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Groundwater recharge modeling with integration of land use/land cover and climate change projections in Surakarta City, Indonesia
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Abstract: Increased population mobility in urban areas drives higher water demand and significant changes in Land Use and Land Cover (LULC), which directly impact groundwater recharge capacity. This study aims to predict LULC changes in 2030 and 2040, analyse groundwater recharge quantities for historical, current, and projected conditions, and evaluate the combined impacts of LULC and climate change. The Cellular Automata-Artificial Neural Network (CA-ANN) method was employed to predict LULC changes, using classified and interpreted land use data from Landsat 7 ETM+ (2000 and 2010) and Landsat 8 OLI (2020) imagery. The Soil and Water Assessment Tool (SWAT) model was used to simulate groundwater recharge. Input data for the SWAT model included Digital Elevation Model (DEM), soil type, LULC, slope, and climate data. Climate projections were based on five Regional Climate Models (RCMs) for two time periods, 2021–2030 and 2031–2040, under Shared Socioeconomic Pathways (SSP) scenarios 2–45 and 5–85. The results indicate a significant increase in built-up areas, accounting for 71.08% in 2030 and 71.83% in 2040. Groundwater recharge projections show a decline, with average monthly recharge decreasing from 83.85 mm/month under SSP2-45 to 78.25 mm/month under SSP5-85 in 2030, and further declining to 82.10 mm/month (SSP2-45) and 77.44 mm/month (SSP5-85) in 2040. The expansion of impervious surfaces due to urbanization is the primary factor driving this decline. This study highlights the innovative integration of CA-ANN-based LULC predictions with climate projections from RCMs, offering a robust framework for analysing urban groundwater dynamics. The findings underscore the need for sustainable urban planning and water resource management to mitigate the adverse effects of urbanization and climate change. Additionally, the methodological framework and insights gained from this research can be applied to other urban areas facing similar challenges, thus contributing to broader efforts in groundwater conservation.
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Key words:
- Groundwater Recharge /
- Climate Change /
- Remote Sensing /
- Socioeconomic Pathways /
- SWAT
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Figure 1. Study area; (a) Hydrogeology; (b) Elevation; (c) SWAT-based delineation of Surakarta City within the Bengawan Solo Basin (modified from Putranto et al. 2016, 2017)
Figure 2. Hydrogeological profiles of two typical cross-sections (modified from Putranto et al. 2016)
Figure 3. Overall methodological framework for assessing the impact of climate and land use change on the spatiotemporal variations in groundwater recharge
Figure 4. Schematic diagram illustrating the bias correction of global climate model rainfall outputs, adjusted against observed rainfall data at each monitoring station
Figure 8. Comparison of observed and scenario rainfall data: a) before bias correction; b) after bias correction
Figure 9. Delineation results of the Bengawan Solo watershed in Surakarta City using the SWAT model
Figure 10. Comparison of observed and simulated discharge for 2011–2020, before calibration (left) and after calibration (right)
Figure 13. Average baseline and projected climate and groundwater recharge in Surakarta City
Figure 15. Spatiotemporal pattern of groundwater recharge: a) 2030 SSP2-45; b) 2030 SSP5-85; c) 2040 SSP2-45; d) 2040 SSP5-85
Table 1. Datasets used in this study
Data Spatial Resolution Temporal Resolution Period Source Digital Elevation Model (DEM) 8 m - - DEMNAS LULC maps (Landsat 7-ETM and Landsat 8-OLI) 30 m - 3 maps (2000, 2010, 2020) USGS Soil type maps 1:50000 - - BBSDLP Rainfall 0.05° × 0.05° Daily 1991–2020 CHIRPS Temperature, solar radiation, wind speed, and relative humidity 0.25° × 0.25° Daily 1991–2020 ERA5 Observed streamflow (m3/s) - Daily 2010–2021 BBWS Bengawan Solo Pos Jurug Population density - - 2000–2021 BPS Kota Surakarta ACCESS-ESM1-5 1.9° × 1.2° Daily 1991–2040 In association with Australia Weather and Climate Research, Australia Government BCC-CSM2-MR 1.9° × 1.9° Daily 1991–2040 Beijing Climate Center (BCC), China FGOALS-f3-L 1.3° × 1° Daily 1991–2040 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), China MIROC6 1.4° × 1.4° Daily 1991–2040 Model for Interdisciplinary Research on Climate (MIROC), Japan MRI-ESM2-0 1.1° × 1.1° Daily 1991–2040 Meteorological Research Institute (MRI), Japan 
