Journal
ENVIRONMENTAL MONITORING AND ASSESSMENT
Volume 184, Issue 2, Pages 797-810Publisher
SPRINGER
DOI: 10.1007/s10661-011-2002-1
Keywords
Hydrological budget; Land use; Playa lake; Water level
Categories
Funding
- MICINN [CGL2009 11-384]
- Andalusian [RNM-126, RNM-3713, RNM-188]
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Medina playa lake, a Ramsar site in western Andalusia, is a brackish lowland lake of 120 ha with an average depth of 1 m. Water flows into Medina from its 1,748-ha watershed, but the hydrology of the lake has not previously been studied. This paper describes the application of a water budget model on a monthly scale over a 6-year period, based on a conceptual hydrological model, and considers different future scenarios after calibration to improve the understanding of the lake's hydrological functioning. Climatic variables from a nearby weather station and observational data (water-level evolution) were used to develop the model. Comparison of measured and predicted values demonstrated that each model component provided a reasonable output with a realistic interaction among the components. The model was then used to explore the potential consequences of land-use changes. Irrigation of olive groves would significantly reduce both the hydroperiod (becoming dry 15% of the time) and the average depth of the lake (water level < 0.5 m 40% of the time). On the other hand, removal of an artificial overflow would double the average flooded surface area during high-water periods. The simulated water balance demonstrates that the catchment outputs are dominated by lake evaporation and surface outflow from the lake system to a creek. Discrepancies between predicted and observed water levels identify key areas of uncertainty for future empirical research. The study provides an improved basis for future hydrological management of the catchment and demonstrates the wider utility of this methodology in simulating this kind of system. This methodology provides a realistic appraisal of potential land-use management practices on a catchment-wide scale and allows predictions of the consequences of climate change.
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