Integrated Environmental Modelling – What is the Vision? Is it Achievable?

Keynote Lecture

DRIHM - An Infrastructure To Advance Hydro-Meteorological Research

One of the main challenges in hydro-meteorological research (HMR) is predicting the impact of weather and climate changes on the environment, society and economy, including local severe hazards such as floods and landslides. At the heart of this challenge lies the ability to have easy access to hydro-meteorological data and models, and facilitate the collaboration across discipline boundaries. Within the DRIHM project (Distributed Research Infrastructure for Hydro-Meteorology, www.drihm.eu, EC funded FP7 project 2011-2015) we develop a prototype e-Science environment to facilitate this collaboration and provide end-to-end HMR services (models, datasets, and post-processing tools) at the European level, with the ability to expand to global scale. The objectives of DRIHM are to lead the definition of a common long-term strategy, to foster the development of new HMR models, workflows and observational archives for the study of severe hydro-meteorological events, to promote the execution and analysis of high-end simulations, and to support the dissemination of predictive models as decision analysis tools. For this we implement a service portal to construct heterogeneous simulation workflows that can include deterministic and ensemble runs on a heterogeneous infrastructure consisting of HPC, grid and Windows cloud resources. Via another FP7 project called DRIHM2US (www.drihm2us.eu) we collaborate with the NSF funded SCIHM project (www.scihm.org) to build a wider international collaborative network. This contribution will provide a sketch of the DRIHM architecture and show some use cases such as the November 2011 Genoa flooding

Probabilistic seasonal dengue forecasting in Vietnam: A modelling study using superensembles

Background: With enough advanced notice, dengue outbreaks can be mitigated. As a climate-sensitive disease, environmental conditions and past patterns of dengue can be used to make predictions about future outbreak risk. These predictions improve public health planning and decision-making to ultimately reduce the burden of disease. Past approaches to dengue forecasting have used seasonal climate forecasts, but the predictive ability of a system using different lead times in a year-round prediction system has been seldom explored. Moreover, the transition from theoretical to operational systems integrated with disease control activities is rare. Methods and findings: We introduce an operational seasonal dengue forecasting system for Vietnam where Earth observations, seasonal climate forecasts, and lagged dengue cases are used to drive a superensemble of probabilistic dengue models to predict dengue risk up to 6 months ahead. Bayesian spatiotemporal models were fit to 19 years (2002–2020) of dengue data at the province level across Vietnam. A superensemble of these models then makes probabilistic predictions of dengue incidence at various future time points aligned with key Vietnamese decision and planning deadlines. We demonstrate that the superensemble generates more accurate predictions of dengue incidence than the individual models it incorporates across a suite of time horizons and transmission settings. Using historical data, the superensemble made slightly more accurate predictions (continuous rank probability score [CRPS] = 66.8, 95% CI 60.6–148.0) than a baseline model which forecasts the same incidence rate every month (CRPS = 79.4, 95% CI 78.5–80.5) at lead times of 1 to 3 months, albeit with larger uncertainty. The outbreak detection capability of the superensemble was considerably larger (69%) than that of the baseline model (54.5%). Predictions were most accurate in southern Vietnam, an area that experiences semi-regular seasonal dengue transmission. The system also demonstrated added value across multiple areas compared to previous practice of not using a forecast. We use the system to make a prospective prediction for dengue incidence in Vietnam for the period May to October 2020. Prospective predictions made with the superensemble were slightly more accurate (CRPS = 110, 95% CI 102–575) than those made with the baseline model (CRPS = 125, 95% CI 120–168) but had larger uncertainty. Finally, we propose a framework for the evaluation of probabilistic predictions. Despite the demonstrated value of our forecasting system, the approach is limited by the consistency of the dengue case data, as well as the lack of publicly available, continuous, and long-term data sets on mosquito control efforts and serotype-specific case data. Conclusions: This study shows that by combining detailed Earth observation data, seasonal climate forecasts, and state-of-the-art models, dengue outbreaks can be predicted across a broad range of settings, with enough lead time to meaningfully inform dengue control. While our system omits some important variables not currently available at a subnational scale, the majority of past outbreaks could be predicted up to 3 months ahead. Over the next 2 years, the system will be prospectively evaluated and, if successful, potentially extended to other areas and other climate-sensitive disease systems

Interactions between climate change, urban infrastructure and mobility are driving dengue emergence in Vietnam.

Dengue is expanding globally, but how dengue emergence is shaped locally by interactions between climatic and socio-environmental factors is not well understood. Here, we investigate the drivers of dengue incidence and emergence in Vietnam, through analysing 23 years of district-level case data spanning a period of significant socioeconomic change (1998-2020). We show that urban infrastructure factors (sanitation, water supply, long-term urban growth) predict local spatial patterns of dengue incidence, while human mobility is a more influential driver in subtropical northern regions than the endemic south. Temperature is the dominant factor shaping dengue's distribution and dynamics, and using long-term reanalysis temperature data we show that warming since 1950 has expanded transmission risk throughout Vietnam, and most strongly in current dengue emergence hotspots (e.g., southern central regions, Ha Noi). In contrast, effects of hydrometeorology are complex, multi-scalar and dependent on local context: risk increases under either short-term precipitation excess or long-term drought, but improvements in water supply mitigate drought-associated risks except under extreme conditions. Our findings challenge the assumption that dengue is an urban disease, instead suggesting that incidence peaks in transitional landscapes with intermediate infrastructure provision, and provide evidence that interactions between recent climate change and mobility are contributing to dengue's expansion throughout Vietnam

Using Spatio-temporal Feature Type Structures for Coupling Environmental Numerical Models to Each Other and to Data Sources

