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We investigate the design of small hydropower plants under multiple sources of uncertainty and contrast it with the conventional deterministic practice that leads to a unique solution. In particular, we emphasize three sources of uncertainty, referring to: (a) the rainfall process, (b) the rainfall-runoff transformation, and (c) the flow-energy conversion. The first is due to the natural (i.e., hydroclimatic) variability, and is represented through stochastic approaches. Regarding the rainfall-runoff uncertainty, this arises from inherent structural shortcomings and poor parameter identifiability across the calibration procedure. In fact, hydrological model parameterizations using only historical data are often insufficient for accurately predicting catchment behavior over the long term, as they may not capture the full range of hydroclimatic conditions that the catchment may be subjected to. To address this issue, we use synthetic time series as drivers to parameterize the model and validate it against observed data. This approach preserves the probabilistic properties and dependence structure of the observed data while also providing a much wider range of hydroclimatic conditions for model training. In addition, it allows for assessing and quantifying the total model uncertainty. The final source of uncertainty is depicted by means of probabilistic efficiency curves. This Monte Carlo simulation-optimization framework is formalized as a modular procedure, where the different sources of uncertainty, as well as the full context, is tested through the design of a small hydropower plant in Epirus, Western Greece.

Although storage-reliability-yield (SRY) relationships have been widely used in the design and planning of water supply reservoirs, their application in hydroelectricity is practically nil. Here, w...

The energy autonomy of small non-interconnected islands in the Mediterranean, taking advantage of their high renewable energy potential, has been a long-standing objective of local communities and stakeholders. This is also in line with the recently implemented European Green Deal, which has set the goal of increasing the renewable energy penetration in European countries’ power systems. However, the islands have further challenges than the large-scale inland areas. On the one hand, their population fluctuates significantly across seasons, as result of tourism, which is their key economic activity. The footprint of tourism is a substantial stress to all associated resources and infrastructures during the summer period. On the other hand, most of these areas suffer from both water and land scarcity. These features raise several challenges regarding the development of really autonomous energy systems, based on renewables and essential storage works to regulate the energy surpluses and deficits in the long run. Taking as example the Cycladic island of Sifnos, Greece, we investigate the design of a hybrid power system, combining wind, solar and hydroelectric energy. A major component of the proposed layout is the pumped-storage system. Due to the limited surface water resources of the island, we configure an upper tank at an elevation of 320 m, recycling seawater. This peculiarity introduces a significant level of uncertainty in hydraulic calculations, as well as various technical challenges, such as the erosion of pipes and the electromechanical equipment, and the waterproofing of the tank. An additional challenge is raised by the peculiar wind regime of the island, that makes essential to choose a hub height of turbines to minimize the frequency of power cut-offs. The basis of a rational design procedure for the main system components is the financial optimization that ensures a desirable level of reliability. This is achieved through a stochastic simulation approach that takes into account the stochastic nature of the underlying hydrometeorological drivers (wind velocity and solar radiation) and the energy demand.

Motivated by the challenges induced by the so-called Target Model and the associated changes to the current structure of the energy market, we revisit the problem of day-ahead prediction of power production from Small Hydropower Plants (SHPPs) without storage capacity. Using as an example a typical run-of-river SHPP in Western Greece, we test alternative forecasting schemes (from regression-based to machine learning) that take advantage of different levels of information. In this respect, we investigate whether it is preferable to use as predictor the known energy production of previous days, or to predict the day-ahead inflows and next estimate the resulting energy production via simulation. Our analyses indicate that the second approach becomes clearly more advantageous when the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP is incorporated within the model training procedure. Beyond these, we also focus on the predictive uncertainty that characterize such forecasts, with overarching objective to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of power production. Finally, we discuss the use of the proposed forecasting procedure under uncertainty in the real-world electricity market.

The large-scale surface-water monitoring infrastructure for Greece Open Hydrosystem Information Network (Openhi.net) is presented in this paper. Openhi.net provides free access to water data, incorporating existing networks that manage their own databases. In its pilot phase, Openhi.net operates three telemetric networks for monitoring the quantity and the quality of surface waters, as well as meteorological and soil variables. Aspiring members must also offer their data for public access. A web-platform was developed for on-line visualization, processing and managing telemetric data. A notification system was also designed and implemented for inspecting the current values of variables. The platform is built upon the web 2.0 technology that exploits the ever-increasing capabilities of browsers to handle dynamic data as a time series. A GIS component offers web-services relevant to geo-information for water bodies. Accessing, querying and downloading geographical data for watercourses (segment length, slope, name, stream order) and for water basins (area, mean elevation, mean slope, basin order, slope, mean CN-curve number) are provided by Web Map Services and Web Feature Services. A new method for estimating the streamflow from measurements of the surface velocity has been advanced as well to reduce hardware expenditures, a low-cost ‘prototype’ hydro-telemetry system (at about half the cost of a comparable commercial system) was designed, constructed and installed at six monitoring stations of Openhi.net.

