Untitled Item
Creators: name: Staub, Caroline
Publisher: name: figshare
Year: 2018
Subjects: subject: Atmospheric Sciences
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: Climate Science

description: <b>Data and methods for producing mean monthly rainfall totals and standard erros.</b> <b>C. G. Staub</b> <i> </i> <i>Reference: Staub, C.G., Stevens, F.R., Waylen, P.R. 2014. The geography of rainfall: Modelling the relationship between mean annual rainfall and landscape characteristics on Mauritius. Applied Geography, 45: 222– 234.</i> <b> </b> <b>Datasets</b> Mean annual precipitation (used as a dependent variable) is calculated from the complete monthly rainfall records of 85 stations across the island from 2000-2011. The data are derived from Monthly Meteorological Summaries (Mauritius Meteorological Services), which were made available by the Mauritius Sugar Industry Research Institute (MSIRI) archives department. Mean annual precipitation values are standardized using long-term means and variances. Station elevation, slope, aspect, distance from the nearest coast, latitude and longitude (independent variables) are derived using an Aster Digital Elevation Model (30 m cell size) which is distributed by the Land Processes Distributed Active Archive Center (LP DAAC), and Arc GIS Desktop 10 (ESRI, 2011). The topographic attributes of slope and aspect are processed using Arc GIS Spatial Analyst surface contour tools.We calculate the distance between individual stations (points) to the coast by creating a binary mask of the outline and using the Straight Line tool to create a surface of distance to coast before extracting values from the points of interest (Appendix A). <b> </b> <b>Procedure </b> <br>For details on the modeling of mean annual and mean monthly rainfall, which constitute the fist two steps in the development of the monthly totals and standard errors, please refer to the following paper:Staub, C.G., Stevens, F.R., Waylen, P.R. 2014. <br>The geography of rainfall: Modelling the relationship between mean annual rainfall and landscape characteristics on Mauritius. Applied Geography, 45: 222-234. We provide below an explanation of the final steps involved in generating the monthly totals and associated standard errors that are available on figshare.<br>To generate monthly rainfall totals, we first remove the variability characteristic to each month between 1997 and 2011 from the observed data by subtracting the predicted value at each of the 82 stations from the observed values for each individual month in the record. We then interpolate these values across the surface of the island using Ordinary Kriging (using Paul Hiemstra’s “automap” package in R (R Core Team, 2013)), thereby producing a gridded surface of residuals (kriged estimates) and associated variance for each month. We compute the square root of the variance on a cell by cell basis and produce a surface of standard errors. We then add the spatial mean surface to the corresponding monthly surface of residuals e.g. mean November rainfall + residuals for November 1997 = November 1997 totals. Estimate and variance values from each month are used to calculate 95% confidence interval surfaces for each month to which mean monthly rainfall surface was then added back to, as described above. The confidence intervals are calculated using the following equations: Upper bound confidence intervals CU = estimate + qnorm(0.975)*sqrt(variance) Lower bound confidence intervals LU = estimate - qnorm(0.975)*sqrt(variance) Finally, an external dataset comprising monthly totals from 30 stations are used for validation purposes. Root mean square error (RMSE), Mean Absolute Error (MAE) and Mean Error (ME) statistics are calculated to assess the goodness of fit between the simulated data and observed validation data. All statistical procedures are conducted within the R statistical computing environment (c).