Hydrodynamic coupling between ground, pipe, river and sea: critical review and future directions

Flash floods in Mediterranean regions pose significant threats to lives, infrastructures, and economies. Recent episodes of extreme rainfall in one such region led to devastating flash floods, resulting in loss of life, destruction of homes, and widespread disruption of transportation networks. Therefore, there is a critical need for advanced methods to monitor and analyze the flood dynamics, especially in urban areas. This study investigates the use of two advanced image-based techniques, Fudaa-LSPIV (Coz et al., 2014) and SSISM-Flow (Ljubičić et al., 2024) for surface velocity and discharge estimation of urban flash floods. The research used videos or images of historical urban flood events and estimated the surface velocity. To analyze the urban floods, Matera, a city of southern Italy, was selected as case study. Matera was chosen because its historical city center, the “Sassi”, was affected by extreme rainfall events in the last few years, e.g. 2014, 2018, 2019, and 2023. Five extreme past flood events occurred on 3 Aug 2018, 12 Nov 2019, 2 Jun 2023, and 2 & 21 July 2024 were recorded for estimation of surface velocity. Fudaa-LSPIV works according to the Particle Image Velocimetry (PIV) principles, while SSISM-Flow is a user-friendly and Python-based innovative tool with OpenCV integration for precise surface velocity filed extraction. These methods involve steps such as image stabilization, camera calibration, orthorectifications, and velocity calculation. Both techniques were evaluated based on their accuracy, performance, and application to overcome the limitations of analyzing the surface flow of urban floods. This study is innovative in comparing methods to estimate surface velocity of real-time flash floods in urban areas. Using these techniques, the surface velocities were estimated along key transects, and results were cross-validated using the Float Time method as benchmark. The outcomes of both approaches turned out to be consistent with benchmark data, confirming their reliability in monitoring urban floods. This comprehensive flow analysis provided insights for calibrating flood models and enhanced risk management. This study introduced a novel application of these techniques in real-time urban flood monitoring. Furthermore, it contributes to the development of an early warning system, enhances management strategies, and mitigates flood risks in vulnerable areas.ReferenceLjubičić, R., et al., 2024.  SSIMS-flow: image velocimetry workbench for open-channel flow rate estimation. Environ. Model. Softw. 173, 105938.Coz, Jérôme Le, wt al., 2014. Image-Based Velocity and Discharge Measurements in Field and Laboratory River Engineering Studies Using the Free Fudaa-LSPIV Software. In Proc.of the Inter.  Conf. on Fluvial Hydraulics, River Flow, 1961–67.AcknowledgmentsThis work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

Urban floods are becoming increasingly more frequent, which has led to tremendous economic losses. The application of inundation modeling to predict and simulate urban flooding is an effective approach for disaster prevention and risk reduction, while also addressing the uncertainty problem in the model is always a challenging task. In this study, a cellular automaton (CA)-based model combining a storm water management model (SWMM) and a weighted cellular automata 2D inundation model was applied and a physical-based model (LISFLOOD-FP) was also coupled with SWMM for comparison. The simulation performance and the uncertainty factors of the coupled model were systematically discussed. The results show that the CA-based model can achieve sufficient accuracy and higher computational efficiency than can a physical-based model. The resolution of terrain and rainstorm data had a strong influence on the performance of the CA-based model, and the simulations would be less creditable when using the input data with a terrain resolution lower than 15 m and a recorded interval of rainfall greater than 30 min. The roughness value and model type showed limited impacts on the change of inundation depth and occurrence of the peak inundation area. Generally, the CA-based coupled model demonstrated laudable applicability and can be recommended for fast simulation of urban flood episodes. This study also can provide references and implications for reducing uncertainty when constructing a CA-based coupled model.

