scholarly journals A hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e1727 ◽  
Author(s):  
Benjamin H. Letcher ◽  
Daniel J. Hocking ◽  
Kyle O’Neil ◽  
Andrew R. Whiteley ◽  
Keith H. Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Temperature Estimation ◽  
Small Stream ◽  
Primary Driver ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59°C), identified a clear warming trend (0.63 °C decade−1) and a widening of the synchronized period (29 d decade−1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (∼0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (∼0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network.

scholarly journals A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

2015 ◽  
Author(s):  
Benjamin H Letcher ◽  
Daniel J Hocking ◽  
Kyle O'Neill ◽  
Andrew R Whiteley ◽  
Keith H Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Spatial Scales ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Modeling Framework ◽  
Small Stream ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

scholarly journals A robust hierarchical model of daily stream temperature using air-water temperature synchronization, autocorrelation, and time lags

2015 ◽  
Author(s):  
Benjamin H Letcher ◽  
Daniel J Hocking ◽  
Kyle O'Neill ◽  
Andrew R Whiteley ◽  
Keith H Nislow ◽  
...  
Keyword(s):  
Missing Data ◽  
Water Temperature ◽  
Spatial Scales ◽  
Warming Trend ◽  
Stream Temperature ◽  
Time Lags ◽  
Stream Network ◽  
Modeling Framework ◽  
Small Stream ◽  
Yearly Period

Water temperature is a primary driver of stream ecosystems and commonly forms the basis of stream classifications. Robust models of stream temperature are critical as the climate changes, but estimating daily stream temperature poses several important challenges. We developed a statistical model that accounts for many challenges that can make stream temperature estimation difficult. Our model identifies the yearly period when air and water temperature are synchronized, accommodates hysteresis, incorporates time lags, deals with missing data and autocorrelation and can include external drivers. In a small stream network, the model performed well (RMSE = 0.59 °C), identified a clear warming trend (0.063 °C · y-1) and a widening of the synchronized period (2.9 d · y-1). We also carefully evaluated how missing data influenced predictions. Missing data within a year had a small effect on performance (~ 0.05% average drop in RMSE with 10% fewer days with data). Missing all data for a year decreased performance (~ 0.6 °C jump in RMSE), but this decrease was moderated when data were available from other streams in the network. Straightforward incorporation of external drivers (e.g. land cover, basin size) should allow this modeling framework to be readily applied across multiple sites and at multiple spatial scales.

scholarly journals Temporal Effects of Groundwater on Physical and Biotic Components of a Karst Stream

Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1299 ◽  
Author(s):  
Tao Tang ◽  
Shuhan Guo ◽  
Lu Tan ◽  
Tao Li ◽  
Ryan M. Burrows ◽  
...  
Keyword(s):  
Water Temperature ◽  
Algal Biomass ◽  
Groundwater Discharge ◽  
Stream Network ◽  
Lotic Ecosystems ◽  
Chl A ◽  
Discharge Water ◽  
Downstream Reach ◽  
Insight Into

Although most lotic ecosystems are groundwater dependent, our knowledge on the relatively long-term ecological effects of groundwater discharge on downstream reaches remains limited. We surveyed four connected reaches of a Chinese karst stream network for 72 consecutive months, with one reach, named Hong Shi Zi (HSZ), evidently affected by groundwater. We tested whether, compared with other reaches, HSZ had (1) milder water temperature and flow regimes, and (2) weaker influences of water temperature and flow on benthic algal biomass represented by chlorophyll a (Chl. a) concentrations. We found that the maximum monthly mean water temperature in HSZ was 0.6 °C lower than of the adjacent upstream reach, and the minimum monthly mean water temperature was 1.0 °C higher than of the adjacent downstream reach. HSZ had the smallest coefficient of variation (CV) for water temperature but the largest CV for discharge. Water temperature and discharge displayed a significant 12-month periodicity in all reaches not directly groundwater influenced. Only water temperature displayed such periodicity in HSZ. Water temperature was an important predictor of temporal variation in Chl. a in all reaches, but its influence was weakest in HSZ. Our findings demonstrate that longer survey data can provide insight into groundwater–surface water interactions.

scholarly journals Assessing the impacts of hydrologic and land use alterations on water temperature in the Farmington River basin in Connecticut

2019 ◽  
Vol 23 (11) ◽  
pp. 4491-4508 ◽  
Author(s):  
John R. Yearsley ◽  
Ning Sun ◽  
Marisa Baptiste ◽  
Bart Nijssen
Keyword(s):  
Land Use ◽  
Water Temperature ◽  
River Basin ◽  
Land Surface ◽  
Canopy Cover ◽  
River System ◽  
Stream Temperature ◽  
Hydrologic Model ◽  
Riparian Buffers ◽  
Connecticut River

