Danish Hydraulic Institute

Hydraulic roughness (e.g., expressed in terms of Manning’s or Strickler’s coefficient) is an essential input to numerical hydrodynamic models. One way to estimate roughness parameters is by hydraulic inversion, using observed water surface elevation (WSE) collected from gauging stations, satellite platforms, or Unmanned Aerial System (UAS) altimeters. Specifically, UAS altimetry provides close to instantaneous observations of longitudinal profiles and seasonal variations of WSE for various river types, which are helpful for calibrating roughness parameters. However, it is computationally expensive to run high-resolution hydrodynamic models thousands to millions of times as required for the global optimization of distributed parameter sets (e.g., spatio-temporally varying river roughness).This study presents an efficient calibration approach for hydrodynamic models using a simplified steady-state hydraulic solver, UAS altimetry datasets, and in-situ observations. The calibration approach minimized the weighted sum of a misfit term, spatial smoothness penalty, and an a-priori sinusoidal temporal variation constraint. The approach was first demonstrated for several synthetic calibration experiments, and the results indicated that the global search algorithm accurately recovered the variations of Strickler coefficient (Ks) for short river reaches in temporal (due to the seasonal growth cycle of the aquatic vegetation) and spatial (e.g., due to spatially variable density of submerged vegetation) scales. Subsequently, the calibration approach was demonstrated for a real WSE dataset collected at a Danish test site (Vejle Å). In this river, friction is dominated by submerged vegetation growing in the stream and shows strong seasonal and longitudinal variations due to the plant growth cycle and variable vegetation density and species composition. Results indicated that spatio-temporal variation of Ks was required to fit in-situ observations and UAS altimetry accurately. This study illustrates how UAS altimetry and hydrodynamic modeling can be combined to achieve an improved understanding and better parameterization of small and medium-sized rivers, where river channel conveyance is controlled by vegetation growth and other spatio-temporally variable factors.

Nanoparticles and colored substances can inhibit algal growth by light shading and chemical toxicity. This study presents two complementary approaches to account for shading in algal growth inhibition tests of engineered nanomaterials (ENMs) and colored substances. The first approach distinguishes between shading effects and toxicity by varying the light path in parallel algal growth inhibition tests. This Multiple Path-Length (MPL) test was applied to TiO2 ENMs and the colored substances sodium picramate and Rhodamine B. A left shifting of concentration-response curves, with increasing light path lengths, indicated shading for Rhodamine B, sodium picramate and TiO2 ENMs. EC50-values obtained at the shortest light path length were generally found best suited to quantify the toxicity of ENMs and colored substances. The second approach addresses shading at the cellular level, where particles can attach to the cell surface and affect photo-pigment content and composition. Pigments associated with photosystem I and II were determined at varying light intensities and concentrations of TiO2 ENMs. The photo-pigments that increased in response to physical shading, decreased after TiO2 ENMs exposure. This indicates that toxicity rather than cellular shading dominated the response of algae exposed to TiO2 ENMs. Additional tests were conducted with the nanomaterials CeO2 and goethite to evaluate the applicability of this approach to other ENMs. On this basis, we recommend MPL testing for determining EC50-values that are not confounded by shading in the test solution, and the pigment-based approach for investigating shading on the cellular level.

Reservoir release is an essential variable as it affects hydrological processes and water availability downstream. This study aims to estimate reservoir release using a satellite-based approach, specially focusing on the impacts of inflow simulations and reservoir water storage change (RWSC) on release estimates. Ten inflow simulations based on hydrological models and blending schemes are used in combination with three RWSC estimates based on two satellite-based approaches. A case study is performed at the Ankang reservoir, China. The results demonstrate that release estimates show high skill, with normalized root-mean-square error (NRMSE) less than 0.12 and Kling-Gupta Efficiency (KGE) over 0.65. The performance of release estimates is varying with and influenced by inflow simulations and RWSC estimates, with NRMSE ranging from 0.09–0.12 and KGE from 0.65–0.74. Based on time-varying Bayesian Model Averaging (BMA) approaches and synthetic aperture radar (SAR) satellite datasets, more accurate inflow and RWSC estimates can be obtained, thus facilitating substantially release estimates. With multi-source satellite datasets, temporal scale of reservoir estimates is increased (monthly and bi-weekly), acting as a key supplement to in situ records. Overall, this study explores the possibility to reconstruct and facilitate reservoir release estimates in poorly gauged dammed basins using hydrological modeling techniques and multi-source satellite datasets.

