Search

The past few years have seen the raise of unmanned aerial vehicles (UAV) in geosciences for generating highly accurate digital elevation models (DEM) at low costs, which promises to be an interesting alternative to satellite data for small river basins. The reliability of UAV-derived topography as input to hydraulic modelling is still under investigation: here, we analyse potentialities and highlight challenges of employing UAV-derived topography in hydraulic modelling in a tropical environment, where weather conditions and remoteness of the study area might affect the quality of the retrieved data. We focused on a stretch of the Limpopo River in Mozambique, where detailed ground survey and airborne data were available. First, we tested and compared topographic data derived by UAV (25 cm), RTK-GPS (50 cm DEM), LiDAR (1 m DEM) and SRTM (30 m DEM); then, we used each DEM as input data to a hydraulic model and compared the performance of each DEM-based model against the LiDAR based model, currently used as benchmark by practitioners in the area. Despite the challenges experienced during the field campaign—and described here—, the degree of accuracy in terrain modelling produced errors in water depth calculations within the tolerances adopted in this typology of studies and comparable in magnitude to the ones obtained from high-precision topography models. This suggests that UAV is a promising source of geometric data even in natural environments with extreme weather conditions.

The effects of land use alteration, migration and urbanization are key aspects in flood management, as human activities can strongly influence the capacity of ecosystems to provide flood regulating ecosystem services and determine their demand. This study analyzes spatio–temporal dynamics of flood regulating ecosystem services to support watershed management planning. A methodology for mapping the supply and demand of flood regulation is proposed and applied to the Arno River basin, in central Italy. The spatial explicit analysis of flood regulating ecosystem services supply is carried out with SWAT - Soil and Water Assessment Tool, whose outputs are synthetized by two indicators to evaluate the retention capacity of each land use class originating from CORINE data sets. Quantification of demand for flood regulating ecosystem services is based on flood hazard classes derived from the existing local flood management plans (i.e., PAI-Piano per l’Assetto Idrogeologico and PGRA-Piano di Gestione del Rischio Alluvioni). Supply and demand data are then combined to obtain budget maps of flood regulating ecosystem services and their evolution, between 1990 and 2018. The results show how both demand and supply of ecosystem services have changed in the last decades, highlighting the main hotspots at the catchment and sub-catchment scales. With the increasing urbanization, the demand values have grown in the Arno floodplains, where residential, industrial and commercial zones are located. At the same time, land use changes have altered the water regulation supply, resulting in a generalized decrease of the basin capacity to provide flood regulation services. The maps and tables obtained show the fundamental role of forest and other vegetated areas whose protection is a priority to assure future flood regulation and associated co-benefits (e.g., regulation of air quality, reduction of erosion, improvement of water quality, wood fuel). The assessment of flood regulating here proposed is a powerful tool for decision makers to improve flood regulation and provides a sound base of knowledge to identify and locate flood prevention and mitigation measures.

There are currently several large-scale gridded archives available for the study of flood exposure, and the results will inevitably depend on the datasets included in the analysis. The purpose of this work is to demonstrate how country flood exposure, here represented as the presence of population within floodplains, is influenced by dataset choices.We conduct this geographical analysis in two parts. First, we conduct a global analysis showing how different flood exposure metrics influence comparisons between countries. Second, we overlay five commonly used gridded archives (three population archives and two floodplain archives) for 32 countries. The purpose is to quantify the influence of data choices, while also giving an overview of the various dataset methodologies. We finally zoom in on areas where the five datasets yield very dissimilar results, to exemplify typical differences among the datasets.

Human activities can strongly influence the capacity of ecosystems to provide flood regulating ecosystem services (ES). Therefore, the effects of land use alteration, population migration and urbanization are key aspects to be considered when dealing with flood management. This study aims at analyzing the spatio‑temporal dynamics of flood regulating ES to support watershed management planning. The spatial explicit analysis of flood regulating ES is carried out with SWAT - Soil and Water Assessment Tool, using daily meteorological data between 2000 and 2014. Two indicators are elaborated in order to evaluate the retention capacity of each land use setting and to map the ES supply. Demand quantification is obtained from the information derived by the existing flood management plans (i.e. PAI-Piano per l’Assetto Idrogeologico and PGRA-Piano di Gestione del Rischio Alluvioni) which contain the identification and the perimeter of hydraulic hazard classes. Supply and demand data are then merged in order to obtain budget maps of flood regulating ES and their evolution from 1960 up to 2012 (1960, 1990, 2000 and 2012). The results show that both the demand and the supply of ecosystem services change during the time. With the increasing urbanization, the demand values have grown in the Arno floodplains, where residential, industrial and commercial zones are located. At the same time, land use changes (e.g. intensive agriculture) have caused negative effects on water regulation supply. This work shows the advantages of assessing flood regulating ES to improve flood regulation in the Arno river basin and provide a sound base of knowledge to identify floods prevention and mitigation measures.

