The utility of using Volunteered Geographic Information (VGI) for evaluating pluvial flood models.

Pluvial floods are increasingly threatening urban environments worldwide due to human-induced climate change. High-resolution, state-of-the-art pluvial flood models are urgently needed to inform climate change adaptation and disaster risk reduction measures but are generally not empirically tested because of the rarity of local high-intensity precipitation events and the lack of monitoring capabilities. Volunteered Geographic Information (VGI) collected by professionals, non-professionals and citizens and made available on the internet can be used to monitor the dynamic extent of a pluvial flood during and after an extreme rain event but is sometimes considered to be unreliable. In this paper, we explore the general utility of VGI to evaluate the performance of pluvial flood models and gain new insights to improve these models. As background for our research, we use the capital city of Budapest, which recently suffered three heavy rainfall events in just five years (2015, 2017 and 2020). For each pluvial flood event, we collected photographic evidence from different online media sources and estimated the associated water depths at various locations in the city from the image context. These were compared with the results of a 2D pluvial flood model that has been shown to provide comparable results to other state-of-the-art inundation models and is easily transferred to other urban areas due to its reliance on open data sources. We introduce a general methodology for comparing VGI with model data by probing different spatial resolutions. Our findings highlight untapped potential and fundamental challenges in using VGI for model evaluation. It is proposed that VGI may become an essential tool and improve the confidence in model-based risk assessments for climate change adaptation and disaster risk reduction.

Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor.

There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations.

Model-based ensembles: Lessons learned from retrospective analysis of COVID-19 infection forecasts across 10 countries.

Mathematical models of different types and data intensities are highly used by researchers, epidemiologists, and national authorities to explore the inherently unpredictable progression of COVID-19, including the effects of different non-pharmaceutical interventions. Regardless of model complexity, forecasts of future COVID-19 infections, deaths and hospitalization are associated with large uncertainties, and critically depend on the quality of the training data, and in particular how well the recorded national or regional numbers of infections, deaths and recoveries reflect the the actual situation. In turn, this depends on, e.g., local test and abatement strategies, treatment capacities and available technologies. Other influencing factors including temperature and humidity, which are suggested by several authors to affect the spread of COVID-19 in some countries, are generally only considered by the most complex models and further serve to inflate the uncertainty. Here we use comparative and retrospective analyses to illuminate the aggregated effect of these systematic biases on ensemble-based model forecasts. We compare the actual progression of active infections across ten of the most affected countries in the world until late November 2020 with "re-forecasts" produced by two of the most commonly used model types: (i) a compartment-type, susceptible-infected-removed (SIR) model; and (ii) a statistical (Holt-Winters) time series model. We specifically examine the sensitivity of the model parameters, estimated systematically from different subsets of the data and thereby different time windows, to illustrate the associated implications for short- to medium-term forecasting and for probabilistic projections based on (single) model ensembles as inspired by, e.g., weather forecasting and climate research. Our findings portray considerable variations in forecasting skill in between the ten countries and demonstrate that individual model predictions are highly sensitive to parameter assumptions. Significant skill is generally only confirmed for short-term forecasts (up to a few weeks) with some variation across locations and periods.

Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology.

The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections.

Short-Term Statistical Forecasts of COVID-19 Infections in India.

COVID-19 cases in India have been steadily increasing since January 30, 2020 and have led to a government-imposed lockdown across the country to curtail community transmission with significant impacts on societal systems. Forecasts using mathematical-epidemiological models have played and continue to play an important role in assessing the probability of COVID-19 infection under specific conditions and are urgently needed to prepare health systems for coping with this pandemic. In many instances, however, access to dedicated and updated information, in particular at regional administrative levels, is surprisingly scarce considering its evident importance and provides a hindrance for the implementation of sustainable coping strategies. Here we demonstrate the performance of an easily transferable statistical model based on the classic Holt-Winters method as means of providing COVID-19 forecasts for India at different administrative levels. Based on daily time series of accumulated infections, active infections and deaths, we use our statistical model to provide 48-days forecasts (28 September to 15 November 2020) of these quantities in India, assuming little or no change in national coping strategies. Using these results alongside a complementary SIR model, we find that one-third of the Indian population could eventually be infected by COVID-19, and that a complete recovery from COVID-19 will happen only after an estimated 450 days from January 2020. Further, our SIR model suggests that the pandemic is likely to peak in India during the first week of November 2020.

