Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

Tephra fallout hazard assessment for a Plinian eruption scenario at Volcán de Colima (Mexico)

Volcanic ash fallout associated with renewal of explosive activity at Colima, represents a serious threat to the surrounding urbanized area. Here we assess the tephra fallout hazard associated with a Plinian eruption scenario. The eruptive history of Volcán de Colima shows that Plinian eruptions occur approximately every 100 years and the last eruption, the 1913, represents the largest historic eruption of this volcano. We used the last eruption as a reference to discuss volcanic hazard and risk scenarios connected with ash fallout. Tephra fallout deposits are modeled using HAZMAP, a model based on a semi-analytical solution of the advection– diffusion–sedimentation equation for volcanic particles. Based on a statistical study of wind profiles at Colima region, we first reconstructed ash loading maps and then computed ground load probability maps for different seasons. The obtained results show that a Plinian eruptive scenario at Volcán de Colima, could seriously damage more than 10 small towns and ranches, and potentially affect big cities located at tens of kilometers from the eruptive center. The probability maps obtained are aimed to give support to the risk mitigation strategies

Validation of the FALL3D ash dispersion model using observations of the 2010 Eyjafjallajökull volcanic ash clouds

During April-May 2010 volcanic ash clouds from the Icelandic Eyjafjallajökull volcano reached Europe causing an unprecedented disruption of the EUR/NAT region airspace. Civil aviation authorities banned all flight operations because of the threat posed by volcanic ash to modern turbine aircraft. New quantitative airborne ash mass concentration thresholds, still under discussion, were adopted for discerning regions contaminated by ash. This has implications for ash dispersal models routinely used to forecast the evolution of ash clouds. In this new context, quantitative model validation and assessment of the accuracies of current state-of-the-art models is of paramount importance. The passage of volcanic ash clouds over central Europe, a territory hosting a dense network of meteorological and air quality observatories, generated a quantity of observations unusual for volcanic clouds. From the ground, the cloud was observed by aerosol lidars, lidar ceilometers, sun photometers, other remote-sensing instru- ments and in-situ collectors. From the air, sondes and multiple aircraft measurements also took extremely valuable in-situ and remote-sensing measurements. These measurements constitute an excellent database for model validation. Here we validate the FALL3D ash dispersal model by comparing model results with ground and airplane-based measurements obtained during the initial 14e23 April 2010 Eyjafjallajökull explosive phase. We run the model at high spatial resolution using as input hourly- averaged observed heights of the eruption column and the total grain size distribution reconstructed from field observations. Model results are then compared against remote ground-based and in-situ aircraft-based measurements, including lidar ceilometers from the German Meteorological Service, aerosol lidars and sun photometers from EARLINET and AERONET networks, and flight missions of the German DLR Falcon aircraft. We find good quantitative agreement, with an error similar to the spread in the observations (however depending on the method used to estimate mass eruption rate) for both airborne and ground mass concentration. Such verification results help us understand and constrain the accuracy and reliability of ash transport models and it is of enormous relevance for designing future operational mitigation strategies at Volcanic Ash Advisory Centers.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.

Multimodel assessment of the upper troposphere and lower stratosphere: Extratropics

A multimodel assessment of the performance of chemistry-climate models (CCMs) in the extratropical upper troposphere/lower stratosphere (UTLS) is conducted for the first time. Process-oriented diagnostics are used to validate dynamical and transport characteristics of 18 CCMs using meteorological analyses and aircraft and satellite observations. The main dynamical and chemical climatological characteristics of the extratropical UTLS are generally well represented by the models, despite the limited horizontal and vertical resolution. The seasonal cycle of lowermost stratospheric mass is realistic, however with a wide spread in its mean value. A tropopause inversion layer is present in most models, although the maximum in static stability is located too high above the tropopause and is somewhat too weak, as expected from limited model resolution. Similar comments apply to the extratropical tropopause transition layer. The seasonality in lower stratospheric chemical tracers is consistent with the seasonality in the Brewer-Dobson circulation. Both vertical and meridional tracer gradients are of similar strength to those found in observations. Models that perform less well tend to use a semi-Lagrangian transport scheme and/or have a very low resolution. Two models, and the multimodel mean, score consistently well on all diagnostics, while seven other models score well on all diagnostics except the seasonal cycle of water vapor. Only four of the models are consistently below average. The lack of tropospheric chemistry in most models limits their evaluation in the upper troposphere. Finally, the UTLS is relatively sparsely sampled by observations, limiting our ability to quantitatively evaluate many aspects of model performance.

