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Canal dikes in low‐lying polders, as well as in other regions worldwide, are critical infrastructure for flood protection and water management. The subsurface water conditions can cause dike failures during excessive rainfall and prolonged periods of drought. There is a lack of multi‐year monitoring of subsurface water conditions in canal dikes and an insufficient understanding of their geohydrological behavior. This study provides and analyses a novel multiyear data set of soil moisture and hydraulic heads (from February 2020 until March 2023) from a monitoring network covering various canal dikes with different characteristics in the western Netherlands. The data, including two extremely dry summers, highlight the impact of meteorological variations on the subsurface water conditions. Non‐hydrostatic hydraulic head levels were observed during droughts that can be detrimental to dike stability and that are often not accounted for in safety assessments for drought situations. The effectiveness of various meteorological drought indicators applied to subsurface water conditions was evaluated: the precipitation deficit is the most reliable measure and outperforms the standardized drought indicators (SPEI and SPI). The drought recovery of dikes was analyzed to understand seasonal transitions and the sequence of different failure mechanisms, during dry and wet situations. This analysis also reveals differences between meteorological, soil moisture, and groundwater droughts, highlighting soil's storage capacity after drought and the limitations of meteorological drought indicators as proxies for soil moisture and groundwater. The insights from this study enhance assessments, inspection procedures and the identification of weak spots of dikes and other earthworks of infrastructure. Key Points Novel multiyear observations of soil moisture and hydraulic heads from various canal dikes reveal geohydrological behavior The precipitation deficit emerges as the most reliable meteorological drought indicator and can be used as an indicator of dike safety The drought recovery lasted 4.5 months in 2022, which is important for the transition between seasons and different failure mechanisms

Coastal aquaculture monitoring is vital for sustainable offshore aquaculture management. However, the dense distribution and various sizes of aquacultures make it challenging to accurately extract the boundaries of aquaculture ponds. In this study, we develop a novel combined framework that integrates UNet++ with a marker-controlled watershed segmentation strategy to facilitate aquaculture boundary extraction from fully polarimetric GaoFen-3 SAR imagery. First, four polarimetric decomposition algorithms were applied to extract 13 polarimetric scattering features. Together with the nine other polarisation and texture features, a total of 22 polarimetric features were then extracted, among which four were optimised according to the separability index. Subsequently, to reduce the “adhesion” phenomenon and separate adjacent and even adhering ponds into individual aquaculture units, two UNet++ subnetworks were utilised to construct the marker and foreground functions, the results of which were then used in the marker-controlled watershed algorithm to obtain refined aquaculture results. A multiclass segmentation strategy that divides the intermediate markers into three categories (aquaculture, background and dikes) was applied to the marker function. In addition, a boundary patch refinement postprocessing strategy was applied to the two subnetworks to extract and repair the complex/error-prone boundaries of the aquaculture ponds, followed by a morphological operation that was conducted for label augmentation. An experimental investigation performed to extract individual aquacultures in the Yancheng Coastal Wetlands indicated that the crucial features for aquacultures are Shannon entropy (SE), the intensity component of SE (SE_I) and the corresponding mean texture features (Mean_SE and Mean_SE_I). When the optimal features were introduced, our proposed method performed better than standard UNet++ in aquaculture extraction, achieving improvements of 1.8%, 3.2%, 21.7% and 12.1% in F1, IoU, MR and insF1, respectively. The experimental results indicate that the proposed method can handle the adhesion of both adjacent objects and unclear boundaries effectively and capture clear and refined aquaculture boundaries.

Earthen flood protection structures are planned and constructed with an expected service life of several decades while being exposed to environmental impacts that may lead to structural or hydraulic failure. Current maintenance procedures involve only repairing external damage, leaving internal processes contributing to structural damage often undetected. Through structural health monitoring (SHM), structural deficits can be detected before visible damage occurs. To improve maintenance workflows and support predictive maintenance of dikes, this paper reports on the integration of digital twin concepts with SHM strategies, referred to as “digital-twin-based SHM”. A digital twin concept, including a standard-compliant building information model, is proposed and implemented in terms of a digital twin environment. For integrating monitoring and sensor data into the digital twin environment, a customized webform is designed. A communication protocol links preprocessed sensor data stored on a server with the digital twin environment, enabling model-based visualization and contextualization of the sensor data. As will be shown in this paper, a digital twin environment is set up and managed in the context of SHM in compliance with technical standards and using well-established software tools. In conclusion, digital-twin-based SHM, as proposed in this paper, has proven to advance predictive maintenance of dikes, contributing to the resilience of critical infrastructure against environmental impacts.

Artificial hydraulic structures like dams or dikes used for water level regulations or flood prevention are continuously under the influence of the weather and variable river regimes. Thus, ongoing monitoring and simulation is crucial in order to determine the inner condition. Potentially life-threatening situations, in extreme case a failure, must be counteracted by all available means. Nowadays flood warning systems rely exclusively on water level forecast without considering the state of the structure itself. Area-covering continuous knowledge of the inner state including time dependent changes increases the capability of recognizing and locating vulnerable spots for early treatment. In case of a predicted breach, advance warning time for alerting affected citizens can be extended. Our approach is composed of smart sensors integrated in a service-oriented geospatial architecture to monitor and simulate artificial hydraulic structures continuously. The sensors observe the inner state of the construction like the soil moisture or the stress and deformation over time but also various external influences like water levels or wind speed. They are interconnected in distributed network architecture by a so-called sensor bus system based on lightweight protocols like Message Queue Telemetry Transport for Sensor Networks (MQTT-SN). These sensor data streams are transferred into an OGC Sensor Web Enablement (SWE) data structure providing high-level geo web services to end users. Bundled with 3rd party geo web services (WMS etc.) powerful processing and simulation tools can be invoked using the Web Processing Service (WPS) standard. Results will be visualized in a geoportal allowing user access to all information.