Deltas on the move; Making deltas cope with the effects of climate c

KvR report 001/2006For the river we explored the discharge of water versus the transport of sediment. Indeltaic environments, the discharge of river water can also be important as a variablepotentially related to flooding. The seawater related variables in WDD (spring tideand wave power and height, that were elaborated before) could be combined with,e.g., height of the dikes from Diva (sections 2 and 3 in this Annex) for further analysisof probability of flooding by the sea. The outlier responsible for decreasing thecorrelation coefficients is Huang He (Yellow River), which seems to have anextremely high sediment discharge (980 x 109 kg/yr) for its water discharge.Figure 4:Correlations of river water discharge and sediment discharge.<strong>Deltas</strong> on the moveThis analysis was performed on a selection of the WDD dataset as described inTable 5.3 without sediment discharge and wave power, because of a relatively lownumber of available observations for these variables. For the WDD dataset:- The first axis comprises annual average discharge, maximum discharge,minimum discharge and delta area and shoreline. It explains 37 % of the totalvariation. This component describes river-form relationships.- The second component comprised subaerial/subaqueous and offshore slope, andexplains 18.5 % of the variation. These describe sediment availability and form.- The third component comprises spring tide, rms wave height and number of rivermouths, and explains 14.5 % of the variation. It seems to describe marine forcesversus a form relationship number of river mouths (Number of river mouths ishigher on a gently sloping delta).sed discharge (10 9 kg/yr)sed discharge (10 9 kg/yr)1000.00y = 0.444x 0.63800.00R 2 = 0.45600.00400.00200.000.000 10000 20000 30000annual ave discharge (m 3 /s)1000.00y = 46.88e 0.0002x800.00R 2 = 0.25600.00400.00200.000.000 3000 6000 9000 12000min discharge (m 3 /s)sed discharge (10 9 kg/yr)annual ave discharge (m 3 /s)1000.00800.00600.00400.00200.00y = 0.923x 0.508R 2 = 0.270.000 20000 40000 60000 80000 1000003000020000100000max discharge (m 3 /s)y = 0.37x + 878.88R 2 = 0.830 20000 40000 60000 80000 100000max discharge (m 3 /s)In total 70% of the variation in the (tested part of the) WDD dataset was describedwith these 3 axes. Using only one separate variable from each axis would alreadyallow characterizing the form of the deltas from causes related to river, sedimentavailability, and marine forces.2 DIVA indicator testingCombining of the 43 WDD deltas with the 115 rivers available in DIVA did not resultin a complete match. Several rivers end up in lakes (e.g. Amu Darya in the Aral Seaand the Salenga in Lake Baikal), some deltas are not covered in the DIVA river set(e.g. Baram, Colville, Grijalva, Klang, Ord). Many DIVA rivers obviously have noclear delta. This led to a coupled set of 34 rivers.Physical vulnerabilityTwo major compound indicators available in DIVA are the segment vulnerabilityscore (CSVS) and accommodation space (ASPACE). The deltas studied hereappear to vary only slightly in CSVS. This scaled, categorical indicator variesbetween 2.5 and 3.5 only (Figure 5). In contrast, accommodation space shows awider span. It must be noted that a high score in DIVA indicates high vulnerability,thus a high value for ASPACE suggests that there is little room for accommodation inthe adjacent hinterland. Apparently, the bulk (28 of 34) of the deltas is estimated tohave substantial accommodation space for inland migration of the present coastline.Finally, a principal component analysis was performed. Principal componentsanalysis (PCA) is a statistical technique that can be used to simplify a dataset (cfJongman et al., 1987). It is a linear transformation that chooses a new coordinatesystem for the data set such that the greatest variance by any projection of the dataset comes to lie on the first axis (also called the first principal component), thesecond greatest variance on the second axis, and so on. PCA can be used forreducing dimensionality in a dataset while retaining those characteristics of thedataset that contribute most to its variance, by keeping lower-order principalcomponents and ignoring higher-order ones. The idea is that such low-ordercomponents often contain the most important aspects of the data. However, this isnot necessarily the case, depending on the application.The two outliers are the Niger in Nigeria and the Mangoky in Madagascar. One of theconstituent variables of the CSVS is sediment supply (SEDSUP). This again is acategorical indicator with 5 classes and class 1 indicating high sediment supply thuslow vulnerability, whereas 5 indicates high vulnerability. All 34 deltas and 115 DIVArivers, however, have been classified as belonging to category 3 (moderate) or 4(high to moderate vulnerability), limiting the usefulness of this indicator. Waveclimate, maximum surge height, tidal range and coastal slope all span a substantialrange: clearly hydrodynamic conditions in adjacent coastal waters differ amongdeltas.8889