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Urban surface runoff management via best management practices (BMP) and low impact development (LID) has earned significant recognition owing to positive environmental and ecological impacts. However, due to the complexity of the parameters involved, the estimation of LID efficiency in attenuating the urban surface runoff at the watershed scale is challenging. A planning analysis of employing Green Roofs and Infiltration Trenches as BMPs/LIDs practices for urban surface runoff control is presented in this study. A multi-objective optimization decision-making framework is established by coupling SWMM (Storm Water Management Model) with NSGA-II models to check the performance of BMPs/LIDs concerning the cost-benefit analysis of LID at the watershed scale. Two urbanized areas belonging to Central Delhi in India were used as case studies. The results showed that the SWMM model is useful in simulating optimization problems for managing urban surface runoff. The optimum scenarios efficiently minimized the urban runoff volume while maintaining the BMPs/LIDs implementation costs and size. With BMPs/LIDs implementation, the reduction in runoff volume increases as expenses increase initially; however, there is no noticeable reduction in flood volume after a certain threshold. Contrasted with the haphazard arrangement of BMPs/LIDs, the proposed approach demonstrates 22%-24% runoff reductions for the same expenditures in watershed 1 and 23%-26% in watershed 2. The result of the study provides insights into planning and management of the urban surface runoff control with LID practices. The proposed framework assists the hydrologists in optimum selection and placements of BMPs/LIDs practices to acquire the most extreme ecological advantages with the least expenses.
Van Allen Probes measurements revealed the presence of the most unusual structures in the ultra-relativistic radiation belts. Detailed modeling, analysis of pitch angle distributions, analysis of the difference between relativistic and ultra-realistic electron evolution, along with theoretical studies of the scattering and wave growth, all indicate that electromagnetic ion cyclotron (EMIC) waves can produce a very efficient loss of the ultra-relativistic electrons in the heart of the radiation belts. Moreover, a detailed analysis of the profiles of phase space densities provides direct evidence for localized loss by EMIC waves. The evolution of multi-MeV fluxes shows dramatic and very sudden enhancements of electrons for selected storms. Analysis of phase space density profiles reveals that growing peaks at different values of the first invariant are formed at approximately the same radial distance from the Earth and show the sequential formation of the peaks from lower to higher energies, indicating that local energy diffusion is the dominant source of the acceleration from MeV to multi-MeV energies. Further simultaneous analysis of the background density and ultra-relativistic electron fluxes shows that the acceleration to multi-MeV energies only occurs when plasma density is significantly depleted outside of the plasmasphere, which is consistent with the modeling of acceleration due to chorus waves.
We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES-15 flux values and an upper boundary condition scaling factor (BF). The GOES-15 flux model utilizes an input and feedback delay of 2 and 2 time steps (i.e., 5 min time steps) with the most efficient number of hidden layers set to 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES-15 flux and therefore were used as the exogenous inputs. The NARX-derived upper boundary condition scaling factor was used in conjunction with the Versatile Electron Radiation Belt (VERB) code to produce reconstructions of the radiation belts during the period of July-November 1990, independent of in-situ observations. Here, Kp was chosen as the sole exogenous input to be more compatible with the VERB code. This Combined Release and Radiation Effects Satellite-era reconstruction showcases the potential to use these neural network-derived boundary conditions as a method of hindcasting the historical radiation belts. This study serves as a companion paper to another recently published study on reconstructing the radiation belts during Solar Cycles 17-24 (Saikin et al., 2021, ), for which the results featured in this paper were used.
Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.
Climate Benefits of Cleaner Energy Transitions in East and South Asia Through Black Carbon Reduction
(2022)
The state of air pollution has historically been tightly linked to how we produce and use energy. Air pollutant emissions over Asia are now changing rapidly due to cleaner energy transitions; however, magnitudes of benefits for climate and air quality remain poorly quantified. The associated risks involve adverse health impacts, reduced agricultural yields, reduced freshwater availability, contributions to climate change, and economic costs. We focus particularly on climate benefits of energy transitions by making first-time use of two decades of high quality observations of atmospheric loading of light-absorbing black carbon (BC) over Kanpur (South Asia) and Beijing (East Asia) and relating these observations to changing energy, emissions, and economic trends in India and China. Our analysis reveals that absorption aerosol optical depth (AAOD) due to BC has decreased substantially, by 40% over Kanpur and 60% over Beijing between 2001 and 2017, and thus became decoupled from regional economic growth. Furthermore, the resultant decrease in BC emissions and BC AAOD over Asia is regionally coherent and occurs primarily due to transitions into cleaner energies (both renewables and fossil fuels) and not due to the decrease in primary energy supply or decrease in use of fossil use and biofuels and waste. Model simulations show that BC aerosols alone contribute about half of the surface temperature change (warming) of the total forcing due to greenhouse gases, natural and internal variability, and aerosols, thus clearly revealing the climate benefits due to a reduction in BC emissions, which would significantly reduce global warming. However, this modeling study excludes responses from natural variability, circulation, and sea ice responses, which cause relatively strong temperature fluctuations that may mask signals from BC aerosols. Our findings show additional benefits for climate (beyond benefits of CO2 reduction) and for several other issues of sustainability over South and East Asia, provide motivation for ongoing cleaner energy production, and consumption transitions, especially when they are associated with reduced emissions of air pollutants. Such an analysis connecting the trends in energy transitions and aerosol absorption loading, unavailable so far, is crucial for simulating the aerosol climate impacts over Asia which is quite uncertain.
