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Proceedings of the 6th IAHR Europe Congress : hydro-environment research and engineering - no frames, no borders (Warsaw, February 15-18, 2021)
Influence of water depth on wave overtopping
The wave overtopping discharge during extreme storm conditions largely determines the required crest height in dike reinforcement projects. In case of future sea level rise, crest heights should be increased significantly, which potentially leads to significant costs and environmental and societal consequences. The available empirical design formulas for wave overtopping are based on data sets that show a relatively large scatter, potentially leading to inefficient design of crest level heights. In this paper a novel approach is presented in which the water depth in front of the dike is incorporated in the wave overtopping formulation leading to a lower scatter and in the case of shallow foreshores also a lower required crest height. The present approach is based on the use of an existing overtopping database (CLASH-EurOtop) and additional generated data by conducting physical experiments (Scheldt Flume of Deltares) and numerical experiments (OpenFOAM) in which the water depth was varied systematically for a variety of test conditions. Based on these three approaches, it is concluded that the wave overtopping discharge is dependent on the water depth. Since this dependency is not included in the present wave overtopping formulation, this paper suggests a method to incorporate this dependency in the present existing formulas by implementing an influence factor for water depth.
FLOODrisk 2020 - proceedings of the 4th European Conference on Flood Risk Management : science and practice for an uncertain future (Budapest, 21 June - 25 June 2021)
Operational optimization of district heating systems with temperature limited sources
Future district heating systems (DHS) will be supplied by renewable sources, most of which are limited in temperature and flow rate. Therefore, operational optimization of DHS is required to maximize the use of renewable sources and minimize (fossil) peak loads. In this paper, we present a robust and fast model-predictive control approach to use the thermal mass of buildings as a daily storage without violating temperature constraints. The novelty of this paper includes two elements. First, the focus on an operational control strategy that explicitly accounts for temperature-limited renewable sources, like a geothermal source. Secondly, the optimization problem is formulated as a (nearly) convex optimization problem, which is required for adoption of model-predictive control in practice. The examples show that the peak heating demand can be reduced by 50%, if the thermal inertia of the buildings is used and the heating setpoints are adapted. Furthermore, the operational optimization finds the proper balance between benefits of pre-heating using renewable sources with limited capacity and costs of additional heat losses due to pre-heating.
Optimal planning of flood defence system reinforcements using a greedy search algorithm
Climate change and deterioration require a continuous effort to reinforce flood defences and meet reliability requirements. To efficiently upgrade flood defence systems, insight in costs and benefits of measures at a system level is required throughout the process of planning and design. Due to the size of flood defence systems the number of possible decisions is large, which hampers system optimization. We describe a greedy search algorithm that can find (near-)optimal combinations of reinforcement measures for dike segments. The algorithm has been validated by comparing results for 2800 different dike segments to an integer programming implementation. The difference in objective value (Total Cost) is only 0.04% on average, which is small compared to other uncertainties in assessment and design of dike segments. The algorithm is applied to a reinforcement project for a dike segment of 41 independent sections, and compared to the common design practice which uses reliability-based requirements on a section level. It is found that the resulting reinforced dike segment is 42% cheaper to construct than the one obtained from the common approach, based on the same input information. This illustrates the practical and societal value of the design approach using a greedy search algorithm in this context.
Processes controlling the flux of legacy phosphorus to surface waters at the farm scale
Phosphorus (P) leaching from agriculture is a major driver of water eutrophication in downstream rivers and lakes. A reduction in the fertilizer applications may be insufficient to improve the water quality in the short term as P has accumulated in the soil during decades of high fertilization and it may continue leaching for many years. A complementary approach to reduce P exports from agriculture is to implement edge-of-field mitigation measures at the farm scale. The selection of effective measures requires a detailed insight into the chemical and hydrological transport mechanisms. Here, we determined the main P sources, processes, and transport routes at the farm scale to support the selection of appropriate mitigation measures. We quantified the legacy P, the different P pools stored in the upper soil, and related it to the yearly P export downstream. To do this, we combined high-resolution monitoring data from the soil, groundwater, surface water, and ditch sediments. The legacy P in the topsoil was high, about 2,600 kg/ha. The predominant subsurface flow and the subsoils' P sorption capacity retained the P mobilized from the topsoil and explained the relative moderate flux of P to surface waters (0.04 kg/ha during the 2018-2019 drainage season). The dissolved P entering the drainage ditch via groundwater discharge was bound to iron-containing particles formed due to the oxidation of dissolved ferrous iron. Once leached from the soil to the drainage ditch, resuspension of P-rich sediment particles during flow peaks were the most important P transport mechanism (78%). Hence, hydraulic constructions that reduce flow velocities and promote sedimentation of P-containing particles could reduce the export of P further downstream.
