include: natural logarithms and log10, squares, cubes, fourth powers, inversion, lag interval and binomial, together with the inverse transformations of many of the above. These transformations should be applied to standard predictor variables prior to Model Calibration, to produce non linear regression models (e.g., use power transformations for polynomial models).

Backward change

Computes the difference between the current value (event t), and the previous value (t minus 1) for the entire time series. This transformation might be applied whenever changes in the predictor may be a significant explanatory factor.

Lag n

Shifts data series forwards or backwards by n time steps to produce lagged predictor variables (where n is an integer, specified by the User).

If Wrap is selected the data are wrapped around, otherwise when the data are lagged the missing values are replaced with the current Missing Data Identifier to keep the file the same length.

If lag value (n) is negative, the new file has its last n values or n Missing Data Identifier values inserted at the start (again the file maintains its original length): depending on whether wrap is selected or not. Negative n should be used if preceding conditions influence current conditions.

If lag value (n) is positive, Transform appends either the first n data items to the end of the file or the current missing code (thus keeping the file the same length): depending on whether wrap is selected or not.

Lag of zero is a straight copy.

Binomial

Creates a file containing ones (1) for values greater than entered threshold, and zeros for values less than or equal to the entered threshold value.  Note:  the default threshold is the Event Threshold value in Settings.

Note: blank carriage returns at the end of files are processed; therefore ensure that files do not end with a blank line.

Box Cox

Applies a Box Cox transformation of the form:

The data are ‘right shifted’ (by the minimum value) if they contain negative values to ensure all values are positive before applying the transformation.  The data are then ‘left shifted’ by the same amount following the transformation.  The algorithm uses a trial-and-error approach to ‘home in’ on the optimum lamda (ë) value for data in the range [-2, 2] based on finding a minimum from Hinkley’s (1977) equation:

The inverse Box Cox option inverts the data based on the entered values.  The lamda value must be between -2 and +2 and the Shift value represents the amount the data must be ‘right shifted’ by before applying the transformation.  The Shift value is usually zero unless the original data contained negative values that needed ‘right shifting’ before applying a Box Cox transformation as explained above.  The inverse Box Cox transformation is defined as:


Quality Control

Few meteorological stations have complete and/or fully accurate data sets. Handling of missing and imperfect data is, therefore, necessary for most practical situations.

Click on the Select File button. An Open file window will appear:  browse through until you have located the directory and file to be quality checked. Click on the required data file then on Open. To activate the quality control procedure, click on the Check File button at the top of the page.

Quality Control reports the data minimum, maximum, mean, the number of values in the file, the number of missing values (see Missing Data Identifier), the number of acceptable values, and the Missing Data Identifier value used to check the data. The default reference value may be changed in Settings.  It also presents a count of the number of values above the threshold (set in Settings as Event Threshold), the maximum difference between every sequential pair of data points (which could indicate an error in the data, for example if temperature swings from 50 one day to 5 the next) and the values of the two data points at which this maximum difference occurs.


Scatterplot

To create a Scatterplot to compare two sets of data, select the predictand file as normal and select ONLY one predictor file. The presentation quality of the Scatterplot may be customized, as required, by doubling clicking on any of the axis legends, titles or data points. Additional windows enable changes to be made to chart font, style, size, colour, etc.

To incorporate the Scatterplot in a Word document, first use the Copy button at the top of the window, then in Word use Paste Special (Picture).


Chart Settings

Used to change (or remove) tick marks, y axis labels, chart titles or y axis scales in either the Line or Bar chart.

Enter the required values/text then click on the Make Changes button (to change text) and/or click on the Apply Ticks button to change tick marks. Similarly, click on the Clear Ticks, Show Legend, or Clear Legend buttons as required. Then click on the Back button to return to the plot.

To change the colour scheme of the lines or bars, double click on the object required. A Color palette will appear. Select the desired colour, then on OK to return to the chart. Similarly, to change the Font, double click on title and/or y axis title. To change the position of text, single click then drag to the required location on the chart.


