Abstract

Since about 1950 there has been a trend in the California Sierra Nevada toward a decreasing portion of the total annual streamflow occurring during April through July, while the streamflow during autumn and winter has increase. This trend not only has important ramifications with regard to water management, it also brings up the question of whether this represents a shift toward earlier release of the snowpack resulting from greenhouse warming. Therefore, the observed record has been examined in terms of relative influences of temperature and precipitation anomalies on the timing of streamflow in this region. To carry out this study, the fraction of annual streamflow (called the fractional streamflow) occurring in November-January (NDJ), February-April (FMA), and May-July (MJJ) at low, medium, and high elevation basins in California and 0regon was examined. Linear regression models were used to relate precipitation and temperature to the fractional streamflow at the three elevations for each season. Composites of monthly temperature and precipitation were employed to further examine the fractional streanflow in its high and low tercile extremes. Long time series of climatic and hydrologic data were also looked at to infer the causes in the trend toward earlier runoff.

For the low-elevation basins, there is a dominant influence of precipitation on seasonal fractional streamflow. Middle-elevation basins exhibit a longer memory of precipitation and temperature in relation to their fractional stream-flow. In-season precipitation is still the most important influence upon NDJ and FMA fractional streamflow; however, the influence of temperature in melting the snowpack is seen on MJJ fractional streamflow, whose strongest influence is FMA temperature. At higher elevation prior-season precipitation exerts a greater influence than at low and middle elevations, and seasonal temperature anomalies have an effect on all seasonal streamflow fractions.

There are several causes for the trend toward decreasing fractional streamflow in the spring and summer. Concomitant with the trend in the timing of streamflow was an increase in NDJ (most notably November) precipitation. There also has been a trend toward higher spring temperatures over most of the western United States, but since them has also been a trend toward decreasing temperatures in the southeast, we do not interpret this as a signal of anthropogenic warming. Other factors in the trend toward earlier streamflow may include a decrease in MJJ precipitation and an increase in August–October precipitation.

Abstract

Streamflow Q estimation in ungauged catchments is one of the greatest challenges facing hydrologists. Observed Q from 3000 to 4000 small-to-medium-sized catchments (10–10 000 km²) around the globe were used to train neural network ensembles to estimate Q characteristics based on climate and physiographic characteristics of the catchments. In total, 17 Q characteristics were selected, including mean annual Q, baseflow index, and a number of flow percentiles. Testing coefficients of determination for the estimation of the Q characteristics ranged from 0.55 for the baseflow recession constant to 0.93 for the Q timing. Overall, climate indices dominated among the predictors. Predictors related to soils and geology were relatively unimportant, perhaps because of their data quality. The trained neural network ensembles were subsequently applied spatially over the entire ice-free land surface, resulting in global maps of the Q characteristics (at 0.125° resolution). These maps possess several unique features: they represent observation-driven estimates, they are based on an unprecedentedly large set of catchments, and they have associated uncertainty estimates. The maps can be used for various hydrological applications, including the diagnosis of macroscale hydrological models. To demonstrate this, the produced maps were compared to equivalent maps derived from the simulated daily Q of four macroscale hydrological models, highlighting various opportunities for improvement in model Q behavior. The produced dataset is available online (http://water.jrc.ec.europa.eu/GSCD).

Abstract

ScaleX is a collaborative measurement campaign, collocated with a long-term environmental observatory of the German Terrestrial Environmental Observatories (TERENO) network in the mountainous terrain of the Bavarian Prealps, Germany. The aims of both TERENO and ScaleX include the measurement and modeling of land surface–atmosphere interactions of energy, water, and greenhouse gases. ScaleX is motivated by the recognition that long-term intensive observational research over years or decades must be based on well-proven, mostly automated measurement systems, concentrated in a small number of locations. In contrast, short-term intensive campaigns offer the opportunity to assess spatial distributions and gradients by concentrated instrument deployments, and by mobile sensors (ground and/or airborne) to obtain transects and three-dimensional patterns of atmospheric, surface, or soil variables and processes. Moreover, intensive campaigns are ideal proving grounds for innovative instruments, methods, and techniques to measure quantities that cannot (yet) be automated or deployed over long time periods. ScaleX is distinctive in its design, which combines the benefits of a long-term environmental-monitoring approach (TERENO) with the versatility and innovative power of a series of intensive campaigns, to bridge across a wide span of spatial and temporal scales. This contribution presents the concept and first data products of ScaleX-2015, which occurred in June–July 2015. The second installment of ScaleX took place in summer 2016 and periodic further ScaleX campaigns are planned throughout the lifetime of TERENO. This paper calls for collaboration in future ScaleX campaigns or to use our data in modelling studies. It is also an invitation to emulate the ScaleX concept at other long-term observatories.