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Synoptic sampling of streams is an inexpensive way to gain insight into the spatial distribution of dissolved constituents in the subsurface critical zone. Few spatial synoptics have focused on urban watersheds although this approach is useful in urban areas where monitoring wells are uncommon. Baseflow stream sampling was used to quantify spatial variability of water chemistry in a highly developed Piedmont watershed in suburban Baltimore, MD having no permitted point discharges. Six synoptic surveys were conducted from 2014 to 2016 after an average of 10 days of no rain, when stream discharge was composed of baseflow from groundwater. Samples collected every 50 m over 5 km were analyzed for nitrate, sulfate, chloride, fluoride, and water stable isotopes. Longitudinal spatial patterns differed across constituents for each survey, but the pattern for each constituent varied little across synoptics. Results suggest a spatially heterogeneous, three‐dimensional pattern of localized groundwater contaminant zones steadily contributing solutes to the stream network, where high concentrations result from current and legacy land use practices. By contrast, observations from 35 point piezometers indicate that sparse groundwater measurements are not a good predictor of baseflow stream chemistry in this geologic setting. Cross‐covariance analysis of stream solute concentrations with groundwater model/backward particle tracking results suggest that spatial changes in base‐flow solute concentrations are associated with urban features such as impervious surface area, fill, and leaking potable water and sanitary sewer pipes. Predicted subsurface residence times suggest that legacy solute sources drive baseflow stream chemistry in the urban critical zone.

Nonpoint source urban nutrient loading into streams and receiving water bodies is widely recognized as a major environmental management challenge. A dominant research and management paradigm assumes that loading primarily derives from elevated stormwater. However, baseflow can account for a large portion of total loading, especially for low development intensity watersheds which comprise the largest urban areas. We investigated the sources and drivers of nonpoint source baseflow nitrogen loading across 27 headwater catchments in the urbanized Piedmont region of North Carolina, USA. Nitrate isotopes, predictors of concentration‐discharge (CQ) slopes, and predictors of mean annual total dissolved nitrogen (TDN) loading suggest that wastewater was a major baseflow nonpoint source of nitrogen across developed catchments likely contributing 61% of nitrate loading from septic served catchments and 49% from sewer served catchments. Our findings suggest that subsurface TDN was abundant, loading was largely transport limited, and the hydrogeomorphic position of sanitary infrastructure strongly influences transport. We developed an empirical model showing catchment loading increased with the topographic wetness index of sanitary sewer location, convergent sloping land area, parcel density, and residual agricultural landcover (R² = 0.78). We extended this model to the study region's 1,436 developed small (0.3–20.8 sq km) catchments. We estimated up to 92.7% of nonpoint source baseflow TDN loading comes from low and medium development intensity catchments, and sanitary infrastructure in wet areas of the landscape accounts for 39% of regional baseflow loading. Our research indicates that managing baseflow loading will require addressing lower development intensity catchments and sanitary infrastructure.

The spatial variation of soil moisture over very small areas (<100 m²) can have nonlinear impacts on cycling and flux rates resulting in bias if it is not considered, but measuring this variation is difficult over extensive temporal and spatial scales. Most studies examining spatial variation of soil moisture were conducted at hillslope (0.01 km²) to multi‐catchment spatial scales (1000 km²). They found the greatest variation at mid wetness levels and the smallest variation at wet and dry wetness levels forming a concave down relationship. There is growing evidence that concave down relationships formed between spatial variation of soil moisture and average soil moisture are consistent across spatial scales spanning several orders of magnitude, but more research is needed at very small, plot scales (<100 m²). The goal of this study was to characterise spatial variation in shallow soil moisture at the plot scale by relating the mean of measurements collected in a plot to the standard deviation (SD). We combined data from a previous study with thousands of new soil moisture measurements from 212 plots in eight catchments distributed across the US Mid‐Atlantic Region to (1) test for a generalisable mean–SD relationship at plot scales, (2) characterise how landcover, land use, season, and hillslope position contribute to differences in mean–SD relationships, and (3) use these generalised mean–SD relationships to quantify their impacts on catchment scale nitrification and denitrification potential. Our study found that 98% of all measurements formed a generalised mean–SD relationship like those observed at hillslope and catchment spatial scales. The remaining 2% of data comprised a mean–SD relationship with greater spatial variation that originated from two riparian plots reported in a previous study. Incorporating the generalised mean–SD relationship into estimates of nitrification and denitrification potential revealed strong bias that was even greater when incorporating mean–SD observations from the two riparian plots with significantly greater spatial variation.

Current land‐use classifications used to assess urbanization effects on stream water quality date back to the 1980s when limited information was available to characterize watershed attributes that mediate non‐point source pollution. With high resolution remote sensing and widely used GIS tools, there has been a vast increase in the availability and precision of geospatial data of built environments. In this study, we leverage geospatial data to expand the characterization of developed landscapes and create a typology that allows us to better understand the impact of complex developed landscapes across the rural to urban gradient. We assess the ability of the developed landscape typology to reveal patterns in stream water chemistry previously undetected by traditional land‐cover based classification. We examine the distribution of land‐cover, infrastructure, topography and geology across 3876 National Hydrography Dataset Plus catchments in the Piedmont region of North Carolina, USA. From this dataset, we generate metrics to evaluate the abundance, density and position of landscape features relative to streams, catchment outlets and topographic wetness metrics. While impervious surfaces are a key distinguishing feature of the urban landscape, sanitary infrastructure, population density and geology are better predictors of baseflow stream water chemistry. Unsupervised clustering was used to generate a distinct developed landscape typology based on the expanded, high‐resolution landscape feature information. Using stream chemistry data from 37 developed headwater catchments, we compared the baseflow water chemistry grouped by traditional land‐cover based classes of urbanization (rural, low, medium and high density) to our composition and structure‐based classification (a nine‐class typology). The typology based on 22 metrics of developed landscape composition and structure explained over 50% of the variation in NO₃⁻‐N, TDN, DOC, Cl⁻, and Br⁻concentration, while the ISC‐based classification only significantly explained 23% of the variation in TDN. These results demonstrate the importance of infrastructure, population and geology in defining developed landscapes and improving discrete classes for water management.

