Emmanouil A. Varouchakis

Environmental drivers of the distribution of nitrogen functional genes at a watershed scale

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.

Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application

Abstract The present research study investigates the application of nonlinear normalizing data transformations in conjunction with ordinary kriging (OK) for the accurate prediction of groundwater level spatial variability in a sparsely-gauged basin. We investigate three established normalizing methods, Gaussian anamorphosis, trans-Gaussian kriging and the Box-Cox method to improve the estimation accuracy. The first two are applied for the first time to groundwater level data. All three methods improve the mean absolute prediction error compared to the application of OK to the non-transformed data. In addition, a modified Box-Cox transformation is proposed and applied to normalize the hydraulic heads. The modified Box-Cox transformation in conjunction with OK is found to be the optimal spatial model based on leave-one-out cross-validation. The recently established Spartan semivariogram family provides the optimal model fit to the transformed data. Finally, we present maps of the groundwater level and the kriging variance based on the optimal spatial model. Editor D. Koutsoyiannis; Associate editor A. Montanari Citation Varouchakis, E.A., Hristopoulos, D.T., and Karatzas, G.P., 2012. Improving kriging of groundwater level data using nonlinear normalizing transformations—a field application. Hydrological Sciences Journal, 57 (7), 1404–1419.

An agricultural flash flood loss estimation methodology: the case study of the Koiliaris basin (Greece), February 2003 flood

Abstract 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 velocity–flood 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.

Environmental drivers of soil microbial community distribution at the Koiliaris Critical Zone Observatory.

Data on soil microbial community distribution at large scales are limited despite the important information that could be drawn with regard to their function and the influence of environmental factors on nutrient cycling and ecosystem services. This study investigates the distribution of Archaea, Bacteria and Fungi as well as the dominant bacterial phyla (Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes), and classes of Proteobacteria (Alpha- and Betaproteobacteria) across the Koiliaris watershed by qPCR and associate them with environmental variables. Predictive maps of microorganisms distribution at watershed scale were generated by co-kriging, using the most significant predictors. Our findings showed that 31-79% of the spatial variation in microbial taxa abundance could be explained by the parameters measured, with total organic carbon and pH being identified as the most important. Moreover, strong correlations were set between microbial groups and their inclusion on variance explanation improved the prediction power of the models. The spatial autocorrelation of microbial groups ranged from 309 to 2.226 m, and geographic distance, by itself, could explain a high proportion of their variation. Our findings shed light on the factors shaping microbial communities at a high taxonomic level and provide evidence for ecological coherence and syntrophic interactions at the watershed scale.

Modeling Soil Salinity in Greenhouse Cultivations Under a Changing Climate With SALTMED: Model Modification and Application in Timpaki, Crete

Abstract Soil salinity is a major soil degradation threat especially for arid coastal environments where it hinders agricultural production, thus imposing a desertification risk. In the prospect of a changing climate, soil salinity caused by brackish water irrigation introduces additional uncertainties regarding the viability of deficit irrigation and intensive cultivation practices such as greenhouse cropping. Here, we propose a modification of the SALTMED leaching requirement model to account for greenhouse cultivation conditions. The model is applied in the RECARE Project Case Study of Timpaki, a semiarid region in south-central Crete, Greece, where greenhouse horticulture is an important land use. Excessive groundwater abstractions toward irrigation have resulted in a drop of the groundwater level in the coastal part of the aquifer, thus leading to seawater intrusion and in turn to soil salinization. Crop yield and soil profile electrical conductivity (EC) sensitivity to initial soil EC (up to 2 dS m−1) and irrigation water EC (up to 3 dS m−1) are modeled for the locally popular horticultural crops of Solanum lycopersicum, Solanum melongena, and Capsicum annuum. Climate model data obtained from nine general circulation models for the “worst case” representative concentration pathway of 8.5 W m−2 of the fifth phase of the Coupled Model Intercomparison Project are corrected for bias against historical observations with the Multisegment Statistical Bias Correction method and used to estimate crop yield and soil profile EC sensitivity in a warmer future. Results show that the effects of climate change on S. lycopersicum greenhouse cultivations of Timpaki will be detrimental, whereas S. melongena and C. annuum cultivations may show greater resilience.

Stochastic Modeling of Aquifer Level Temporal Fluctuations Based on the Conceptual Basis of the Soil-Water Balance Equation

Abstract The formulation of a model that can reliably simulate the temporal groundwater level fluctuations of an aquifer is important for effective water resource management and for the prevention of possible desertification effects. Mires Basin at the island of Crete, Greece, is part of a major watershed with significantly reduced groundwater resources because of overexploitation during the past 30 years. In this work, the interannual variability of groundwater level is modeled with a discrete time autoregressive exogenous variable (ARX) model that is based on physical grounds (soil-water balance equation). Precipitation surplus is used as an exogenous variable in the ARX model. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale; the second considers a larger time delay in terms of the groundwater level input; and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. Modeling results for the time series of the spatially averaged groundwater level show very good agreement, after an initial adaptation period, with measured data. Among the three modified versions of the original ARX model considered in this work, the third model version shows significantly better agreement with measured data.

Spatial variability estimation and risk assessment of the aquifer level at sparsely gauged basins using geostatistical methodologies

The spatial variability evaluation of the water table level of an aquifer provides useful information in water resources management plans. Three different approaches are applied to estimate the spatial variability of the water table in the study basin. All of them are based on the Kriging methodology. The first is the classical Ordinary Kriging approach, while the second involves information from a secondary variable (surface elevation) and the application of Residual Kriging. The third calculates the probability to lie below a certain groundwater level limit that could cause significant problems in groundwater resources availability. The latter is achieved by means of Indicator Kriging. A recently developed non-linear normalization method is used to transform both data and residuals closer to normal distribution for improved prediction results. In addition, the recently developed Spartan variogram model is applied to determine the spatial dependence of the measurements. The latter proves to be the optimal model, compared to a series of models tested, which provides in combination with the Kriging methodologies the most accurate cross validation estimations. The variogram form is explained with respect to the radius of influence of the pumping wells representing the spatial impact of the pumping activity. Groundwater level and probability maps are developed providing the ability to assess the spatial variability of the groundwater level in the basin and the risk that certain locations have in terms of a safe groundwater level limit that has been set for the sustainability of the groundwater resources of the basin.

Utilizing Successive Linearization Optimization to Control the Saltwater Intrusion Phenomenon in Unconfined Coastal Aquifers in Crete, Greece

In the present work, a simulation-optimization method is employed in order to manage saltwater intrusion in two unconfined coastal aquifers in Crete, Greece. The optimization formulation seeks to maximize groundwater withdrawal rates while maintaining the saltwater intrusion front at the current location or inhibiting it at locations closer to the coast. A combination of a groundwater flow model (MODFLOW) with the Ghyben-Herzberg saltwater front approximation and a sequential implementation of the Simplex algorithm (GWM) are employed. The results show that under the current pumping strategies, the saltwater intrusion front will continue to move inland, posing a serious threat to the groundwater quality of these regions. Optimal groundwater withdrawal scenarios that take into consideration the water needs of the local communities and environmental concerns are presented and discussed. In both case studies, significant reductions in pumping are required in order for the saltwater intrusion front to retract closer to the shoreline.