応用水文 = Applied hydrology

Low impact development techniques to mitigate the impacts of climate-change-induced urban floods: Current trends, issues and challenges

https://research-information.bris.ac.uk/files/178389620/1_s2.0_S0043135418310091_main.pdf

Recent insights on uncertainties present in integrated catchment water quality modelling

. In many countries, urban flooding due to local,
intense rainfall is expected to become more frequent because of climate
change and urbanization. Cities trying to adapt to this growing risk are
challenged by a chronic lack of surface flooding data that are needed for
flood risk assessment and planning. In this work, we propose a new approach
that exploits existing surveillance camera systems to provide qualitative
flood level trend information at scale. The approach uses a deep
convolutional neural network (DCNN) to detect floodwater in surveillance
footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water
level fluctuations visible from a surveillance camera's viewpoint. To
demonstrate the approach, we trained the DCNN on 1218 flooding images
collected from the Internet and applied it to six surveillance videos
representing different flooding and lighting conditions. The SOFI signal
obtained from the videos had a 75 % correlation to the actual
water level fluctuation on average. By retraining the DCNN with a few frames from a
given video, the correlation is increased to 85 % on average. The results
confirm that the approach is versatile, with the potential to be applied to
a variety of surveillance camera models and flooding situations without the
need for on-site camera calibration. Thanks to this flexibility, this
approach could be a cheap and highly scalable alternative to conventional
sensing methods.

/pdf/scalable-flood-level-trend-monitoring-with-surveillance-h6dtf6agev.pdf

Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network

Towards a smart water city: A comprehensive review of applications, data requirements, and communication technologies for integrated management

Lost in calibration: why people still do not calibrate their models, and why they still should – a case study from urban drainage modelling

Low cost sensors are emerging alongside innovative data transfer technologies, allowing the integration of smart solutions for the decentralisation of water infrastructure. In this work, the smart rain barrel (SRB) concept is introduced as an IoT-based solution for advanced rainwater harvesting. The SRB consists of a rain barrel extended by a discharge valve, which is centrally controlled. This concept offers (1) individual control of each implemented SRB with its incorporation into the integrated water system management and (2) a simple large-scale implementation of additional storage units as an alternative to future expansion of existing infrastructure. The open-source software “Smartin” is developed by hypothetically retrofitting an Alpine municipality with SRBs to evaluate the effects of the latter on the urban water infrastructure. Compared to uncontrolled rain barrels, a simple coordinated control strategy already clearly improves the performance of the integrated system by reducing combined sewer overflow and addressing drinking water demand.

Integrated urban water management with micro storages developed as an IoT-based solution – The smart rain barrel

Rain barrels are micro-scale applications which are used as temporary storage and for rainwater harvesting They can be easily implemented into existing stormwater infrastructure Recent advances in the field of Internet of Things (IoT) have opened up new possibilities for real-time monitoring and control of such structures, that enable the reduction of urban flooding or combined sewer overflows The special feature of our smart rain barrel is its integration into a pilot project for smart cities, where every water inflow and outflow of the university campus in Innsbruck (Austria) is measured Weather forecasts and time-controlled filling levels of different Low Impact Developments (LID) structures and the connected sewer system are used for real-time control (RTC) In a first step, the smart rain barrels are implemented into a SWMM-model with the objective of reducing the peak runoff rate by using the filling level in the main conduit as the control variable for real-time control Results show that depending on the installation site and the storage volume of the rain barrel, a flood volume reduction of 18–40% can be achieved although only a simplified automatic control system has been implemented

Smart Rain Barrels: Advanced LID Management Through Measurement and Control

Advanced Rainwater Harvesting through Smart Rain Barrels

River basin wide planning for assessing the ecological and chemical status of surface water bodies calls for integrated modelling of the river and the connected urban water systems. Challenges were identified in the effort to set up such models and in general data availability for such studies. The Austrian interuniversity project IMW3 is dedicated to data acquisition in a pilot catchment in Baden, Austria. Four online measurement stations are installed in a river section and the effluent of an urban drainage system. This contribution focuses on two parts of this project: i) the set-up of a framework for data validation and post processing with focus on implementation of meta information and ii) the transformation of data to information by data analysis and a model implementation. We show that while a lot of effort was put into data acquisition, obtaining reliable data over a longer period is still challenging. We discuss the necessity of a proper meta-information management and show that even with in-depth analysis manual data treatment cannot be completely avoided. Statistical methods are used to identify and verify processes in the receiving water prior to setting up a conceptual model. However, their applicability strongly depends on data quality.

Carolina Kinzel

Papers

Lost in calibration: why people still do not calibrate their models, and why they still should – a case study from urban drainage modelling

Integrated urban water management with micro storages developed as an IoT-based solution – The smart rain barrel

Smart Rain Barrels: Advanced LID Management Through Measurement and Control

Advanced Rainwater Harvesting through Smart Rain Barrels

Integrated assessment of receiving water quality based on validated high resolution data and water quality modelling