Water, energy, land, and food are vital elements with multiple interactions. In this context, the concept of a water–energy–food (WEF) nexus was manifested as a natural resource management approach, aiming at promoting sustainable development at the international, national, or local level and eliminating the negative effects that result from the use of each of the four resources against the other three. At the same time, the transition to green energy through the application of renewable energy technologies is changing and perplexing the relationships between the constituent elements of the nexus, introducing new conflicts, particularly related to land use for energy production vs. food. Specifically, one of the most widespread “green” technologies is photovoltaic (PV) solar energy, now being the third foremost renewable energy source in terms of global installed capacity. However, the growing development of PV systems results in ever expanding occupation of agricultural lands, which are most advantageous for siting PV parks. Using as study area the Thessaly Plain, the largest agricultural area in Greece, we investigate the relationship between photovoltaic power plant development and food production in an attempt to reveal both their conflicts and their synergies.

The generation of hydrometeorological time series that exhibit a given probabilistic and stochastic behavior across multiple temporal levels, traditionally expressed in terms of specific statistical characteristics of the observed data, is a crucial task for risk-based water resources studies, and simultaneously a puzzle for the community of stochastics. The main challenge stems from the fact that the reproduction of a specific behavior at a certain temporal level does not imply the reproduction of the desirable behavior at any other level of aggregation. In this respect, we first introduce a pairwise coupling of Nataf-based stochastic models within a disaggregation scheme, and next we propose their puzzle-type configuration to provide a generic stochastic simulation framework for multivariate processes exhibiting any distribution and any correlation structure. Within case studies we demonstrate two characteristic configurations, i.e., a three-level one, operating at daily, monthly and annual basis, and a two-level one to disaggregate daily to hourly data. The first configuration is applied to generate correlated daily rainfall and runoff data at the river basin of Achelous, Western Greece, which preserves the stochastic behavior of the two processes at the three temporal levels. The second configuration disaggregates daily rainfall, obtained from a meteorological station at Germany, to hourly. The two studies reveal the ability of the proposed framework to represent the peculiar behavior of hydrometeorological processes at multiple temporal resolutions, as well as its flexibility on formulating generic simulation schemes.

Hydrological calibrations with historical data are often deemed insufficient for deducing safe estimations about a model structure that imitates, as closely as possible, the anticipated catchment behaviour. Ιn order to address this issue, we investigate a promising strategy, using as drivers synthetic time series, which preserve the probabilistic properties and dependence structure of the observed data. The key idea is calibrating a model on the basis of synthetic rainfall-runoff data, and validating against the full observed data sample. To this aim, we employed a proof of concept on few representative catchments, by testing several lumped conceptual hydrological models with alternative parameterizations and across two time-scales, monthly and daily. Next, we attempted to reinforce the validity of the recommended methodology by employing monthly stochastic calibrations in 100 MOPEX catchments. As before, a number of different hydrological models were used, for the purpose of proving that calibration with stochastic inputs is independent of the chosen model. The results highlight that in most cases the new approach leads to stronger parameter identifiability and stable predictive capacity across different temporal windows, since the model is trained over much extended hydroclimatic conditions.

The storage-reliability-yield (SRY) relationship is a well-established tool for preliminary design of reservoirs fulfilling consumptive water uses, yet rarely employed within hydropower planning studies. Here, we discuss the theoretical basis for representing the trade-offs between reservoir size and expected revenues from hydropower production, under uncertain inflows, by taking advantage of the stochastic simulation-optimization approach. We also demonstrate that under some assumptions, the complex and site-specific problem, mainly induced by the nonlinearity of storage-head-energy conversion, can be significantly simplified and generalized as well. The methodology is tested across varying runoff regimes and under a wide range of potential reservoir geometries, expressed in terms of a generic shape parameter of the head-storage relationship. Based on the outcomes of these analyses we derive empirical expressions that link reliable energy with summary inflow statistics, reservoir capacity and geometry.

The design of hydropower works typically follows a top-down approach, starting from a macroscopic screening of the broader region of interest, to select promising clusters for hydroelectric exploitation, based on easily retrievable information. Manual approaches are very laborious and may fail to detect sites of significant hydropower potential. In order to facilitate this kind of studies, we provide a novel geomorphological approach to assess the hydropower potential across river networks. The method is based on the discretization of the stream network into segments of equal length, thus providing a background layer of head differences between potential abstraction and power production sites. Next, at each abstraction point, we estimate the so-called unit geo-hydro-energy index (UGHE), which is a key concept of our approach. UGHE is defined as the ratio of annual potential energy divided by the upstream catchment area, the head difference, and the unit annual runoff of the catchment, which is set equal to 1000 mm. The method is further expanded, to estimate the actual hydropotential, if spatially distributed runoff data are available. All analyses are automatized by taking advantage of the high-level interpreted programming language Python and the open-source QGIS tool. The proposed framework is demonstrated at the regional scale, involving the siting of run-of-river hydroelectric works in the Peneios river basin.