Time Series Chart Settings

Used to make various adjustments to the time series chart produced from the Time Series Plot screen,

Lines

For each line in the chart you can adjust its width and its legend text here (to adjust the colour of the line double click on the line in the chart itself).  Make the changes required and click the Make Changes button to apply the changes to the chart.  You can adjust the width of a line to any value between 1 and 100 inclusive (the default width is 30).

Legend

You can choose to show the legend on the chart or remove the legend by clicking the appropriate buttons at the bottom of this screen.

Y axis ticks

These refer to the tick lines drawn across the chart on the y axis.  The default is no tick lines (except for the line y=0).  If you wish to apply y axis tick lines, enter the number you require in the text box and click the Make Changes button.  Click the Clear Y Ticks button to remove these tick lines.

Y axis extent

You can adjust the extent of the Y axis by entering appropriate minimum and maximum values on this page and clicking the Make Changes button.

X Labels

For analysed data year markers are shown on the X axis.  For raw data it is possible to apply a number of data markers (a counter) on the x axis.

X axis labels can be added by entering an appropriate number in the X axis labels gap text box.  This specifies the gap you want between X axis labels/markers.  The default for analysed data is 1 (i.e. year markers appear every year).  The default for raw data is 0 (i.e. no markers appear).  If you enter 0, no markers are applied to the X axis.

Your changes can be made by clicking the Make Changes button.  X axis labels are removed by clicking the Clear X Labels button.

Note: when plotting SPI data series the X axis labels are determined by the total number of months available.  In this case it may better to remove the labels entirely to avoid overcrowding labels on the X axis.

Labels

You can adjust the text appearing on the X  and Y axis and also the chart title by typing in the appropriate text on this screen and clicking the Make Changes button.


Scatter Plot Settings

Adjust Chart title, X axis label and Y axis label as appropriate.

Click on Make Changes for modifications to take effect.


Compare Results

The Compare Results page enables the User to plot monthly statistics produced by the Summary Statistics screen. Having specified the necessary input file, either bar or line charts may be chosen for display purposes. The graphing option allows simultaneous comparison of two data sets and hence rapid assessment of downscaled versus observed, or current versus future climate scenarios.

Input File n

To choose a results (*.TXT) file, click on Select First File button. An Open file window appears; browse through until the correct directory and file are reached, then click on the appropriate file name. The name of the file will appear beneath the button, along with a list of available statistics. Repeat the process by clicking on the Select Second File button.

Select Statistic

Click on the required statistic listed under Select Statistic. The live statistic will be highlighted in blue. If necessary, select the same statistic from the second list.

Bar

Having specified the Input File(s) and required statistic click the Bar button to produce a bar chart.

Line

Having specified the Input File(s) and required statistic click the Line button to produce a line chart.


Time Series Analysis

The Time Series Analysis page allows the user to produce a time series plot of chosen data file(s): up to a maximum of five files can be plotted simultaneously on the same chart.

Time Period

Select from Raw Data, Month, Season, Annual or Water Year.  Raw Data simply plots the data from the chosen files as a time series plot for the selected fit period (set by the User as Start and End).   No statistics are derived for Raw Data.

When selecting a Month, Season, Annual or Water Year Time Period, SDSM calculates the chosen statistics (in Select Statistic) for this time period and plots them on a line graph.  For example, if you choose a Time Period of January, and select Sum, SDSM will plot an annual series of January Sums for the selected fit period: i.e. the sum for January 1961, sum for January 1962, sum for January 1963 etc. as a line chart.

Note, Water Year runs from October to September and is referred to by the year in which it starts.

File Selection

Using the Drive, Directory and File Selection boxes, select up to five files to plot.  Note, two File Selection windows are provided to allow you to select files from different directories.  You can only select up to a maximum of five files from the two File Selection windows in total.

Data

Select the period that you wish to analyse here. Note, if you attempt to plot a period longer than the available data set, an error message will appear.

Save Results To

You can choose to save a summary of the calculated results to a data file.  This option will not work if you are plotting the Raw Data as no summary statistics are calculated for this option.   The default is a *.CSV file (text file that is comma separated) that can be opened in a spreadsheet for further analysis.  Click on the Clear button to deselect the selected file.  Note, if you repeat a plot with different selections but leave the selected file the same, the contents of the file will be over written with the new results.