Water use in electricity generation for water-energy nexus analyses: The European case.

With almost 40% of the global population suffering from water scarcity, the need to manage water resources is evidently urgent. While water and energy systems are intrinsically linked, the availability of comprehensive, integrated data sets across the domains of water and energy is generally lacking. As a result, estimated indicators representing volumes of water usage per unit of electricity or fuel produced are often required to analyse the water-energy nexus. In this paper, an "ensemble" of indicators is assembled representing water usage spanning different electricity-generation technologies based on previously published works in an attempt to depict the level or lack of detail in current large-scale energy-sector water-usage data. Based on these, the degree in which using such estimates is suitable for reproducing electricity-production water-usage at coarser spatio-temporal scales is assessed. The performance of the ensemble median/min/max as a predictor of water use is evaluated for the period from 1980 to 2015 using additional information about the constituents of the European energy system. Comparing with the reported values for 1980-2015, the median provides a skillful reproduction of historical yearly water use for the EU (EU28) as a whole. A further analysis for 2015 indicates that reasonable agreement is also seen at the country level. Thus, the results suggest that an "ensemble-based approach" has the potential to provide sturdy estimates of yearly water use by energy systems for analyses at both the country and regional levels.

Simulation of Optimal Decision-Making Under the Impacts of Climate Change.

Climate change causes transformations to the conditions of existing agricultural practices appointing farmers to continuously evaluate their agricultural strategies, e.g., towards optimising revenue. In this light, this paper presents a framework for applying Bayesian updating to simulate decision-making, reaction patterns and updating of beliefs among farmers in a developing country, when faced with the complexity of adapting agricultural systems to climate change. We apply the approach to a case study from Ghana, where farmers seek to decide on the most profitable of three agricultural systems (dryland crops, irrigated crops and livestock) by a continuous updating of beliefs relative to realised trajectories of climate (change), represented by projections of temperature and precipitation. The climate data is based on combinations of output from three global/regional climate model combinations and two future scenarios (RCP4.5 and RCP8.5) representing moderate and unsubstantial greenhouse gas reduction policies, respectively. The results indicate that the climate scenario (input) holds a significant influence on the development of beliefs, net revenues and thereby optimal farming practices. Further, despite uncertainties in the underlying net revenue functions, the study shows that when the beliefs of the farmer (decision-maker) opposes the development of the realised climate, the Bayesian methodology allows for simulating an adjustment of such beliefs, when improved information becomes available. The framework can, therefore, help facilitating the optimal choice between agricultural systems considering the influence of climate change.

Local control on precipitation in a fully coupled climate-hydrology model.

The ability to simulate regional precipitation realistically by climate models is essential to understand and adapt to climate change. Due to the complexity of associated processes, particularly at unresolved temporal and spatial scales this continues to be a major challenge. As a result, climate simulations of precipitation often exhibit substantial biases that affect the reliability of future projections. Here we demonstrate how a regional climate model (RCM) coupled to a distributed hydrological catchment model that fully integrates water and energy fluxes between the subsurface, land surface, plant cover and the atmosphere, enables a realistic representation of local precipitation. Substantial improvements in simulated precipitation dynamics on seasonal and longer time scales is seen for a simulation period of six years and can be attributed to a more complete treatment of hydrological sub-surface processes including groundwater and moisture feedback. A high degree of local influence on the atmosphere suggests that coupled climate-hydrology models have a potential for improving climate projections and the results further indicate a diminished need for bias correction in climate-hydrology impact studies.