Small hydropower plants (SHPPs) are subject to multiple uncertainties and complexities, despite their limited scale. These uncertainties are often ignored in the typical engineering practice, which results in risky design. As this type of renewable energy rapidly penetrates the electricity mix, the impacts of their uncertainties, exogenous and endogenous, become critical. In this vein, we develop a stochastic simulation-optimization framework tailored for small hydropower plants. First, we investigate the underlying multicriteria design problem and its peculiarities, in order to determine a best-compromise performance metric that ensures efficient and effective optimizations. Next, we adjust to the optimal design problem a modular uncertainty assessment procedure. This combines statistical and stochastic approaches to quantify the uncertainty of the inflow process per se, the associated input data, the initial selection of efficiency curves for the turbine mixing in the design phase, as well as the drop of efficiency due to aging effects. Overall, we propose a holistic framework for the optimal design of SHPPs, highlighting the added value of considering the stochasticity of input processes and parameters. The novelty of this approach is the transition from the conventional to the uncertainty-aware design; from the unique value to Pareto-optimality, and finally to the reliability of the expected performance, in terms of investment costs, hydropower production, and associated revenues.

The highly-competitive electricity market over EU and the challenges induced by the so-called “Target Model”, introduce significant uncertainties to day-ahead trades involving renewable energy, since most of these sources are driven by non-controllable weather processes (wind, solar, hydro). Here, we explore the case of small hydropower plants that have negligible storage capacity, and thus their production is just a nonlinear transformation of inflows. We discuss different forecasting approaches, which take advantage of alternative sources of information, depending on data availability. Among others, we investigate whether is it preferable to employ day-ahead predictions based on past energy production data per se, or use these data in order to retrieve past inflows, which allows for introducing hydrological knowledge within predictions. Overall objective is to move beyond the standard, yet risky, point forecasting methods, providing a single expected value of hydropower production, thus quantifying the overall uncertainty of each forecasting method. Power forecasts are evaluated in terms of economic efficiency, accounting for the impacts of over- and under-estimations in the real-world electricity market.

The fundamental concepts in the field of water-energy systems and their historical evolution with emphasis on recent developments are reviewed. Initially, a brief history of the relation of water and energy is presented, and the concept of the water-energy nexus in the 21th century is introduced. The investigation of the relationship between water and energy shows that this relationship comprises both conflicting and synergistic elements. Hydropower is identified as the major industry of the sector and its role in addressing modern energy challenges by means of integrated water-energy management is highlighted. Thus, the modelling steps of designing and operating a hydropower system are reviewed, followed by an analysis of theory and physics behind energy hydraulics. The key concept of uncertainty, which characterises all types of renewable energy, is also presented in the context of the design and management of water-energy systems. Subsequently, environmental considerations and impacts of using water for energy generation are discussed, followed by a summary of the developments in the emerging field of maritime energy. Finally, present challenges and possible future directions are presented.

Over the last 30 years, numerous water resources planning and management studies in Greece have been conducted by using state-of-the-art methodologies and associated computational tools that have been developed by the Itia research team at the National Technical University of Athens. The spearhead of Itia’s research toolkit has been the Hydronomeas decision support system (which stands for “water distributer” in Greek) supporting multi-reservoir hydrosystem management. Its methodological framework has been based on the parameterization-simulation-optimization approach comprising stochastic simulation, network linear optimization for the representation of water and energy fluxes, and multicriteria global optimization, ensuring best-compromise decision-making. In its early stage, Hydronomeas was implemented in Object Pascal – Delphi. Currently, the software is being substantially redeveloped and its improved version incorporates new functionalities, several model novelties and interconnection with other programs, e.g., EPANET. Hydronomeas 2020 will be available at the end of 2020 as a free and open-source Python package. In this work we present the key methodological advances and improved features of the current version of the software, demonstrated in the modelling of the extensive and challenging raw water supply system of the city of Athens, Greece.

The Open Hydrosystem Information Network (OpenHi.net) is a state-of-the-art information infrastructure for the collection, management and free dissemination of hydrological and environmental information related to Greece’s surface water resources. It was launched two years ago as part of a the national research infrastructure “Hellenic Integrated Marine Inland water Observing, Forecasting and offshore Technology System” (HIMIOFoTS), which also comprises a marine-related component (https://www.himiofots.gr/). The OpenHi.net system receives and processes real-time data from automatic telemetric stations that are connected to a common web environment (https://openhi.net/). In particular, for each monitoring site it accommodates stage measurements, raw and automatically post-processed. Furthermore, in some specially selected sites time series related to water quality characteristics (pH, water temperature, salinity, DO, electrical conductivity) are provided. The web platform also offers automatically-processed information in terms of discharge data, statistics, and graphs, alerts for extreme events, as well as geographical data associated with surface water bodies. At the present time, the network comprises about 20 stations. However, their number is continuously increasing, due to the open access policy of the system (the platform is fully accessible to third-parties uploading their data). In the long run, it is envisioned that a national-scale hydrometric infrastructure will be established, covering all important rivers, lakes and reservoirs of the country.

