**3. Results**

### *3.1. Relative Importance of Factors*

The concluding step in creating layers for incorporation into correlational analysis is the compilation of "Relative Importance Value" of the RUNOFF layers. The negative (and/or positive) influences of the runoff of SEE factors are diluted by the amount of water flowing through the same sample site. As mentioned before, runoff units are all in pixels where each pixel is equivalent to 977.21 m2. The assumption is that one unit of precipitation falls on each pixel unit of the watershed, or in the case of categories, on each pixel unit of the category. After compilation of runoff, factor importance values are found by dividing the factor runoff by the rainfall runoff and multiplying by 100. The formal equation for this calculation can be given by:

$$I\_f = \frac{F\_f}{R\_f} \times 100\tag{1}$$

where

> *If* = Importance values of Factor f

*Ff* = Factor *f* runoff

*Rf* = Normalized Rain runoff

Figure 10 represents the runoff extraction protocol for different SEE factors based on point vector-, line vector- and raster-based GIS layers. Given that rainfall over the study area varies from 2751 mm per year to 4648 mm per year, the normalized rainfall surface as a percentage of maximum varies from 0.592 to 1.000, and the effect of transformation due to the rainfall pattern can have a significant effect on importance values of the runoff of anthropogenic and environmental variables. As a result of the rainfall variation in the Bumbu Watershed, the importance values fall in the range of 0 to 200. Figures 11 and 12 present the spatial distribution of the relative importance values of the factors involved.

**Figure 10.** Flow diagram to represent the procedure to extract runoff values from different GIS layers.

**Figure 11.** Distribution of (**a**) normalized rainfall pattern, (**b**) ROAD RUNOFF importance values (IV) scaled 1 to 200 overlain on roads/streets layer, (**c**) DENSE FOREST RUNOFF importance values (IV) scaled 1 to 200 overlain on dense forest polygons and (**d**) REGEN FOREST RUNOFF importance values (IV) scaled 1 to 200 overlain on regen forest polygons.

In practice, it is more practical to extract RUNOFF values from all layers in one step and to perform the "importance value" calculation in a spreadsheet. As mentioned earlier, a [SAMPLING SITE] layer was created by projecting the sampling site locations onto a raster with the same dimensions and location as the SRTM 1 arc second DEM supplied by USGS [21]. By overlaying the sampling site layer on a raster group consisting of [NORMALIZED RAINFALL], [ROAD RUNOFF], [DENSE FOREST], [REGEN FOREST], [GREEN SPACE], [SEMI URBAN], [HIGHLY URBAN] and [HABITATION/POPULATION], all the variables are captured in a spreadsheet in a single step. Tables 2 and 3 below report the results of this capture and importance value computation.

**Figure 12.** Distribution of RUNOFF importance values (IV) scaled 1 to 200 for (**a**) GREEN SPACE overlain on green space polygons, (**b**) SEMI-URBAN overlain on semi-urban polygons, (**c**) HIGHLY URBAN overlain on highly urban polygons and (**d**) POPULATION/HABITATION overlain on habitation/population layers.


**Table 2.** Raw runo ff values for di fferent factors at 22 WQ sampling sites in the Bumbu Watershed.

**Table 3.** Relative importance values (IV) for di fferent factor runo ff at 22 WQ sampling sites in the Bumbu Watershed.


Table 2 represents the raw runo ff at the 22 water sampling sites across the Bumbu Watershed. Herein, it becomes necessary that the relative impact of each factor is understood in the context of surface water's travel history and the varying level of influence each factor can have on runo ff as it moves downstream. In most cases, it is not feasible to directly measure the exact quantity and potential impact of the anthropogenic and environmental inputs at these sites where water quality is measured. It is important to note that the potential impacts of the di fferent factors are present in the form of several physical and chemical constituents carried by the Bumbu river. As precipitation moves across

the landscape and downstream to the sea, it accumulates and transports the potential impacts of these factors in the runo ff surface water and in the percolating ground water. This potential impact of various factors is diluted by the amount of water involved in their transport. The best that we can do is to infer their relative presence and concentration at the 22 spatially distributed water quality sampling stations and attempt to assess the correlation of their relative presence with accepted measures of water quality. In this regard, we utilized normalized rainfall runo ff values to calculate the impact of these di fferent factors on surface water which are enumerated in Table 3 in the form of relative importance values. The 22 water sampling sites are located across di fferent geographical landscapes in both upstream and downstream regions of the watershed. As mentioned before, these landscapes are classified based on land use/land cover into various categories characteristic of varying levels of vegetation and urbanization. The water sampling sites of the main channel of Bumbu river are represented by the UA series. It can be observed that the class "Dense Forest" has high importance values of 93.25 and 92.30 for the sampling points UA1 and UA2 which are located in the upstream portion of the basin. As one moves the downstream (UA3–UA8), the impact of Dense Forest class is found to be continuously decreasing, accounted by a slight increase in values of other classes, namely regenerating forest, habitation and green space. The impact of the vegetation classes (Dense Forest, Regen Forest and Green Space) is usually present in surface water in the form of minerals, metallic ions, salts and several other geochemical and natural organic matter inputs.

In our future studies, we intend to verify whether the decrease in the values of the vegetation classes correlates with a simultaneous decrease in the measurement of various geochemical inputs at the water sampling sites. Road Runo ff and Highly Urban runo ff values are found to be insignificant when compared with other classes for the UA series. The sampling points of left hand Bumbu feeder streams are represented by the UB series, whereas those of right hand Bumbu feeder streams are represented by the UC series. Road runo ff influence is negligible for all the points in the series except for UB4, UC5 and UC6 which lie in the close vicinity of residential/urban areas. The class "Green space" has a considerable influence on runo ff with values 23.52, 23.29, 24.82 and 38.78 for the sampling points UB4, UB5, UB7 and UB8, respectively. This class depicts cultivated garden plots of otherwise vacant land adjacent to urban and semi-urban areas. Levels of turbidity, nitrates, nitrites and Total Phosphorous (TP) can also be examined for a possible relationship with importance values of the above classes. From the table, it can be discerned that UB1, UB2, UB3 and UB6 have high values of 87.18, 71.69, 94.11 and 65.22 for Dense Forest Runo ff. The Regen Forest Runo ff class has moderate relative importance values of 20.79, 41.12, 32.89, 37.22 and 36.35 for UB2, UB4, UB5, UB7 and UB8, respectively, whereas UC2, UC3 and UC4 have high values of 82.91, 58.97 and 77.91, respectively. UB7, UB8, UC5 and UC6 lie in the urban region of the city of Lae in the downstream area of the Bumbu Watershed and have high measurements for highly urban and semi-urban runo ff values. The Bumbu river at these sites receives e ffluents from di fferent industries located nearby, human detritus from residential areas, pollutants, waste discharge and other forms of organic matter (Figure 2) [14]. Some other physicochemical characteristics of water which can be inspected for correlation with importance values at these points for UA, UB and UC series include Total Suspended Solids (TSS), coliforms, alkalinity, pH and temperature. Thus, it can be seen that the Bumbu river experiences a wide range of influences from di fferent landscape classes which can possibly influence its water quality as its surface water moves from the upstream regions to the downstream regions of the watershed. To accurately examine the relationship of the relative importance values with physicochemical characteristics, other social and economic parameters, a thorough correlation analysis is required, the prospects of which are further elaborated in the discussion section.
