The contribution of savannas to global carbon storage is poorly understood, in part due to lack of knowledge of the amount of belowground biomass. In these ecosystems, the coexistence of woody and herbaceous life forms is often explained on the basis of belowground interactions among roots. However, the distribution of root biomass in savannas has seldom been investigated, and the dependence of root biomass on rainfall regime remains unclear, particularly for woody plants. Here we investigate patterns of belowground woody biomass along a rainfall gradient in the Kalahari of southern Africa, a region with consistent sandy soils. We test the hypotheses that (1) the root depth increases with mean annual precipitation (root optimality and plant hydrotropism hypothesis), and (2) the root-to-shoot ratio increases with decreasing mean annual rainfall (functional equilibrium hypothesis). Both hypotheses have been previously assessed for herbaceous vegetation using global root data sets. Our data do not support these hypotheses for the case of woody plants in savannas. We find that in the Kalahari, the root profiles of woody plants do not become deeper with increasing mean annual precipitation, whereas the root-to-shoot ratios decrease along a gradient of increasing aridity.

The recent mountain pine beetle epidemic in the Colorado River Basin has resulted in widespread tree mortality in pine stands across the Colorado Plateau. Because of complex micro‐scale (i.e. tree well scale) interactions between vegetation and snow processes, one of the most significant issues resulting from this epidemic is the potential hydrologic impacts of the effects of changing forest structure. Using SNTHERM, we conducted a comparative modelling scenario analysis of the snowpack along a transect between two trees over the course of the snow ablation season (28 February–30 June) under four forest stand conditions to assess changes in snowpack characteristics because of loss of canopy biomass. We found that the red phase scenario (intermediate phase of tree death) exhibited a 4‐day earlier snow disappearance date than the living stand scenario and grey phase scenario (advanced phase of tree death), although the timing of isothermal conditions at 0 °C was identical. The modelled clearcut scenario snowpack became isothermal at 0 °C 10 days earlier than the living, red phase, or grey phase scenarios. The clearcut modelling scenario also exhibited the greatest homogenization of snow properties, and the spatio‐temporal distribution of snow disappearance at the tree well scale was 70% as variable as the living, red phase and grey phase modelling scenarios. These results provide insight to the processes responsible for changing hydrologic dynamics in snow‐dominated forest ecosystems with the onset of vegetation stress and death and may help inform future forest management strategies. Copyright © 2012 John Wiley & Sons, Ltd.

Runoff from the Upper Colorado River Basin (UCRB) is an important water resource in the western United States. The majority of annual runoff is derived from spring snowmelt, and therefore April–July water supply volume (WSV) forecasts produced by the Colorado Basin River Forecast Center are critical to basin water management. The primary objective of this study was to evaluate the impact of snow data assimilation (DA) in a distributed hydrologic model on WSV forecasting error and skill in headwater catchments of the UCRB. To do this, a framework was built to use the Localized Ensemble transform Kalman filter to update modeled snow water equivalent (SWE) states in the Hydrology Laboratory‐Research Distributed Hydrologic Model with SNOTEL SWE observations and spatially and temporally complete MODIS Snow Covered‐Area and Grain size data. The DA approach was assessed by evaluating ensemble streamflow prediction forecasts over a 20‐year period for 23 catchments in the UCRB. Overall, the DA approach improved water supply forecast skill in 80% of pilot basins with an average improvement in the median continuous rank probability skill score of 4%. A research‐to‐operations transition was facilitated by automating the implementation of the DA approach. This work demonstrates the capacity for gridded and point snow products to be used to objectively update model states in an operational forecasting setting.

This study compares stream nitrate (NO 2 3 ) concentrations to spatially distributed snowmelt in two alpine catchments, the Green Lakes Valley, Colorado (GLV4) and Tokopah Basin, California (TOK). A snow water equivalent reconstruction model and Landsat 5 and 7 snow cover data were used to estimate daily snowmelt at 30 m spatial resolution in order to derive indices of new snowmelt areas (NSAs). Estimates of NSA were then used to explain the NO 2 3 flushing behavior for each basin over a 12 year period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007). To identify the optimal method for defining NSAs and elucidate mechanisms underlying catchment NO 2 3 flushing, we conducted a series of regression analyses using multiple thresholds of snowmelt based on temporal and volumetric metrics. NSA indices defined by volume of snowmelt (e.g., snowmelt 30 cm) rather than snowmelt duration (e.g., snowmelt 9 days) were the best predictors of stream NO flushing behavior suggests that streamflow in TOK was primarily influenced by overland flow and shallow subsurface flow, whereas GLV4 appeared to be more strongly influenced by deeper subsurface flow paths.

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Developing environmental flow standards requires an empirical understanding of the relationship between species' ecology and instream flow. However, when congruent biological and hydrologic data are lacking, the accurate simulation of hydrologic metrics (HMs) corresponding to the locations of biological data is needed. Methods to predict HMs vary in formulation (i.e., statistical vs. process‐based hydrologic models), ability to simulate HMs across the full range of the hydrologic regime (i.e., magnitude, duration, frequency, rate of change and timing) and ability to transfer HMs from gaged to ungaged locations. Yet, despite the breadth of modelling approaches, less attention has been paid to the variability in HMs associated with each approach. In this study, we apply a distributed hydrologic model to the diverse watersheds of South Carolina to examine the predictability of HMs from simulated daily time series of streamflow across ecoregions, stream classifications and level of human alteration. In doing so, we contextualize the predictability of HMs, giving managers and researchers in South Carolina the flexibility of choosing HMs that are best suited for quantifying flow–ecology relationships based on the location, flow regime components of interest and uncertainty of HMs. We found that at least one HM within each of the five flow regime components (out of a selected subset of 41 non‐redundant HMs) was consistently and accurately predicted across the diverse streams of the study area. We discuss the patterns of predictability related to site characterizations and individual HMs and their implications for developing environmental flow standards.

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