We report an empirical analysis of the hydrologic response of three small, highly impervious urban watersheds to pulse rainfall events, to assess how traditional stormwater management (SWM) alters urban hydrographs. The watersheds vary in SWM coverage from 3% to 61% and in impervious cover from 45% to 67%. By selecting a set of storm events that involved a single rainfall pulse with >96% of total precipitation delivered in 60 min, we reduced the effect of differences between storms on hydrograph response to isolate characteristic responses attributable to watershed properties. Watershed‐average radar rainfall data were used to generate local storm hyetographs for each event in each watershed, thus compensating for the extreme spatial and temporal heterogeneity of short‐duration, intense rainfall events. By normalizing discharge values to the discharge peak and centring each hydrograph on the time of peak we were able to visualize the envelope of hydrographs for each group and to generate representative composite hydrographs for comparison across the three watersheds. Despite dramatic differences in the fraction of watershed area draining to SWM features across these three headwater tributaries, we did not find strong evidence that SWM causes significant attenuation of the hydrograph peak. Hydrograph response for the three watersheds is remarkably uniform despite contrasts in SWM, impervious cover and spatial patterns of land cover type. The primary difference in hydrograph response is observed on the recession limb of the hydrograph, and that change appears to be associated with higher storm‐total runoff in the watersheds with more area draining to SWM. Our findings contribute more evidence to the work of previous authors suggesting that SWM is less effective at attenuating urban hydrographs than is commonly assumed. Our findings also are consistent with previous work concluding that percent impervious cover may have greater influence on runoff volume than percent SWM coverage.

Soils derived from different lithologies and their controls on preferential flow remain underexplored in forested landscapes. In the same lithology, the propensity for preferential flow occurrence at different hillslope positions also remains largely elusive. By utilizing a soil moisture response time method, we compared preferential flow occurrence between a shale site (Shale Hills, silt loam soils) and a sandstone site (Garner Run, sandy loam soils) at four hillslope positions: ridge‐top, North‐ and South‐facing mid‐slopes and toe slope, for over 2 years. The catchments are neighbouring and covered by temperate forest. For the four hillslope positions, Shale Hills had higher preferential flow frequencies compared to Garner Run. Between these two catchments, the South‐facing mid‐slope sites showed the highest contrasts in preferential flow frequency (33.5% of events at Shale Hills vs. 8.8% at Garner Run) while the ridge‐top sites showed the lowest contrasts (18.7 vs. 13.2%). Additionally, over the unfrozen period, for seven out of eight monitoring sites, drier antecedent conditions tended to be more favourable for preferential flows to occur, with significant (p < .01) relationships at two sites. Except for the South‐facing mid‐slope sites, both Shale Hills and Garner Run had two preferential flow pathways. The characteristic preferential flow pathways at Shale Hills were the B_wand C horizons, and for Garner Run, preferential flow moved from the E/AE horizon to the B_whorizon. This study shows that shale‐derived soils tended to have higher preferential flow occurrence than sandstone soils, but hillslope positions exhibit different levels of contrasts. More effort should be paid to study the impact of lithology on preferential flows in the context of land surface modelling and biogeochemical reactions to improve ecosystem services of headwater catchments.

Large‐scale models often use a single grid to represent an entire catchment assuming homogeneity; the impacts of such an assumption on simulating evapotranspiration (ET) and streamflow remain poorly understood. Here, we compare hydrological dynamics at Shale Hills (PA, USA) using a complex model (spatially explicit, >500 grids) and a simple model (spatially implicit, two grids using “effective” parameters). We asked two questions:What hydrological dynamics can a simple model reproduce at the catchment scale? What processes does it miss by ignoring spatial details?Results show the simple model can reproduce annual runoff ratios and ET, daily discharge peaks (e.g., storms, floods) but not discharge minima (e.g., droughts) under dry conditions. Neither can it reproduce different streamflow from the two sides of the catchment with distinct land surface characteristics. The similar annual runoff ratios between the two models indicate spatial details are not as important as climate in reproducing annual scale ET and discharge partitioning. Most of the calibrated parameters in the simple model are within the ranges in the complex model, except that effective porosity has to be reduced to 40% of the average porosity from the complex model. The form of the storage‐discharge relationship is similar. The effective porosity in the simple model however represents the dynamic and mobile water storage in the effective drainage area of the complex model that connects to the stream and contributes to high streamflow; it does not represent the passive, immobile water storage in the often disconnected uphill areas. This indicates that an additional uphill functioning unit is needed in the simple model to simulate the full spectrum of high‐low streamflow dynamics in natural catchments.