Season is referred to by the year in which it falls. Winter (December, January, February) is referred to by the year in which the January, February fall.

Select Statistic

You can select the summary statistics to be plotted by clicking in the appropriate check button in this section (default is Sum).  SDSM will calculate the chosen statistic for the selected Time Period, repeated across the range of the fit period, and plot these calculated statistics on a time series chart.

Note, Winter/Summer ratio is calculated as the sum of the winter data (December, January, February), divided by the sum of the summer data (June, July, August).  It is referred to by the year in which the summer period falls.

Sum, Mean, Maximum: are standard measures for the selected time period.

Winter/Summer ratio: is calculated as the sum of the winter data (December, January, February), divided by the sum of the following summer data (June, July, August).  It is referenced to the year in which the summer period falls.

Maximum dry (wet) spell:  the maximum dry (wet) spell length in days for the given time period.

Dry (wet) day persistence: the total number of consecutive dry (wet) days for a period divided by the total number of dry (wet) days in that period.

Mean dry (wet) spell:  mean dry (wet) spell length for the period.

Median dry (wet) spell: median dry (wet) spell length for the period.

SD dry (wet) spell: standard deviation of dry (wet) spell length for the period.

Spell length correlation: calculated as mean wet day persistence - (1 - mean dry day persistence) for the period.

Partial Duration Series: is calculated as the sum of data values less than or equal to the chosen threshold for the selected Time Period.  The default is the threshold value set in the main Settings screen.  This value can be adjusted by entering the required threshold in the text box (this will not affect the threshold value set in the main Settings screen and applied elsewhere).

Percentile calculates the specified percentile for the chosen Time Period.  The default is 90% but this can be adjusted by entering the required value in the text box.

Standard Precipitation Index (SPI):  This is calculated for monthly time series only, so the Time Period selection is ignored when SPI is chosen.  It is derived by first calculating the monthly sums of the data, then calculating a moving average of these monthly sums (smoothing) across the time period entered by the User in the adjacent text box.  The default moving average period is 3 months.  The smoothed data are then normalised by subtracting the mean of all the data in the fit range and dividing by the standard deviation of the smoothed data for each month.

Peaks Over Threshold (POT).  This counts the number of events greater than the user specified threshold for the chosen time period.

Nth largest: determines the nth largest value when the data are sorted into descending order for a given time period.

Largest n day total: calculates the n day total for all possible windows in the time period and presents the largest value.

%Prec>annual %ile: Percentage of total precipitation above annual percentile.

% All precip from events>long term %ile:  Percentage of all precipitation from events that are greater than the long term percentile.

No. of events > long term %ile: Count of the total number of events in the time period that are greater than the long term percentile.

Plot

Click the Plot button to plot the selected statistics as a time series graph.


Partial Correlation

A recursive algorithm is implemented to calculate partial correlations from the correlation matrix, using the following equation:

where r12.34 is the partial correlation between variables 1 and 2, with the influence of variables 3 and 4 removed. This statistic helps to identify the amount of explanatory power that is unique to each predictor.


Predictand

The Predictand is defined as the target variable of interest (e.g., temperature, precipitation totals, etc.) at the regional scale, or less.

Statistical downscaling procedures commence with the assembly of coincident Predictor and predictand data sets. Although predictands are typically individual daily weather series, obtained from meteorological observations at single sites (e.g., daily precipitation, maximum and minimum temperature, hours of sunshine, wind speed, etc.), the methodology is applicable to other environmental predictands (e.g., air quality parameters, sea levels, snow cover, etc.).

SDSM employs TWO types of predictor predictand relationship:

In Unconditional models a direct link is assumed between the predictors and predictand (e.g., local wind speeds may be a function of regional airflow indices).

In Conditional models, there is an intermediate occurrence process between regional forcing and local weather (e.g., local precipitation amounts depend on wet /dry day occurrence, which in turn depend on regional scale predictors such as humidity and atmospheric pressure).


Ensemble

Individual ensemble members are considered equally plausible local climate scenarios realised by a common set of regional scale predictors.  The extent to which ensemble members differ depends on the relative significance of the deterministic and stochastic components of the regression models used for downscaling. For example, local temperatures are largely determined by regional forcing whereas precipitation series display more “noise” arising from local factors.

