This study examines the relative impact of regional land‐cover/land‐use patterns and projected future climate change on hydrologic processes. Historic, present and projected future land cover data were used to drive the variable infiltration capacity (VIC) model using observed meteorological forcing data for 1983–2007 over Wisconsin (USA). The current and projected future (year 2030) land cover data were developed using the land transformation model (LTM). The VIC model simulations were driven using downscaled and bias‐corrected projected future climate forcing from three different Intergovernmental Panel for Climate Change (IPCC) AR4 general circulation models (GCMs): HadCM3, PCM and GFDL. Sensitivity results conducted on a single grid cell show that annual average surface runoff and baseflow were increased by 8 and 6 mm, respectively, while evapotranspiration was reduced by 15 mm when a fully forested grid was converted to cropland. Results also indicate that annual average net radiation and sensible heat flux were reduced considerably due to forest‐to‐cropland conversion, and the reduction was more prominent in winter and spring seasons due to effect of snow albedo. Forest‐to‐cropland conversion also resulted in increased latent heat flux in summer (JJA) while this land transformation increased the snow water equivalent in winter (DJF) and spring (MAM). Complete conversion of forest to cropland resulted in a decrease of the radiative surface temperature on an annual basis with more cooling occurring in winter and summer. Impacts of historic deforestation were similar to what was expected based on a single grid sensitivity analysis. Copyright © 2010 Royal Meteorological Society

Impervious surface area (ISA) has different surface characteristics from the natural land cover and has great influence on watershed hydrology. To assess the urbanization effects on streamflow regimes, the authors analyzed the U.S. Geological Survey (USGS) streamflow data of 16 small watersheds in the White River [Indiana (IN)] basin. Correlation between hydrologic metrics (flow distribution, daily variation in streamflow, and frequency of high-flow events) and ISA was investigated by employing the nonparametric Mann–Kendall method. Results derived from the 16 watersheds show that urban intensity has a significant effect on all three hydrologic metrics. The Variable Infiltration Capacity (VIC) model was modified to represent ISA in urbanized basins using a bulk parameterization approach. The model was then applied to the White River basin to investigate the potential ability to simulate the water and energy cycle response to urbanization. Correlation analysis for individual VIC grid cells indicates that the VIC urban model was able to reproduce the slope magnitude and mean value of the USGS streamflow metrics. The urban model also reproduced the urban heat island (UHI) seen in the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature products, especially for the grids encompassing the city of Indianapolis, IN. The difference of the hydrologic metrics obtained from the VIC model with and without urban representation indicates that the streamflow regime in the White River has been modified because of urban development. The observed data, together with model analysis, suggested that 3%–5% ISA in a watershed is the detectable threshold, beyond which urbanization effects start to have a statistically significant influence on streamflow regime.

Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.

An understanding of riverine water‐quality dynamics in regional mixed‐land use watersheds is the foundation for advances in landscape biogeochemistry and informed land management. A differential implementation of the statistical/process‐based model SPAtially Referenced Regressions on Watershed attributes (SPARROW; Smith et al., https://doi.org/10.1029/97wr02171) is proposed to empirically relate a regional pattern of changes in flow‐normalized constituent flux, over a multiyear period, to contemporaneous changes in spatially referenced explanatory variables. In a pilot application, the differential model, called Spatiotemporal Watershed Accumulation of Net effects (SWAN), is used to explore factors influencing changes in flow‐normalized flux of total nitrogen over the period 1990–2010 at 43 sites in the nontidal Chesapeake Bay watershed. A seven‐parameter model explains 80% of the transformed variability in independently estimated flux changes, indicating that storage effects having characteristic time scales greater than 20 years had a small influence, relative to changes in inputs, on regional water‐quality response. Results suggest that 1990–2010 changes in total‐nitrogen flux are largely the outcome of increased nonpoint‐source pollution associated with urban and suburban development, modulated to the point of negation by terrestrial losses stemming from widespread increases in air temperature and precipitation. The loss mechanism is qualitatively consistent with denitrification; however, increases in aboveground biomass, agricultural nitrogen exports, or hydrologic flushing are also plausible contributors. Although qualified by a small sample size and constraints on explanatory data availability, the pilot suggests that SWAN is a promising approach for broadening scientific understanding of factors driving regional water‐quality change and for supporting evidence‐based land‐management decisions.