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Table 2. LULC classification scheme
LULC type Description Build-up area Urban, residential, industrial, and other construction land Vegetation Green space (RTH), rice, and shrubs Roads Motorways and roads in residential areas Waterbody River, lake
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Table 3. Land use and land cover area in Surakarta City (2000–2020)
LULC Type 2000 2010 2020 Km2 % Km2 % Km2 % Built-up area 31.27 66.93 31.66 67.77 32.54 69.65 Vegetation 9.55 20.44 8.37 17.92 7.28 15.58 Roads 5.2 11.13 5.84 12.50 6.05 12.95 Waterbody 0.7 1.50 0.85 1.82 0.85 1.82
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Table 4. Comparison of LULC in 2020: Existing vs. projected conditions
LULC Type Observation Model Kappa value (Overall) Km2 % Km2 % Built-up area 32.54 69.65 33.13 70.91 0.82 Vegetation 7.28 15.58 6.93 14.83 Roads 6.05 12.95 5.77 12.35 Waterbody 0.85 1.82 0.89 1.90
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Table 5. Accuracy of LULC predictions
LULC Type 2030 2040 RTRW Km2 % Km2 % Km2 % Build-up area 33.21 71.08 33.56 71.83 40.78 87.29 Vegetation 6.13 13.12 5.54 11.86 3.71 7.94 Roads 6.53 13.98 6.77 14.49 0.52 1.11 Waterbody 0.85 1.82 0.85 1.82 1.71 3.66
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Table 6. Extent of conformity between LULC prediction results and the Surakarta City RTRW
Suitability 2030/km2 % 2040/km2 % Suitable 35.71 76.43 36.16 77.39 Not suitable 11.01 23.57 10.56 22.61
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Table 7. Evaluation of monthly rainfall model performance during the historical periods (1991–2020)
No Model R MAE R* MAE* 1 ACCESS-ESM1-5 0.28 138.55 0.62 65.06 2 BCC-CSM2-MR 0.18 158.61 0.45 83.15 3 FGOALS-f3-L 0.05 134.02 0.67 59.02 4 MIROC6 0.12 120.32 0.55 75.96 5 MRI-ESM2-0 0.24 145.83 0.60 62.83 6 MMA 0.42 92.35 0.73 52.02 *corrected
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Table 8. Parameters used in the SWAT model calibration process
Parameters Value range Value of terp Min Max 1 R__CN2.mgt −0.25 0.3 −0.27 2 V__GW_DELAY.gw 200 250 245 3 V__ALPHA_BNK.rte 0.4 0.5 0.45 4 V__CH_K2.rte 0 0.125 0.03125 5 R__SOL_AWC(..).sol 0.3 0.35 0.32 6 R__SOL_K(..).sol 0 300 75 7 R__SOL_BD(..).sol 1.8 1.9 1.7 8 V__CH_K1.sub 0 250 187.5 9 V__CH_N1.sub 0.014 0.025 0.02225
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Table 9. Comparison results between observed and simulated data
Default value Parameterization R2 0.18 0.85 NSE 0.40 0.62 PBIAS 33.5 7.91 KGE 0.37 0.75
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Table 10. Annual groundwater recharge (GWR) for the historical period 1991–2020 (mm/year)
Year GWR Year GWR Year GWR 1991 57,468.8 2001 43,542.4 2011 37,893.8 1992 57,451.4 2002 36,991.6 2012 34,216.2 1993 57,406.1 2003 38,434.1 2013 30,139.3 1994 42,266.7 2004 43,201.7 2014 30,911.3 1995 52,614.7 2005 42,889.6 2015 32,318.4 1996 55,375.4 2006 47,907.3 2016 36,665.6 1997 63,570.0 2007 45,755.6 2017 31,588.7 1998 56,615.6 2008 49,361.1 2018 36,357.9 1999 44,909.0 2009 44,891.4 2019 31,395.0 2000 57,747.3 2010 72,755.2 2020 31,907.8 Mean 54,542.5 Mean 46,573.0 Mean 33,339.4
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Table 11. Annual Groundwater Recharge (GWR) quantity for the projection period 2021–2040 under climate scenarios SSP2-45 and SSP5-85 (mm/year)
SSP2-45 SSP5-85 SSP2-45 SSP5-85 Year GWR Year GWR Year GWR Year GWR 2021 26,182.6 2021 26,795.4 2031 20,954.8 2031 17,832.3 2022 27,095.4 2022 21,941.6 2032 24,800.7 2032 29,151.7 2023 35,455.7 2023 22,788.8 2033 30,742.1 2033 26,593.7 2024 26,716.6 2024 25,394.4 2034 27,387.9 2034 22,420.5 2025 25,510.5 2025 25,658.8 2035 20,172.7 2035 29,144.8 2026 41,246.3 2026 28,468.7 2036 34,223.9 2036 24,122.8 2027 32,872.4 2027 26,923.8 2037 27,736.7 2037 16,103.5 2028 25,403.0 2028 28,906.8 2038 22,509.7 2038 29,340.3 2029 26,075.2 2029 26,219.6 2039 20,289.0 2039 23,973.1 2030 32,116.3 2030 44,648.1 2040 32,286.6 2040 22,115.4 Mean 29,867.4 Mean 27,774.6 Mean 26,110.4 Mean 24,079.8
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