Remote sensing and information value (IV) model for regional mapping of fluvial channels and topographic wetness in the Saudi Arabia

Figures & data

Figure 1. Reference map showing the major fluvial channels palaeolakes mapped from SIR-C (e.g., Edgell 2006, Dabbagh, Al-Hinai, and Khan 1997), and aerial photographs (e.g., Clarck 1989). Black line highlights water divide and green polygons highlight the circular farms densities in the region. The yellow line divides the region into renewable and non-renewable groundwater zones. Features in the figure reproduced with kind permission from Springer and Business Media. For full color versions of the figures in this paper, please see the online version.

Figure 2. Geological map of the study area showing lithological units and structural geology control groundwater accumulation and quality.

Figure 3. Rainfall map showing the spatial distribution of rainfall over the region. Red points highlight the International Wheat Improvement sites (IWIN).

Table 1. Major fossil water aquifers in Saudi Arabia.

Aquifer	Storage (MCM	Available (MCM)	Area (km^2)	Outcrop area (km^2)	Thickness (m)	Fall in water table (1981–2002)	Comments
Saq sandstone	258,400	103,360	300,000	51,245	400–1200	16–76	Largest aquifer. High potential for development
Wajid sandstone	237,500	95,000	170,000	31,534	200–900	–	Only limited geological understanding
Tabuk (Tawil)	109,800	43920	~142,000	86,540	500–950	34	Upper unit of three sandstones separated by aquitards/aquicludes
Minjur sandstones	171,300	111,340	~48,000	29,648	400	80–85	The indicated surface area includes the Dhruma aquifer
Wasia/Biyadh/Sakah sandstones	66000	33,300		29,797	600	–	Largest system in the Kingdom after the Saq
Umm Er Raduma limestone	6000	3000		58,331	300–700	52–97	Lack of understanding of many physical parameters

Source: Country Report.

Table 2. Fluvial channel and mega-basin weights and information values (IVs). These fluvial channel have positive association to the topographic wetness and are, therefore, enhancing the predictive power. Mega-basins that have negative or no association to the spatial distribution of palaeochannels are shown in brackets (*).

	Mega-basins	Area (km^2)	Area (%)	Stream area (km^2)	Stream area (%)	Distribution good (DG)	Non-stream area (km^2)	Non-stream area (%)	Distribution bad (DB)	WOE	C = DG − DB	IV = C*WOE	Predictive power
1	Ar Rub Al Kali	93,172	4.86	28,657	30.75	0.17	64,515	69.25	0.03	1.73	0.14	0.2422	Very strong
2	Ad Dawasir	539,293	28.13	20,267	3.75	0.12	519,026	96.25	0.29	−1.23	−0.17	0.2091	Strong
3	Sahba	233,584	12.18	37,263	15.95	0.22	196,321	84.05	0.11	0.69	0.11	0.0759	Medium
4	Asulb	211,796	11.04	32,770	15.47	0.19	179,026	84.53	0.10	0.64	0.09	0.0576	Medium
5	Arrimah	226,081	11.79	12,041	5.32	0.07	214,040	94.68	0.12	−0.53	−0.05	0.0265	Medium
6	Nafud	153,934	8.03	9421	6.12	0.05	144,513	93.88	0.08	−0.47	−0.03	0.0141	Weak
7	Tabuk	181,393	9.46	8389	4.62	0.05	171,589	95.36	0.09	−0.58	−0.04	0.0232	Medium
8	Arran	121,743	6.35	6272	5.15	0.03	115,471	94.85	0.06	−0.69	−0.03	0.0207	Medium
9	*Coastal basin	152,866	7.97	10,263	6.71	0.06	142,603	93.29	0.08	−0.28	−0.02	0.0056	Useless
	Total	1,913,862		165,343			1,747,104

Notes: distribution good = stream area/total stream area. Distribution bad = non stream area/total area.

Weight of evidence (WOE) = ln (distribution good/distribution bad). *Coastal basin.

Figure 4. Flow chart of the methodology used for mapping fluvial channels and indexing topographic wetness. W+ and W– highlight the areas of good and bad fluvial channels distribution, respectively.

Table 3. Predictive soil wetness values of each mega-basin in the region.

No.	Mega-basins	Catchments area (a)	Topographic slope angle (β) (°)	Topographic wetness TWI = ln (a/β)	Predictive power
1	Rub Al Kali	28,657	0.01	14	High
2	Ad Dawasir	20,267	0.11	16.7	Very high
3	Sahba	37,263	0.07	13.18	High
4	Asulb	32,770	0.16	12.22	Medium
5	Arrimah	12,041	0.07	12	Medium
6	Nafud	9421	0.08	11.67	Medium
7	Tabuk	8389	0.10	11.33	Low
8	Arran	6272	0.23	10.21	Low
9	Coastal basin	10,263	0.70	9.60	Very low
	Total	165,343

Figure 5. Fluvial channel and mega-basin maps derived from SRTM DEM using D8 algorithm (a), and ENE–WSW topographic profile across the Saudi Arabia, showing flow direction and flow type in the region.

Figure 6. Zooms of fluvial channels derived from DEM using a D8 algorithm (a and b), from the corresponding Landsat 8 (c), and SIR-C image (d), showing detail of fluvial channels (wadis) on SRTM DEM, traces on Landsat 8 image, and fluvial fans on SIR-C image. These show the capability of the SRTM and SIR-C SAR sensors to imagine details of fluvial channels than optical sensors.

Figure 7. Maps of fluvial channel distribution (a), fluvial channel density (b), IV (c), and topographic wetness index (TWI) (d), showing the influence of the spatial distribution of palaeochnnels and slope on the topographic wetness in the entire region sensors.

Figure 8. Graphs of topographic slope vis IV (a) and mega-basin area vis IV (b).

Dabbagh, A. E., K. G. Al-Hinai, and M. A. Khan. 1997. “Detection of Sand-Covered Geologic Features in the Arabian Peninsula Using SIR-C/X-SAR Data.” Remote Sensing of Environment 59 (2): 375–382. doi:10.1016/S0034-4257(96)00160-5.

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