Hydropower accounts for almost half of all electricity production in Sweden, while also being the most important energy balancing resource. Nuclear power plants are gradually being decommissioned up to 2045 in Sweden according to the government’s plans. It means there is a need for a dramatic expansion of renewable energy production, especially for hydropower as a balancing resource. The availability of renewable energy fluctuates with the weather, seasons, and between years, which is an important factor for the coordination of renewable energy production.The aim of this research is to investigate how runoff forecasts affect hydropower production planning when the account is taken to hydro-climatic variations. This problem is studied by using production planning models fed by runoff forecasts that exhibit climate-driven periodicity. Statistical ensemble predictions and receding horizon control are implemented to reveal the effect on the production. Further, we utilize half-a-century long daily hydro-climatological data to runoff forecasts that are aggregated in ensembles particularly reflecting the bi-annual climate periodicity apparent from spectral analysis of the data.Dalälven River Basin is used as a study case, which is a watershed stretching from western Sweden to the Baltic in the east and which has more than 30 hydropower stations. Four forecast ensemble scenarios have been analyzed out by studying the periodicity of discharge data in Dalälven River Basin. According to the seasonal and two-year periodicity, the four scenario ensembles are defined as: a) Odd year, wet month; b) Even year, dry month; c) Odd year, dry month; d) Even year, wet month. [A1] A large-scale optimal hydropower production model was built in MATLAB, in order to simulate hydropower production in Dalälven River Basin. It includes 13 reservoirs and 36 hydropower plants, and applies the sequential-quadratic-programming (SQP ) as the optimization method. Receding horizon control is embedded into the optimal production programming, which can correct the systematic error. The historical data shows that the one-year-long sampling has a consistent biannual periodicity, with wet and dry years of different strength depending on the start month of the data selection in the year. It indicates the discharge data selection start from November and December gives the most dramatic effect on periodicity, while starting from May and July have a lower impact. Dynamic programming of hydropower production shows for both dry and wet runoff conditions that matching the forecast ensemble with the right phase of the actual climate conditions has a significant effect on the production profit.

The viability of a renewable electricity system depends on long-term climate variations, uneven spatiotemporal distribution of renewable energy, and technical constraints. A major problem is to achieve a sustainable balance of water usage and consumption, as well as adequate energy and water distribution and storage capacities. In particular, hydropower offers a large capacity for energy storage and production flexibility, but only stands for a minor part of the total energy potential. In this study we explored the spatial and temporal variance of hydropower availability for a 35-year period based on historical hydro-meteorological data from large parts of Europe. A spectral analysis of these historical time-series shows that spatiotemporal coordination of the hydropower system covered in the Global Reservoir and Dam Database (GRanD) can potentially contribute with a “virtual” energy storage capacity that is up to four times the actual energy storage capacity contained in the existing hydropower reservoirs. Such virtual energy storage capacity implies reduced water storage demand, hence, indirectly contributes to reduced constraints of the food-water-energy nexus also in a wider system perspective. We found that the most significant benefits from a spatiotemporal management arise at distances of 1,200 – 3,000 km, i.e. on the continental scale, which can have implications for a future renewable energy system at large. The analysis also covers what we denote “energy-domain-specific drought”, which implies a shortage of energy storage capacity to avoid a deficit of energy for a given time period, and which may be reduced by the spatiotemporal coordination of power production.

Our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models has made tremendous progress over the past decade. However, most studies have only analysed the performance of one or few datasets, have used global precipitation datasets to force lumped models on regional/large-scale basins, or have adopted more complex distributed models but applied them to small basin scales.We aimed at addressing these gaps in the literature: in particular, we compared the performance of 18 different precipitation datasets used as input in a grid-based distributed hydrological model to assess streamflow in large-scale river basins. These datasets are classified as Uncorrected Satellites, Corrected Satellites, and Reanalysis-Gauges based datasets. The hydrological model is applied to 8 large scale river basins (Amazon, Brahmaputra, Congo, Danube, Godavari, Mississippi, Rhine and Volga) with different sizes, presence of hydraulic structures, human footprint, hydrometeorological characteristics, and precipitation gauge network density were selected.The results of this study showed that there is not a unique best performing precipitation dataset for all basins and results are very sensitive to the basin characteristics. However, there are few datasets which persistently outperform the others: SM2RAIN-ASCAT for Class 1, CHIRPS V2.0, MSWEP V2.1, and CMORPH-CRTV1.0 for Class 2, GPCC and WFEDEI GPCC for Class 3. The use of a distributed modelling approach rather than lumped is supported by the fact that precipitation datasets showing the highest model result at the basin outlet do not show the same high performance at internal locations of the basin. In addition, precipitation datasets belonging to Class 2 outperform the other datasets in basins with Tropical and Temperate-Arid climate (e.g. Congo, Mississippi and Godavari), while Class 3 datasets show the highest NSE values in Temperate and Temperate-Cold basins (e.g. Danube, Rhine and Volga).

