The prediction of ground water recharge of landslide areas in Dnipropetrovsk region


Keywords: groundwater, forecast, level, hydrodynamic schematization

Abstract

The forecast of groundwater level together with the observation network of wells are important and mandatory components of hydrogeological monitoring. The reliability of predictive calculations is achieved by reasonable definition of boundary conditions (inverse hydrogeological problem), correct calculation of hydrogeological parameters (inverse problem), and reasoned choice of methods (inductive problem). The classical forecast of the level regime of groundwater is  a direct hydrogeological problem.The calculated dependences are proposed for four variants of hydrogeological conditions in relation to landslide-prone areas. The first and second variants are watered ravine with boundary conditions of the first and second kind.    The third option is a special case of boundary conditions of second kind “impermeable boundary”. The fourth option considers the periodic watercourse formation in the water intervals of the time climate series. A comparative analysis of four hydrodynamic schemes “infiltration band” in unlimited and semi-bounded layers and half-plane is performed under the same conditions to estimate the error of schematization. Significant differences in the calculation results confirm the need for a clear choice of the design scheme. A method of accounting for evaporation from the groundwater surface lying above the critical depth was proposed. This scientific approach allows accurate and detailed characterization of the average monthly groundwater regime in the course of a year. Multivariate calculations allow us to assert that the main mode – forming factor at the depth of groundwater below the critical depth is infiltration replenishment. Evaporation is a negative component of the water balance. Its value depends on the depth of groundwater, lithological composition of the host rocks, vegetation cover and complex climatic factors. Accounting for the evaporation of ground water in the forward estimates is required if they lie above the critical depth. The critical depth for the territory of Dnipropetrovsk region is assumed to be 2.0 m.   At this depth of groundwater level from the earth’s surface, the evaporation rate is zero. The maximum evaporation or evaporability corresponds to the position of groundwater at the surface of the earth. The maximum evaporation is 800 – 820 mm for Dnipropetrovsk region.The evaporation value increases inversely with the depth of its occurrence from the surface of the earth when the rise of the groundwater level occurs above the critical depth. The process of changes in the groundwater level in the unsteady filtration regime  is described by two-dimensional differential equations of the second order in partial derivatives of parabolic type. This equation has analytical partial solutions for all considered variants of boundary conditions with regard to the problems of meliorative hydrogeology. It is possible to transform correctly to hydrogeological conditions of landslide slopes using numerical forecast.Infiltration nutrition indices were calculated by comparing the monitoring data with the values of evaporation through the soil surface.

Author Biographies

G. P. Yevgrashkina
Oles Honchar Dnipro National University
T. P. Mokrytskaya
Oles Honchar Dnipro National University
M. M. Kharytonov
Dnipro State Agrarian and Economic University