The aim of this research is to facilitate the interaction of data between different environmental numerical models and between numerical models and observed data sources, thereby giving rise to more accurate simulations involving a variety of environmental phenomena. The thesis includes ten publications. The first gives an overall context to the field of integrated environmental modelling, which seeks increased interoperability and extensibility between numerical models and data sources. This is followed by a series of four papers introducing and discussing associated enabling technologies: the development of a web portal for the display and comparison of model and in-situ marine data; the FluidEarth 2 implementation of the OpenMI 2.0 standard passing data between models in spatio-temporal structures aggregated from lower level component parts; an extension of the ISO19115 metadata standard to provide aspects related to model coupling; and the Model MAP – a set of necessary concepts for numerical models to be coupled together. These technologies are then applied in a series of papers describing the DRIHM e-Infrastructure for research into hydro-meteorology. These papers focus on the meteorological aspects; the user interface; the back-end computing infrastructures; and two key integrating technologies with an applied example. The final paper gives another example of the use of spatio-temporal feature type structures as part of a new numerical modelling technology. Overall, this series of papers points towards a structured representation of the data supporting environmental numerical models in terms of the underlying spatio-temporal characteristics. This suggests the adoption of a new vocabulary for describing these characteristics. Adoption of this vocabulary and new representation of environmental modelled data will speed the uptake of coupling strategies and create demand for established and new standards to support integrated environmental modelling

Deconfliction and Surface Generation from Bathymetry Data Using LR B-splines

A set of bathymetry point clouds acquired by different measurement techniques at different times, having different accuracy and varying patterns of points, are approximated by an LR B-spline surface. The aim is to represent the sea bottom with good accuracy and at the same time reduce the data size considerably. In this process the point clouds must be cleaned by selecting the “best” points for surface generation. This cleaning process is called deconfliction, and we use a rough approximation of the combined point clouds as a reference surface to select a consistent set of points. The reference surface is updated using only the selected points to create an accurate approximation. LR B-splines is the selected surface format due to its suitability for adaptive refinement and approximation, and its ability to represent local detail without a global increase in the data size of the surface.acceptedVersio

Low overtopping discharges over sea walls and breakwaters

<p>The design of seawalls and breakwaters is often required to achieve very low target overtopping discharges when these structures protect vulnerable infrastructure or activities. The balance between economically viable protection and performance requirements is often difficult to achieve without good knowledge on low overtopping. The paucity of data in this space and the higher uncertainty associated with existing methods, increases the challenge. The occurrence of low number of overtopping waves has the consequence that any test results are substantially more affected by the inherent variation of random waves, therefore more uncertain. Within the multi-institute project RECIPE under the HYDRALAB+ project, experimental studies for RECIPE Task 8.2 have generated new data on the response of seawalls, breakwaters and related coastal structures with the aim of improving future model testing. Tests by HRW have explored wave overtopping with contributions of data from UPORTO and LNEC. These physical model test results explore these issues and provide example data. The tests were successful in obtaining low to very low overtopping discharge test data. For low / very low overtopping discharges, these test data present considerable scatter relative to the latest empirical prediction. A number of repetitions were performed for wave conditions resulting in very low overtopping discharges, which illustrated the inherent uncertainty associated with low overtopping.</p

The Effects of Meteorological Factors on Dengue Cases in Malaysia

Dengue is a vector-borne disease affected by meteorological factors and is commonly recorded from ground stations. Data from ground station have limited spatial representation and accuracy, which can be overcome using satellite-based Earth Observation (EO) recordings instead. EO-based meteorological recordings can help to provide a better understanding of the correlations between meteorological variables and dengue cases. This paper aimed to first validate the satellite-based (EO) data of temperature, wind speed, and rainfall using ground station data. Subsequently, we aimed to determine if the spatially matched EO data correlated with dengue fever cases from 2011 to 2019 in Malaysia. EO data were spatially matched with the data from four ground stations located at states and districts in the central (Selangor, Petaling) and east coast (Kelantan, Kota Baharu) geographical regions of Peninsular Malaysia. Spearman&rsquo;s rank-order correlation coefficient (&rho;) was performed to examine the correlation between EO and ground station data. A cross-correlation analysis with an eight-week lag period was performed to examine the magnitude of correlation between EO data and dengue case across the three time periods (2011&ndash;2019, 2015&ndash;2019, 2011&ndash;2014). The highest correlation between the ground-based stations and corresponding EO data were reported for temperature (mean &rho; = 0.779), followed by rainfall (mean &rho; = 0.687) and wind speed (mean &rho; = 0.639). Overall, positive correlations were observed between weekly dengue cases and rainfall for Selangor and Petaling across all time periods with significant correlations being observed for the period from 2011 to 2019 and 2015 to 2019. In addition, positive significant correlations were also observed between weekly dengue cases and temperature for Kelantan and Kota Baharu across all time periods, while negative significant correlations between weekly dengue cases and temperature were observed in Selangor and Petaling across all time periods. Overall negative correlations were observed between weekly dengue cases and wind speed in all areas from 2011 to 2019 and 2015 to 2019, with significant correlations being observed for the period from 2015 to 2019. EO-derived meteorological variables explained 48.2% of the variation in dengue cases in Selangor. Moderate to strong correlations were observed between meteorological variables recorded from EO data derived from satellites and ground stations, thereby justifying the use of EO data as a viable alternative to ground stations for recording meteorological variables. Both rainfall and temperature were found to be positively correlated with weekly dengue cases; however, wind speed was negatively correlated with dengue cases

Position paper: Open web-distributed integrated geographic modelling and simulation to enable broader participation and applications

Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field