Luminescence dating of buried cobble surfaces from sandy beach ridges: a case study from Denmark

Here we investigate the use of optically stimulated luminescence (OSL) for dating cobbles from the body of successive beach ridges and compare cobble surface‐derived ages to standard quartz OSL ages from sand. Between four and eight cobbles and sand samples (age control) were dated with the luminescence method, taken from the modern beach and from beach ridges on the south and north extremes of a prograding spit on the westernmost coast of Lolland, Denmark. Luminescence‐depth profiles perpendicular to the surfaces of the cobbles show that the feldspar infrared signals stimulated at 50 °C were fully reset to various depths into the cobbles prior to final deposition; as a result, the equivalent doses determined from close to the surface of such cobbles can be used to calculate burial ages. Beach‐ridge burial ages given by the average of ages of individual cobbles taken from the same site are consistent, within errors, with the ages derived from the sand samples. Cobble‐ and sand‐derived ages show that the southernmost beach ridge at Albuen was formed around 2 ka ago, indicating that this sandy spit is younger than other coastal systems in Denmark. The agreement between ages derived from clasts and from standard quartz OSL in this study confirms that, even in the absence of sandy sediments, we can reliably date sites using OSL by targeting larger clasts. In addition, the record of prior light exposure contained in the shape of the cobbles’ luminescence‐depth profile removes one of the major uncertainties (i.e. the degree of signal reset prior to burial) in the luminescence dating of high latitude sites.     

The Kings River Experimental Watersheds: Infrastructure and data

The Kings River Experimental Watersheds (KREW) were established in 2002 to expand our knowledge of catchment physical, chemical, and biological processes in Sierra Nevada headwater forests, and to better understand the impacts of prescribed burning and forest thinning on these processes. Two elevation strata (high and low) were selected for the KREW sites, with four independent catchments and one nested catchment within each stratum. Both high and low elevation study areas were instrumented for continuous measurements of meteorology, streamflow, and turbidity. Atmospheric and stream chemistry, suspended sediment concentration, and bedload sediment delivery were measured on a regular schedule. Soil chemical and physical properties and vegetation were systematically sampled before and after the initial thinning and prescribed burning treatments, which were implemented between 2012 and 2016. Post-treatment data collection continues today as we explore opportunities for the second round of possible treatments. The critical research infrastructure and long-term baseline data collection has been instrumental in building partnerships with downstream managers, end users, non-governmental organizations, academic researchers, and national research programmes. Contributions to date include fundamental understanding of magnitude and variability of nutrient deposition; carbon, nutrient, and major ion dynamics in headwater streams; aquatic algae and macroinvertebrate populations; vegetation composition and structure; and streamflow responses to precipitation in the two elevation strata. Data from the experimental watersheds also support calibration and validation of diverse hydrologic models used for water resources planning.     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

Improving soil moisture accounting and streamflow prediction in SWAT by incorporating a modified time‐dependent Curve Number method

The objective of this study is to incorporate a time‐dependent Soil Moisture Accounting (SMA) based Curve Number method (SMA_CN) in Soil and Water Assessment Tool (SWAT) and compare its performance with the existing CN method in SWAT by simulating the hydrology of two agricultural watersheds in Indiana, USA. Results show that fusion of the SMA_CN method causes decrease in runoff volume and increase in profile soil moisture content, associated with larger groundwater contribution to the streamflow. In addition, the higher amount of moisture in the soil profile slightly elevates the actual evapotranspiration. The SMA‐based SWAT configuration consistently produces improved goodness‐of‐fit scores and less uncertain outputs with respect to streamflow during both calibration and validation. The SMA_CN method exhibits a better match with the observed data for all flow regimes, thereby addressing issues related to peak and low flow predictions by SWAT in many past studies. Comparison of the calibrated model outputs with field‐scale soil moisture observations reveals that the SMA overhauling enables SWAT to represent soil moisture condition more accurately, with better response to the incident rainfall dynamics. While the results from the modification of the CN method in SWAT are promising, more studies including watersheds with various physical and climatic settings are needed to validate the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco,Italy

This study deals with landslide susceptibility mapping in the northern part of Lecco Province, Lombardy Region, Italy. In so doing, a valid landslide inventory map and thirteen predisposing factors (including elevation, slope aspect, slope degree, plan curvature, profile curvature, distance to waterway, distance to road, distance to fault, soil type, land use, lithology, stream power index, and topographic wetness index) form the spatial database within geographic information system. The used predictive models comprise a bivariate statistical approach called frequency ratio (FR) and two machine learning tools, namely multilayer perceptron neural network (MLPNN) and adaptive neuro-fuzzy inference system (ANFIS). These models first use landslide and non-landslide records for comprehending the relationship between the landslide occurrence and predisposing factors. Then, landslide susceptibility values are predicted for the whole area. The accuracy of the produced susceptibility maps is measured using area under the curve (AUC) index, according to which, the MLPNN (AUC?=?0.916) presented the most accurate map, followed by the ANFIS (AUC?=?0.889) and FR (AUC?=?0.888). Visual interpretation of the susceptibility maps, FR-based correlation analysis, as well as the importance assessment of predisposing factors, all indicated the significant contribution of the road networks to the crucial susceptibility of landslide. Lastly, an explicit predictive formula is extracted from the implemented MLPNN model for a convenient approximation of landslide susceptibility value.

     

Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models

Natural Hazards - On March 25, 2019, widespread flood events occurred across Iran’s provinces and set a new record for socioeconomic losses and casualties. In hindsight, it opened an...     

Socioeconomic impacts of environmental risks in the western Makran zone (Chabahar,Iran)

Natural Hazards - The western Makran subduction zone is capable of producing considerable tsunami run-up heights that penetrate up to 5 km inland. In this study, we show how climate change...     

Assessing factors affecting drought,earthquake, and flood risk perception: empirical evidence from Bangladesh

Natural Hazards - Understanding household disaster risk perception is crucial to formulate and apply disaster risk reduction strategies. Using survey data from 300 households from three highly...     

FEA of Urban Rock Tunnels Under Impact Loading at Targeted Velocity

Geotechnical and Geological Engineering - This paper presents the effect of impact load and weathering of surrounding rockmass on the deformation behavior of urban underground structures. The FEM...     

WASP-121b Secondary Eclipses Revisited Using TESS Observation

Astronomy Reports - We present the detection and characterization of the ultrahot Jupiter WASP-121b ( $${{R}_{p}} \simeq 1.865{\kern 1pt} {{R}_{J}}$$ , $${{M}_{p}} \simeq 1.184{\kern 1pt}...