No abstract

In sparsely monitored basins, accurate mapping of the spatial variability of groundwater level requires the interpolation of scattered data. This paper presents a comparison of deterministic interpolation methods, i.e. inverse distance weight (IDW) and minimum curvature (MC), with stochastic methods, i.e. ordinary kriging (OK), universal kriging (UK) and kriging with Delaunay triangulation (DK). The study area is the Mires Basin of Mesara Valley in Crete (Greece). This sparsely sampled basin has limited groundwater resources which are vital for the island's economy; spatial variations of the groundwater level are important for developing management and monitoring strategies. We evaluate the performance of the interpolation methods with respect to different statistical measures. The Spartan variogram family is applied for the first time to hydrological data and is shown to be optimal with respect to stochastic interpolation of this dataset. The three stochastic methods (OK, DK and UK) perform overall better than the deterministic counterparts (IDW and MC). DK, which is herein for the first time applied to hydrological data, yields the most accurate cross-validation estimate for the lowest value in the dataset. OK and UK lead to smooth isolevel contours, whilst DK and IDW generate more edges. The stochastic methods deliver estimates of prediction uncertainty which becomes highest near the southeastern border of the basin.

River flooding causes significant losses to crops and negatively affects local agriculture economies, particularly in rural riverine areas. In this work, a techno-economic methodology for the monetary estimation of crop losses due to flash flooding is presented. The methodology takes into account flood depth and flow velocity, as provided by MIKE FLOOD, as well as the season of flood occurrence, and provides monetary estimates of crop damage based on synthetic logistic flow velocity-flood depth-crop damage surfaces. The development of the flood damage surfaces involved a questionnaire survey targeting practicing and research agronomists. Subsequently, a weighted Monte Carlo simulation was performed in order to enhance the questionnaire-based loss estimate information. Finally, synthetic flow velocity-flood depth-crop damage surfaces were developed for every crop under study and for every month using logistic regression analysis. The damage surfaces are an essential component of the developed model which was implemented in Python, enabling the GIS visualization of the estimated agricultural damage. The aforementioned methodology was applied for estimating the damage caused by a flash flood that took place in the Koiliaris River Basin in Crete for which no historical data exist. The novelty of the proposed methodology is the development of local synthetic flow velocityflood depth-crop damage surfaces. Furthermore, the velocity parameter, which is taken into account, makes the methodology suitable for flash flood events, where significant discharges and high velocities dominate, or for flood event cases which are characterized by high flow velocities. The methodology identifies rural areas and agricultural land uses that are most prone to flooding and serious crop damages and thus require greater attention. Furthermore, the methodology aptitude for developing local damage surfaces could be modulated in order to confront different flood scenarios on various crops distributions and be used to address agricultural planning activities.

No abstract

No abstract

To date only few studies have dealt with the biogeography of microbial communities at large spatial scales, despite the importance of such information to understand and simulate ecosystem functioning. Herein, we describe the biogeographic patterns of microorganisms involved in nitrogen (N)-cycling (diazotrophs, ammonia oxidizers, denitrifiers) as well as the environmental factors shaping these patterns across the Koiliaris Critical Zone Observatory, a typical Mediterranean watershed. Our findings revealed that a proportion of variance ranging from 40 to 80% of functional genes abundance could be explained by the environmental variables monitored, with pH, soil texture, total organic carbon and potential nitrification rate being identified as the most important drivers. The spatial autocorrelation of N-functional genes ranged from 0.2 to 6.2 km and prediction maps, generated by cokriging, revealed distinct patterns of functional genes. The inclusion of functional genes in statistical modeling substantially improved the proportion of variance explained by the models, a result possibly due to the strong relationships that were identified among microbial groups. Significant relationships were set between functional groups, which were further mediated by land use (natural versus agricultural lands). These relationships, in combination with the environmental variables, allow us to provide insights regarding the ecological preferences of N-functional groups and among them the recently identified clade II of nitrous oxide reducers.

No abstract

No abstract