Multiple-project discount rates for cost-benefit analysis in construction projects: A formal risk model for microgeneration renewable energy technologies

The traditional economic approach for appraising the costs and benefits of construction project Net Present Values involves the calculation of net returns for each investment option under different discount rates. An alternative approach consists of multiple-project discount rates based on risk modelling. The example of a portfolio of microgeneration renewable energy technology (MRET) is presented to demonstrate that risks and future available budget for re-investment can be taken into account when setting discount rates for construction project specifications in presence of uncertainty. A formal demonstration is carried out through a reversed intertemporal approach of applied general equilibrium. It is demonstrated that risk and the estimated available budget for future re-investment can be included in the simultaneous assessment of the costs and benefits of multiple projects.

Variation in the global-scale impacts of climate change on crop productivity due to climate model uncertainty and adaptation

Crop production is inherently sensitive to fluctuations in weather and climate and is expected to be impacted by climate change. To understand how this impact may vary across the globe many studies have been conducted to determine the change in yield of several crops to expected changes in climate. Changes in climate are typically derived from a single to no more than a few General Circulation Models (GCMs). This study examines the uncertainty introduced to a crop impact assessment when 14 GCMs are used to determine future climate. The General Large Area Model for annual crops (GLAM) was applied over a global domain to simulate the productivity of soybean and spring wheat under baseline climate conditions and under climate conditions consistent with the 2050s under the A1B SRES emissions scenario as simulated by 14 GCMs. Baseline yield simulations were evaluated against global country-level yield statistics to determine the model's ability to capture observed variability in production. The impact of climate change varied between crops, regions, and by GCM. The spread in yield projections due to GCM varied between no change and a reduction of 50%. Without adaptation yield response was linearly related to the magnitude of local temperature change. Therefore, impacts were greatest for countries at northernmost latitudes where warming is predicted to be greatest. However, these countries also exhibited the greatest potential for adaptation to offset yield losses by shifting the crop growing season to a cooler part of the year and/or switching crop variety to take advantage of an extended growing season. The relative magnitude of impacts as simulated by each GCM was not consistent across countries and between crops. It is important, therefore, for crop impact assessments to fully account for GCM uncertainty in estimating future climates and to be explicit about assumptions regarding adaptation.

Hypothesis management in situation assessment

A situation assessment uses reports from sensors to produce hypotheses about a situation at a level of aggregation that is of direct interest to a military commander. A low level of aggregation could mean forming tracks from reports, which is well documented in the tracking literature as track initiation and data association. In this paper there is also discussion on higher level aggregation; assessing the membership of tracks to larger groups. Ideas used in joint tracking and identification are extended, using multi-entity Bayesian networks to model a number of static variables, of which the identity of a target is one. For higher level aggregation a scheme for hypothesis management is required. It is shown how an offline clustering of vehicles can be reduced to an assignment problem.