Beach nourishment has complex implications for the future of sandy shores
Beach nourishment, the addition of sand to increase the width or sand volume of the beach, is a widespread coastal management technique to counteract coastal erosion. Globally, rising sea levels, storms and diminishing sand supplies threaten beaches and the recreational, ecosystem, groundwater and flood protection services they provide. Consequently, beach nourishment practices have evolved from focusing on maximizing the time sand stays on the beach to also encompassing human safety and water recreation, groundwater dynamics and ecosystem impacts. In this Perspective, we present a multidisciplinary overview of beach nourishment, discussing physical aspects of beach nourishment alongside ecological and socio- economic impacts. The future of beach nourishment practices will vary depending on local vulnerability, sand availability, financial resources, government regulations and efficiencies, and societal perceptions of environmental risk, recreational uses, ecological conservation and social justice. We recommend co- located, multidisciplinary research studies on the combined impacts of nourishments, and explorations of various designs to guide these globally diverse nourishment practices.
A hydrography upscaling method for scale invariant parametrization of distributed hydrological models
Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow-direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream-downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives sub-grid river attributes such as drainage area, river length, slope and width. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (~1 km), 5 arcmin (~10 km) and 15 arcmin (~30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often applied methods. Furthermore, we show that using IHU-derived hydrography data minimizes the errors made in timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.
A simple spatio–temporal data fusion method based on linear regression coefficient compensation
High spatio–temporal resolution remote sensing images are of great significance in the dynamic monitoring of the Earth’s surface. However, due to cloud contamination and the hardware limitations of sensors, it is diffcult to obtain image sequences with both high spatial and temporal resolution. Combining coarse resolution images, such as the moderate resolution imaging spectroradiometer (MODIS), with fine spatial resolution images, such as Landsat or Sentinel-2, has become a popular means to solve this problem. In this paper, we propose a simple and ecient enhanced linear regression spatio–temporal fusion method (ELRFM), which uses fine spatial resolution images acquired at two reference dates to establish a linear regression model for each pixel and each band between the image reflectance and the acquisition date. The obtained regression coefficients are used to help allocate the residual error between the real coarse resolution image and the simulated coarse resolution image upscaled by the high spatial resolution result of the linear prediction. The developed method consists of four steps: (1) linear regression (LR), (2) residual calculation, (3) distribution of the residual and (4) singular value correction. The proposed method was tested in dierent areas and using dierent sensors. The results show that, compared to the spatial and temporal adaptive reflectance fusion model (STARFM) and the flexible spatio–temporal data fusion (FSDAF) method, the ELRFM performs better in capturing small feature changes at the fine image scale and has high prediction accuracy. For example, in the red band, the proposed method has the lowest root mean square error (RMSE) (ELRFM: 0.0123 vs. STARFM: 0.0217 vs. FSDAF: 0.0224 vs. LR: 0.0221). Furthermore, the lightweight algorithm design and calculations based on the Google Earth Engine make the proposed method computationally less expensive than the STARFM and FSDAF.
Does plastic waste kill mangroves? : a field experiment to assess the impact of anthropogenic waste on mangrove growth, stress response and survival
The value of mangroves has been widely acknowledged, but mangrove forests continue to decline due to numerous anthropogenic stressors. The impact of plastic waste is however poorly known, even though the amount of plastic litter is the largest in the region where mangroves are declining the fastest: South East Asia. In this study, we examine the extent of the plastic waste problem in mangroves along the north coast of Java, Indonesia. First, we investigate how much of the forest floor is covered by plastic in the field (in number of items per m2 and in percentage of the forest floor covered by plastic), and if plastic is also buried in the upper layers of the sediment. We then experimentally investigate the effects of a range of plastic cover percentages (0%, 50% and 100%) on root growth, stress response of the tree and tree survival over a period of six weeks. Field monitoring showed that plastic was abundant, with 27 plastic items per m2 on average, covering up to 50% of the forest floor at multiple locations. Moreover, core data revealed that plastic was frequently buried in the upper layers of the sediment where it becomes immobile and can create prolonged anoxic conditions. Our experiment subsequently revealed that prolonged suffocation by plastic caused immediate pneumatophore growth and potential leaf loss. However, trees in the 50%-plastic cover treatment proved surprisingly resilient and were able to maintain their canopy over the course of the experiment, whereas trees in the 100%-plastic cover treatment had a significantly decreased leaf area index and survival by the end of the experiment. Our findings demonstrate that mangrove trees are relatively resilient to partial burial by plastic waste. However, mangrove stands are likely to deteriorate eventually if plastic continues to accumulate.