Monthly Rainfall and its Inter-Annual Variability (December)

Monthly rainfall totals are necessary to many water resources as well as agricultural problems and decisions for which MAPs, or even wet/dry season precipitation totals be they high or low, are of relatively little consequence, because an intra-year distribution of rainfall is required (Schulze, 1997). Monthly rainfall values then serve as an important tool in describing such an intra-year distribution. It should, however, be borne in mind that the use of the calendar month is but a time step of convenience for describing temporal patterns of rainfall, in that it breaks up annual precipitation into components of time long enough to smooth out many of the irregularities of daily rainfalls (Schulze, 1997). Nevertheless, large differences in rainfall can exist from one month to the next. Some of these differences result from major rainfall generating mechanisms changing from one month to the next. How the maps of median monthly rainfall were derived since time series of monthly rainfall are more variable than those of annual rainfall, the raster surfaces of median monthly rainfall at 1 arc minute spatial resolution (i.e. at 1` x 1` latitude/longitude spacing; 1.7 x 1.7 km; with 429 700 raster points making up South Africa) were calculated by expressing the median rainfall value of a given month at each qualifying rainfall station as a ratio of the MAP surface which was generated by Lynch (2004) using Geographically Weighted Regression. These ratios were then interpolated by Inverse Distance Weighting (IDW) onto the rectangular raster of 1 arc minute. This interpolated raster was then multiplied by the raster of MAP values to give 1` x 1` values of that month's median rainfall. The procedure was then repeated for each of the 12 months of the year (Lynch, 2004).

月降水量总量对于诸多水资源研究、农业问题与决策而言至关重要，而年平均降水量（Mean Annual Precipitation, MAP）乃至干湿季降水总量（无论数值高低）在此类场景中相对意义有限——因为相关研究与决策需要明确降水的年内分布特征（Schulze, 1997）。月降水量数据便成为描述此类年内降水分布的重要工具。但需注意，以自然月作为时间步长仅为描述降水时间分布的便捷方式：该方式将年降水量划分为若干时长足够的时段，从而平滑掉多数日降水的不规则波动（Schulze, 1997）。尽管如此，不同月份间的降水量仍可能存在显著差异，其中部分差异源于主要降水形成机制随月份发生变化。鉴于月降水量时间序列较年降水量序列波动更强，研究人员通过以下流程生成月降水量中值栅格：针对每个符合要求的降水站点，将其某一月份的降水量中值与Lynch（2004）通过地理加权回归（Geographically Weighted Regression, GWR）生成的年平均降水量栅格表面的比值作为基准，随后通过反距离权重插值（Inverse Distance Weighting, IDW）将该比值插值至1弧分空间分辨率的矩形栅格中，该栅格的经纬度网格间距为1′×1′，实地分辨率约1.7 km×1.7 km，覆盖南非全境的栅格总数为429700个；再将插值得到的比值栅格与年平均降水量栅格相乘，最终得到该月份1弧分分辨率的月降水量中值。随后针对全年12个月份重复上述流程，即可生成完整的月降水量中值栅格数据集（Lynch, 2004）。