Construction of Master Recession Curve Using Genetic Algorithms
The article describes a new methodology of using genetic algorithms to assemble a natural time series of discharge recession, from which a master recession curve can be interpreted both for streams and for springs. Presented approach can avoid obstacles such as limited time-series datasets, incomplete recessions or too many recessionary segments in many recession series, different time intervals of observations (daily or weekly frequencies). Short time-series intervals, imprecise or mistaken measurements and different types of datasets (averaged or directly measured data) are taken into account as well. Even rough measurements of discharges with inaccurate sensing range can be analysed, if sufficiently long observation is available. Complicated hydrograph shapes in the case of e.g. karstic springs (often caused by combination of laminar and turbulent discharge sub-regimes due to karst network settings) can be processed as well. Subsequent construction of master recession curve is much easier an offers better conditions for its interpretation. Presented algorithm was already implemented to a programme solution, completed on the user form.
The risk of diffuse pollution of groundwater by nitrogen substances from agricultural land is perceived as a result of the interaction of groundwater vulnerability (determined by the characteristics of the environment overlying groundwater in relation to water transport or soil solution) and loading of overlying environment by nitrogen. Index of groundwater vulnerability was assessed on the basis of four parameters, namely, the amount of effective rainfall in the period from October to March, the capacity of soil to accumulate water, the average depth of the groundwater table and the permeability of the rock environment. Assessment of the index of loading of overlying environment by nitrogen was based on two parameters, namely, nitrogen balance and crop cover on agricultural land in the winter half on districts level in 2012, which corresponds with current state of the load. The resulting risk of groundwater pollution by nitrogen was expressed by the formula counting with the transformed values of groundwater vulnerability index and the index of loading of overlying environment by nitrogen. From practical point of view, the above mentioned indexes, as well as the subsequent risk of diffuse groundwater pollution, were spatially expressed via three associated categories. Based on the evaluation of relevant parameters, 5.18% of agricultural land falls into the category of very high and high risk, 42.20% in the medium risk category and 52.62% in the category of low and very low risk of diffuse pollution of groundwater by nitrogen from agricultural land.
Monitoring temporal variations of 18O and 2H isotopes in precipitation, groundwater and surface water was performed in the region of Kakheti (East Georgia). Data were collected from three meteorological stations at altitudes between 400 - 1,100 m a.s.l., from two shallow and one deep hydrogeological boreholes, and from two surface water monitoring stations (Alazani River and Patmasuri karstic stream). 18O values in precipitation show an annual variation between -22 ‰ and +1 ‰ and a distinct altitude effect. A clear correlation exists between the seasonal isotope composition of precipitation, shallow groundwater and surface water. A five-fold amplitude dampening and a delay of 10-15 days was observed. The data show that precipitation in the Caucasus Mountains to the North infiltrates into the Upper Jurassic - Lower Cretaceous karstic aquifer and travels to the Alazani valley towards south-east. The isotopic signature of winter precipitation is reflected in stream water as well as in shallow groundwater isotope data of groundwater in a 2,000-m-deep hydrogeological borehole at Heretiskari show a distinctly different character with δ18O ranging between -2.8 ‰ to -2.2 ‰ and a deuterium excess of -25 ‰.