Simplified VarKarst Semi-distributed Model Applied to Joint Simulations of Discharge and Piezometric Variations in Villanueva Del Rosario Karst System (Malaga, Southern Spain)
Numerical modeling provides well-established tools for advancing in water management. In this study, a simplified version of the semi-distributed VarKarst approach has been developed to reduce modeling routine and, therefore, the time of calculation needed for jointly simulating spring discharge and piezometric head variations in the same karst system located in southern Spain. Simulated spring outflows were compared with spring flow data derived from a previous application of the original VarKarst. Scatter correlation of spring flows yields Kling-Gupta efficiency (KGE) and a Pearson’s coefficient (R2) of 0.90 and 0.89, respectively. The modified approach includes new equations that consider the distance between sea level and the basement of the aquifer, from which the piezometric-level variations were calculated. The KGE, R2, and the root-mean-squared error results obtained of groundwater level were 0.79, 0.85, and 3.07 m, respectively. We conclude that the simplified VarKarst numerical code can provide realistic hydrodynamic results in the karst system, as original VarKarst, concerning both discharge and groundwater level dynamics. This capacity of simulation could help to reduce uncertainty in model routines.
KeywordsKarst (carbonate) aquifer Semi-distributed modeling VarKarst Hydrodynamic simulation
This chapter is a contribution to the project CGL2015-65858R and to the research group 308 of Andalusian Government.
- Brenner, S, Coxon, G, Howden, NJK, Freer, J, Hartmann, A (2018) Process-based modelling to evaluate simulated ground-water levels and frequencies in a Chalk catchment in south-western England. Nat. Hazards Earth Syst. Sci. 18, 445–461. https://doi.org/10.5194/nhess-18-445-2018.CrossRefGoogle Scholar
- COST (1995), COST 65: Hydrogeological aspects of groundwater protection in karstic areas, Final report (COST action 65), Rep. EUR 16547, 446 pp., Brussel, Belgium.Google Scholar
- Hartmann A, Barberá JA, Lange J, Andreo B, Weiler M (2013) Progress in the hydrologic simulation of time variant recharge areas of karst systems – Exemplified at a karst spring in Southern Spain. Advances in Water Resources 54: 149–160, https://doi.org/10.1016/j.advwatres.2013.01.010.CrossRefGoogle Scholar
- Jimenez-Martinez, J, Smith, M, Pope, D (2016) Prediction of groundwater induced flooding in a chalk aquifer for future climate change scenarios, Hydrol. Process., 30, 573–587. https://doi.org/10.1002/hyp.10619, 2016.
- Ladouche, B, Marechal, JC, Dorfliger, N (2014) Semi-distributed lumped model of a karst system under active management, J. Hydrol., 509, 215–230. https://doi.org/10.1016/j.hydrol.2013.11.017.
- Martín-Algarra M (1987) Evolución geológica alpìna del contacto entre las Zonas Internas y Externas de la Corduillera Bética. Ph.D. Thesis, University of Granada.Google Scholar
- Mudarra M (2012) Importancia relative de la zona no saturada y zona saturada en el funcionamiento hidrogeológico de los acuíferos carbonáticos. Caso de la Alta Cadena, sierra de Enmedio y área de Los Tajos (provincia de Málaga). Ph.D. Thesis, University of Málaga.Google Scholar
- Mudarra M, Andreo B, Marín AI, Vadillo I, Barberá JA (2014) Combined use of natural and artificial reacers to determine the hydrogeological functioning of a karst aquifer: the Villanueva del Rosario system (Andalusia, Southern Spain). Hydrogeological Journal 22: 1027–1039, https://doi.org/10.1002/2014wr015685.CrossRefGoogle Scholar
- Vrugt JA, ter Braak CJF, Clark MP, Hyman JM, Robinson BA (2008) Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain monte Carlo simulation. Water Resources Research, 44: W00B09. https://doi.org/10.1029/2007wr006720.
- Vrugt JA, ter Braak CJF, Diks CGH, Robinson BA, Hyman JM, Higdon D (2009) Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling. International Journal of Nonlinear Sciences and Numerical Simulation, 10(3), 271–288.Google Scholar