Modelling of piping collapses and gully headcut landforms:Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)tech-niques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable's importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model's result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.     

Considering potential seismic sources in earthquake hazard assessment for Northern Iran

Located on the Alpine-Himalayan earthquake belt, Iran is one of the seismically active regions of the world. Northern Iran, south of Caspian Basin, a hazardous subduction zone, is a densely populated and developing area of the country. Historical and instrumental documented seismicity indicates the occurrence of severe earthquakes leading to many deaths and large losses in the region. With growth of seismological and tectonic data, updated seismic hazard assessment is a worthwhile issue in emergency management programs and long-term developing plans in urban and rural areas of this region. In the present study, being armed with up-to-date information required for seismic hazard assessment including geological data and active tectonic setting for thorough investigation of the active and potential seismogenic sources, and historical and instrumental events for compiling the earthquake catalogue, probabilistic seismic hazard assessment is carried out for the region using three recent ground motion prediction equations. The logic tree method is utilized to capture epistemic uncertainty of the seismic hazard assessment in delineation of the seismic sources and selection of attenuation relations. The results are compared to a recent practice in code-prescribed seismic hazard of the region and are discussed in detail to explore their variation in each branch of logic tree approach. Also, seismic hazard maps of peak ground acceleration in rock site for 475- and 2,475-year return periods are provided for the region.     

From flood risk mapping toward reducing vulnerability: the case of Addis Ababa

Natural Hazards - Flood risk maps for the built environment can be obtained by integrating geo-spatial information on hazard, vulnerability and exposure. They provide precious support for strategic...     

Investigating the ability of periodically correlated (PC) time series models to forecast the climate index

Stochastic Environmental Research and Risk Assessment - Considering the importance of climate change and its effects, especially in recent decades, the forecast of future climate conditions can be...     

Characterizing Water Flow in Vegetated Lysimeters with Stable Water Isotopes and Modeling

We have used stable water isotopes (δ¹⁸O, δ²H) in combination with lumped-parameter modeling for characterizing unsaturated flow in two lysimeters vegetated with maize. The lysimeters contained undisturbed soil cores dominated by sandy gravel (Ly1) and clayey sandy silt (Ly2). Stable water isotopes were analyzed in precipitation and lysimeter outflow water over about 3 years. The mean transit time of water T and dispersion parameter P_D, obtained from modeling, were higher for the silt soil in Ly2 than for the gravel soil in Ly1 (T of 362 vs. 129 d, P_D of 0.7 vs. 0.12). The consideration of preferential flow (PF) paths could substantially improve the model curve fits, with 13 and 11% contribution of PF for Ly1 and Ly2 as best estimates. Different assumptions were compared to estimate the input function, that is, stable water isotope content in the recharging water. Using the isotopic composition of precipitation as input (no modification) resulted in reasonable model estimations. Best model fits for the entire observation were obtained by weighting the recharging isotopes according to average precipitation within periods of 3 and 6 months, in correspondence to changing vegetation phases and seasonal influences. Input functions that consider actual evapotranspiration could significantly improve modeling at some periods, however, this led to deviations between modeled and observed δ¹⁸O at other periods. This may indicate the influence of variable flow, so that dividing the whole observation period into hydraulically characteristic sub-periods for lumped-parameter modeling (which implements steady-state flow) is recommended for possible further improvement.     

Soil Characterization Using Joint Interpretation of Remote Sensing,Resistivity and Induced Polarization Data along the Coast of the Nile Delta

Natural Resources Research - Soil characterization in coastal areas is essential for strategic coastal engineering implementation and for understanding the paleo-environmental conditions. However,...     

High-resolution coastal hazard assessment along the French Riviera from co-seismic tsunamis generated in the Ligurian fault system

Natural Hazards - The French Riviera is a densely populated and touristic coast. It is also one of the most seismically active areas of the Western Mediterranean. This is evidenced by the Mw...