Traditionally, the use of stochastic models within water resources management aim to provide synthetically-generated inflow time series that reproduce the statistical regime of the historical data. On the other hand, the water uses are typically handled as steady-state elements, which follow a constant seasonal pattern over the entire simulation horizon. However, given that the demands are associated with highly uncertain hydroclimatic and socioeconomic factors, they should also be considered as random variables, as made for inflows. Using as example a complex hydrosystem in Western Thessaly, Greece, comprising both surface and groundwater resources to serve irrigation, water supply, environmental and hydroelectric uses, we demonstrate the advantages of a fully stochastic setting of the water management problem over its traditional configuration. Among others, we investigate the use of synthetic demands that are correlated with inflows, given that both are driven by hydroclimatic processes. Data syntheses are employed with the recently introduced AnySim stochastic simulation package (https://www.itia.ntua.gr/en/softinfo/33/).

Due to their negligible storage capacity, small hydroelectric plants cannot offer regulation of flows, thus making the prediction of energy production a very difficult task, even for small time horizons. Further uncertainties arise due to the limited hydrological information, in terms of upstream inflow data, since usually the sole available measurements refer to the power production, which is a nonlinear transformation of the river discharge. In this context, we develop a stochastic modelling framework comprising two steps. Initially, we extract past inflows on the basis of energy data, which may be referred to as the inverse problem of hydropower. Key issue of this approach is that the model error is expressed in stochastic terms, which allows for embedding uncertainties within calculations. Next, we generate stochastic forecasting ensembles of future inflows and associated hydropower production, spanning from small (daily to weekly) to meso-scale (monthly to seasonal) time horizons. The methodology is tested in the oldest (est. 1926) small hydroelectric plant of Greece, located at Glafkos river, in Northern Peloponnese. Among other complexities, this comprises a mixing of Pelton and Francis turbines, which makes the overall modelling procedure even more challenging.

As made for most of renewable energy sources, wind energy is driven by highly uncertain and thus unpredictable meteorological processes. In the context of wind power scheduling and control, reliable wind predictions across scales is a challenging problem. However, since the generation of wind energy is, in fact, a nonlinear transformation of wind velocity through the power curve of each specific turbine, the errors in meteorological predictions have different impacts on wind power forecasts. It is well-known that for quite a large range of wind velocity values, the wind power production is either zero or constant, thus independent of the individual wind velocity value. This interesting feature allows for ensuring better predictions of the output, i.e. the energy production, with respect to input, i.e. wind velocity. Taking advantage of this, we present a hybrid stochastic framework for multi-step ahead wind velocity predictions and their evaluation by means of power production and economic efficiency. The methodology is tested for different wind regimes and different layouts of wind turbine systems, emphasizing to mixing of different turbine types, which allows for minimizing uncertainties. Finally, we investigate the use of this index in the technical and operational optimization of wind energy systems.

Wind turbines are large-scale engineering infrastructures that may cause significant social reactions, due to the anticipated aesthetic nuisance. On the other hand, aesthetics is a highly subjective issue, thus any attempt towards its quantification requires accounting for the uncertainty induced from subjectivity. In this work, taking as example the Aegean island of Tinos, Cyclades, Greece, we present a stochastic-based methodology for evaluating the feasibility of developing wind parks in terms of their aesthetic impacts. At first, a field analysis is been conducted along with photographic surveying, 3D representation and the opinion of the target population regarding the development of wind parks across the island. Subsequently, the landscape transformations that will be caused from the wind turbines are assessed according to the theory of aesthetics, which are depicted by using suitable spatial analysis tools in GIS environment. The 3D representation images along with the maps are finally assessed through stochastic analysis, in order to quantify the visual impacts to the landscape and the nuisance to local community.

In November 2017, a storm event of substantial but unknown local intensity caused a flash flood in Western Attica, Greece, which was responsible for 24 human fatalities and large-scale economical losses. Our focus is to the neighbouring catchment of Sarantapotamos, which has been equipped with an automatic stage recorder that was destroyed during the rising of the flood. Our overall objective is the estimation of the rainfall over the broader area of interest, through a reverse rainfall-runoff modelling approach at this specific catchment. Several sources of information are accounted for in order to reproduce the “observed” flood hydrograph, including photos and videos. We then employ Monte Carlo simulations to evaluate the uncertainty induced from limited and even missing data. Utilising the outcome of these analyses, we provide probabilistic estimations of the modelled rainfall, as well as risk evaluations, by estimating the maximum intensities and associated return periods of the storm event across multiple time scales.