Marine ambient sound levels have risen due to noisy human activities, such as shipping, fishing, seismic surveys and piling for windfarms. Marine mammals and fishes are two prominent taxonomic groups that are exposed to this noise pollution, which may experience detrimental effects at the population level. Acoustic effects on individual behaviour such as deterrence, disturbance, distraction and masking of biologically relevant sounds, can be translated energetically to changes in vital rates (growth, maturation, reproduction and survival) in a population consequences of acoustic disturbance (PCAD) approach. However, we typically neglect spatial variation in species distributions and noise pollution, while abiotic factors like temperature, bathymetry and currents, as well as habitat quality in terms of feeding or hiding opportunities, will also have a geographically variable impact on potential consequences. We here address the conceptual integration of agent based models (ABM) into the PCAD framework, as a suitable theoretical tool with high potential for the exploration of these spatial factors and their modifying role in noise impact assessment studies. We review five ABM case studies, including investigations into: 1) effects of movement strategy on the impact of explosions in harbour porpoise; 2) effects of disturbance sensitivity on pile driving impact on migrating cod; 3) impact of seismic survey sounds on Atlantic mackerel distribution and movement; 4) population‐level impact of mitigation of harbour porpoise bycatch with pingers; and 5) population effects of alternative windfarm construction scenarios in harbour porpoise. We discuss similarities and differences among these studies in sound and species mapping approaches and we evaluate model realism and pattern validation. We believe that ABMs are a valuable tool for integrating spatial information into ecological impact studies that investigate acoustic disturbance, for any type of sound source, and for both marine mammals and fish.

Recent advances in numerical modeling, satellite data, and coastal processes, together with the rapid evolution of CMEMS products and the increasing pressures on coastal zones, suggest the timeliness of extending such products toward the coast. The CEASELESS EU H2020 project combines Sentinel and in-situ data with high-resolution models to predict coastal hydrodynamics at a variety of scales, according to stakeholder requirements. These predictions explicitly introduce land discharges into coastal oceanography, addressing local conditioning, assimilation memory and anisotropic error metrics taking into account the limited size of coastal domains. This article presents and discusses the advances achieved by CEASELESS in exploring the performance of coastal models, considering model resolution and domain scales, and assessing error generation and propagation. The project has also evaluated how underlying model uncertainties can be treated to comply with stakeholder requirements for a variety of applications, from storm-induced risks to aquaculture, from renewable energy to water quality. This has led to the refinement of a set of demonstrative applications, supported by a software environment able to provide met-ocean data on demand. The article ends with some remarks on the scientific, technical and application limits for CMEMS-based coastal products and how these products may be used to drive the extension of CMEMS toward the coast, promoting a wider uptake of CMEMS-based predictions.

Climate and land-use change drive a suite of stressors that shape ecosystems and interact to yield complex ecological responses (that is, additive, antagonistic and synergistic effects). We know little about the spatial scales relevant for the outcomes of such interactions and little about effect sizes. These knowledge gaps need to be filled to underpin future land management decisions or climate mitigation interventions for protecting and restoring freshwater ecosystems. This study combines data across scales from 33 mesocosm experiments with those from 14 river basins and 22 cross-basin studies in Europe, producing 174 combinations of paired-stressor effects on a biological response variable. Generalized linear models showed that only one of the two stressors had a significant effect in 39% of the analysed cases, 28% of the paired-stressor combinations resulted in additive effects and 33% resulted in interactive (antagonistic, synergistic, opposing or reversal) effects. For lakes, the frequencies of additive and interactive effects were similar for all spatial scales addressed, while for rivers these frequencies increased with scale. Nutrient enrichment was the overriding stressor for lakes, with effects generally exceeding those of secondary stressors. For rivers, the effects of nutrient enrichment were dependent on the specific stressor combination and biological response variable. These results vindicate the traditional focus of lake restoration and management on nutrient stress, while highlighting that river management requires more bespoke management solutions.