Large-stream nitrate retention patterns shift during droughts: Seasonal to sub-daily insights from high-frequency data-model fusion.

High-frequency nitrate-N (NO3--N) data are increasingly available, while accurate assessments of in-stream NO3--N retention in large streams and rivers require a better capture of complex river hydrodynamic conditions. This study demonstrates a fusion framework between high-frequency water quality data and hydrological transport models, that (1) captures river hydraulics and their impacts on solute signal propagation through river hydrodynamic modeling, and (2) infers in-stream retention as the differences between conservatively traced and reactively observed NO3--N signals. Using this framework, continuous 15-min estimates of NO3--N retention were derived in a 6th-order reach of the lower Bode River (27.4 km, central Germany), using long-term sensor monitoring data during a period of normal flow from 2015 to 2017 and a period of drought from 2018 to 2020. The unique NO3--N retention estimates, together with metabolic characteristics, revealed insightful seasonal patterns (from high net autotrophic removal in late-spring to lower rates, to net heterotrophic release during autumn) and drought-induced variations of those patterns (reduced levels of net removal and autotrophic nitrate removal largely buffered by heterotrophic release processes, including organic matter mineralization). Four clusters of diel removal patterns were identified, potentially representing changes in dominant NO3--N retention processes according to seasonal and hydrological conditions. For example, dominance of autotrophic NO3--N retention extended more widely across seasons during the drought years. Such cross-scale patterns and changes under droughts are likely co-determined by catchment and river environments (e.g., river primary production, dissolved organic carbon availability and its quality), which resulted in more complex responses to the sequential droughts. Inferences derived from this novel data-model fusion provide new insights into NO3- dynamics and ecosystem function of large streams, as well as their responses to climate variability. Moreover, this framework can be flexibly transferred across sites and scales, thereby complementing high-frequency monitoring to identify in-stream retention processes and to inform river management.

Sensors in the Stream: The High-Frequency Wave of the Present.

New scientific understanding is catalyzed by novel technologies that enhance measurement precision, resolution or type, and that provide new tools to test and develop theory. Over the last 50 years, technology has transformed the hydrologic sciences by enabling direct measurements of watershed fluxes (evapotranspiration, streamflow) at time scales and spatial extents aligned with variation in physical drivers. High frequency water quality measurements, increasingly obtained by in situ water quality sensors, are extending that transformation. Widely available sensors for some physical (temperature) and chemical (conductivity, dissolved oxygen) attributes have become integral to aquatic science, and emerging sensors for nutrients, dissolved CO2, turbidity, algal pigments, and dissolved organic matter are now enabling observations of watersheds and streams at time scales commensurate with their fundamental hydrological, energetic, elemental, and biological drivers. Here we synthesize insights from emerging technologies across a suite of applications, and envision future advances, enabled by sensors, in our ability to understand, predict, and restore watershed and stream systems.

Predicting successful aging in a population-based sample of georgia centenarians.

Used a population-based sample (Georgia Centenarian Study, GCS), to determine proportions of centenarians reaching 100 years as (1) survivors (43%) of chronic diseases first experienced between 0-80 years of age, (2) delayers (36%) with chronic diseases first experienced between 80-98 years of age, or (3) escapers (17%) with chronic diseases only at 98 years of age or older. Diseases fall into two morbidity profiles of 11 chronic diseases; one including cardiovascular disease, cancer, anemia, and osteoporosis, and another including dementia. Centenarians at risk for cancer in their lifetime tended to be escapers (73%), while those at risk for cardiovascular disease tended to be survivors (24%), delayers (39%), or escapers (32%). Approximately half (43%) of the centenarians did not experience dementia. Psychiatric disorders were positively associated with dementia, but prevalence of depression, anxiety, and psychoses did not differ significantly between centenarians and an octogenarian control group. However, centenarians were higher on the Geriatric Depression Scale (GDS) than octogenarians. Consistent with our model of developmental adaptation in aging, distal life events contribute to predicting survivorship outcome in which health status as survivor, delayer, or escaper appears as adaptation variables late in life.

Understanding centenarians' psychosocial dynamics and their contributions to health and quality of life.

While it is understood that longevity and health are influenced by complex interactions among biological, psychological, and sociological factors, there is a general lack of understanding on how psychosocial factors impact longevity, health, and quality of life among the oldest old. One of the reasons for this paradox is that the amount of funded research on aging in the US is significantly larger in the biomedical compared to psychosocial domains. The goals of this paper are to highlight recent data to demonstrate the impact of four pertinent psychosocial domains on health and quality of life of the oldest old and supplement recommendations of the 2001 NIA Panel on Longevity for future research. The four domains highlighted in this paper are (1) demographics, life events, and personal history, (2) personality, (3) cognition, and (4) socioeconomic resources and support systems.