Statistical analysis of rainfall and runoff extremes plays a crucial role in hydrological design and flood risk management. Usually this analysis is performed separately for the two processes of interest, thus ignoring their dependencies, which appear at multiple temporal scales. Actually, the generation of a flood strongly depends on soil moisture conditions, which in turn depends on past rainfall. Using daily rainfall and runoff data from about 400 catchments in USA, retrieved from the MOPEX repository, we investigate the statistical behavior of the corresponding annual rainfall and streamflow maxima, also accounting for the influence of antecedent soil moisture conditions. The latter are quantified by means of accumulated daily rainfall at various aggregation scales (i.e., from 5 up to 30 days) before each extreme rainfall and streamflow event. Analysis of maxima is employed by fitting the Generalized Extreme Value (GEV) distribution, using the L-moments method for extracting the associated parameters (shape, scale, location). Significant attention is paid for ensuring statistically consistent estimations of the shape parameter, which is empirically adjusted in order to minimize the influence of sample uncertainty. Finally, we seek for the possible correlations among the derived parameter values and hydroclimatic characteristics of the studied basins, and also depict their spatial distribution across USA.

We present a distributed hydrological model with minimal calibration requirements, which represents the rainfall-runoff transformation and the flow routing processes. The generation of surface runoff is based on a modified NRCS-CN scheme. Key novelty is the use of representative CN values, which are initially assigned to model cells on the basis of slope, land cover and permeability maps, and adjusted to antecedent soil moisture conditions. For the propagation of runoff to the basin outlet two flow types are considered, i.e. overland flow across the terrain and channel flow along the river network. These are synthesized by employing a novel velocity-based approach, where the assignment of velocities along the river network is based on macroscopic hydraulic information. It also uses the concept of varying time of concentration, which is considered function of the average runoff intensity across the catchment. This configuration is suitable for event-based flood simulation and requires the specification of only two lumped inputs, which are either manually estimated or inferred through calibration. The model can also run in continuous mode, by employing a soil moisture accounting scheme that produces both the surface (overland) runoff and the interflow through the unsaturated zone. The two model configurations are demonstrated in the representation of observed flows across Nedontas river basin at South Peloponnese, Greece.

The preliminary design of small hydropower plants is typically relied on empirically-derived flow-duration curves (FDCs). This approach allows for estimating characteristic quantities of interest, such as the mean annual energy production, the mean annual water volume captured by the turbines and the mean annual time of turbine operation. In this work, we aim to parameterize the daily FDCs in statistical terms, i.e. by fitting suitable distribution functions and express their uncertainty through confidence intervals. The fitting procedure emphasizes to the accurate representation of the main body of the distribution, since the high flows cannot be captured by hydropower plants without sufficient storage capacity, while the lower ones are reserved for environmental purposes. The parametric FDCs are next used to provide statistical predictions of the desirable design variables. The methodology is applied to a sample of Mediterranean catchments with different hydroclimatic and geomorphological characteristics. Based on the outcomes of this analysis, we also attempt to establish regional relationships, by associating key statistical and design quantities with lumped properties and hydrological signatures of the studied catchments.

We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modeling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms on antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modeling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically derived quantiles for given return periods. Lastly, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.

The objective of this article is to provide a simple and effective tool for low flow forecasting up to six months ahead, with minimal data requirements, i.e. flow observations retrieved at the end of wet period (first half of April, for the Mediterranean region). The core of the methodological framework is the exponential decay function, while the typical split-sample approach for model calibration, which is known to suffer from the dependence on the selection of the calibration data set, is enhanced by introducing the so-called Randomly Selected Multiple Subsets (RSMS) calibration procedure. Moreover, we introduce and employ a modified efficiency metric, since in this modelling context the classical Nash-Sutcliffe efficiency yields unrealistically high performance. The proposed framework is evaluated at 25 Mediterranean rivers of different scales and flow dynamics, including streams with intermittent regime. Initially, signal processing and data smoothing techniques are applied to the raw hydrograph, in order to cut-off high flows that are due to flood events occurring in dry periods, and allow for keeping the decaying form of the baseflow component. We then employ the linear reservoir model to extract the annually varying recession coefficient, and, then, attempt to explain its median value (over a number of years) on the basis of typical hydrological indices and the catchment area. Next, we run the model in forecasting mode, by considering that the recession coefficient of each dry period ahead is a linear function of the observed flow at the end of the wet period. In most of the examined catchments, the model exhibits very satisfactory predictive capacity and is also robust, as indicated by the limited variability of the optimized model parameters across randomly selected calibration sets.

From the origins of hydrology, the time of concentration, tc, has conventionally been tackled as a constant quantity. However, theoretical proof and empirical evidence imply that tc exhibits signif...

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.