: The goals of this PI are to understand the physical processes that control the air-sea interaction and its impact on a wide rang of weather and climate systems and improve coupled atmosphere-ocean prediction through development of innovative coupled models and observations. The specific objectives of this study are to obtain coincident measurements of the lower atmosphere, air-sea interface, and the upper ocean adequate for coupled model evaluation; better understand the physics of air-sea coupling and its impact on convective organization including convectively induced cold pool structure; examine corresponding air-sea fluxes and boundary layer recovery that affects time scales of convection; diagnose high-resolution coupled model such as COAMPS forecasts of convective cloud systems and convective cold pool structure to determine the effects of air-sea coupling on the convective organization in the coupled model on diurnal, 2-day, and synoptic variability and their up-scaling influence in both convectively active and suppressed phases of MJO; and improve physical representation of the air-sea coupling processes in coupled models through airborne and satellite observations over the Indian Ocean.

Well-known problems trouble coupled general circulation models of the eastern Atlantic and Pacific Ocean basins. Model climates are significantly more symmetric about the equator than is observed. Model sea surface temperatures are biased warm south and southeast of the equator, and the atmosphere is too rainy within a band south of the equator. Near-coastal eastern equatorial SSTs are too warm, producing a zonal SST gradient in the Atlantic opposite in sign to that observed. The U.S. Climate Variability and Predictability Program (CLIVAR) Eastern Tropical Ocean Synthesis Working Group (WG) has pursued an updated assessment of coupled model SST biases, focusing on the surface energy balance components, on regional error sources from clouds, deep convection, winds, and ocean eddies; on the sensitivity to model resolution; and on remote impacts. Motivated by the assessment, the WG makes the following recommendations: 1) encourage identification of the specific parameterizations contributing to the biases in individual models, as these can be model dependent; 2) restrict multimodel intercomparisons to specific processes; 3) encourage development of high-resolution coupled models with a concurrent emphasis on parameterization development of finer-scale ocean and atmosphere features, including low clouds; 4) encourage further availability of all surface flux components from buoys, for longer continuous time periods, in persistently cloudy regions; and 5) focus on the eastern basin coastal oceanic upwelling regions, where further opportunities for observational–modeling synergism exist.

Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm.

Enhancing transparency in data-driven urban pluvial flood prediction using an explainable CNN model

In this paper, we first briefly review the history of air-sea coupled models, and then introduce the current status and recent advances of regional air-sea coupled models. In particular, we discuss the core technical and scientific issues involved in the development of regional coupled models, including the coupling technique, lateral boundary conditions, the coupling with sea waves (ices), and data assimilation. Furthermore, we introduce the application of regional coupled models in numerical simulation and dynamical downscaling. Finally, we discuss the existing problems and future directions in the development of regional air-sea coupled models.

Floods are the most common disaster in Canada. As results of rapid urbanization and climate changes, both frequency and risks of floods have been increased in Canadian urbanized areas, where the disasters have usually costlier impacts than in rural areas. Imagery data and technologies of optical remote sensing are helpful and can be applied for urban flood response and pre-disaster preparation. Especially high and very high optical remote sensing can be used for precise mapping of the floodwater distribution in dense urban areas and providing key information for disaster response management. In addition, the geospatial information about urban land surface and urban growth derived from optical remote sensing imagery can be the key inputs for urban flood risk analyses. In recent years, several case studies for different urban flood types, including fluvial (Calgary 2013, Ottawa-Gatineau 2017) and pluvial (the Greater Toronto Area) floods in Canada, have been carried out at Canada Centre for Mapping and Earth Observation, Natural Resources Canada. Methodologies/framework for urban floodwater mapping have been developed based on high resolution optical data, as well as the impacts of urban growth on the urban flash flood risks have been investigated using model simulations with remote sensing derived maps as inputs. This presentation demonstrates results from three Canadian urban flood case studies and introduces remote-sensing-based methodologies for different types of urban floods.