As a general rule, the standard error of the ensemble mean, changes in proportion to the inverse square root of the Ensemble Size.


Analyse in Screen Variables

The strongest correlation in each month is shown in red.

As can be seen, the strength of individual predictors can vary markedly on a month by month basis.

The User should, therefore, be judicious concerning the most appropriate combination(s) of predictor(s) for a given season and predictand. The choice of predictors ultimately depends on the season of interest and on the application of the scenario.

The local knowledge base should also be considered when determining sensible combinations of predictors.


Correlation in Screen Variables

The correlation matrix shows the strength of inter variable associations. Interpretation of the matrix is complicated by the fact that few variables are truly independent.

Partial correlations are also reported for the selected predictors and predictand. These statistics help to identify the amount of explanatory power that is unique to each predictor.

The p value shows the probability that a given correlation could have occurred by chance alone. For most practical applications a p value greater than 0.05 would indicate that the correlation is statistically insignificant.


Bibliography

This Bibliography provides references to papers containing full technical details of SDSM, along with example case studies. Additional background material is also recommended for other downscaling methods, as well as selected review papers in which various downscaling methods have been compared.

Abdel-Fattah, S.L. 2012. DownScaling the Great Lakes: Techniques for adaptive policy. Unpublished PhD Thesis, McMaster University, Ontario, Canada, 236pp.

Abdo, K.S., Fiseha, B.M., Rientjes, T.H.M., Gieske, A.S.M. and Haile, A.T. 2009. Assessment of climate change impacts on the hydrology of Gilgel Abay catchment in Lake Tana basin, Ethiopia. Hydrological Processes, 23, 3661-3669.

Abkar, A., Habibnajad, M., Solaimani, K. and Naghavi, H. 2014. Investigation efficiency SDSM miodel to simulate temperature indexes in arid and semi-arid regions. Iranian Journal of Irrigation and Water Engineering, 4, 1-17.

Abraham, L.Z. 2006. Climate change impact on Lake Ziway watershed water availability, Ethiopia. Unpublished MSc Thesis, University of Applied Sciences, Cologne, pp123.

Adem, A.A., Melesse, A.M., Tilahun, S.A., Setegn, S.G., Ayana, E.K., Wale, A. and Assefa, T.T. 2014. Climate change projections in the Upper Gilgel Abbay River catchment, Bule Nile Basin Ethiopia. In: Melesse, A.M.,

Abtew, W. and Setgn, S.G. (eds.) Nile River Basin, Chapter 19, 363-388. Springer, Switzerland.

Adimo, O.A., Bosco, N.J. and Leonard, W. 2011. Net primary productivity response to climate change in the Mount Kenya ecosystem. In: Filho, W.L. (ed.) Experiences of Climate Change Adaptation in Africa. Chapter 14, Springer.

Aguilar, M.Y., Pacheco, T.R., Tobar, J.M. and Quiñónez, J.C. 2009. Vulnerability and adaptation to climate change of rural inhabitants in the central coastal plain of El Salvador. Climate Research, 40, 187-198.

Aherne, J., Futter, M.N. and Dillon, P.J. 2008. The impacts of future climate change and sulphur emission reductions on acidification recovery at Plastic Lake, Ontario. Hydrology and Earth System Sciences, 12, 383-392.

Ahmadi, A., Moridi, A., Lafdani, E.K. and Kianpisheh, G. 2014. Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models - a case study. Journal of Earth System Science, 123, 1603-1618.

Appiah-Adjei, E.K. and Allen, D.M. 2009. Quantifying the impact of climate change on groundwater recharge to fractured-rock aquifers: A case study from Canada. Groundwater and climate in Africa Conference, IAHS Publication 334, 210-218.

Appiah-Adjei, E.K., Shu, L., Adjei, K.A. and Lu, C. 2013. Evaluation of climate change impact on sustainability of Tailan underground reservoir in China. Advanced Materials Research, 726-731, 3249-3255.

Artlert, K., Chaleeraktrakoon, C. and Nguyen, V.-T.-V. 2013. Modeling and analysis of rainfall processes in the context of climate change for Mekong, Chi and Mun River Basins (Thailand). Journal of Hydro-environment Research, 7, 2-17.

