Stochastic simulation of hydrological timeseries for data scarce regions - Case study at the Municipality of Western Mani 

Abstract

<p>West Mani, an attractive place in western Peloponnese, Greece, faces water shortage. The problem lies not only in the quantity but also in the quality of the available water. Investigating the options for the sustainable management of water resources, utilizing surface water seems to be the optimal solution. However, the complex geomorphology and geology of the study area, and its particular its karstic structure, when combined with the scarcity of hydrological data, makes the estimation of surface water availability challenging. As a result, it is considered necessary to take hydrological uncertainty into account using stochastic analysis. To this aim, we generate synthetic rainfall and streamflow timeseries based on available meteorological data from basins near the area of interest. We then appropriately adjust them so that they represent the magnitude and the variability of the rainfall and streamflow of the study area. For the timeseries generation algorithm, we employ a stochastic model following the Hurst-Kolmogorov dynamics by reproducing marginal distribution, seasonality and persistence.</p>

Stochastic Evaluation of the Investment Risk by the Scale of Water Infrastructures—Case Study: The Municipality of West Mani (Greece)

“…An important property of natural processes is that they exhibit the so-called Hurst-Kolmogorov (HK) behavior, which is also known as long-range dependence or Long-Term Persistence (LTP) [93]. Using HK dynamics, [94,95], we are able to generate synthetic timeseries by preserving the same stochastic characteristics as the historical timeseries [96]. The synthetic timeseries were used to calculate stream flows, which are considered the main source of water, whereas the source of desalination is the sea.…”

Modern Use of Traditional Rainwater Harvesting Practices: An Assessment of Cisterns’ Water Supply Potential in West Mani, Greece

“…Rainfall seasonality is preserved through a linear standardization procedure, i.e., subtracting from each month its monthly mean and dividing by its monthly standard deviation, and at the end, is recovered by the reverse procedure. Figure 5b shows the synthetic timeseries sharing the above stochastic properties with the historical [37].…”

“…standard deviation, and at the end, is recovered by the reverse procedure. Figure 5b shows the synthetic timeseries sharing the above stochastic properties with the historical [37].…”

The role of technology in the water–energy–food nexus. A case study: Kerinthos, North Euboea, Greece

“…In our perspective, the essence of prosperity resides in recognizing and understanding the intricate interplay among water, energy, and food systems, collectively referred to as the waterenergy-food (WEF) nexus (Allouche et al, 2015;Al-Saidi and Elagib, 2017;Wichelns, 2017;IRENA, 2019;Al-Saidi and Hussam, 2021;Siganou et al, 2022;The Water, Energy & Food Security Resource Platform, n.d.;WEF Background, n.d.). While the term WEF nexus was formally introduced by The World Economic Forum Water Initiative (2011), the conceptualization and acknowledgment of the interconnectedness among these elements had been identified earlier in scholarly work by Koutsoyiannis et al (2009).…”