Distributed lag models for hydrological data

The distributed lag model (DLM), used most prominently in air pollution studies, finds application wherever the effect of a covariate is delayed and distributed through time. We explore the use of modified formulations of DLMs to provide flexible varying-coeficient models with smoothness constraints, applicable in any setting in which lagged covariates are regressed on a time-dependent response. The models are applied to simulated flow and rainfall data and to flow data from a Scottish mountain river, with particular emphasis on approximating the relationship between environmental covariates and flow regimes in order to detect the influence of unobserved processes. It was found that under certain rainfall conditions some of the variability in the influence of rainfall on flow arises through a complex interaction between antecedent ground wetness and the time-delay in rainfall. The models are able to identify subtle changes in rainfall response, particularly in the location of peak influence in the lag structure and offer a computationally attractive approach for fitting DLMs

Analysis and use of VAS satellite data

A series of interrelated investigations has examined the analysis and use of VAS (VISSR Atmospheric Sounder) satellite data. A case study of VAS-derived mesoscale stability parameters suggested that they would have been a useful supplement to conventional data in the forecasting of thunderstorms on the day of interest. However, the meteorological significance of small or short lived stability features was uncertain. A second investigation examined the roles of first guess and VAS radiometric data in producing sounding retrievals. The radiance data often did not have a decisive influence on the final satellite soundings. Broad-scale patterns of the first guess, radiances, and retrievals frequently were similar, whereas small scale retrieval features, especially in the dew points, were often of uncertain origin

Analysis and use of VAS satellite data

Four interrelated investigations have examined the analysis and use of VAS satellite data. A case study of VAS-derived mesoscale stability parameters suggested that they would have been a useful supplement to conventional data in the forecasting of thunderstorms on the day of interest. A second investigation examined the roles of first guess and VAS radiometric data in producing sounding retrievals. Broad-scale patterns of the first guess, radiances, and retrievals frequently were similar, whereas small-scale retrieval features, especially in the dew points, were often of uncertain origin. Two research tasks considered 6.7 micron middle tropospheric water vapor imagery. The first utilized radiosonde data to examine causes for two areas of warm brightness temperature. Subsidence associated with a translating jet streak was important. The second task involving water vapor imagery investigated simulated imagery created from LAMPS output and a radiative transfer algorithm. Simulated image patterns were found to compare favorably with those actually observed by VAS. Furthermore, the mass/momentum fields from LAMPS were powerful tools for understanding causes for the image configurations

Long Term Behavioral Health Surveillance of Former Astronauts at the NASA Johnson Space Center

No abstract availabl

Ensemble evaluation of hydrological model hypotheses

It is demonstrated for the first time how model parameter, structural and data uncertainties can be accounted for explicitly and simultaneously within the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. As an example application, 72 variants of a single soil moisture accounting store are tested as simplified hypotheses of runoff generation at six experimental grassland field-scale lysimeters through model rejection and a novel diagnostic scheme. The fields, designed as replicates, exhibit different hydrological behaviors which yield different model performances. For fields with low initial discharge levels at the beginning of events, the conceptual stores considered reach their limit of applicability. Conversely, one of the fields yielding more discharge than the others, but having larger data gaps, allows for greater flexibility in the choice of model structures. As a model learning exercise, the study points to a “leaking” of the fields not evident from previous field experiments. It is discussed how understanding observational uncertainties and incorporating these into model diagnostics can help appreciate the scale of model structural error

Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface

There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future. Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE

Digital catchment observatories: A platform for engagement and knowledge exchange between catchment scientists, policy makers, and local communities