A framework for adapting survey design through time for wildlife population assessment

Sampling strategies for monitoring the status and trends in wildlife populations are often determined before the first survey is undertaken. However, there may be little information about the distribution of the population and so the sample design may be inefficient. Through time, as data are collected, more information about the distribution of animals in the survey region is obtained but it can be difficult to incorporate this information in the survey design. This paper introduces a framework for monitoring motile wildlife populations within which the design of future surveys can be adapted using data from past surveys whilst ensuring consistency in design-based estimates of status and trends through time. In each survey, part of the sample is selected from the previous survey sample using simple random sampling. The rest is selected with inclusion probability proportional to predicted abundance. Abundance is predicted using a model constructed from previous survey data and covariates for the whole survey region. Unbiased design-based estimators of status and trends and their variances are derived from two-phase sampling theory. Simulations over the short and long-term indicate that in general more precise estimates of status and trends are obtained using this mixed strategy than a strategy in which all of the sample is retained or all selected with probability proportional to predicted abundance. Furthermore the mixed strategy is robust to poor predictions of abundance. Estimates of status are more precise than those obtained from a rotating panel design.

Deriving global flood hazard maps of fluvial floods through a physical model cascade

Global flood hazard maps can be used in the assessment of flood risk in a number of different applications, including (re)insurance and large scale flood preparedness. Such global hazard maps can be generated using large scale physically based models of rainfall-runoff and river routing, when used in conjunction with a number of post-processing methods. In this study, the European Centre for Medium Range Weather Forecasts (ECMWF) land surface model is coupled to ERA-Interim reanalysis meteorological forcing data, and resultant runoff is passed to a river routing algorithm which simulates floodplains and flood flow across the global land area. The global hazard map is based on a 30 yr (1979–2010) simulation period. A Gumbel distribution is fitted to the annual maxima flows to derive a number of flood return periods. The return periods are calculated initially for a 25×25 km grid, which is then reprojected onto a 1×1 km grid to derive maps of higher resolution and estimate flooded fractional area for the individual 25×25 km cells. Several global and regional maps of flood return periods ranging from 2 to 500 yr are presented. The results compare reasonably to a benchmark data set of global flood hazard. The developed methodology can be applied to other datasets on a global or regional scale.

Assessment of surface winds over the Atlantic, Indian and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: historical bias, forcing response, and state dependence

An assessment of the fifth Coupled Models Intercomparison Project (CMIP5) models’ simulation of the near-surface westerly wind jet position and strength over the Atlantic, Indian and Pacific sectors of the Southern Ocean is presented. Compared with reanalysis climatologies there is an equatorward bias of 3.7° (inter-model standard deviation of ± 2.2°) in the ensemble mean position of the zonal mean jet. The ensemble mean strength is biased slightly too weak, with the largest biases over the Pacific sector (-1.6±1.1 m/s, 27 -22%). An analysis of atmosphere-only (AMIP) experiments indicates that 41% of the zonal mean position bias comes from coupling of the ocean/ice models to the atmosphere. The response to future emissions scenarios (RCP4.5 and RCP8.5) is characterized by two phases: (i) the period of most rapid ozone recovery (2000-2049) during which there is insignificant change in summer; and (ii) the period 2050-2098 during which RCP4.5 simulations show no significant change but RCP8.5 simulations show poleward shifts (0.30, 0.19 and 0.28°/decade over the Atlantic, Indian and Pacific sectors respectively), and increases in strength (0.06, 0.08 and 0.15 m/s/decade respectively). The models with larger equatorward position biases generally show larger poleward shifts (i.e. state dependence). This inter-model relationship is strongest over the Pacific sector (r=-0.89) and insignificant over the Atlantic sector (r=-0.50). However, an assessment of jet structure shows that over the Atlantic sector jet shift is significantly correlated with jet width whereas over the Pacific sector the distance between the sub-polar and sub-tropical westerly jets appears to be more important.