The viability of a renewable electricity system depends on a relatively small share of hydropower storage resources to regulate climate variations and the spatially uneven distribution of renewable energy. By spatio-temporal coordination of hydropower production over larger regions, the energy storage demand will be reduced and contribute to a “virtual” energy storage gain that in Europe was found to be almost twice the actual energy storage capacity of hydropower reservoirs. In an attempt to quantify this gain, hydropower availability was simulated for most parts of the European continent for a 35-year period based on historical hydrometeorological data. The most significant benefits from spatio-temporal management arise at distances between 1200 and 3000 km, i.e., on the continental scale, which can have implications for a future renewable energy system at large. Furthermore, we discuss a condition termed “energy-domain-specific drought”, which is a risk that can be reduced by the spatio-temporal management of power production. Virtual energy storage gain is not explicitly considered in the management models of hydropower production systems but could in principle complement existing management incentives.

Over the past decades, a variety of valuable research studies has helped to advance our understanding of the advantages and limitations of satellite derived precipitation datasets as a forcing to hydrological models, in combination with or as an alternative to gauge data. However, most studies have assessed the performance of only one single dataset (or a few), have used global precipitation datasets to force lumped models on regional/large-scale basins or have tested more complex distributed models only at small-scale basins. In addition, only few studies have re-calibrated the model for each precipitation dataset or have investigated reanalysis-based precipitation datasets. We aimed at addressing these gaps in the literature: in particular, we compared the performance of 18 different precipitation datasets when used as main forcing in a grid-based distributed hydrological model to assess streamflow in medium to large-scale river basins. These datasets are classified as Uncorrected Satellites (Class 1), Corrected Satellites (Class 2) and Reanalysis - Gauges based datasets (Class 3). To provide a broad-based analysis, 8 large-scale river basins (Amazon, Brahmaputra, Congo, Danube, Godavari, Mississippi, Rhine and Volga) having different sizes, hydrometeorological characteristics, and human influence were selected. The distributed hydrological model was recalibrated for each precipitation dataset individually. We found that there is not a unique best performing precipitation dataset for all basins and that results are very sensitive to the basin characteristics. However, a few datasets persistently outperform the others: SM2RAIN-ASCAT for Class 1, CHIRPS V2.0, MSWEP V2.1, and CMORPH-CRTV1.0 for Class 2, GPCC and WFEDEI GPCC for Class 3. Surprisingly, precipitation datasets showing the highest model accuracy at basin outlets do not show the same high performance in internal locations, supporting the use of distributed modelling approach rather than lumped.

The population growth and the economic development that the five Countries (Argentina, Bolivia, Brazil, Paraguay and Uruguay) located in La Plata Basin (LPB) have gone through over the last decades have risen the necessity to assess the current and future electricity demand and compare it to the energy production. In this region, the water resources of LPB are one of the most important sources of energy. In this context, the CLARIS EU (European Union) FP7 project has carried out an extensive research work which, in terms of investigating hydropower potential and vulnerability, has produced the following outcomes: assessment of current hydropower production and electricity demand; calculation of the maximum potential hydropower in order to establish whether and when shortage in terms of hydropower generation is to be expected; analysis of potential residual hydropower taking into account future user demands and analysis of hydropower production taking into account climate change scenarios, in order to iden...

Population growth, economic development, urbanization, changes in hydrological regimes and land use are the main drivers affecting allocation and exploitation of water resources. The pressure exerted by these global changes on the five countries -Argentina, Bolivia, Brazil, Paraguay and Uruguay- located in La Plata Basin over the last decades has raised the need for assessing trends in future electricity demand and energy production in the basin. The aim of this research is to assess whether (and when) shortage and vulnerabilities in terms of hydropower generation are to be expected in La Plata basin in the next decades. The methodology proposed has focused on two aspects to reach the objectives: 1) assessment of hydropower production and electricity demand in the basin over the last twenty years (1987–2008), in order to establish growing trends for the next thirty years; 2) computation of maximum potential hydropower using the newly developed Arc-GIS based tool VAPIDRO-ASTE. The assessment and calculation have been applied to La Plata River and its main tributaries: Paranaiba, Grande River, Tiete, Paranapanema, Iguacu, Uruguay, Negro River, Paraguay and Parana. The first outcomes of this research show that La Plata Basin has high hydropower potential. About 40% of the hydropower potential is already used to produce and supply energy. Out of the remaining 60% potential, about 25% could hardly be exploited because of environmental issues or low cost/benefit ratio. Thus, the estimated residual potential hydropower is about 35% of the maximum potential hydropower calculated.