Fluvial floods are one of the primary natural hazards to our society, and the associated flood risk should always be evaluated for present and future conditions. The European Union’s (EU) Floods Directive highlights the importance of flood mapping as a key stage for detecting vulnerable areas, assessing floods’ impacts, and identifying damages and compensation plans. The implementation of the EU Flood Directive in Greece is challenging because of its geophysical and climatic variability and diverse hydrologic and hydraulic conditions. This study addressed this challenge by modeling of design rainfall at the sub-watershed level and subsequent estimation of flood design hydrographs using the Natural Resources Conservation Service (NRCS) Unit Hydrograph Procedure. The HEC-RAS 2D model was used for flood routing, estimation of flood attributes (i.e., water depths and flow velocities), and mapping of inundated areas. The modeling approach was applied at two complex and ungauged representative basins: The Lake Pamvotida basin located in the Epirus Region of the wet Western Greece, and the Pinios River basin located in the Thessaly Region of the drier Central Greece, a basin with a complex dendritic hydrographic system, expanding to more than 1188 river-km. The proposed modeling approach aimed at better estimation and mapping of flood inundation areas including relative uncertainties and providing guidance to professionals and academics.

In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget. Display Omitted The novel Surrogate-Enhanced Evolutionary Annealing Simplex algorithm (SEEAS) is proposed.Surrogate model is used as global search routine and for identifying promising transitions within simplex-based operators.SEEAS outperforms alternative methods in 6 test functions, in 15 & 30 dimensions and for 500 & 1000 function evaluations.SEEAS handles typical peculiarities of water optimization in hydrological calibration and multi-reservoir management.

Summary One-dimensional and quasi-two-dimensional hydraulic freeware models (HEC-RAS, LISFLOOD-FP and FLO-2d) are widely used for flood inundation mapping. These models are tested on a benchmark test with a mixed rectangular–triangular channel cross section. Using a Monte-Carlo approach, we employ extended sensitivity analysis by simultaneously varying the input discharge, longitudinal and lateral gradients and roughness coefficients, as well as the grid cell size. Based on statistical analysis of three output variables of interest, i.e. water depths at the inflow and outflow locations and total flood volume, we investigate the uncertainty enclosed in different model configurations and flow conditions, without the influence of errors and other assumptions on topography, channel geometry and boundary conditions. Moreover, we estimate the uncertainty associated to each input variable and we compare it to the overall one. The outcomes of the benchmark analysis are further highlighted by applying the three models to real-world flood propagation problems, in the context of two challenging case studies in Greece.

The ever-increasing energy demand has led to overexploitation of fossil fuels deposits, while renewables offer a viable alternative. Since renewable energy resources derive from phenomena related to either atmospheric or geophysical processes, unpredictability is inherent to renewable energy systems. An innovative and simple stochastic tool, the climacogram, was chosen to explore the degree of unpredictability. By applying the climacogram across the related timeseries and spatial-series it was feasible to identify the degree of unpredictability in each process through the Hurst parameter, an index that quantifies the level of uncertainty. All examined processes display a Hurst parameter larger than 0.5, indicating increased uncertainty on the long term. This implies that only through stochastic analysis may renewable energy resources be reliably manageable and cost efficient. In this context, a pilot application of a hybrid renewable energy system in the Greek island of Astypalaia is discussed, for which we show how the uncertainty (in terms of variability) of the input hydrometeorological processes alters the uncertainty of the output energy values.

Since the prime days of stochastic hydrology back in 1960s, autoregressive (AR) and moving average (MA) models (as well as their extensions) have been widely used to simulate hydrometeorological processes. Initially, AR(1) or Markovian models with Gaussian noise prevailed due to their conceptual and mathematical simplicity. However, the ubiquitous skewed behavior of most hydrometeorological processes, particularly at fine time scales, necessitated the generation of synthetic time series to also reproduce higher-order moments. In this respect, the former schemes were enhanced to preserve skewness through the use of non-Gaussian white noise&mdash, a modification attributed to Thomas and Fiering (TF). Although preserving higher-order moments to approximate a distribution is a limited and potentially risky solution, the TF approach has become a common choice in operational practice. In this study, almost half a century after its introduction, we reveal an important flaw that spans over all popular linear stochastic models that employ non-Gaussian white noise. Focusing on the Markovian case, we prove mathematically that this generating scheme provides bounded dependence patterns, which are both unrealistic and inconsistent with the observed data. This so-called &ldquo, envelope behavior&rdquo, is amplified as the skewness and correlation increases, as demonstrated on the basis of real-world and hypothetical simulation examples.

