We compare the extent of SWOT-observable rivers with GRDC and USGS gauge datasets, the most complete datasets freely available to the global scientific community.
To compute the uncertainty component for any routine gauging, the four most similar gaugings among the reference stream-gaugings dataset are selected using an analog approach, where analogy includes both riverbed shape and flow distribution complexity.
A merged satellite-gauge precipitation dataset from the Tropical Rainfall Measuring Mission (TRMM) version 3B42V6 is used.
As part of the study, we evaluate these products against a rain gauge-based dataset using both graphical- and metrics-based diagnostics.
A comparative analysis of TMPA-3B42V7 wIMD IMD gauge-based gridded dataset was carried out on daily, monthly, seasonal and yearly basis for 16 years (1998 2013).
The study revealed that TMPA estimates performed reasonably well with the gauge-based gridded dataset, however, some significant biases were also observed.
Once the enhancers were assigned to their probable targets we then set out to use these associations as a training dataset to gauge the maximum distance at which an enhancer can act upon its target gene body.
Once the enhancers were assigned to their probable targets the next, we sought to use these associations as a training dataset to gauge the maximum distance at which an enhancer can act upon its target gene body.
We compare basin-mean precipitation derived from this approach with basin-mean precipitation from a precipitation gauge-based, 1/16° gridded dataset that has been used to simulate and evaluate trends in Western United States streamflow and snowpack over the 20th century.
In this study, three different daily gauge based gridded rainfall datasets, namely Indian Meteorological Department (IMD), Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Climate Prediction Center (CPC) unified rain gauge data are compared over India for the monsoon season of 1979 2007.