An integrated model that couples surface and subsurface models was developed for a huge carbonate oil reservoir overlain by a large gas-cap located in the Middle East region. The main objective of the integrated model is to quickly evaluate changes in production strategy and provide more accurate forecast of field performance than with conventional approaches where surface and subsurface performance are evaluated separately. Building a fully integrated model is a very challenging task, due to the complex nature of the field process, including compositional variations, NGL processing and evaluation of gas disposition options. The surface network model was developed to allow evaluation of liquid and gas velocity in the flowlines and trunklines, and erosional velocity and back pressure to every well in the network. Trunklines were modeled with detailed elevation profiles to capture the complex nature of desert terrain found in the field. The subsurface model is a huge resolution model with more than 60 million grid-cells. The reservoir simulation model is compositional, having nine-components and runs on a state-of-the-art in-house simulator, GigaPOWERSTM. This paper highlights the process in building the fully coupled model by a multidisciplinary team, including the subsurface model, wellbore models, surface network model, and the integration layer between those different standalone models. The paper also discusses the issues encountered during building the integrated model and how those challenges were resolved. Introduction Integrated modeling is becoming quite popular recently in the oil and gas industry (Huang et al., 2012; Malik et al., 2012; Roadifer et al., 2012). Coupling of subsurface and surface models refer to the process of linking fluid flow equations from porous media, to the surface facilities through wellbores. During history matching process, field historical data - mainly fluid rates and pressures - are used to calibrate simulation models. The use of decoupled models at this level is enough most of the time. To predict field performance, coupled models are necessary, especially when production constrains are controlled from surface (Coats et al, 2004). Different approaches have been implemented to bring the various components of the coupled model together. Implicit coupling is a method where fluid flow equations for the surface and the subsurface are solved simultaneously at each time step. Another approach is the explicit coupling where subsurface model is solved separately and then results are passed to surface network model through a connection layer (Al-Mutairi et al., 2010). A team was created to develop an integrated flow model to rapidly evaluate changes in production strategy, provide more accurate production forecast compared with evaluating reservoir and surface separately and to allow optimization to reduce or maintain gas-oil ratio (GOR) taking into consideration system production constraints. Due to the complex nature of the process, with planned NGL processing, frequently evaluation of gas disposition options required and the complex nature of the reservoir simulation model (compositional, dual porosity-dual permeability, etc.), the subject field was considered a suitably challenging candidate for development of an integrated model process that could be applied across other fields once demonstrated.

