Space-time variability of low streamflows in river networks

Using river distances in the space/time estimation of dissolved oxygen along two impaired river networks in New Jersey.

Understanding surface water quality is a critical step towards protecting human health and ecological stability. Because of resource deficiencies and the large number of river miles needing assessment, there is a need for a methodology that can accurately depict river water quality where data do not exist. The objective of this research is to implement a methodology that incorporates a river me...

Impact of subsurface drainage on streamflows in the Red River of the North basin

http://dx.doi.org/10.1016/j.jhydrol.2014.01.070 0022-1694/ 2014 Elsevier B.V. All rights reserved. ⇑ Corresponding author. Tel.: +1 701 231 7118; fax: +1 701 231 1008. E-mail address: [email protected] (Z. Lin). 1 Present address: Department of Geography, University College London, Gower Street, London WC1E 6BT, UK. Mohammed M. Rahman , Zhulu Lin a,⇑, Xinhua Jia , Dean D. Steele , Thomas M. De...

Synchrony: quantifying variability in space and time

1. There is growing recognition that linking patterns to their underlying processes in interconnected and dynamic ecological systems requires data sampled atmultiple spatial and temporal scales. 2. However, spatially explicit and temporally resolved data sets can be difficult to analyze using classical statistical methods because the data are typically autocorrelated and thus violate the assump...

Time-Space-Wavelength Networks for Low-Complexity Processor Interconnection

This paper studies a flexible hierarchic design approach of large processor networks with distributed media access. The cluster-based interconnection combines passive metal buses and passive optical star couplers at two hierarchic levels, independently employing interleaved TDMA for conflict-free interprocessor communication. The system delay analysis highlights the tradeoffs of arbitrarily com...

Space-time kriging extension of precipitation variability