Global system of rivers: Its role in organizing continental land mass and defining land‐to‐ocean linkages

The spatial organization of the Earth\u27s land mass is analyzed using a simulated topological network (STN‐30p) representing potential flow pathways across the entire nonglacierized surface of the globe at 30‐min (longitude × latitude) spatial resolution. We discuss a semiautomated procedure to develop this topology combining digital elevation models and manual network editing. STN‐30p was verified against several independent sources including map products and drainage basin statistics, although we found substantial inconsistency within the extant literature itself. A broad suite of diagnostics is offered that quantitatively describes individual grid cells, river segments, and complete drainage systems spanning orders 1 through 6 based on the Strahler classification scheme. Continental and global‐scale summaries of key STN‐30p attributes are given. Summaries are also presented which distinguish basins that potentially deliver discharge to an ocean (exorheic) from those that potentially empty into an internal receiving body (endorheic). A total of 59,122 individual grid cells constitutes the global nonglacierized land mass. At 30‐min spatial resolution, the cells are organized into 33,251 distinct river segments which define 6152 drainage basins. A global total of 133.1 × 106 km2 bear STN‐SOp flow paths with a total length of 3.24 × 106 km. The organization of river networks has an important role in linking land mass to ocean. From a continental perspective, low‐order river segments (orders 1‐3) drain the largest fraction of land (90%) and thus constitute a primary source area for runoff and constituents. From an oceanic perspective, however, the small number (n=101) of large drainage systems (orders 4‐6) predominates; draining 65% of global land area and subsuming a large fraction of the otherwise spatially remote low‐order rivers. Along river corridors, only 10% of land mass is within 100 km of a coastline, 25% is within 250 km, and 50% is within 750 km. The global mean distance to river mouth is 1050 km with individual continental values from 460 to 1340 km. The Mediterranean/Black Sea and Arctic Ocean are the most land‐dominated of all oceans with land:ocean area ratios of 4.4 and 1.2, respectively; remaining oceans show ratios from 0.55 to 0.13. We discuss limitations of the STN‐30p together with its potential role in future global change studies. STN‐30p is geographically linked to several hundred river discharge and chemistry monitoring stations to provide a framework for calibrating and validating macroscale hydrology and biogeochemical flux models

Monitoring the impact of land cover change on surface urban heat island through google earth engine. Proposal of a global methodology, first applications and problems

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to "Make cities inclusive, safe, resilient and sustainable". In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992-2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities

Mass storage system experiences and future needs at the National Center for Atmospheric Research

A summary and viewgraphs of a discussion presented at the National Space Science Data Center (NSSDC) Mass Storage Workshop is included. Some of the experiences of the Scientific Computing Division at the National Center for Atmospheric Research (NCAR) dealing the the 'data problem' are discussed. A brief history and a development of some basic mass storage system (MSS) principles are given. An attempt is made to show how these principles apply to the integration of various components into NCAR's MSS. Future MSS needs for future computing environments is discussed

Mass storage system experiences and future needs at the National Center for Atmospheric Research

This presentation is designed to relate some of the experiences of the Scientific Computing Division at NCAR dealing with the 'data problem'. A brief history and a development of some basic Mass Storage System (MSS) principles are given. An attempt is made to show how these principles apply to the integration of various components into NCAR's MSS. There is discussion of future MSS needs for future computing environments

A Taxonomy of Data Grids for Distributed Data Sharing, Management and Processing

Data Grids have been adopted as the platform for scientific communities that need to share, access, transport, process and manage large data collections distributed worldwide. They combine high-end computing technologies with high-performance networking and wide-area storage management techniques. In this paper, we discuss the key concepts behind Data Grids and compare them with other data sharing and distribution paradigms such as content delivery networks, peer-to-peer networks and distributed databases. We then provide comprehensive taxonomies that cover various aspects of architecture, data transportation, data replication and resource allocation and scheduling. Finally, we map the proposed taxonomy to various Data Grid systems not only to validate the taxonomy but also to identify areas for future exploration. Through this taxonomy, we aim to categorise existing systems to better understand their goals and their methodology. This would help evaluate their applicability for solving similar problems. This taxonomy also provides a "gap analysis" of this area through which researchers can potentially identify new issues for investigation. Finally, we hope that the proposed taxonomy and mapping also helps to provide an easy way for new practitioners to understand this complex area of research.Comment: 46 pages, 16 figures, Technical Repor

The Northern Eurasia Earth Science Partnership: An Example of Science Applied to Societal Needs

Northern Eurasia, the largest landmass in the northern extratropics, accounts for ~20% of the global land area. However, little is known about how the biogeochemical cycles, energy and water cycles, and human activities specific to this carbon-rich, cold region interact with global climate. A major concern is that changes in the distribution of land-based life, as well as its interactions with the environment, may lead to a self-reinforcing cycle of accelerated regional and global warming. With this as its motivation, the Northern Eurasian Earth Science Partnership Initiative (NEESPI) was formed in 2004 to better understand and quantify feedbacks between northern Eurasian and global climates. The first group of NEESPI projects has mostly focused on assembling regional databases, organizing improved environmental monitoring of the region, and studying individual environmental processes. That was a starting point to addressing emerging challenges in the region related to rapidly and simultaneously changing climate, environmental, and societal systems. More recently, the NEESPI research focus has been moving toward integrative studies, including the development of modeling capabilities to project the future state of climate, environment, and societies in the NEESPI domain. This effort will require a high level of integration of observation programs, process studies, and modeling across disciplines

British Geological Survey Annual Science Review 2012-13

The British Geological Survey (BGS) is part of the Natural Environment Research Council and is its principal supplier of national capability in geoscience. We advance understanding of the structure, properties and processes of the solid Earth system through interdisciplinary surveys, monitoring, modelling and research for the benefit of society. We are the UK’s premier provider of objective and authoritative geoscientific data, information and knowledge for creating wealth, using natural resources sustainably, reducing risk and living with the impacts of environmental change. Our vision To be the world’s leading centre for geoscience impact

Assessment of contemporary Arctic river runoff based on observational discharge records

We describe the contemporary hydrography of the pan‐Arctic land area draining into the Arctic Ocean, northern Bering Sea, and Hudson Bay on the basis of observational records of river discharge and computed runoff. The Regional Arctic Hydrographic Network data set, R‐ArcticNET, is presented, which is based on 3754 recording stations drawn from Russian, Canadian, European, and U.S. archives. R‐ArcticNET represents the single largest data compendium of observed discharge in the Arctic. Approximately 73% of the nonglaciated area of the pan‐Arctic is monitored by at least one river discharge gage giving a mean gage density of 168 gages per 106 km2. Average annual runoff is 212 mm yr−1 with approximately 60% of the river discharge occurring from April to July. Gridded runoff surfaces are generated for the gaged portion of the pan‐Arctic region to investigate global change signals. Siberia and Alaska showed increases in winter runoff during the 1980s relative to the 1960s and 1970s during annual and seasonal periods. These changes are consistent with observations of change in the climatology of the region. Western Canada experienced decreased spring and summer runoff