Aspen Global Change Institute (AGCI), 2006. Climate Change and Aspen: An Assessment of Impacts and Potential Responses. Appendix B, p107-111. Aspen Global Change Institute, Colorado, pp147.

Assefa, K., Woodbury, A. and Holländer, H. 2014. Impact of soil data parameterization on recharge for climate change studies. EGU General Assembly, Vienna, Austria, id 14988.

Ayalew, D., Tesfaye, K., Mamo, G., Yitaferu, B. and Bayu, W. 2012. Outlook of future climate in northwestern Ethiopia, 3, xx-xx

Babel, M.S. and Turyatunga, E. 2015. Evaluation of climate change impacts and adaptation measures for maize cultivation in the western Uganda agro-ecological zone. Theoretical and Applied Climatology, 119, 239-254.

Bekele, H.M. 2009. Evaluation of climate change impact on Upper Blue Nile Basin reservoir (case study on Gilgel Abay reservoir, Ethiopia). Unpublished MSc thesis, Arba Minch, Ethiopia.

Bettolli, M.L., Carmen, M.A.A., Brasesco, G.C., Rudorff, F., Ortiz, A.M., Arroyo, J. and Armoa, Y.J. 2010. Pastura natural de salto (Uruguay): Relacion con la variabilidad climitica y analisis de contextos futuros de cambio climatico. Revista Brasileira de Meteorologia, 25, 248-259.

Bishaw, Y. 2012. Evaluation of climate change impact on Omo Gibe basin (case study of Gilgel Gibe III reservoir). Unpublished MSc thesis, Addis Ababa University, Ethiopia.

Bootsma, A., Gameda, S. and McKenney, D.W. 2005. Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada. Canadian Journal of Soil Science, 85, 329-343.

Chaleeraktrakoon, C. and Khwanket, U. 2013. A Statistical Downscaling Model for Extreme Daily Rainfalls at a Single Site. World Environmental and Water Resources Congress ASCE 2013, pp1058-1067.

Chaleeraktrakoon, C. and Punlun, P. 2010. Statistical analysis and downscaling for the minimum, average, and maximum daily temperatures of the Chi and Mun River basins. Thammasat International Journal of Science and Technology, 15, 64-81.

Chaowiwat, W. and Likitdecharote, K. 2008. Effect of climate change on potential evapotranspiration case study: Lower Chaopraya Basin. Proceedings of the 1st Nakhon Pathom Rajabhat University (NPRU) Academic Conference, SCI-09, 75-83.

Cheema, S.B., Rasul, G., Ali, G. and Kazmi, D.H. 2011. A comparison of minimum temperature trends with model projections. Pakistan Journal of Meteorology, 8, 39-52.

Chen A. 2006. The threat of dengue fever in the Caribbean: Impacts and adaptation. Final Report submitted to Assessments of Impacts and Adaptations to Climate Change (AIACC), Project No. SIS 06. International START Secretariat, Washington DC.

Chen, A., Taylor, M., Centella, A. and Farrell, D. 2008. Climate trends and scenarios. Report of Working Group I, Climate Change and Biodiversity in Caribbean Island, CANARI Technical Report No.381, December 2008.

Chen, H., Guo, J., Guoi, S-L. and Xu, V.-Y. 2014. Comparison of different statistical downscaling methods and evaluation indicators in climate change impact on runoff. Journal of Hydraulic Engineering.

Chen, H., Guo, J., Guoi, S-L., Xu, V.-Y. and Singh, V.P. 2009. Analysis of climate change impact on precipitation in Dajiangkou reservoir basin using statistical downscaling method. Hydrological Modelling and Integrated Water Resources Management in Ungauged Mountainous Watersheds. IAHS, 335, 291-299.

Chen, H., Xu, C.-Y. and Guo, S. 2012. Comparison and evaluation of multiple GCMs, statistical downscaling and hydrological models in the study of climate change impacts on runoff. Journal of Hydrology, 434-435, 36-45.

Chen, J., Brissette, F.P. and Leconte, R. 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401, 190-202.

Chen, J., Brissette, F.P. and Leconte, R. 2012. Coupling statistical and dynamical methods for spatial downscaling of precipitation. Climatic Change, 114, 509-526.

