Hemipelagic to pelagic(H/P)marls,representing pelitic deposits,accumulated within the foredeep sub-basin of the Dinaric Foreland Basin(northern Neotethyan margin,present-day Croatia)during the Middle to Late Eocene.Sy...Hemipelagic to pelagic(H/P)marls,representing pelitic deposits,accumulated within the foredeep sub-basin of the Dinaric Foreland Basin(northern Neotethyan margin,present-day Croatia)during the Middle to Late Eocene.Syn-sedimentary tectonic movements,paleogeographic position and exchanges of short-lived hyperthermal episodes affected the sedimentation and related mineral and geochemical record of these deposits.Mineral(clay)assemblages bear signature of prevailing physical weathering with significant illite and chlorite content,but climatic seasonality is suggested by smectite-interlayered phases and sporadical increase of kaolinite content.Illite crystallinity varies significantly,and the lowest crystallinity is recorded by the Lutetian samples.Illite chemistry index is always bellow 0.5,being characteristic for Fe-Mg-rich illite.The geochemical records are the most prominent(CIA up to 76,CIW up to 91)for the Istrian Lutetian(42.3-40.5 Ma),but also for Priabonian(35.8-34.3 Ma)samples of Hvar Island.The ICV values(the lowest 1.40 and the highest 10.85)of all studied samples fall above PAAS(ICV=0.85)and point to their chemical immaturity.The Ga/Rb ratios are lower than 0.2 and K_(2)O/Al_(2)O_(3) ratios are also low(0.16-0.22),implying transition between cold and dry,and warm and humid climate,obviously trending among several warming episodes.展开更多
Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibili...Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge.Notably,the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions.The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context.In the currently evolving field of space–time landslide modelling,we recommend focusing on data error handling,model interpretability,and geomorphic plausibility,rather than allocating excessive resources to algorithm and case study comparisons.展开更多
基金supported by Croatian Science Foundation Research Project Dinaridic Foreland Basin between Two Eocene Thermal Optima:A Possible Scenario for the Northern Adriatic BREEMECO(No.2019-04-5775)。
文摘Hemipelagic to pelagic(H/P)marls,representing pelitic deposits,accumulated within the foredeep sub-basin of the Dinaric Foreland Basin(northern Neotethyan margin,present-day Croatia)during the Middle to Late Eocene.Syn-sedimentary tectonic movements,paleogeographic position and exchanges of short-lived hyperthermal episodes affected the sedimentation and related mineral and geochemical record of these deposits.Mineral(clay)assemblages bear signature of prevailing physical weathering with significant illite and chlorite content,but climatic seasonality is suggested by smectite-interlayered phases and sporadical increase of kaolinite content.Illite crystallinity varies significantly,and the lowest crystallinity is recorded by the Lutetian samples.Illite chemistry index is always bellow 0.5,being characteristic for Fe-Mg-rich illite.The geochemical records are the most prominent(CIA up to 76,CIW up to 91)for the Istrian Lutetian(42.3-40.5 Ma),but also for Priabonian(35.8-34.3 Ma)samples of Hvar Island.The ICV values(the lowest 1.40 and the highest 10.85)of all studied samples fall above PAAS(ICV=0.85)and point to their chemical immaturity.The Ga/Rb ratios are lower than 0.2 and K_(2)O/Al_(2)O_(3) ratios are also low(0.16-0.22),implying transition between cold and dry,and warm and humid climate,obviously trending among several warming episodes.
基金The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano-Südtirol/Alto Adige.
文摘Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge.Notably,the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions.The practical applicability of the approach is demonstrated in a hindcasting and scenario-building context.In the currently evolving field of space–time landslide modelling,we recommend focusing on data error handling,model interpretability,and geomorphic plausibility,rather than allocating excessive resources to algorithm and case study comparisons.