La Plata Basin is shared by five countries (Argentina, Bolivia, Brazil, Paraguay and Uruguay), which have fast growing economies in South America. These countries need energy for their sustainable development; hence, hydropower can play a very important role as a renewable clean source of energy. This paper presents an analysis of the current hydropower production and electricity demand in La Plata Basin (LPB), and it analyses the maximum and residual hydropower potential of the basin for a horizon of 30 yr (i.e. year 2040). Current hydropower production is estimated based on historical available data, while future energy production is deduced from the available water in the catchment (estimated based on measured hydrographs of the past years), whereas electricity demand is assessed by correlating existing electricity demand with the estimated population growth and economic development. The maximum and residual hydropower potential of the basin were assessed for the mean annual flows of the present hydrological regime (1970–2000) and topographical characteristics of the area. Computations were performed using an integrated GIS environment called VAPIDRO-ASTE released by the Research on Energy System (Italy). The residual hydropower potential of the basin is computed considering first that the water supply needs for population, industry and agriculture are served, and then hydropower energy is produced. The calculated hydropower production is found to be approximately half of the estimated electricity demand, which shows that there is a need to look for other sources of energy in the future.

The presence of a bridge spanning over a river and its floodplain alters the condition of flow. In subcritical conditions, which are typical in alluvial rivers, this flow alteration results in the so-called backwater effect, that is, an increase of the water surface elevation upstream of the structure as a response to increased energy losses. The extension of the expected backwater affected area and the increase in water surface elevation are highly dependent on the river morphology, bridge geometry, flow and floodplain characteristics and may originate upstream flooding. Thus, in many countries, at the stage of design a new bridge (or renovation works), a specific investigation on the backwater effect has to be undertaken to analyse its effect on the flooding of its vicinity. Bridge waterway hydraulics has been widely investigated in terms of practical research and some standard guidelines have been suggested for modelling the backwater effect at bridge crossings. Although the scientific literature has widely proven that hydraulic modelling is affected by many sources of uncertainty, the hydraulic modelling of backwater effects is still undertaken within a deterministic approach, based on the outcomes of a calibrated hydraulic model. This article aims at approaching the prediction of backwater effects at bridge crossings by accounting for the main sources of uncertainty affecting the hydraulic modelling exercise. A bridge over a highly vegetated floodplain (Tallahala Creek, Mississippi, USA) was considered for this analysis, and uncertainty in the model parameters and input data was assessed to predict backwater flood design profiles. Copyright © 2012 John Wiley & Sons, Ltd.

It is widely recognised that remote sensing can support flood monitoring, modelling and management. In particular, satellites carrying Synthetic Aperture Radar (SAR) sensors are valuable as radar wavelengths can penetrate cloud cover and are insensitive to daylight. However, given the strong inverse relationship between spatial resolution and revisit time, monitoring floods from space in near real time is currently only possible through low resolution (about 100 m pixel size) SAR imagery. For instance, ENVISAT-ASAR (Advanced Synthetic Aperture Radar) in WSM (wide swath mode) revisit times are of the order of 3 days and the data can be obtained within 24 h at no (or low) cost. Hence, this type of space-borne data can be used for monitoring major floods on medium-to-large rivers. This paper aims to discuss the potential for, and uncertainties of, coarse resolution SAR imagery to monitor floods and support hydraulic modelling. The paper first describes the potential of globally and freely available space-borne data to support flood inundation modelling in near real time. Then, the uncertainty of SAR-derived flood extent maps is discussed and the need to move from deterministic binary maps (wet/dry) of flood extent to uncertain flood inundation maps is highlighted.

A flow-duration curve (FDC) illustrates the relationship between the frequency and magnitude of streamflow. Applications of FDC are of interest for many hydrological problems related to hydropower generation, river and reservoir sedimentation, water quality assessment, water-use assessment, water allocation and habitat suitability. This study addresses the problem of FDC estimation for ungauged river basins, assessing the effectiveness and reliability of several regional approaches. The study refers to a wide region of eastern central Italy and adopts a jack-knife cross-validation procedure to evaluate the uncertainty of regional FDC’s, comparing it with the uncertainty of empirical FDC’s constructed from short samples of streamflow data. The results (a) provide an evaluation of the reliability of the regional FDC’s for ungauged sites, (b) show that the reliability of the three best performing regional models are similar to one another, and (c) demonstrate that empirical FDC’s based on limited data samples generally provide a better fit of the long-term FDC’s than regional FDC’s.

The levee effect refers to the paradox that the construction of a levee to protect from flooding might induce property owners to invest more in their property, increasing the potential damages shou ...