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Most South Asian countries have challenges in ensuring water, energy, and food (WEF) security, which are often interacting positively or negatively. To address these challenges, the nexus approach provides a framework to identify the interactions of the WEF sectors as an integrated system. However, most nexus studies only qualitatively discuss the interactions between these sectors. This study conducts a systematic analysis of the WEF security nexus in South Asia by using open data sources at the country scale. We analyze interactions between the WEF sectors statistically, defining positive and negative correlations between the WEF security indicators as synergies and trade-offs, respectively. By creating networks of the synergies and trade-offs, we further identify most positively and negatively influencing indicators in the WEF security nexus. We observe a larger share of trade-offs than synergies within the water and energy sectors and a larger share of synergies than trade-offs among the WEF sectors for South Asia. However, these observations vary across the South Asian countries. Our analysis highlights that strategies on promoting sustainable energy and discouraging fossil fuel use could have overall positive effects on the WEF security nexus in the countries. This study provides evidence for considering the WEF security nexus as an integrated system rather than just a combination of three different sectors or securities.
India is facing a double burden of malnourishment with co-existences of under- and over-nourishment. Various socioeconomic factors play an essential role in determining dietary choices. Agriculture is one of the major emitters of greenhouse gases (GHGs) in India, contributing 18% of total emissions. It also consumes freshwater and uses land significantly. We identify eleven Indian diets by applying k-means cluster analysis on latest data from the Indian household consumer expenditure survey. The diets vary in calorie intake [2289-3218 kcal/Consumer Unit (CU)/day] and dietary composition. Estimated embodied GHG emissions in the diets range from 1.36 to 3.62 kg CO2eq./CU/day, land footprint from 4 to 5.45 m(2)/CU/day, whereas water footprint varies from 2.13 to 2.97m(3)/CU/day. Indian diets deviate from a healthy reference diet either with too much or too little consumption of certain food groups. Overall, cereals, sugar, and dairy products intake are higher. In contrast, the consumption of fruits and vegetables, pulses, and nuts is lower than recommended. Our study contributes to deriving required polices for the sustainable transformation of food systems in India to eliminate malnourishment and to reduce the environmental implications of the food systems. (c) 2020 Elsevier B.V. All rights reserved.
Porphyry copper deposits are formed by fluids released from felsic magmatic intrusions of batholithic dimensions, which are inferred to have been incrementally built up by a series of sill injections. The growth of the magma chamber is primarily controlled by the volumetric injection rate from deeper-seated magma reservoirs, but can further be influenced by hydrothermal convection and fluid release. To quantify the interplay between magma chamber growth, volatile expulsion and hydrothermal fluid flow during ore formation, we used numerical simulations that can model episodic sill injections in concert with multi-phase fluid flow. To build up a magma chamber that constantly maintains a small region of melt within a period of about 50 kyrs, an injection rate of at least 1.3 x 10(-3) km(3)/y is required. Higher magma influxes of 1.9 to 7.6 x 10(-3) km(3)/y are able to form magma chambers with a thickness of 2 to 3 km. Such an intrusion continuously produces magmatic volatiles which can precipitate a copper ore shell in the host rock about 2 km above the fluid injection location. The steady fluid flux from such an incrementally growing magma chamber maintains a stable magmatic fluid plume, precipitating a copper ore shell in a more confined region and resulting in higher ore grades than the ones generated by an instantaneous emplacement of a voluminous magma chamber. Our simulation results suggest that magma chambers related to porphyry copper deposits form by rapid and episodic injection of magma. Slower magma chamber growth rates more likely result in barren plutonic rocks, although they are geochemically similar to porphyry-hosting plutons. However, these low-frequency sill injection events without a significant magma chamber growth can generate magmatic fluid pulses that can reach the shallow subsurface and are typical for high-sulfidation epithermal deposits.
Accelerograms are the primary source for characterizing strong ground motion. It is therefore of paramount interest to have high-quality recordings free from any nonphysical contamination. Frequently, accelerograms are affected by baseline jumps and drifts, either related to the instrument and/or a major earthquake. In this work, I propose a correction method for these undesired baseline drifts based on segmented linear least squares. The algorithm operates on the integrated waveforms and combines all three instrument components to estimate a model that modifies the baseline to be at zero continuously. The procedure consists of two steps: first a suite of models with variable numbers of discontinuities is derived for all three instrument components. During this process, the number of discontinuities is reduced in a parsimonious way, for example, two very close discontinuities are merged into a single one. In the second step, the optimal model is selected on the basis of the Bayesian information criterion. I exemplify the application on synthetic waveforms with known discontinuities and on observed waveforms from a unified strong-motion database of the Japan Meteorological Agency (JMA) and the National Research Institute for Earth Science and Disaster Prevention (NIED, Japan) networks for the major events of the 2016 Kumamoto earthquakes. After the baseline jump correction, the waveforms are furthermore corrected for displacement according to Wang et al.(2011). The resulting displacements are comparable to the Interferometric Synthetic Aperture Radar-derived displacement estimates for the Kumamoto earthquake sequence.
