Baseflow is essential for stream ecosystems and human water uses, particularly in areas with Mediterranean climates. Yet the factors controlling the temporal and spatial variability of baseflow and its sources are poorly understood. Measurements of oxygen and hydrogen isotopic composition (δ¹⁸O and δ²H) were used to evaluate controls on baseflow in the stream network of a 64‐km² catchment in western Oregon. A total of 607 water samples were collected to contrast baseflow in a year of near average precipitation (2016) to a year with low winter snowpack and subsequent summer drought conditions (2015). Spatial autocorrelation structures and relationships between surface water isotopic signatures and geologic and topographic metrics throughout the network were determined using Spatial Stream Network models. Isotope values varied widely in space and between years, indicating disparate baseflow water sources. During average flow conditions, the spatial variation in δ¹⁸O was primarily related to elevation, reflecting the influence of prior precipitation and input of water from snowmelt at higher elevation. In contrast, during drought conditions, the spatial variation in δ¹⁸O was also related to terrain slope and roughness—proxies for local water storage in deep‐seated earthflows and other Quaternary deposits. A prominent spring‐fed tributary with high unit baseflow discharge illustrated the importance of subsurface water storage in porous volcanic bedrock. As drought increases in a warming climate, baseflow in mountain catchments may become more dependent on storage in geologic and geomorphic features.

Rainfall runoff erosivity (R) is one key climate factor that controls water erosion. Quantifying the effects of climate change–induced erosivity change is important for identifying critical regions prone to soil erosion under a changing environment. In this study we first evaluate the changes of R from 1970 to 2090 across the United States under nine climate conditions predicted by three general circulation models for three emissions scenarios (A2, A1B, and B1) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Then, we identify watersheds that are most vulnerable to future climate change in terms of soil erosion potential. We develop a novel approach to evaluate future trends of R magnitude and variance by incorporating both the rate of change with time as well as the level of agreement between climatic projections. Our results show that mean decadal R values would increase with time according to all nine climatic projections considered between 1970 and 2090. However, these trends vary widely spatially. In general, catchments in the northeastern and northwestern United States are characterized by strong increasing trends in R, while the trends in the midwestern and southwestern United States are either weak or inconsistent among the nine climatic projections considered. The northeastern and northwestern United States will likely experience a significant increase in annual variability of R (i.e., increase in extreme events). Conversely the variability of R is unlikely to change in large areas of the Midwest. At the watershed scale (8-digit Hydrologic Unit Code), the mean vulnerability to erosion scores vary between −0.12 and 0.35 with a mean of 0.04. The five hydrologic regions with the highest mean vulnerability to erosion are 5, 6, 2, 1, and 17, with values varying between 0.06 and 0.09. These regions occupy large areas of Ohio, Maryland, Indiana, Vermont, and Illinois, with mean erosion vulnerability score statewide above 0.08. Future watershed management aiming at reducing soil erosion should focus on areas with the highest soil erosion vulnerability identified by this study.

This study focuses on the investigation of the mean transit time (MTT) of water and its spatial variability in a tropical high-elevation ecosystem (wet Andean páramo). The study site is the Zhurucay River Ecohydrological Observatory (7.53 km²) located in southern Ecuador. A lumped parameter model considering five transit time distribution (TTD) functions was used to estimate MTTs under steady-state conditions (i.e., baseflow MTT). We used a unique data set of the δ¹⁸O isotopic composition of rainfall and streamflow water samples collected for 3 years (May 2011 to May 2014) in a nested monitoring system of streams. Linear regression between MTT and landscape (soil and vegetation cover, geology, and topography) and hydrometric (runoff coefficient and specific discharge rates) variables was used to explore controls on MTT variability, as well as mean electrical conductivity (MEC) as a possible proxy for MTT. Results revealed that the exponential TTD function best describes the hydrology of the site, indicating a relatively simple transition from rainfall water to the streams through the organic horizon of the wet páramo soils. MTT of the streams is relatively short (0.15–0.73 years, 53–264 days). Regression analysis revealed a negative correlation between the catchment's average slope and MTT (R² = 0.78, p < 0.05). MTT showed no significant correlation with hydrometric variables, whereas MEC increases with MTT (R² = 0.89, p < 0.001). Overall, we conclude that (1) baseflow MTT confirms that the hydrology of the ecosystem is dominated by shallow subsurface flow; (2) the interplay between the high storage capacity of the wet páramo soils and the slope of the catchments provides the ecosystem with high regulation capacity; and (3) MEC is an efficient predictor of MTT variability in this system of catchments with relatively homogeneous geology.