Integrating Hydrodynamic and Water Quality Models

2021 ◽  
pp. 157-174
Author(s):  
Wu-Seng Lung
2016 ◽  
Vol 15 (0) ◽  
pp. 9781780408323-9781780408323
Author(s):  
D. L. Clark ◽  
G. Hunt ◽  
M. S. Kasch ◽  
P. J. Lemonds

1987 ◽  
Vol 19 (7) ◽  
pp. 1197-1202 ◽  
Author(s):  
A. Van Der Beken

Water ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 88
Author(s):  
Xiamei Man ◽  
Chengwang Lei ◽  
Cayelan C. Carey ◽  
John C. Little

Many researchers use one-dimensional (1-D) and three-dimensional (3-D) coupled hydrodynamic and water-quality models to simulate water quality dynamics, but direct comparison of their relative performance is rare. Such comparisons may quantify their relative advantages, which can inform best practices. In this study, we compare two 1-year simulations in a shallow, eutrophic, managed reservoir using a community-developed 1-D model and a 3-D model coupled with the same water-quality model library based on multiple evaluation criteria. In addition, a verified bubble plume model is coupled with the 1-D and 3-D models to simulate the water temperature in four epilimnion mixing periods to further quantify the relative performance of the 1-D and 3-D models. Based on the present investigation, adopting a 1-D water-quality model to calibrate a 3-D model is time-efficient and can produce reasonable results; 3-D models are recommended for simulating thermal stratification and management interventions, whereas 1-D models may be more appropriate for simpler model setups, especially if field data needed for 3-D modeling are lacking.


2012 ◽  
Vol 55 (4) ◽  
pp. 1241-1247 ◽  
Author(s):  
D. N. Moriasi ◽  
B. N. Wilson ◽  
K. R. Douglas-Mankin ◽  
J. G. Arnold ◽  
P. H. Gowda

2019 ◽  
Vol 24 (1) ◽  
pp. 04018057 ◽  
Author(s):  
Yogesh Khare ◽  
Christopher J. Martinez ◽  
Rafael Muñoz-Carpena ◽  
Adelbert “Del” Bottcher ◽  
Andrew James

2020 ◽  
pp. 349-382
Author(s):  
J.R. Williams ◽  
J.G. Arnold ◽  
C.A. Jones ◽  
V.W. Benson ◽  
R.H. Griggs

Author(s):  
P López-Jiménez ◽  
F Martínez-Solano ◽  
Gonzalo López-Patiño ◽  
Vicente Fuertes-Miquel

2021 ◽  
Author(s):  
◽  
Martha Trodahl

<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios.  Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale.  To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables.  The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island.  This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI.  Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists.  Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application.  Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>


2011 ◽  
Vol 63 (1) ◽  
pp. 171-177
Author(s):  
G. T. Parker

As water quality models and their implementation have become increasingly diverse, complex and proprietary, a need for more thorough understanding of the differences between each alternative arises. The work presented here proposes a novel visualization paradigm for water quality applications which can be used to understand difference between implementations of identical and different conceptual models. A proof-of-concept visualization tool was developed and tested again three scenarios for four different conceptual models of biochemical kinetics. Results show representative figures illustrating how the approach can communicate differences in model complexity and dynamic behaviour. The proposed tool should help ensure more suitable application of water quality models in varied contexts. A discussion of quantifying model complexity in a single metric is also presented, and recommendations are made on the selection of various representational forms for communicating and exploring specific model characteristics.


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