Earthquake source arrays
(2020)
A collection of earthquake sources recorded at a single station, under specific conditions, are considered as a source array (SA), that is interpreted as if earthquake sources originate at the station location and are recorded at the source location. Then, array processing methods, that is array beamforming, are applicable to analyse the recorded signals. A possible application is to use source array multiple event techniques to locate and characterize near-source scatterers and structural interfaces. In this work the aim is to facilitate the use of earthquake source arrays by presenting an automatic search algorithm to configure the source array elements. We developed a procedure to search for an optimal source array element distribution given an earthquake catalogue including accurate origin time and hypocentre locations. The objective function of the optimization process can be flexibly defined for each application to ensure the prerequisites (criteria) of making a source array. We formulated four quantitative criteria as subfunctions and used the weighted sum technique to combine them in one single scalar function. The criteria are: (1) to control the accuracy of the slowness vector estimation using the time domain beamforming method, (2) to measure the waveform coherency of the array elements, (3) to select events with lower location error and (4) to select traces with high energy of specific phases, that is, sp- or ps-phases. The proposed procedure is verified using synthetic data as well as real examples for the Vogtland region in Northwest Bohemia. We discussed the possible application of the optimized source arrays to identify the location of scatterers in the velocity model by presenting a synthetic test and an example using real waveforms.
LiCSBAS
(2020)
For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (similar to km) relative displacements with an accuracy of <1 cm/epoch and similar to 2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit.
In this investigation, we examine the uncertainties using the horizontal-to-vertical spectral ratio (HVSR) technique on earthquake recordings to detect site resonant frequencies at 207 KiK-net sites. Our results show that the scenario dependence of response (pseudospectral acceleration) spectral ratio could bias the estimates of resonant frequencies for sites having multiple significant peaks with comparable amplitudes. Thus, the Fourier amplitude spectrum (FAS) should be preferred in computing HVSR. For more than 80% of the investigated sites, the first peak (in the frequency domain) on the average HVSR curve over multiple sites coincides with the highest peak. However, for sites with multiple peaks, the highest peak frequency (f(p)) is less susceptible to the selection criteria of significant peaks and the extent of smoothing to spectrum than the first peak frequency (f(0)). Meanwhile, in comparison to the surface-to-borehole spectral ratio, f(0) tends to underestimate the predominant frequency (at which the largest amplification occurs) more than f(p). In addition, in terms of characterizing linear site response, f(p) shows a better overall performance than f(0). Based on these findings, we thus recommend that seismic network operators provide f(p) on the average HVSRFAS curve as a priority, ideally together with the average HVSRFAS curve in site characterization.
In near- surface geophysics, ground-based mapping surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we have developed a multiscale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the a trous algorithm, which allows to efficiently compute a multiscale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, whereas our archaeogeophysical case study from northeast Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, whereas our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multiscale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.
Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.
In recent years, nature-based solutions are receiving increasing attention in the field of disaster risk reduction and climate change adaptation as inclusive, no regret approaches. Ecosystem-based adaptation (EbA) can mitigate the impacts of climate change, build resilience and tackle environmental degradation thereby supporting the targets set by the 2030 Agenda, the Paris Agreement and the Sendai Framework. Despite these benefits, EbA is still rarely implemented in practice. To better understand the barriers to implementation, this research examines policy-makers' perceptions of EbA, using an extended version of Protection Motivation Theory as an analytical framework. Through semi-structured interviews with policy-makers at regional and provincial level in Central Vietnam, it was found that EbA is generally considered a promising response option, mainly due to its multiple ecosystem-service benefits. The demand for EbA measures was largely driven by the perceived consequences of natural hazards and climate change. Insufficient perceived response efficacy and time-lags in effectiveness for disaster risk reduction were identified as key impediments for implementation. Pilot projects and capacity building on EbA are important means to overcome these perceptual barriers. This paper contributes to bridging the knowledge-gap on political decision-making regarding EbA and can, thereby, promote its mainstreaming into policy plans.