In this paper, a new methodology for delineating Hydrological Response Units (HRUs), based on the Curve Number (CN) concept, is presented. Initially, a semi-automatic procedure in a GIS environment is used to produce basin maps of distributed CN values as the product of the three classified layers, soil permeability, land use/land cover characteristics and drainage capacity. The map of CN values is used in the context of model parameterization, in order to identify the essential number and spatial extent of HRUs and, consequently, the number of control variables of the calibration problem. The new approach aims at reducing the subjectivity introduced by the definition of HRUs and providing parsimonious modelling schemes. In particular, the CN-based parameterization (1) allows the user to assign as many parameters as can be supported by the available hydrological information, (2) associates the model parameters with anticipated basin responses, as quantified in terms of CN classes across HRUs, and (3) reduces the effort for model calibration, simultaneously ensuring good predictive capacity. The advantages of the proposed approach are demonstrated in the hydrological simulation of the Nedontas River Basin, Greece, where parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS model. A modelling experiment with a varying number of HRUs, where the parameter estimation problem was handled through automatic optimization, showed that the parameterization with three HRUs, i.e., equal to the number of flow records, ensured the optimal performance. Similarly, tests with alternative HRU configurations confirmed that the optimal scores, both in calibration and validation, were achieved by the CN-based approach, also resulting in parameters values across the HRUs that were in agreement with their physical interpretation.

River basins are by definition temporally-varying systems: changes are apparent at every temporal scale, in terms of changing meteorological inputs and catchment characteristics due to inherently uncertain natural processes and anthropogenic interventions. In an operational context, the ultimate goal of hydrological modelling is predicting responses of the basin under conditions that are similar or different to those observed in the past. Since water management studies require that anthropogenic effects are considered known and a long hypothetical period is simulated, the combined use of stochastic models, for generating the inputs, and deterministic models that also represent the human interventions in modified basins, is found to be a powerful approach for providing realistic and statistically consistent simulations (in terms of product moments and correlations, at multiple time scales, and long-term persistence). The proposed framework is investigated on the Ferson Creek basin (USA) that exhibi...

A time series generator is presented, employing a robust three-level multivariate scheme for stochastic simulation of correlated processes. It preserves the essential statistical characteristics of historical data at three time scales (annual, monthly, daily), using a disaggregation approach. It also reproduces key properties of hydrometeorological and geophysical processes, namely the long-term persistence (Hurst-Kolmogorov behaviour), the periodicity and intermittency. Its efficiency is illustrated through two case studies in Greece. The first aims to generate monthly runoff and rainfall data at three reservoirs of the hydrosystem of Athens. The second involves the generation of daily rainfall for flood simulation at five rain gauges. In the first emphasis is given to long-term persistence - a dominant characteristic in the management of large-scale hydrosystems, comprising reservoirs with carry-over storage capacity. In the second we highlight to the consistent representation of intermittency and asymmetry of daily rainfall, and the distribution of annual daily maxima. We provide a multivariate generator based on a three-level disaggregation scheme.The model preserves the statistical behaviour of observed data at multiple scales.Long-term persistence is reproduced through a used-defined autocovariance function.Highly-skewed and intermittent processes, e.g. rainfall, are effectively modelled.The methodology is applicable for both steady-state and terminating simulations.

We present and validate a global parametric model of potential evapotranspiration (PET) with two parameters that are estimated through calibration, using as explanatory variables temperature and extraterrestrial radiation. The model is tested over the globe, taking advantage of the Food and Agriculture Organization (FAO CLIMWAT) database that provides monthly averaged values of meteorological inputs at 4300 locations worldwide. A preliminary analysis of these data allows for explaining the major drivers of PET over the globe and across seasons. The model calibration against the given Penman-Monteith values was carried out through an automatic optimization procedure. For the evaluation of the model, we present global maps of optimized model parameters and associated performance metrics, and also contrast its performance against the well-known Hargreaves-Samani method. Also, we use interpolated values of the optimized parameters to validate the predictive capacity of our model against monthly meteorological time series, at several stations worldwide. The results are very encouraging, since even with the use of abstract climatic information for model calibration and the use of interpolated parameters as local predictors, the model generally ensures reliable PET estimations. Exceptions are mainly attributed to irregular interactions between temperature and extraterrestrial radiation, as well as because the associated processes are influenced by additional drivers, e.g., relative humidity and wind speed. However, the analysis of the residuals shows that the model is consistent in terms of parameters estimation and model validation. The parameter maps allow for the direct use of the model wherever in the world, providing PET estimates in case of missing data, that can be further improved even with a short term acquisition of meteorological data.

Despite the great scientific and technological advances in flood hydrology, everyday engineering practices still follow simplistic approaches, such as the rational formula and the SCS-CN method combined with the unit hydrograph theory that are easy to formally implement in ungauged areas. In general, these "recipes" have been developed many decades ago, based on field data from few experimental catchments. However, many of them have been neither updated nor validated across all hydroclimatic and geomorphological conditions. This has an obvious impact on the quality and reliability of hydrological studies, and, consequently, on the safety and cost of the related flood protection works. Preliminary results, based on historical flood data from Cyprus and Greece, indicate that a substantial revision of many aspects of flood engineering procedures is required, including the regionalization formulas as well as the modelling concepts themselves. In order to provide a consistent design framework and to ensure realistic predictions of the flood risk (a key issue of the 2007/60/EU Directive) in ungauged basins, it is necessary to rethink the current engineering practices. In this vein, the collection of reliable hydrological data would be essential for re-evaluating the existing "recipes", taking into account local peculiarities, and for updating the modelling methodologies as needed.

