TY - JOUR T1 - Annual precipitation regulates spatial and temporal drivers of lake water clarity JF - Ecological Applications Y1 - 2017 A1 - Rose, Kevin C A1 - Greb, Steven R. A1 - Diebel, Matthew A1 - Turner, Monica G. KW - land use KW - land-water interactions KW - landscape ecology KW - precipitation KW - remote sensing KW - Water quality AB - Understanding how and why lakes vary and respond to different drivers through time and space is needed to understand, predict, and manage freshwater quality in an era of rapidly changing land use and climate. Water clarity regulates many characteristics of aquatic ecosystems and is responsive to watershed features, making it a sentinel of environmental change. However, whether precipitation alters the relative importance of features that influence lake water clarity or the spatial scales at which they operate is unknown. We used a dataset of thousands of north temperate lakes and asked: (1) How does water clarity differ between a very wet versus dry year? (2) Does the relative importance of different watershed features, or the spatial extent at which they are measured, vary between wet and dry years? (3) What lake and watershed characteristics regulate long-term water clarity trends? Among lakes, water clarity was reduced and less variable in the wet year than in the dry year; furthermore, water clarity was reduced much more in high-clarity lakes during the wet year than in low-clarity lakes. Climate, land use/land cover, and lake morphometry explained most variance in clarity among lakes in both years, but the spatial scales at which some features were important differed between the dry and wet years. Watershed percent agriculture was most important in the dry year, whereas riparian zone percent agriculture (around each lake and upstream features) was most important in the wet year. Between 1991 and 2012, water clarity declined in 23% of lakes and increased in only 6% of lakes. Conductance influenced the direction of temporal trend (clarity declined in lakes with low conductance), whereas the proportion of watershed wetlands, catchment-to-lake-area ratio, and lake maximum depth interacted with antecedent precipitation. Many predictors of water clarity, such as lake depth and landscape position, are features that cannot be readily managed. Given trends of increasing precipitation, eliminating riparian zone agriculture or keeping it <10% of area may be an effective option to maintain or improve water clarity. VL - 27 SN - 1939-5582 UR - http://dx.doi.org/10.1002/eap.1471 IS - 2 ER - TY - JOUR T1 - Urban heat island impacts on plant phenology: intra-urban variability and response to land cover JF - Environmental Research Letters Y1 - 2016 A1 - Samuel C Zipper A1 - Jason Schatz A1 - Aditya Singh A1 - Christopher J Kucharik A1 - Philip A Townsend A1 - Steven P Loheide KW - land surface phenology KW - remote sensing KW - sensor network KW - urban climate KW - urban ecology KW - urban heat island KW - vegetation phenology AB - Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is knownabout spatial or temporal variability in plant response to the UHI. Using an automated temperaturesensor network in conjunction with Landsat-derived remotely sensed estimates of start/end of thegrowing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI(USA) for the 2012–2014 growing seasons. Median urban growing season length (GSL) estimated fromtemperature sensors is ∼5 d longer than surrounding rural areas, and UHI impacts on GSL arerelatively consistent from year-to-year. Parks within urban areas experience a subdued expression ofGSL lengthening resulting from interactions between the UHI and a park cool island effect. Acrossall growing seasons, impervious cover in the area surrounding each temperature sensor explains >50%of observed variability in phenology. Comparisons between long-term estimates of annual meanphenological timing, derived from remote sensing, and temperature-based estimates of individualgrowing seasons show no relationship at the individual sensor level. The magnitude of disagreementbetween temperature-based and remotely sensed phenology is a function of impervious and grass coversurrounding the sensor, suggesting that realized GSL is controlled by both local land cover andmicrometeorological conditions. VL - 11 SN - 1748-9326 UR - http://stacks.iop.org/1748-9326/11/i=5/a=054023 IS - 5 ER -