A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations

Cheng, Su-Ting; Wiley, Michael J.

doi:10.1016/j.envsoft.2016.04.015

Cited by 11 publications

(9 citation statements)

References 50 publications

(73 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We used perturbation analysis, and the result obtained shows that the correction factor for solar radiation (CFSEAX) parameter and the infiltration index (INFILT) parameter were the most sensitive parameters. The result obtained is similar to the result demonstrated by Cheng and Wiley [40]. They illustrate that stream temperature is more sensitive to solar radiation and depth.…”

Section: Discussionsupporting

confidence: 80%

“…The results were presented using graphical plots showing changes in the water temperature and DO concentration at each station due to the influence of parameters for hydrology, water temperature and DO calibration. The visual result allowed us to understand the impact of a single parameter to water temperature and DO concentration and enabled us to measure their influence on the model performance [40].…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…We combined the range of parameter values and designed a factor perturbation that was used to adjust the parameters from their based values (as calibrated). Each parameter and input considered was adjusted by the factors of 0.3, 0.6, 0.9, 1.2, 1.5, 1.8, and 2 while allowing all other parameters to remain fixed to provide a basis for precision and comparison between each parameter [40]. The parameters evaluated by the sensitivity analysis are summarized in Table 2.…”

Section: Sensitivity Analysismentioning

confidence: 99%

See 2 more Smart Citations

Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers

Bello

Hashim

Haniffah

2017

Climate

View full text Add to dashboard Cite

Predicting the impact of climate change and human activities on river systems is imperative for effective management of aquatic ecosystems. Unique information can be derived that is critical to the survival of aquatic species under dynamic environmental conditions. Therefore, the response of a tropical river system under climate and land-use changes from the aspects of water temperature and dissolved oxygen concentration were evaluated. Nine designed projected climate change scenarios and three future land-use scenarios were integrated into the Hydrological Simulation Program FORTRAN (HSPF) model to determine the impact of climate change and land-use on water temperature and dissolved oxygen (DO) concentration using basin-wide simulation of river system in Malaysia. The model performance coefficients showed a good correlation between simulated and observed streamflow, water temperature, and DO concentration in a monthly time step simulation. The Nash-Sutcliffe Efficiency for streamflow was 0.88 for the calibration period and 0.82 for validation period. For water temperature and DO concentration, data from three stations were calibrated and the Nash-Sutcliffe Efficiency for both water temperature and DO ranged from 0.53 to 0.70. The output of the calibrated model under climate change scenarios show that increased rainfall and air temperature do not affects DO concentration and water temperature as much as the condition of a decrease in rainfall and increase in air temperature. The regression model on changes in streamflow, DO concentration, and water temperature under the climate change scenarios illustrates that scenarios that produce high to moderate streamflow, produce small predicted change in water temperatures and DO concentrations compared with the scenarios that produced a low streamflow. It was observed that climate change slightly affects the relationship between water temperatures and DO concentrations in the tropical rivers that we include in this study. This study demonstrates the potential impact of climate and future land-use changes on tropical rivers and how they might affect the future ecological systems. Most rivers in suburban areas will be ecologically unsuitable to some aquatic species. In comparison, rivers surrounded by agricultural and forestlands are less affected by the projected climate and land-uses changes. The results from this study provide a basis in which resource management and mitigation actions can be developed.

show abstract

Section: Discussionsupporting

confidence: 80%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Sensitivity Analysismentioning

confidence: 99%

See 1 more Smart Citation

Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers

Bello

Hashim

Haniffah

2017

Climate

View full text Add to dashboard Cite

show abstract

“…In order to quantify the impact of riparian shading, existing regional-scale stream temperature models usually rely on theoretical values regarding vegetation characteristics (Sun et al, 2015;Loinaz et al, 2013), on simplified assumptions regarding shading process (Haag and Luce, 2008;Cheng and Wiley, 2016), or incorporate shading data from low-resolution DEMs (Cox and Bolte, 2007). Nowadays however, LiDAR can provide accurate data at a large scale.…”

Section: Introductionmentioning

confidence: 99%

Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data

Loicq

Moatar

Jullian

et al. 2018

Science of The Total Environment

View full text Add to dashboard Cite

Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (T) ranging from 3.0°C (upstream) to 1.3°C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of T simulated by the T-NET model by 0.62°C on average between April and September. However, difference between the shading routines reaches up to 2°C (monthly average) at the upstream-most station. Standard deviation of errors on T is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.

show abstract

“…In other studies, these meteorological and hydrological processes are diversified into different sub-factors. For example, Cheng and Wiley (2016) describe the radiative processes such as shortwave and longwave solar radiation as explanatory meteorological factors in thermal dynamics of streams (Figure 1). Hydrological characteristics such as water depth, surface area, runoff, and groundwater contribution/withdrawal are also included as the key processes that determine the thermodynamics in a stream (Zorn et.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Data Granularity on Temperature Gradient Modeling in Michigan’s Streams

DERTLİ¹,

Hayes

Zorn

2022

Turkish J. Water Sci. Manag.

View full text Add to dashboard Cite

Stream temperature is a critical characteristic for aquatic ecosystems. Many physical, chemical and biological components are influenced by this environmental variable; therefore, it is crucial to understand the factors that take place in thermodynamic processes in these ecosystems. Regression models are useful tools that help us comprehend and explain the drivers of these thermal processes since they can be used for quantifying the magnitude and the type of the relationship between the independent variables (i.e., air temperature, discharge) and the response variable (i.e., stream temperature). However, selection of data granularity (or time aggregation) of data may often be a key decision for modelers. Although granularity of data is selected based on the ecological relevance of data to the question of interest in many cases, it may arbitrarily be selected by the researchers in many other cases. However, data granularity can substantially influence model coefficients, can affect the model predictions, and influence evaluation of model fitness and interpretation of model outputs. In this article, we adopted regression models and applied different data granularity scenarios to investigate the consequences of data granularity selection in modeling approaches. Our findings showed that using different data granularities resulted in considerable changes in regression coefficients in the models. Our results also revealed that overall model fitness increased with coarser-scale data granularity and model selection was influenced by the type of data granularity. This study might be helpful for modelers and environmental managers since it highlights the significance of selection of data granularity, and proposes a different point of view in model design, evaluation and application from the perspective of data selection.

show abstract

A Reduced Parameter Stream Temperature Model (RPSTM) for basin-wide simulations

Cited by 11 publications

References 50 publications

Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers

Predicting Impact of Climate Change on Water Temperature and Dissolved Oxygen in Tropical Rivers

Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data

The Effect of Data Granularity on Temperature Gradient Modeling in Michigan’s Streams

Contact Info

Product

Resources

About