Nyasha Ashleigh Siziba; Pepukai Chifamba

doi:10.26599/JGSE.2023.9280026

doi: 10.26599/JGSE.2023.9280026

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出版历程
- 收稿日期: 2022-12-10
- 录用日期: 2023-10-24
- 网络出版日期: 2023-12-10
- 刊出日期: 2023-12-31

Using geospatial technologies to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane District, Zimbabwe

Nyasha Ashleigh Siziba^{1
, ,},
Pepukai Chifamba¹

1.
Department of Geospatial Science and Earth Observation, Zimbabwe National Geospatial and Space Agency (ZINGSA), 630 Churchill Ave, Mount Pleasant, Harare Zimbabwe

More Information

Corresponding author: siziba1420@gmail.com

摘要

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Abstract: The main objective of the study was to delineate Ground Water Potential Zones (GWPZ) in Mberengwa and Zvishavane districts, Zimbabwe, utilizing geospatial technologies and thematic mapping. Various factors, including geology, soil, rainfall, land use/land cover, drainage density, lineament density, slope, Terrain Ruggedness Index (TRI), and Terrain Wetness Index (TWI), were incorporated as thematic layers. The Multi Influencing Factor (MIF) and Analytical Hierarchical Process (AHP) techniques were employed to assign appropriate weights to these layers based on their relative significance, prioritizing GWPZ mapping. The integration of these weighted layers resulted in the generation of five GWPZ classes: Very high, high, moderate, low, and very low. The MIF method identified 3% of the area as having very high GWPZ, 19% as having high GWPZ, 40% as having moderate GWPZ, 24% as having low GWPZ, and 14% as having very low GWPZ. The AHP method yielded 2% for very high GWPZ, 14% for high GWPZ, 37% for moderate GWPZ, 37% for low GWPZ, and 10% for very low GWPZ. A strong correlation (ρ of 0.91) was observed between the MIF results and groundwater yield. The study successfully identified regions with abundant groundwater, providing valuable target areas for groundwater exploitation and high-volume water harvesting initiatives. Accurate identification of these crucial regions is essential for effective decision-making, planning, and management of groundwater resources to alleviate water shortages.
- Groundwater resources /
- Analytical Hierarchical Process /
- Multi Influence Factor /
- Lineaments density /
- Terrain Wetness Index /
- Ground Water Potential Zone

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Figure 1. Location of the study area

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Figure 2. Flowchat showing summary of methodology adapted for the research

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Figure 3. Visual agreement between existing and extracted drainage

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Figure 4. Multi Influencing Factor (MIF) framework

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Figure 5. Conditioning factors in groundwater potential mapping a) Rainfall, b) Soil, c) Slope d) Geology, e) Terrain Ruggedness Index (TRI), f) Land use/Land cover (LULC), g) Terrain Wetness Index (TWI), h) Lineament Density, i) Drainage Density

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Figure 6. Ground water potential zones derived from AHP method

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Figure 7. Ground water potential zones derived from MIF method

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Figure 8. Bar chart representing area covered by GWPZ

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Figure 9. Correlation between MIF and Borehole yield

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Figure 10. Scatter plot for MIF derived GWPZ

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Table 1. Data collected, source and use

Data	Source	Use
SRTM DEM	USGS	For slope, TWI, TRI, Drainage density maps
Landsat 8 path 170 and row 74	USGS	For LULC classification and lineament extraction
Soil	Local geodatabase	Soil classification
Rainfall	Metrological services department and CHIRPS rainfall data	Rainfall map
Geology	Geomaps	Rock type classification, lineaments

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Table 2. Determine the weight of conditioning factors using AHP method.

Theme	Normalized theme weight	Feature class	Class weight	Normalized theme weight (x_i)
Geology	0.165	Alluvium Andesitic and dactic metavolcanic Basaltic metavolcanic Dolerites and gabbros Felsites and porphyries Gneiss of various ages Metasediments Norite and gabro Older gneiss complex Serpentinites Ultramafic lavas Young intrusive granites	9 2 3 3 2 3 6 2 4 3 4 1	0.214 0.048 0.071 0.071 0.048 0.071 0.143 0.046 0.095 0.071 0.095 0.024
Rainfall	0.146	<450 450–550 550–650 650–750 >750	2 3 4 5 6	0.100 0.150 0.200 0.250 0.300
Lineament density /km/km²	0.128	0–0.3 0.3–0.6 0.6–0.9 0.9–1.2 >1.2	2 3 5 7 9	0.077 0.115 0.192 0.269 0.346
Drainage density /km/km²	0.092	0–0.3 0.3–0.6 0.6–0.9 0.9–1.2 >1.2	9 7 5 3 2	0.346 0.269 0.192 0.115 0.077
Slope/^o	0.110	0–5 5–10 10–15 15–20 >20	9 7 5 3 2	0.292 0.250 0.208 0.167 0.083
TWI	0.102	0–6 6–12 12–18 18–24 24–30	2 4 5 7 8	0.077 0.154 0.192 0.269 0.308
Soil	0.092	Arenosols Leptosols Luvisols Acrisol Solonchaks Lixisols	9 7 6 2 5 4	0.273 0.212 0.182 0.061 0.152 0.121
LULC	0.110	Water Forest Shrubs and grassland Agricultural fields Bare land Built up Mine dumps Rock outcrops	9 7 6 5 4 2 2 1	0.257 0.200 0.171 0.143 0.086 0.057 0.057 0.029
TRI	0.055	0–2 2–10 10–25 25–40 >40	7 6 5 3 1	0.300 0.250 0.200 0.150 0.100

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Table 3. Determine the weight of conditioning factors using MIF method.

Factor	Major effects (A)	Minor effect(B)	Proposed relative rates (A+B)	Proposed score of each influencing factor (A+B) *100/∑(A+B)
Lineaments density	1+1	0.5	2.5	9.80
Drainage density	1+1	0.5+0.5+0.5	3.5	13.73
Land use/Land cover	1+1+1	0.5	3.5	13.73
Geology	1+1+1+1		4	15.69
Soil	1		1	3.92
Rainfall	1+1	0.5+0.5	3	11.76
Slope	1+1+1	0.5+0.5	4	15.69
TWI	1+1	0.5	2.5	9.80
TRI	1	0.5	1.5	5.88
			∑25.5	100

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Table 4. The percentage and area covered by Ground Water Potential Zones (GWPZ)

Classification	AHP area /Ha	AHP area /%	MIF area /Ha	MIF area /%
Very good	13,942	2	24,748	3
Good	101,436	14	141,217	19
Moderate	281,693	37	300,451	40
Low	282,259	37	184,238	24
Very low	75,195	10	103,871	14

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