References

1. Aver’yanov, S.F., 1978. Bor’ba s zasoleniyem oroshay- emykh zemel. [Combating salinization of irrigat- ed land]. M., Kolos, 240 p. (in Russian).
2. Bochever, F.M., Garmonov, I.V., Lebedev, A.S., Shestakov, V.M., 1969. Osnovy gydrogeologycheskykh ra- schetov [The basis of the hydrogeological calcula- tions], M., Nedra, 367p. (in Russian).
3. Herrera – Pantoja, M., Hiscock, K.M., 2008. The effects of climate change on potential groundwater recharge in Great Britain, Hydrol. Process., 22:73–86.
4. Holman, I., Tascone, D., Hess, T. A., 2009. Compari- son of stochastic and deterministic downscaling methods for modelling potential groundwater recharge under climate change in East Anglia, UK: implications for ground water resource management,Hydrogeol. J., 17: 1629–1641.
5. Jannat, T., Chowdhury, A., Rahaman, M.Z., Islam, K.M. 2014. Estimating rainfall infiltration for ground- water recharge using infiltration index method: A case study in Rajshahi District, Bangladesh. American Journal of Civil Engineering; 2(3): 91- 95, doi: 10.11648/j.ajce.20140203.15
6. Jyrkama, M. I., Sykes, J. F., 2007. The impact of climate change on spatially varying groundwater recharge in the grand river watershed (Ontario), J. Hydrol., 338, 237–250.Kalf, F.R.P., Woolley, D.R.,2005. Applicability and meth- odology of determining sustainable yield in groundwater systems. Hydrogeol. J. 13, 295–312.
7. Malet, J.-P., van Asch, Th. W. J., van Beek, R., Maquaire,O., 2005. Forecasting the behaviour of complex landslides with a spatially distributed hydrologi- cal model, Nat. Hazards Earth Syst. Sci., 5, 71–85, doi:10.5194/nhess-5-71-2005.
8. Mohan, С., Western, A.W., Wei, Y., Saft, M., 2018. Predict- ing groundwater recharge for varying land cover and climate conditions – a global meta-study. Hy- drol. Earth Syst. Sci., 22: 2689–2703, https://doi. org/10.5194/hess-22-2689-2018.
9. Rudakov, V.K., Andreyeva, L.A.,1970. Metody prog- noznykh raschetov vliyaniya oroshenija na rezim gruntovykh vod [Methods for predictive calcula- tions of the effect of irrigation on ground water regime]. Voprosy gydrogeologycheskykh progno- zov v svyazy s irrigatsiey zemel i vodosnabzhe- niem. Izdatelstvo DGU,. 3, 5-96] (in Russian).
10. Sdao, F., Lioi, D. S., Pascale, S., Caniani, D., Mancini, I. M., 2013. Landslide susceptibility assessment by using a neuro-fuzzy model: a case study in the Rupestrian heritage rich area of Matera. Nat. Haz- ards Earth Syst. Sci., 13, 395–407, www.nat-haz- ards-earth-syst-sci.net/13/395/2013/ doi:10.5194/ nhess-13-395-2013.
11. Yeh, H.F., Lee, C.H., Chen, J.F., Chen, W.P., 2007. Estimation of Groundwater Recharge Using Water Balance Model. Water Resources, Vol. 34, No. 2,
:153–162. doi: 10.1134/S0097807807020054.
12. Yevgrashkina, G. P., Mokrickaja, T. P., Marchenko, V. K., Lomova, K.S., 2018. Forecast of the level regime of groundwater in landslide areas (on the example the city of the Dnipro). Dniprop. Univer. bulletin, Geology, geography., 26(1): 227-234. https://doi. org/10.15421/111823.
13. Yevgrashkina, G.P., Mokritskaya, T.P., Marchenko, V.K.,, 2017. Determination of infiltration nutrition of groundwater by analytical and numerical methods. Dniprop. Univer. bulletin, Geology, geography., 25(2), 146-150, https://doi.org/10.15421/111730.
14. Yevgrashkina, G.P., Andreyeva, L.P., 1973. Opyt ana- lytycheskogo prognoza urovennogo rezhi- ma gruntovykh vod na oroshayemykh zem- lyakh Krasnoperekopskogo rayona Krymskoy oblasty [Experience of analytical forecast of groundwater level regime on irrigated lands of Krasnoperekopsky district of the Crimean region]. Issues of hydrogeological forecasts in connection with land irrigation and water supply., Dneprop- etrovsk, DGU, 96-99 (in Russian).
15. Yihdego, Y., Khalil, A., 2017. Groundwater Resources As- sessment and Impact Analysis Using a Conceptual Water Balance Model and Time Series Data Anal- ysis: Case of Decision Making Tool. Hydrology, 4, 25: 1-16.
16. Yihdego, Y., Webb, J.A., 2015. Use of a conceptual hydro- geological model and a time variant water budget analysis to determine controls on salinity in Lake Burrumbeet in southeast Australia. Environmental Earth Sciences. J., 73, 1587–1600.
17. Yihdego, Y. Webb, J.A., 2016. Validation of a model with climatic and flow scenario analysis: Case of Lake Burrumbeet in southeastern Australia J. Environ. Monit. Assess., 188, 1–14.
18. Van Asch, Th. W. J., Malet, J.-P., Bogaard, T. A., 2009. The effect of groundwater fluctuations on the veloc- ity pattern of slow moving landslides, Nat. Haz- ards Earth Syst. Sci., 9, 739–749, doi:10.5194/ nhess-9-739-2009.
19. Zeng, B., Xiang, W., Rohn, J, Ehret, D., Chen, X., 2017. Assessment of shallow landslide susceptibility us- ing an artificial 2 neural network in Enshi region, China. Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-176.
Published
2019-10-12