Vegetation is an important component of the Earth's biosphere and therefore plays a crucial role in the carbon exchange of terrestrial ecosystems. Vegetation variables, such as leaf area index (LAI) and leaf chlorophyll content (Cab), can be monitored at global scale using remote sensing (RS). There are two main categories of approaches for estimating the vegetation variables from RS data: empirical and physically-based approaches. Physically-based approaches are more widely applicable because they rely on radiative transfer (RT) models, which can be adapted to the observation conditions and to the observed vegetation. For estimating the vegetation variables, however, the RT model has to be inverted, and this inversion is usually an ill-posed and under-determined problem. Several regularization methods have been proposed to allow finding stable and unique solutions: model coupling, using multi-angular data, using a priori information, as well as applying spatial or temporal constraints. Traditionally, radiance data measured at top-of the atmosphere (TOA) are pre-processed to top-of-canopy (TOC) reflectances. Corrections for atmospheric effects, and, if needed, for adjacency, directional, or topographic effects are usually applied sequentially and independently. Physically, however, these effects are inter-related, and each correction introduces errors. These errors propagate to the TOC reflectance data, which are used to invert the canopy RT model. The performance of the TOC approach is therefore limited by the errors introduced in the data during the pre-processing steps. This thesis proposes to minimize these errors by directly using measured TOA radiance data. In such a TOA approach, the atmospheric RT model, which is normally inverted to perform the atmospheric correction, is coupled to the canopy RT model. The coupled canopy-atmosphere model is inverted directly using the measured radiance data. Adjacency, directional and topographic effects can then be included in the coupled RT model. The same regularization methods as used for TOC approaches can be applied to obtain stable and unique estimates. The TOA approach was tested using four case studies based on mono-temporal data. A) The performance of the TOA approach was compared to a TOC approach for three Norway spruce stands in the Czech Republic, using near-nadir Compact High Resolution Imaging Spectrometer (CHRIS) data. The coupled model included canopy directional effects and simulated the CHRIS radiance data with similar accuracy as the canopy model simulated the atmospherically-corrected CHRIS data. Local sensitivity analyses showed that the atmospheric parameters had much less influence on the simulations than the vegetation parameters, and that the sensitivity profiles of the latter were very similar for both TOC and TOA approaches. The dimensionality of the estimation problem was evaluated to be 3 for both approaches. Canopy cover (Cv), fraction of bark material (fB), Cab, and leaf dry matter content (Cdm) were estimated using look-up tables (LUT) with similar accuracy with both approaches. B) Regularization using multi-angular data was tested for the TOA approach, using four angular CHRIS datasets, for the same three stands as used in A). The coupled model provided good simulations for all angles. The dimensionality increased from 3 to 6 when using all four angles. Two LUTs were built for each stand: a 4-variable LUT with fB, Cv, Cdm, and Cab, and a 7-variable LUT where leaf brown pigment concentration (Cs), dissociation factor (D), and tree shape factor (Zeta) were added. The results did not fully match the expectation that the more angles used, the more accurate the estimates become. Although their exploitation remains challenging, multi-angular data have higher potential than mono-angular data at TOA level. C) A Bayesian object-based approach was developed and tested on at-sensor Airborne Prism Experiment (APEX) radiance data for an agricultural area in Switzerland. This approach consists of two steps. First, up to six variables were estimated for each crop field object using a Bayesian optimization algorithm, using a priori information. Second, a LUT was built for each object with only LAI and Cab as free variables, thus spatially constraining the values of all other variables to the values obtained in the first step. The Bayesian object-based approach estimated LAI more accurately than a LUT with a Bayesian cost function approach. This case study relied on extensive field data allowing defining the objects and a priori data. D) The Bayesian object-based approach proposed in C) was applied to a simulated TOA Sentinel-2 scene, covering the area around Zurich, Switzerland. The simulated scene was mosaicked using seven APEX flight lines, which allowed including all spatial and spectral characteristics of Sentinel-2. Automatic multi-resolution segmentation and classification of the vegetated objects in four levels of brightness in the visible domain enabled defining the objects and a priori data without field data, allowing successful implementation of the Bayesian object-based approach. The research conducted in this thesis contributes to the improvement of the use of regularization methods in ill-posed RT model inversions. Three major areas were identified for further research: 1) inclusion of adjacency and topography effects in the coupled model, 2) addition of temporal constraints in the inversion, and 3) better inclusion of observation and model uncertainties in the cost function. The TOA approach proposed here will facilitate the exploitation of multi-angular, multi-temporal and multi-sensor data, leading to more accurate RS vegetation products. These higher quality products will support many vegetation-related applications.

Urban flooding has become a global issue due to climate change, urbanization and limitation in the capacity of urban drainage infrastructures. To tackle the growing threats, it is crucial to understand urban surface flood resilience, i.e., how urban drainage catchments can resist against and recover from flooding. This study proposes a grid cell based resilience metric to assess urban surface flood resilience at the urban drainage catchment scale. The new metric is defined as the ratio of the number of unflooded grid cells to the total grid cell number in an urban drainage catchment. A two-dimensional Cellular Automata based model CADDIES is used to simulate urban surface flooding. This methodology is demonstrated using a case study in Dalian, China, which is divided into 31 urban drainage catchments for flood resilience analysis. Results show the high resolution resilience assessment identifies vulnerable catchments and helps develop effective adaptation strategies to enhance urban surface flood resilience. Comparison of the new metric with an existing metric reveals that new metric has the advantage of fully reflecting the changing process of system performance. Effectiveness of adaptation strategies for enhancing urban surface flood resilience is discussed for different catchments. This study provides a new way to characterize urban flood resilience and an in-depth understanding of flood resilience for urban drainage catchments of different characteristics, and thus help develop effective intervention strategies for sustainable sponge city development.

