Preprints
Filtering by Subject: Statistical Models
Projecting Global Changes in Land Use and Ecosystem Services Using SEALS (Spatial Economic Allocation Landscape Simulator)
Published: 2024-12-18
Subjects: Environmental Indicators and Impact Assessment, Environmental Monitoring, Environmental Sciences, Spatial Science, Statistical Models
Understanding how economic systems and ecosystems interact across space is crucial to ensure societal needs are met without compromising environmental quality. Spatially explicit economic models usually describe human activities and ensuing land-use dynamics at a resolution that this is too coarse (typically 10-1000 regions) to understand how these affect many biophysical processes, including [...]
Functional regression for space-time prediction of precipitation-induced shallow landslides in South Tyrol, Italy
Published: 2024-12-10
Subjects: Applied Statistics, Environmental Indicators and Impact Assessment, Environmental Monitoring, Geology, Geomorphology, Hydrology, Multivariate Analysis, Statistical Models
Shallow landslides are geomorphic hazards in mountainous terrains across the globe. Their occurrence can be attributed to the interplay of static and dynamic landslide controls. In previous studies, data-driven approaches have been employed to model shallow landslides on a regional scale, focusing on analyzing the spatial aspects and time-varying conditions separately. Still, the joint assessment [...]
On weighted ensembles of streamflow: bias correct separately and prefer constrained weights for more reliable and predictable outputs
Published: 2024-08-23
Subjects: Earth Sciences, Environmental Engineering, Hydrology, Statistical Models, Water Resource Management
It has become more and more common in hydrology to consider multiple estimates of hydrological variables – ensembles – over single model runs. Ensemble members represent different realisations of various model structures, input data, and/or parametrisations. Improved predictions can be made using weighted ensembles with wide variety of model averaging methods found in the literature, but only a [...]
Uncertainty aware sample mass determination of coarse-grained soils for particle size analyses
Published: 2024-08-23
Subjects: Applied Statistics, Civil and Environmental Engineering, Civil Engineering, Earth Sciences, Engineering, Environmental Engineering, Environmental Monitoring, Geology, Geomorphology, Geotechnical Engineering, Hydraulic Engineering, Hydrology, Other Civil and Environmental Engineering, Other Earth Sciences, Probability, Sedimentology, Soil Science, Statistical Models, Statistics and Probability, Stratigraphy
Determining particle size distributions (PSD) of soils is a basic first step in manygeotechnical analyses and guidance is given in different national standards. Forambiguous reasons, the recommended minimum sample mass (m_min) for the PSD-analyses of soils with a main component of gravel or greater is based on equationsincluding the soil's maximum grain diameter (D_max). We claim that the [...]
Automatic identification of streamlined subglacial bedforms using machine learning: an open-source Python approach
Published: 2024-06-16
Subjects: Geomorphology, Glaciology, Statistical Models
Subglacial processes exert a major control on ice streaming. Constraining subglacial conditions thus allows for more accurate predictions of ice mass loss. Due to the difficulty in observing large‐scale conditions of the modern subglacial environment, we turn to geologic records of ice streaming in deglaciated environments. Morphometric values of streamlined subglacial bedforms provide valuable [...]
Estimating the mass eruption rate of volcanic eruptions from the plume height using Bayesian regression with historical data: the MERPH model
Published: 2024-04-18
Subjects: Applied Statistics, Statistical Models, Statistics and Probability, Volcanology
The mass eruption rate (MER) of an explosive volcanic eruption is a commonly used quantifier of the magnitude of the eruption, and estimating it is importance in managing volcanic hazards. The physical connection between the MER and the rise height of the eruption column results in a scaling relationship between these quantities, allowing one to be inferred from the other. Eruption source [...]
Global patterns of commodity-driven deforestation and associated carbon emissions
Published: 2024-04-18
Subjects: Environmental Monitoring, Natural Resource Economics, Statistical Models, Sustainability
Rapid agriculture-driven deforestation raises significant concerns about achieving climate and biodiversity targets. Linking deforestation to food production is crucial for guiding the development, implementation, and evaluation of forest conservation and climate change mitigation efforts. However, the limited scope and comprehensiveness of available datasets restrict the effectiveness of these [...]