Updated estimate of aerosol direct radiative forcing from satellite observations and comparison against the Hadley Centre climate model

The fourth assessment report of the Intergovernmental Panel on Climate Change (IPCC) includes a comparison of observation-based and modeling-based estimates of the aerosol direct radiative forcing. In this comparison, satellite-based studies suggest a more negative aerosol direct radiative forcing than modeling studies. A previous satellite-based study, part of the IPCC comparison, uses aerosol optical depths and accumulation-mode fractions retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) at collection 4. The latest version of MODIS products, named collection 5, improves aerosol retrievals. Using these products, the direct forcing in the shortwave spectrum defined with respect to present-day natural aerosols is now estimated at −1.30 and −0.65 Wm−2 on a global clear-sky and all-sky average, respectively, for 2002. These values are still significantly more negative than the numbers reported by modeling studies. By accounting for differences between present-day natural and preindustrial aerosol concentrations, sampling biases, and investigating the impact of differences in the zonal distribution of anthropogenic aerosols, good agreement is reached between the direct forcing derived from MODIS and the Hadley Centre climate model HadGEM2-A over clear-sky oceans. Results also suggest that satellite estimates of anthropogenic aerosol optical depth over land should be coupled with a robust validation strategy in order to refine the observation-based estimate of aerosol direct radiative forcing. In addition, the complex problem of deriving the aerosol direct radiative forcing when aerosols are located above cloud still needs to be addressed.

Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes

The wood mouse is a common and abundant species in agricultural landscape and is a focal species in pesticide risk assessment. Empirical studies on the ecology of the wood mouse have provided sufficient information for the species to be modelled mechanistically. An individual-based model was constructed to explicitly represent the locations and movement patterns of individual mice. This together with the schedule of pesticide application allows prediction of the risk to the population from pesticide exposure. The model included life-history traits of wood mice as well as typical landscape dynamics in agricultural farmland in the UK. The model obtains a good fit to the available population data and is fit for risk assessment purposes. It can help identify spatio-temporal situations with the largest potential risk of exposure and enables extrapolation from individual-level endpoints to population-level effects. Largest risk of exposure to pesticides was found when good crop growth in the “sink” fields coincided with high “source” population densities in the hedgerows. Keywords: Population dynamics, Pesticides, Ecological risk assessment, Habitat choice, Agent-based model, NetLogo

Risk assessment of UK skylark populations using life-history and individual-based landscape models

Abstract: Following a workshop exercise, two models, an individual-based landscape model (IBLM) and a non-spatial life-history model were used to assess the impact of a fictitious insecticide on populations of skylarks in the UK. The chosen population endpoints were abundance, population growth rate, and the chances of population persistence. Both models used the same life-history descriptors and toxicity profiles as the basis for their parameter inputs. The models differed in that exposure was a pre-determined parameter in the life-history model, but an emergent property of the IBLM, and the IBLM required a landscape structure as an input. The model outputs were qualitatively similar between the two models. Under conditions dominated by winter wheat, both models predicted a population decline that was worsened by the use of the insecticide. Under broader habitat conditions, population declines were only predicted for the scenarios where the insecticide was added. Inputs to the models are very different, with the IBLM requiring a large volume of data in order to achieve the flexibility of being able to integrate a range of environmental and behavioural factors. The life-history model has very few explicit data inputs, but some of these relied on extensive prior modelling needing additional data as described in Roelofs et al.(2005, this volume). Both models have strengths and weaknesses; hence the ideal approach is that of combining the use of both simple and comprehensive modeling tools.

The application of Regional Climate Model output for the simulation of high-mountain permafrost scenarios

This study investigates the possibilities and limitations of using Regional Climate Model (RCM) output for the simulation of alpine permafrost scenarios. It focuses on the general problem of scale mismatch between RCMs and impact models and, in particular, the special challenges that arise when driving an impact model in topographically complex high-mountain environments with the output of an RCM. Two approaches are introduced that take into account the special difficulties in such areas, and thus enable the use of RCM for alpine permafrost scenario modelling. Intended as an initial example, they are applied at the area of Corvatsch (Upper Engadine, Switzerland) in order to demonstrate and discuss the application of the two approaches, rather than to provide an assessment of future changes in permafrost occurrence. There are still many uncertainties and inaccuracies inherent in climate and impact models, which increase when driving one model with the output of the other. Nevertheless, our study shows that the use of RCMs offers new and promising perspectives for the simulation of high-mountain permafrost scenarios