Chen, K., Guo, S. and Xu, C. 2012. Comparison of different statistical downscaling methods and evaluation indicators in climate change impact on runoff. Journal of Hydraulic Engineering, 43 (8).

Chen, S.T., Yu, P.S. and Tang, Y.H. 2010. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology, 385, 13-22.

Choi, W., Moore, A., Koenig, K. and Rasmussen, P. 2008. Response of hydrological systems to statistically downscaled GCM output in the northern Manitoba (Canada) boreal forest region. Proceedings of the XIIIth IWRA World Water Congress, 1-4 September 2008, Montpellier, France.

Chu, J.T., Xia, J., Xu, C.Y. and Singh, V.P. 2010. Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe, China. Theoretical and Applied Climatology, 99, 149-161.

Chung, E.S., Park, K. and Lee, K.S. 2011. The relative impacts of climate change and urbanization on the hydrological response of a Korean urban watershed. Hydrological Processes, 25, 544-560.

Cohen, S. and Neale, T (eds). 2003. Expanding the dialogue on climate change and water management in the Okanagan Basin, British Columbia. Environment Canada.

Combalicer, E.A., Cruz, R.V.O., Lee, S. and Im, S. 2010. Assessing climate change impacts on water balance in the Mount Makiling forest, Philippines. Journal of Earth System Science, 119, 265-283.

Corzo, G., Jonoski, A., Yimer, G., Xuan, Y. and Solomatine, D. 2009. Downscaling Global Climate Models using modular models and fuzzy committees. Proceedings of the 7th International Symposium Ecohydraulics, Chile.

Coulibaly, P. 2004. Downscaling daily extreme temperatures with genetic programming. Geophysical Research Letters, 31, L16203.

Coulibaly, P. 2009. Multi-model approach to hydrologic impact of climate change. From headwaters to the ocean: Hydrological changes and watershed management. Proceedings of the International Conference on Hydrological Changes and Management from Headwaters to the Ocean, Kyoto, Japan, pp249-255.

Coulibaly, P. and Shi, X. 2005. Identification of the effect of climate change on future design standards of drainage infrastructure in Ontario. Ministry of Transportation of Ontario, Canada.

Coulibaly, P., Dibike, Y.B. and Anctil, F. 2005. Downscaling precipitation and temperature with temporal neural networks. Journal of Hydrometeorology, 6, 483-496.

Crawford, T. 2007. Future climate change: modelling the implications of shifts in rainfall characteristics for runoff in Northern Ireland. Unpublished Ph.D. thesis, Queen's University Belfast, Northern Ireland.

Crawford, T., Betts, N.L. and Favis-Mortlock, D.T. 2007. GCM grid box choice and predictor selection associated with statistical downscaling of daily precipitation over Northern Ireland. Climate Research, 34, 145-160.

Crossman, J., Futter, M.N., Oni, S.K., Whitehead, P.G., Jin, L., Butterfield, D., Baulch, H.M. and Dillon, P.J. 2013. Impacts of climate change on hydrology and water quality: Future proofing management strategies in the Lake Simcoe watershed, Canada. Journal of Great Lakes Research, 39, 19-32.

Cueto, R.O.G., Martinez, A.T. and Ostos, E.J. 2010. Heat waves and heat days in an arid city in the northwest of Mexico: current trends and in climate change scenarios. International Journal of Biometeorology, 54, 335-345.

Dharmarathna, W.R.S.S., Herath, S. and Weerakoon, S.B. 2014. Changing the planting date as a climate change adaptation strategy for rice production in Kurunegala district, Sri Lanka. Sustainability Science, 9, 103-111.

Diaz-Nieto, J. and Wilby, R.L. 2005. A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Climatic Change, 69, 245-268.

Dibike, Y.B. and Coulibaly, P. 2005. Hydrologic impact of climate change in the Saguenay watershed: comparison of downscaling methods and hydrologic models. Journal of Hydrology, 307, 145-163.

Dibike, Y.B. and Coulibaly, P. 2006. Temporal neural networks for downscaling climate variability and extremes. Neural Networks, 19, 135-144.

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