The Potential for Fuel Reduction to Offset Climate Warming Impacts on Wildfire Intensity in California
Published: 2024-02-21
Subjects: Climate, Earth Sciences, Environmental Sciences, Forest Management, Forest Sciences, Meteorology, Oceanography and Atmospheric Sciences and Meteorology, Other Statistics and Probability, Physical Sciences and Mathematics, Probability, Statistical Methodology, Statistical Models, Statistics and Probability
Increasing fuel aridity due to climate warming has and will continue to increase wildfire danger in California. In addition to reducing global greenhouse gas emissions, one of the primary proposals for counteracting this increase in wildfire danger is a widespread expansion of hazardous fuel reductions. Here, we quantify the potential for fuel reduction to reduce wildfire intensity using [...]
Bayesian network modelling of phosphorus pollution in agricultural catchments with high-resolution data
Published: 2024-01-10
Subjects: Agriculture, Biochemistry, Environmental Monitoring, Statistical Models
A Bayesian Belief Network was developed to simulate phosphorus (P) loss in an Irish agricultural catchment. Septic tanks and farmyards were included to represent all P sources and assess their effect on model performance. Bayesian priors were defined using daily discharge and turbidity, high-resolution soil P data, expert opinion, and literature. Calibration was done against seven years of daily [...]
Designing and describing climate change impact attribution studies: a guide to common approaches
Published: 2024-01-06
Subjects: Climate, Earth Sciences, Ecology and Evolutionary Biology, Environmental Public Health, Environmental Studies, Human Geography, Physical and Environmental Geography, Physical Sciences and Mathematics, Probability, Public Health, Spatial Science, Statistical Methodology, Statistical Models, Statistics and Probability
Impact attribution is an emerging transdisciplinary sub-discipline of detection and attribution, focused on the social, economic, and ecological impacts of climate change. Here, we provide an overview of common end-to-end frameworks in impact attribution, focusing on examples relating to the human health impacts of climate change. We propose a typology of study designs based on whether [...]
Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system
Published: 2023-08-03
Subjects: Geomorphology, Statistical Models
The initial inception of the landslide susceptibility concept defined it as a static property of the landscape, explaining the proneness of certain locations to generate slope failures. Since the spread of data-driven probabilistic solutions though, the original susceptibility definition has been challenged to incorporate dynamic elements that would lead the occurrence probability to change both [...]
Demystifying the Dynamics of Global and Regional Sea Level Trends from 1993 to 2021
Published: 2023-07-07
Subjects: Applied Statistics, Earth Sciences, Physical Sciences and Mathematics, Statistical Models
As global sea levels rise, questions persist about the robustness of trends and their dynamics. Here, we offer a fresh perspective by examining the dynamics of global and regional mean sea-level trends using a probabilistic framework applied to the altimetric record. We show that the global mean sea-level (GMSL) rise accelerated from 2.5 mm/yr (1993-2000) to 4.2 mm/yr (2014-2021) with an average [...]
Performance evaluation of a simple feed-forward deep neural network model applied to annual rainfall anomaly index (RAI) over Indramayu, Indonesia
Published: 2023-06-30
Subjects: Climate, Hydrology, Physical Sciences and Mathematics, Statistical Models, Statistics and Probability, Sustainability, Water Resource Management
Indramayu is a district in West Java that is known for being the leading producer of rice and brackish salt. The production of these two commodities is strongly influenced by hydroclimatological conditions, making accurate and reliable long-term estimates crucial. In this study, we evaluated a simple feed-forward deep neural network (DNN) model that could potentially be used as a candidate for [...]
A new OSL dose model to account for post-depositional mixing of sediments
Published: 2023-06-07
Subjects: Earth Sciences, Statistical Models, Statistics and Probability
In applications of optically stimulated luminescence (OSL) dating to unconsolidated sediments, the burial age of a sample of grains is estimated using statistical models of the distribution of the experimentally determined equivalent doses of the grains, together with estimates of the environmental dose rate. For grains that have been vertically mixed after deposition (e.g., due to bioturbation), [...]
Carbon Utilization and Storage through Rehabilitation of Groundwater Wells
Published: 2023-05-20
Subjects: Applied Statistics, Civil and Environmental Engineering, Climate, Earth Sciences, Environmental Sciences, Geochemistry, Hydrology, Longitudinal Data Analysis and Time Series, Oil, Gas, and Energy, Statistical Methodology, Statistical Models, Statistics and Probability
According to the Intergovernmental Panel on Climate Change (IPCC) of the United Nations (UN), rise in atmospheric concentration of carbon dioxide (CO2) due to anthropogenic factors is considered as the primary driver for global climate change. With almost every major corporation around the world working towards their “net-zero goals”, it is becoming increasingly important to have more [...]