Data are the backbone of science.This paper describes the construction of a complex database for social-ecological analysis in Mongolia.Funded through the National Science Foundation(NSF)Dynamics of Coupled Natural an...Data are the backbone of science.This paper describes the construction of a complex database for social-ecological analysis in Mongolia.Funded through the National Science Foundation(NSF)Dynamics of Coupled Natural and Human(CNH)Systems program,the Mongolian Rangelands and Resilience(MOR2)project focused on Mongolian pastoral systems,community adaptive capacity,and vulnerability to climate change.We examine the development of a complex,multi-disciplinary research database of data collected over a three-year period,both in the field and from other sources.This data set captures multiple types of data:ecological,hydrological and social science surveys;remotely-sensed data,participatory mapping,local documents,and scholarly literature.The content,structure,and organization of the database,development of data protocols and issues related to data access,sharing and long-term storage are described.We conclude with recommendations for long-term data management and curation from large multidisciplinary research projects.展开更多
Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry （SNOTEL） stations across the Southern Rocky Mountains were used to estimate spatio...Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry （SNOTEL） stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non- evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series （1990 to 2013） created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.展开更多
基金supported by funds form the NSF Dynamics of Coupled and Human Systems(CNH)Program award BCS-1011801,The World Bank,USAID,American Association of University Women,Open Society Institute,Center for Collaborative Conservation,Colorado State University.
文摘Data are the backbone of science.This paper describes the construction of a complex database for social-ecological analysis in Mongolia.Funded through the National Science Foundation(NSF)Dynamics of Coupled Natural and Human(CNH)Systems program,the Mongolian Rangelands and Resilience(MOR2)project focused on Mongolian pastoral systems,community adaptive capacity,and vulnerability to climate change.We examine the development of a complex,multi-disciplinary research database of data collected over a three-year period,both in the field and from other sources.This data set captures multiple types of data:ecological,hydrological and social science surveys;remotely-sensed data,participatory mapping,local documents,and scholarly literature.The content,structure,and organization of the database,development of data protocols and issues related to data access,sharing and long-term storage are described.We conclude with recommendations for long-term data management and curation from large multidisciplinary research projects.
文摘Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry （SNOTEL） stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non- evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series （1990 to 2013） created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.