Abstract. Aquatic ecosystems can be significantly altered by the construction of dams and modification of riparian buffers, and the effects are often reflected in spatial and temporal changes to water temperature. To investigate the implications for water temperature of spatially and temporally varying riparian buffers and dam-induced hydrologic alterations, we have implemented a modeling system (DHSVM-RBM) within the framework of the state-space paradigm that couples a spatially distributed land surface hydrologic model, DHSVM, with the distributed stream temperature model, RBM. The basic modeling system has been applied previously to several similar-sized watersheds. However, we have made enhancements to DHSVM-RBM that simulate spatial heterogeneity and temporal variation (i.e., seasonal changes in canopy cover) in riparian vegetation, and we included additional features in DHSVM-RBM that provide the capability for simulating the impacts of reservoirs that may develop thermal stratification. We have tested the modeling system in the Farmington River basin in the Connecticut River system, which includes varying types of watershed development (e.g., deforestation and reservoirs) that can alter the streams' hydrologic regime and thermal energy budget. We evaluated streamflow and stream temperature simulations against all available observations distributed along the Farmington River basin. Results based on metrics recommended for model evaluation compare well to those obtained in similar studies. We demonstrate the way in which the model system can provide decision support for watershed planning by simulating a limited number of scenarios associated with hydrologic and land use alterations.

scholarly journals Influence of Water Temperature and Salinity on PH During Dry Season in Lower Dong Nai River System, Vietnam

2015 ◽  
Vol 61 (4) ◽  
pp. 29-35 ◽  
Author(s):  
Dang Quoc Dung ◽  
Pham Anh Duc
Keyword(s):  
Water Temperature ◽  
Ph Value ◽  
Interpolation Method ◽  
Water Environment ◽  
River System ◽  
Dry Season ◽  
Linear Regression Models ◽  
Stream Network ◽  
R Software ◽  
Influence Of Water

Abstract This paper uses the gvSIG 2.2.0 software, IDW interpolation method, river and stream network data, and 36 sampling sites to build the maps of three monitored parameters such as pH, water temperature, and salinity in the Lower Dong Nai River system (2009-2010) in dry season. Based on an analysis of these maps and statistical assessment by using the R software, the correlations between pH, temperature, and salinity are clarified. The results show that the pH and temperature values have a tendency to decrease, whereas the salinity tends to increase annually. The pH value has good and significant correlations with the water temperature and salinity in both simple and multiple linear regression models. The results aim to provide a scientific reference for further research on the water environment in this area.

Seasonal variation in the long-term warming trend in water temperature off the Western Australian coast

2009 ◽  
Vol 60 (2) ◽  
pp. 129 ◽  
Author(s):  
N. Caputi ◽  
S. de Lestang ◽  
M. Feng ◽  
A. Pearce
Keyword(s):  
Seasonal Variation ◽  
Indian Ocean ◽  
Water Temperature ◽  
West Coast ◽  
Climate Models ◽  
Warming Trend ◽  
Temperature Cycle ◽  
Rock Lobster ◽  
Leeuwin Current ◽  
The Indian Ocean

Previous studies have demonstrated that one area of greatest increase in surface sea temperatures (SST) (0.02°C per year) in the Indian Ocean over the last 50 years occurs off the lower west coast of Australia, an area dominated by the Leeuwin Current. The present paper examines water temperature trends at several coastal sites since the early 1970s: two rock lobster puerulus monitoring sites in shallow water (<5 m); four sites from a monitoring program onboard rock lobster vessels that provide bottom water temperature (<36 m); and an environmental monitoring site at Rottnest (0–50 m depth). Two global SST datasets are also examined. These data show that there was a strong seasonal variation in the historic increases in temperature off the lower west coast of Australia, with most of the increases (0.02–0.035°C per year) only focussed on 4–6 months over the austral autumn–winter with little or no increase (<0.01°C per year) apparent in the austral spring–summer period. These increases are also apparent after taking into account the interannual variation in the strength of the Leeuwin Current. The warming trend results in a change to the seasonal temperature cycle over the decades, with a delay in the peak in the temperature cycle during autumn between the 1950s and 2000s of ~10–20 days. A delay in the timing of the minimum temperature is also apparent at Rottnest from August–September to October. This seasonal variation in water temperature increases and its effect on the annual temperature cycle should be examined in climate models because it provides the potential to better understand the specific processes through which climate change and global warming are affecting this region of the Indian Ocean. It also provides an opportunity to further test the climate models to see whether this aspect is predicted in the future projections of how increases will be manifest. Any seasonal variation in water temperature increase has important implications for fisheries and the marine ecosystem because it may affect many aspects of the annual life cycle such as timing of growth, moulting, mating, spawning and recruitment, which have to be taken into account in the stock assessment and management of fisheries.

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