We propose a systematic framework for delineating Hydrological Response Units (HRUs), based on a modified Curve Number (CN) approach. The CN-value accounts for three major physiographic characteristics of a river basin, by means of classes of soil permeability, land use/land cover characteristics, and drainage capacity. A semi-automatic procedure in a GIS environment allows producing basin maps of distributed CN-values as the product of the three classified layers. The map of CN-values is used in the context of model parameterization, in order to identify the essential number and spatial extent of HRUs and, consequently, the number of control variables of the calibration problem. The new approach aims at reducing the subjectivity introduced by the definition of HRUs, and simultaneously at providing parsimonious modelling schemes. In particular, the CN-based parameterization (1) allows the user to assign as many parameters as can be supported by the available hydrological information, (2) associates the model parameters with anticipated basin responses, as quantified in terms of CN classes across HRUs, and (3) reduces the effort for model calibration, simultaneously ensuring good predictive capacity. The advantages of the proposed framework are demonstrated in the hydrological simulation of Nedontas river basin, Greece, in which parameterizations of different complexities are employed in a recently improved version of the HYDROGEIOS modelling framework.

Flow duration curves are useful tools to estimate available surface water resources, at the basin scale. These represent the percentage of time during which discharge values are exceeded, irrespective of their temporal sequence. Annual flow duration curves are useful tools for evaluating all flow quantiles of a river and their confidence intervals, by removing the effects of variability from year to year. However, these tools fail to represent the hydrological regime of ephemeral rivers, since they cannot account for zero flows. In this work we propose a technique for calculating annual flow duration curves and their standard deviation in the case of intermittent rivers. In particular, we propose a generalization of the stochastic index method, in which we use the concept of total probability and order statistics. The method is proposed to determine the conditional distribution of positive flows, for given probability of dryness, and is implemented on three catchments in Italy and Greece, with low ( 40%) frequency of zero flows, respectively. Copyright © 2013 John Wiley & Sons, Ltd.

(1) The objective function of the inverse problem in Soil-Vegetation-Atmosphere-Transfer (SVAT) models can be expressed as the aggregation of two criteria, accounting for the uncertainties of surface soil moisture (� ) and evapotranspiration (ET), retrieved from remote sensing (RS). In this context, we formulate a Weighted-Objective-Function (WOF) with respect to model effective soil hydraulic parameters, comprising of two components for � and ET, respectively, and a dimensionless coefficient w. Given that the sensitivity ofis increased by omitting the periods when soil moisture decoupling occurs, we also introduce within the WOF a threshold, � d, which outlines the decoupling of the surface and root-zone moisture. The optimal values of w andd are determined by using a novel framework, weighted objective function selector algorithm (WOFSA). This performs numerical experiments, assuming known reference conditions. In particular, it solves the inverse problem for different sets ofand ET, considering the uncertainties of retrieving them from RS, and then runs the hydrological model to obtain the simulated water fluxes and their residuals, DWF, against the reference responses. It estimates the two unknown variables, w andd, by maximizing the linear correlation between the WOF and maximum DWF. The framework is tested using a modified Soil-Water-Atmosphere-Plant (SWAP) model, under 22 contrasting hydroclimatic scenarios. It is shown that for each texture class, w can be expressed as function of the averageand ET-fraction, while that for all scenariosd can be modeled as function of the average � , average ET, and standard deviation of ET. Based on the outcomes of this study, we also provide recommendations on the most suitable time period for soil moisture measurements for capturing its dynamics and thresholds. Finally, we propose the implementation of WOFSA within multiobjective calibration, as a generalized tool for recognizing robust solutions from the Pareto front.

Solar radiation, direct and diffuse, is affected by surface characteristics, such as slope, aspect, altitude and shading. The paper examines the effects of topography on radiation, at multiple spatiotemporal scales, using suitable geometric methods for the direct and diffuse components. Two indices are introduced for comparing the direct radiation received by areas at the same and different latitudes. To investigate the profile of direct radiation across the whole of Greece, these are evaluated from an hourly to annual basis, via GIS techniques. Moreover, different approaches are examined for estimating the actual global radiation at operational spatial scales (sub-basin and terrain), according to the available meteorological data. The study indicates that the errors of typical hydrometeorological modelling formulas, which ignore the topographic effects and the seasonal allocation of direct and diffuse radiation, depend on the spatial scale and are non-uniformly distributed in time. In all cases,...