Geo-spatial Approach for Assessing the Impact of Land-Use and Land-Cover Change on Groundwater Recharge

Therefore, this study focused on the analysis of hydrological processes and recharge ability of various land‒use types. A specific modeling tool to estimate recharge that should be mentioned in depth, as it was used in this research project, is the WetSpass modeling. For this study the WetSpass model is to be applied with a focus on how hydrological processes respond to land use change, and what affect various land uses have on groundwater recharge.

Wetspass Model

WetSpass is an acronym for Water and Energy Transfer between Soil, Plants and Atmosphere under quasi-Steady State and it was built as a physically based methodology for estimation of the long-term average, spatially varying, water balance components. It is especially suited for studying long-term effects of land use changes on the water regime in a watershed. The model is completely integrated with GIS ArcView (3.x) as a raster model coded in Avenue, the programming language of ArcView.

The model uses seasonal (summer and winter) geographical information systems (GIS) input grids of the mentioned inputs to estimate annual and seasonal groundwater recharge values:

R = P - S - ET

Where, R is the groundwater recharge, P is the precipitation, S is surface runoff and ET is evapotranspiration, with mm units for all parameters. The surface runoff is calculated using the classical rational formula:

S = CHOR C (P - I)

Where, CHOR is a coefficient that parameterizes the part of the seasonal precipitation that actually contributes to runoff, C is a runoff coefficient based on the rational formula and I is the interception (mm).

METHODS

This study was conducted using data collected from literature, secondary data collected from office, primary data gathered from field survey and laboratory analysis of remote sensing imageries. The methodology used for this study was given below in Fig. 1.

RESULTS AND DISCUSSION

Land-Use and land-Cover change detection

The land‒use maps for 1986, 2000, and 2015 for Akaki catchment are presented in Fig.2 Comparison of classification results suggest that the principal land‒cover changes observed in the study area between 1986 and 2015 are the urban settlement, grass land, forest and cultivated lands. The area and percentage coverage of these five lands‒use classes during the three intervals are presented in Table I. It shows that the urban settlement and cultivated land areas were increased while that of forest and grass land declined continuously from 1986 till 2015.

(Table- I: Land-use/ land-cover categories in the study area during 1986, 2000 and 2015.)

Table I shows the spatial distribution of land-use/land-cover categories of the study area during the three years 1986, 2000 and 2015 in the Akaki catchment. For the year 1986, the land cover map is shown in Fig. 2a. The percentage coverage of each class is shown in Table I and indicates that the highest area of the catchment is covered with cultivated land (48.97%), while forest, grass land, urban settlement and water covered 13.56%, 25.71%, 10.9% and 1.06%, respectively.

For land-use/land-cover map of 2000 as shown in Fig. 2b, the areal coverage of cultivated land, urban settlement and water body was increased to 49.36%, 15.61% and 1.18%, respectively, while the areal coverage of forest land and grass land was decreased to 12.62% and 21.23%, respectively.The LU/LC map for 2015 of the Akaki catchment is presented in Fig. 2c, in which the cultivated land is covered for about 52.44% of the areal extent of Akaki catchment. Whereas forest land, grass land, urban settlement and water body covered 9.9%, 14.67%, 21.86% and 1.1% of the areal coverage of the study area, respectively.

Fig .2. Land‒use map of the years 1986 (a), 2000 (b) and 2015(c)

WetSpass model

WetSpass gives various hydrologic output grids on a yearly and seasonal (summer and winter) basis. Even though the model was originally developed to compute the long-term spatially distributed recharge of a basin, it also simulates runoff, evapotranspiration, interception, transpiration, soil evaporation and errors in water balance. The results of the modeling are given by digital grid maps of the spatial distribution of annual average values of actual evapotranspiration, surface runoff and groundwater recharge for the 29 years period from 1986 to 2015 under different land use scenarios.

Groundwater recharge under different land‒uses scenarios

The WetSpass model was applied for the three different land‒use maps to assess the impact of land‒use and land‒cover change on water balance of the catchment especially ground water recharge. It estimates seasonal and annual long-term spatial distribution amounts of groundwater recharge by subtracting the seasonal and annual surface runoff and evapotranspiration from the seasonal and annual precipitation respectively. A simulated result of WetSpass modeling for groundwater recharge was summarized for each period as an average map Fig. 3.