High-throughput screening of Erwinia chrysanthemi pectin methylesterase variants using carbohydrate microarrays.

Pectin methylesterases (PMEs) catalyse the removal of methyl esters from the homogalacturonan (HG) backbone domain of pectin, a ubiquitous polysaccharide in plant cell walls. The degree of methyl esterification (DE) impacts upon the functional properties of HG within cell walls and plants produce numerous PMEs that act upon HG in muro. Many microbial plant pathogens also produce PMEs, the activity of which renders HG more susceptible to cleavage by pectin lyase and polygalacturonase enzymes and hence aids cell wall degradation. We have developed a novel microarray-based approach to investigate the activity of a series of variant enzymes based on the PME from the important pathogen Erwinia chrysanthemi. A library of 99 E. chrysanthemi PME mutants was created in which seven amino acids were altered by various different substitutions. Each mutant PME was incubated with a highly methyl esterified lime pectin substrate and, after digestion the enzyme/substrate mixtures were printed as microarrays. The loss of activity that resulted from certain mutations was detected by probing arrays with a mAb (JIM7) that preferentially binds to HG with a relatively high DE. Active PMEs therefore resulted in diminished JIM7 binding to the lime pectin substrate, whereas inactive PMEs did not. Our findings demonstrate the feasibility of our approach for rapidly testing the effects on PME activity of substituting a wide variety of amino acids at different positions.

Analysis of a Kalman filter based method for on-line estimation of atmospheric dispersion parameters using radiation monitoring data.

A Kalman filter method is discussed for on-line estimation of radioactive release and atmospheric dispersion from a time series of off-site radiation monitoring data. The method is based on a state space approach, where a stochastic system equation describes the dynamics of the plume model parameters, and the observables are linked to the state variables through a static measurement equation. The method is analysed for three simple state space models using experimental data obtained at a nuclear research reactor. Compared to direct measurements of the atmospheric dispersion, the Kalman filter estimates are found to agree well with the measured parameters, provided that the radiation measurements are spread out in the cross-wind direction. For less optimal detector placement it proves difficult to distinguish variations in the source term and plume height; yet the Kalman filter yields consistent parameter estimates with large associated uncertainties. Improved source term assessment results, when independent estimates of the plume height can be used. Perspectives for using the method in the context of nuclear emergency management are discussed, and possible extensions to the present modelling scheme are outlined, to account for realistic accident scenarios.

Kalman filtration of radiation monitoring data from atmospheric dispersion of radioactive materials.

A Kalman filter method using off-site radiation monitoring data is proposed as a tool for on-line estimation of the source term for short-range atmospheric dispersion of radioactive materials. The method is based on the Gaussian plume model, in which the plume parameters including the source term exhibit a 'random walk' process. The embedded parameters of the Kalman filter are determined through maximum-likelihood estimation making the filter essentially free of external parameters. The method is tested using both real and simulated radiation monitoring data. For simulated data, the method is shown to retrieve the embedded parameters employed in generating the data and to reconstruct the plume model parameters, including the source term. When tested against experimental radiation monitoring data the method is found accurately to uncover the known source term.

Experimental evaluation of gamma fluence-rate predictions from Argon-41 releases to the atmosphere over a nuclear research reactor site.

An experimental study of radionuclide dispersion in the atmosphere has been conducted at the BR1 research reactor in Mol, Belgium. Artificially generated aerosols ('white smoke') were mixed with the routine releases of (41)Ar in the reactor's 60-m tall venting stack. The detailed plume geometry was measured with remote sensing (Lidar) of the aerosol plumes while surface radiation levels were measured under the plume using gamma detectors at downwind distances of up to 1500 m from the release point. A database was built with simultaneous measurements of plume geometry and radiation field from (41)Ar decay, together with in-situ measurements of meteorological parameters. The joint tracer/radiation experimental dataset has been subsequently used to evaluate the accuracy of predictions of dispersion parameters and gamma fluence rates obtained by the atmospheric dispersion and dose rate model RIMPUFF.