In this study, 3-D models of P-wave velocity (Vp) and P-wave and S-wave ratio (Vp/Vs) of the crust and upper mantle in the Eastern and eastern Southern Alps (northern Italy and southern Austria) were calculated using local earthquake tomography (LET). The data set includes high-quality arrival times from well-constrained hypocenters observed by the dense, temporary seismic networks of the AlpArray AASN and SWATH-D. The resolution of the LET was checked by synthetic tests and analysis of the model resolution matrix. The small inter-station spacing (average of similar to 15 km within the SWATH-D network) allowed us to image crustal structure at unprecedented resolution across a key part of the Alps. The derived P velocity model revealed a highly heterogeneous crustal structure in the target area. One of the main findings is that the lower crust is thickened, forming a bulge at 30-50 km depth just south of and beneath the Periadriatic Fault and the Tauern Window. This indicates that the lower crust decoupled both from its mantle substratum as well as from its upper crust. The Moho, taken to be the iso-velocity contour of Vp = 7.25 km/s, agrees with the Moho depth from previous studies in the European and Adriatic forelands. It is shallower on the Adriatic side than on the European side. This is interpreted to indicate that the European Plate is subducted beneath the Adriatic Plate in the Eastern and eastern Southern Alps.
Phase transitions in molecular crystals are often determined by intermolecular interactions. The cage complex of [Co(C12H30N8)](3+) . 3 NO3- is reported to undergo a disorder-order phase transition at T-c1 approximate to 133 K upon cooling. Temperature-dependent neutron and synchrotron diffraction experiments revealed satellite reflections in addition to main reflections in the diffraction patterns below T-c1. The modulation wave vector varies as function of temperature and locks in at T-c3 approximate to 98 K. Here, we demonstrate that the crystal symmetry lowers from hexagonal to monoclinic in the incommensurately modulated phases in T-c1<T<T-c3. Distinctive levels of competitions: trade-off between longer N-H...O and shorter C-H...O hydrogen bonds; steric constraints to dense C-H...O bonds give rise to pronounced modulation of the basic structure. Severely frustrated crystal packing in the incommensurate phase is precursor to optimal balance of intermolecular interactions in the lock-in phase.
In the comment on "Varves of the Dead Sea sedimentary record." Quaternary Science Reviews 215 (Ben Dor et al., 2019): 173-184. by R. Bookman, two recently published papers are suggested to prove that the interpretation of the laminated sedimentary sequence of the Dead Sea, deposited mostly during MIS2 and Holocene pluvials, as annual deposits (i.e., varves) is wrong. In the following response, we delineate several lines of evidence which coalesce to demonstrate that based on the vast majority of evidence, including some of the evidence provided in the comment itself, the interpretation of these sediments as varves is the more likely scientific conclusion. We further discuss the evidence brought up in the comment and its irrelevance and lack of robustness for addressing the question under discussion.
Volcano-seismic signals such as long-period events and tremor are important indicators for volcanic activity and unrest. However, their wavefield is complex and characterization and location using traditional seismological instrumentation is often difficult.
In 2019 we recorded the full seismic wavefield using a newly developed 3C rotational sensor co-located with a 3C traditional seismometer on Etna, Italy. We compare the performance of the rotational sensor, the seismometer and the Istituto Nazionale di Geofisica e Vulcanologia-Osservatorio Etneo (INGV-OE) seismic network with respect to the analysis of complex volcano-seismic signals. We create event catalogs for volcano-tectonic (VT) and long-period (LP) events combining a STA/LTA algorithm and cross-correlations.
The event detection based on the rotational sensor is as reliable as the seismometer-based detection. The LP events are dominated by SH-type waves. Derived SH phase velocities range from 500 to 1,000 m/s for LP events and 300-400 m/s for volcanic tremor. SH-waves compose the tremor during weak volcanic activity and SH- and SV-waves during sustained strombolian activity.
We derive back azimuths using (a) horizontal rotational components and (b) vertical rotation rate and transverse acceleration. The estimated back azimuths are consistent with the INGV-OE event location for (a) VT events with an epicentral distance larger than 3 km and some closer events, (b) LP events and tremor in the main crater area. Measuring the full wavefield we can reliably analyze the back azimuths, phase velocities and wavefield composition for VT, LP events and tremor in regions that are difficult to access such as volcanoes.