The effects of low impact development on urban flooding under different rainfall characteristics

This manuscript focuses on the degrading effects of discretization errors on the simulation results of strongly coupled models. Coupled simulation models, be they multi-scale or multi-physics in nature, in recent years, have gained significant attention for their ability to predict the behavior of complex physical systems by implementing mature, independently developed, constituent models. This investigation evaluates the numerical errors inherent in the predictions of the constituent models and their effects on the predictions of the coupled model. Not only are the discretization errors of each constituent model quantified as they propagate from one constituent to the other during coupling iterations, but their effects on the computational requirements of the coupling procedure are also considered. Furthermore, the sensitivity of the coupled model predictions to each constituent is determined allowing a thorough evaluation of the impact of discretization errors on coupled model predictions. These relationships are demonstrated through a case study of a strongly coupled dynamical system.

In recent decades, low impact development (LID) has become an increasingly important concern as a state-of-the-art stormwater management mode to treat urban flood, preferable to conventional urban drainage systems. However, the effects of the combined use of different LID facilities on urban flooding have not been fully investigated under different rainfall characteristics. In this study, a residential, neighborhood-scale catchment in Shenzhen City, southern China was selected as a case study, where the effects of four LID techniques (bio-retention, bio-swale, rain garden and pervious pavement) with different connection patterns (cascaded, semi-cascaded and paralleled) on runoff reduction efficiency were analyzed by the storm water management model (SWMM), promoted by the U.S. EPA. Three kinds of designed storm events with different return periods, durations and time-to-peak ratios were forced to simulate the flood for holistic assessment of the LID connection patterns. The effects were measured by the runoff coefficient of the whole storm–runoff process and the peak runoff volume. The results obtained indicate that the cascaded connect LID chain can more effectively reduce the runoff than that in the paralleled connect LID chain under different storms. The performances of the LID chains in modeling flood process in SWMM indicate that the runoff coefficient and the peak runoff volume increase with the increase in the rain return periods and the decrease in rain duration. Additionally, the move backward of the peak rain intensity to the end of the storm event slightly affects the peak runoff volume obviously while gives slight influence on the total runoff volume. This study provides an insight into the performance of LID chain designs under different rainfall characteristics, which is essential for effective urban flood management.

The threat posed by urban flooding in most cities of the world is becoming alarming especially within the recent decades. This makes it necessary to Identify and delineate flood risk areas within cities in order to curb it menace. This study employs geospatial technique to delineate flood risk areas within Kano metropolis with a view to mitigating its impact on lives and properties. Digital Elevation Model (ASTER DEM 30m) was used to derive excess surface run-off attributes including flow direction and accumulation. Based on these attributes, flood risk areas were determined and delineated using buffer distances of 500 meters. World View image (30 cm spatial resolution) was used to identify the landuses at risk. The result from the analysis delineated flood risk areas at varying exposure levels (i.e high, moderate and low).It was evident that flood risk level within the metropolis corresponds to the pattern of surface run-off flow accumulation areas. Settlements and farmlands found within high accumulation areas along the floodplains of River Jakara (in the North and North-eastern part) and Kano-Zaria road (southern part) are at higher risk than those found on low accumulation areas. The study concluded that excess surface run-off flow direction and accumulation are among the fundamental factors determining the risk to urban flooding. The study recommends that with the ongoing level of urban development and impervious surface expansion, urban planners and policy makers should make use of the flow direction and accumulation maps in determining safer places for future developments