Increasing pressures on the hydrological cycle from our changing planet have led to calls for a refocus of research in the sciences of hydrology and water resources. Opportunities for new and innovative research into these areas are being facilitated by advances in the use of cyberinfrastructure, such as the development of digital catchment observatories. This is enabling research into hydrological issues such as flooding to be approached differently. The ability to combine different sources of data, knowledge, and modeling capabilities from different groups such as scientists, policy makers, and the general public has the potential to provide novel insights into the way individual catchments respond at different temporal and spatial scales. While the potential benefits of the digital catchment observatory are large, this new way of carrying out research into hydrological sciences is likely to prove challenging on many levels. Along with the obvious technical and infrastructural challenges to this work, an important area for consideration is how to enable a digital observatory to work for a range of potential end-users, paving the way for new areas of research through developing a platform effective for engagement and knowledge exchange. Using examples from the recent local-scale hydrological exemplar in the Environmental Virtual Observatory pilot project (http://www.evo-uk.org), this commentary considers a number of issues around the communication between and engagement of different users, the use of local knowledge and uncertainty with cloud-based models, and the potential for decision support and directions for future research

Identification of temporal consistency in rating curve data : Bidirectional Reach (BReach)

In this paper, a methodology is developed to identify consistency of rating curve data based on a quality analysis of model results. This methodology, called Bidirectional Reach (BReach), evaluates results of a rating curve model with randomly sampled parameter sets in each observation. The combination of a parameter set and an observation is classified as nonacceptable if the deviation between the accompanying model result and the measurement exceeds observational uncertainty. Based on this classification, conditions for satisfactory behavior of a model in a sequence of observations are defined. Subsequently, a parameter set is evaluated in a data point by assessing the span for which it behaves satisfactory in the direction of the previous (or following) chronologically sorted observations. This is repeated for all sampled parameter sets and results are aggregated by indicating the endpoint of the largest span, called the maximum left (right) reach. This temporal reach should not be confused with a spatial reach (indicating a part of a river). The same procedure is followed for each data point and for different definitions of satisfactory behavior. Results of this analysis enable the detection of changes in data consistency. The methodology is validated with observed data and various synthetic stage-discharge data sets and proves to be a robust technique to investigate temporal consistency of rating curve data. It provides satisfying results despite of low data availability, errors in the estimated observational uncertainty, and a rating curve model that is known to cover only a limited part of the observations

A cloud based tool for knowledge exchange on local scale flood risk

There is an emerging and urgent need for new approaches for the management of environmental challenges such as flood hazard in the broad context of sustainability. This requires a new way of working which bridges disciplines and organisations, and that breaks down science-culture boundaries. With this, there is growing recognition that the appropriate involvement of local communities in catchment management decisions can result in multiple benefits. However, new tools are required to connect organisations and communities. The growth of cloud based technologies offers a novel way to facilitate this process of exchange of information in environmental science and management; however, stakeholders need to be engaged with as part of the development process from the beginning rather than being presented with a final product at the end. Here we present the development of a pilot Local Environmental Virtual Observatory Flooding Tool. The aim was to develop a cloud based learning platform for stakeholders, bringing together fragmented data, models and visualisation tools that will enable these stakeholders to make scientifically informed environmental management decisions at the local scale. It has been developed by engaging with different stakeholder groups in three catchment case studies in the UK and a panel of national experts in relevant topic areas. However, these case study catchments are typical of many northern latitude catchments. The tool was designed to communicate flood risk in locally impacted communities whilst engaging with landowners/farmers about the risk of runoff from the farmed landscape. It has been developed iteratively to reflect the needs, interests and capabilities of a wide range of stakeholders. The pilot tool combines cloud based services, local catchment datasets, a hydrological model and bespoke visualisation tools to explore real time hydrometric data and the impact of flood risk caused by future land use changes. The novel aspects of the pilot tool are; the co-evolution of tools on a cloud based platform with stakeholders, policy and scientists; encouraging different science disciplines to work together; a wealth of information that is accessible and understandable to a range of stakeholders; and provides a framework for how to approach the development of such a cloud based tool in the future. Above all, stakeholders saw the tool and the potential of cloud technologies as an effective means to taking a whole systems approach to solving environmental issues. This sense of community ownership is essential in order to facilitate future appropriate and acceptable land use management decisions to be codeveloped by local catchment communities. The development processes and the resulting pilot tool could be applied to local catchments globally to facilitate bottom up catchment management approaches