A framework for a joint hydro-meteorological-social analysis of drought

This article presents an innovative framework for analysing environmental governance challenges by focusing on their Drivers, Responses and Impacts (DRI). It builds on and modifies the widely applied Drivers, Pressures, States, Impacts and Responses (DPSIR) model. It suggests, firstly and most importantly, that the various temporal and spatial scales at which Drivers, Responses and Impacts operate should be included in the DRI conceptual framework. Secondly, the framework focuses on Drivers, Impacts and Responses in order to provide a parsimonious account of a drought system that can be informed by a range of social science, humanities and science data. ‘Pressures’ are therefore considered as a sub-category of ‘Drivers’. ‘States’ are a sub-category of ‘Impacts’. Thirdly, and most fundamentally in order to facilitate cross-disciplinary research of droughts, the DRI framework defines each of its elements, ‘Drivers’, ‘Pressures’, ‘States’, ‘Impacts’ and ‘Responses’ as capable of being shaped by both linked natural and social factors. This is different from existing DPSIR models which often see ‘Responses’ and ‘Impacts’ as located mainly in the social world, while ‘States’ are considered to be states within the natural environment only. The article illustrates this argument through an application of the DRI framework to the 1976 and 2003–6 droughts. The article also starts to address how - in cross-disciplinary research that encompasses physical and social sciences – claims about relationships between Drivers as well as Impacts of and Responses to drought over time can be methodologically justified. While the DRI framework has been inductively developed out of research on droughts we argue that it can be applied to a range of environmental governance challenges

The NASA-Lewis/ERDA solar heating and cooling technology program

Plans by NASA to carry out a major role in a solar heating and cooling program are presented. This role would be to create and test the enabling technology for future solar heating, cooling, and combined heating/cooling systems. The major objectives of the project are to achieve reduction in solar energy system costs, while maintaining adequate performance, reliability, life, and maintenance characteristics. The project approach is discussed, and will be accomplished principally by contract with industry to develop advanced components and subsystems. Advanced hardware will be tested to establish 'technology readiness' both under controlled laboratory conditions and under real sun conditions

Using variograms to detect and attribute hydrological change

There have been many published studies aiming to identify temporal changes in river flow time series, most of which use monotonic trend tests such as the Mann–Kendall test. Although robust to both the distribution of the data and incomplete records, these tests have important limitations and provide no information as to whether a change in variability mirrors a change in magnitude. This study develops a new method for detecting periods of change in a river flow time series, using temporally shifting variograms (TSVs) based on applying variograms to moving windows in a time series and comparing these to the long-term average variogram, which characterises the temporal dependence structure in the river flow time series. Variogram properties in each moving window can also be related to potential meteorological drivers. The method is applied to 91 UK catchments which were chosen to have minimal anthropogenic influences and good quality data between 1980 and 2012 inclusive. Each of the four variogram parameters (range, sill and two measures of semi-variance) characterise different aspects of the river flow regime, and have a different relationship with the precipitation characteristics. Three variogram parameters (the sill and the two measures of semi-variance) are related to variability (either day-to-day or over the time series) and have the largest correlations with indicators describing the magnitude and variability of precipitation. The fourth (the range) is dependent on the relationship between the river flow on successive days and is most correlated with the length of wet and dry periods. Two prominent periods of change were identified: 1995–2001 and 2004–2012. The first period of change is attributed to an increase in the magnitude of rainfall whilst the second period is attributed to an increase in variability of the rainfall. The study demonstrates that variograms have considerable potential for application in the detection and attribution of temporal variability and change in hydrological systems

Optimal Control of Superconducting N-level quantum systems

We consider a current-biased dc SQUID in the presence of an applied time-dependent bias current or magnetic flux. The phase dynamics of such a Josephson device is equivalent to that of a quantum particle trapped in a $1-$D anharmonic potential, subject to external time-dependent control fields, {\it i.e.} a driven multilevel quantum system. The problem of finding the required time-dependent control field that will steer the system from a given initial state to a desired final state at a specified final time is formulated in the framework of optimal control theory. Using the spectral filter technique, we show that the selected optimal field which induces a coherent population transfer between quantum states is represented by a carrier signal having a constant frequency but which is time-varied both in amplitude and phase. The sensitivity of the optimal solution to parameter perturbations is also addressed

Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes

A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid .Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events

Relative influence of changes in hydraulic conductivity with depth and climate change on estimations of borehole yields

Understanding the impact of climate change on borehole yields from fractured aquifers is essential for future management of groundwater resources. Although the impact of changes in hydraulic conductivity with depth (VKD) on groundwater levels is well established, the relative significance of climate change and VKD on borehole yield estimates is poorly understood. We hypothesize that VKD exerts a significant additional control on borehole yields under climate change which has not been considered in yield assessments to date. We developed a radial groundwater flow model of an idealised pumping borehole in the fractured Chalk aquifer of south-east England, and applied 11 VKD profiles based on a simple conceptual representation of variability in hydraulic conductivity with depth in the Chalk. For each VKD profile, we applied 20 climate scenarios and six constant pumping rates for the period 1962 – 2014. We then estimated borehole yields based on the derived lowest pumping water levels during key drought years (e.g. 1976). We show that VKD is more significant (p 0.1) in controlling lowest pumping groundwater levels. Hydraulic conductivity is as significant a control as climate on borehole yields, although responses are highly non-linear associated with pumping water level-pumping rate curves intersecting key yield constraints (e.g. pump intake depth, major inflow horizons). It is recommended that variations in hydraulic conductivity with depth are taken into consideration in future assessments of borehole yields under climate change when developing integrated water resources management plans. The approach presented is generic and can be applied across different aquifers where vertical heterogeneity is present

Can Strong Gravitational Lensing Constrain Dark Energy?

We discuss the ratio of the angular diameter distances from the source to the lens, $D_{ds}$, and to the observer at present, $D_{s}$, for various dark energy models. It is well known that the difference of $D_s$s between the models is apparent and this quantity is used for the analysis of Type Ia supernovae. However we investigate the difference between the ratio of the angular diameter distances for a cosmological constant, $(D_{ds}/D_{s})^{\Lambda}$ and that for other dark energy models, $(D_{ds}/D_{s})^{\rm{other}}$ in this paper. It has been known that there is lens model degeneracy in using strong gravitational lensing. Thus, we investigate the model independent observable quantity, Einstein radius ($\theta_E$), which is proportional to both $D_{ds}/D_s$ and velocity dispersion squared, $\sigma_v^2$. $D_{ds}/D_s$ values depend on the parameters of each dark energy model individually. However, $(D_{ds}/D_s)^{\Lambda} - (D_{ds}/D_{s})^{\rm{other}}$ for the various dark energy models, is well within the error of $\sigma_v$ for most of the parameter spaces of the dark energy models. Thus, a single strong gravitational lensing by use of the Einstein radius may not be a proper method to investigate the property of dark energy. However, better understanding to the mass profile of clusters in the future or other methods related to arc statistics rather than the distances may be used for constraints on dark energy.Comment: 15 pages, 13 figures, Accepted in PR