Ellen M. Douglas

TL;DR: In this paper, the authors evaluated trends in flood and low flows in the US using a regional average Kendall's S trend test at two spatial scales and over two timeframes using a bootstrap methodology to account for the observed regional cross-correlation of streamflows.

TL;DR: This study demonstrates the use of globally available Earth system science data sets for water assessment in otherwise information-poor regions of the world and develops explicit geospatial indicators that link biogeophysical, socioeconomic, and engineering perspectives constitutes an important next step in global water assessment.

TL;DR: In this paper, the authors used a gridded, global-scale water balance model to estimate interannual variability in global irrigation water demand arising from climate data sets and uncertainties arising from agricultural and climate data.

TL;DR: The results show that global mapping of irrigated, rain-fed, and paddy croplands is possible by combining information from multiple data sources, however, regions with rapidly changing irrigation or complex mixtures of irrigations and non-irrigated crops present significant challenges and require more and better data to support high quality mapping of irrigation.

TL;DR: In this paper, the authors used the Regional Atmospheric Modeling System (RAMS) to simulate a rain event from 16 to 20 July 2002 over the Indian region: (i) a control with Global Land Cover land use and observed Normalized Difference Vegetation Index, (ii) an irrigated crop scenario, (iii) a non-irrigated crop and (iv) a scenario for potential (natural) vegetation.