The results indicated that the total annual ground water recharge for Akaki catchment was changed insignificantly from time to time with change in land‒use and land‒cover. The model estimates the simulated total annual ground water recharge of the catchment for land‒use and land‒cover maps of 1986, 2000 and 2015 to be 9,622,050.3 mm/y, 9,490,260.5 mm/y and 9,315,814.2 mm/y respectively.Table II shows the simulation results of WetSpass modeling for ground water recharge under different land‒use and land‒cover scenarios and season (summer and winter). It also shows both the annual and seasonal ground water recharge of the catchment was slightly decreasing with changing in land‒use and land‒cover.

DISCUSSION

Impact of land‒use change on groundwater rechargeBased on three different periods of remote sensing and the longterm observed hydro-meteorological data from the 1986 to 2015, the impacts of land use changes in the groundwater system in the Akaki catchment were analyzed by estimating of groundwater recharge.

Land-use and land-cover changes like urban settlement expansion and change of grass land and forest land in to cultivated land are found to be the major changes in the catchment. From the land‒use land‒cover classification result maps for the periods of 1986, 2000 and 2015, it has been observed that grass land and forest land have changed negatively during 1986 to 2015 change detection phase by -16,196.1 ha and -5,323.16 ha area while urban settlement, cultivated land and water body area have changed positively by 16,046.3 ha, 5,401.6 ha and 71.38ha respectively.

Such dramatic change was caused by the uncontrolled expansion of urbanization, industrialization and increase of population. It has been reported that in many parts of the world the water table is declining at the rate of 1–2 meter/year as a result of population increase and land‒use and land‒cover changes particularly urban expansion and deforestation (Singh et. al., 2010).

Some previous works that make effort to estimate ground water recharge are available in the Akaki catchment such as the one published by Demilie et al. (2006), AAWSA (2000) and Nigussa (2003). The model results for this research were continuously verified by the previous model results and independent runoff data. According to Demilie et al. (2006) estimates ground water recharge of the Akaki catchment by using the chloride mass balance (CMB) method and he was calculated the annual ground water recharge of the catchment to be 265 mm, which amounts to 23% of the weighted mean annual areal precipitation of the catchment. Comparison of this value with the present simulated model result appears to be in a good agreement. On the other hand, Nigussa (2003) estimates the average recharge of the catchment by using empirical model known as DRASTIC and GIS.

However, this study appears to overestimate the recharge values estimated by Demilie et al. (2006), AAWSA (2000) and Nigussa (2003). The variation in the estimated values of ground water recharge by different methods indicates the requirements of relatively high-quality methods of recharge estimation. Hence, for successful estimation of groundwater recharge utilization of a variety of exhaustive and accurate independent methods is mandatory.

CONCLUSION

Humans have exerted large‒scale changes on the terrestrial biosphere, primarily through the land‒use/land-cover changing. However, the impacts of such changes on the hydrologic cycle particularly groundwater recharge are poorly understood. Hence, groundwater recharge estimations under different land‒use scenarios are essential for the management of groundwater aquifers.

The objective of this study was firstly to use RS imageries to see the changes on LU/LC of the catchment for the past 29 years. Secondly, to map areas of recharge and understand how land management practices could affect recharge. In this study, a geospatial analysis approach based on RS-GIS technology and the seasonal steady-state water balance model, WetSpass, was implemented.

Comparison of classification results indicates that the water body, urban settlement and cultivated land areal coverage from 1986 to 2015 increases form 1,552.6 hectare, 16,005.8 hectare and 71,502.9 hectare to 1,624 hectare, 32,052.1 hectare and 76,904.5 hectare respectively. While forest and grass lands are decreased from 19,886.26 hectare and 37,708.3 hectare to 14,563.1 hectare and 21,512.2 hectare respectively. Results from the WetSpass model are in good agreement with some of the recent previous studies and indicates the validity of the simulated recharge.

ACKNOWLEDGEMENT

I want to express my feeling of gratitude to Dr. K.V. Suryabhagavan and Dr. Dessie Nedaw.They have fully utilized their precious time and also providing all the necessary relevant literatures and information that are crucial for the quality in the present study. The financial support from the university of Aksum and Addis Ababa University was essential for my studies and merits great thanks. Autors wants to give special thanks to GSE (Geological Survey of Ethiopia), Ministry of Water Irrigation and Energy (MoWIE) and AAWSA (Addis Ababa Water and Sewerage Authority) for their helps and providing all necessary data.

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