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en:simple [2019-07-14] (current)
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-====== Simple models, motivation ​======+====== Alternative simple models ====== 
 + 
 +===== Simple models, motivation =====
 Hydrogeologists solving practical groundwater problems are facing significant uncertainty,​ which is given by the lack of relevant data and the ambiguity of their interpretation. Mathematical models are used to understand the ongoing processes and to support decision-making. Because of the uncertainty of the data and its interpretation it is appropriate to create multiple simple models – a “multiple model ensemble” (Uusitalo et al. 2015) – that can be later developed into more complex models. Even simple models can help to select a strategy of further exploration and collection of data and to support preliminary decision-making. Hydrogeologists solving practical groundwater problems are facing significant uncertainty,​ which is given by the lack of relevant data and the ambiguity of their interpretation. Mathematical models are used to understand the ongoing processes and to support decision-making. Because of the uncertainty of the data and its interpretation it is appropriate to create multiple simple models – a “multiple model ensemble” (Uusitalo et al. 2015) – that can be later developed into more complex models. Even simple models can help to select a strategy of further exploration and collection of data and to support preliminary decision-making.
 +
 In this context simple models can be closed-form solutions and spatial analyses, which often make use of aggregated data and use simplified assumptions (e.g. geometry of the modelling domain is simplified to a single rectangle). Such models usually compute for example: balance (e.g. of water or chemicals), water flux (Darcy'​s law and continuity equation), flux of solutes or residence time. Slightly different are spatial computations performed mostly by geographical information systems (GIS) – e.g. the pumping rate based on the distance from the fringe of the contaminant plume. Available screening modelling tools for contaminant transport (e.g. length of steady-state contaminant plume) can be considered as simple ones too. To support the formulation of conceptual models it is necessary to visualize and analyze data and to perform data aggregation and common calculation and estimations (e.g. average hydraulic gradient, geochemical background or redox conditions from chemical composition). In this context simple models can be closed-form solutions and spatial analyses, which often make use of aggregated data and use simplified assumptions (e.g. geometry of the modelling domain is simplified to a single rectangle). Such models usually compute for example: balance (e.g. of water or chemicals), water flux (Darcy'​s law and continuity equation), flux of solutes or residence time. Slightly different are spatial computations performed mostly by geographical information systems (GIS) – e.g. the pumping rate based on the distance from the fringe of the contaminant plume. Available screening modelling tools for contaminant transport (e.g. length of steady-state contaminant plume) can be considered as simple ones too. To support the formulation of conceptual models it is necessary to visualize and analyze data and to perform data aggregation and common calculation and estimations (e.g. average hydraulic gradient, geochemical background or redox conditions from chemical composition).
 +
 The above issues mentioned require an information system that facilitates collection of data from various semi-structured sources, visualization and analysis of data in order to create alternative conceptual models and implement the corresponding simple procedural models. Interoperability with third-party modelling software is also a requirement. The above issues mentioned require an information system that facilitates collection of data from various semi-structured sources, visualization and analysis of data in order to create alternative conceptual models and implement the corresponding simple procedural models. Interoperability with third-party modelling software is also a requirement.
  
-==== Quotations about simplicity and complexity of groundwater models====+====== Dealing with Uncertainty — Multiple Simple Groundwater Models ====== 
 +===== Strategy ===== 
 +Heterogeneous subsurface is usually characterized by scarce data – therefore it is reasonable to fully exploit available data and develop multiple simple models. Strategy of multiple simple models and especially alternative (equifinal) simple models is demonstrated on three case studies. Those models have diverse structure, are based on diverse assumptions or they test diverse hypotheses. 
 +{{ :​en:​simple1_schema.png?​direct |}} 
 + 
 +===== Case study 1 ===== 
 +In the first case study four alternative models represent four alternative hypotheses explaining loss of water in the Smědá river in the Czech Republic. One hypothesis (No 4) quantifies a natural process – the effluence to the subglacial channel. The water loss can be explained this way therefore the hypothesis was not rejected. Other hypotheses explain the loss by seepage to the surface coal mine through diverse geological layers. 
 + 
 +Closed-form solution with simplified assumptions (e.g. geometry of the modelling domain is simplified to a single rectangle) was used.  The case study represents the equifinality principle. 
 + 
 +=== Hypotheses 1–3 === 
 +Water loss is caused by the surface mine only. 
 +{{ :​en:​simple2_frydlantsky.png?​direct |}} 
 +Water flows thru: 
 +  - Glacifluvial gravel (**not rejected**) – //green arrow// 
 +  - Sandy layer (max. 10 l/s → **rejected**) – //red arrow// 
 +  - Crystalline rock (could not be quantified → **not rejected**) – //yellow arrow// 
 + 
 +=== Hypothesis 4 === 
 +Surface water seeps the subglacial channel (a trench formed by the quaternary continental glacier filled with sediment). It is the only natural explanation – not caused by mining. Hydraulic conductivity 3∙10<​sup>​–3</​sup>​ m/s of the gravel in the subglacial channel would explain the water loss. Hydraulic conductivity was not measured. The hypothesis was not rejected. 
 +{{ :​en:​simple3_koryto.jpg?​direct&​400 |}} 
 +=== Conclusion === 
 +Overall explanation of the water loss is the combination of all hypotheses. 
 + 
 +===== Case study 2 ===== 
 +The second case study combines different simple models for different parts of a site contaminated by petroleum hydrocarbons. Maximal extent of the contaminant plume (downgradient from the contamination source) is estimated by computing steady-state plume length by three methods implemented in the CoronaScreen (Wilson et al. 2005) code. The maximal permitted pumping rate upgradient is calculated by a simple spatial computation based on the distance from the plume'​s fringe. Pumping rate is limited by recharge from precipitation and lateral influx of the capture zone of the exploited well.  
 +{{ :​en:​simple4_lustenice.jpg?​direct |}} 
 + 
 +=== Downgradient:​ Steady state plume === 
 +Steady state plume length = distance from the source of the pollution where the pollution is depleted by natural attenuation (biodegradation etc.)  
 + 
 +CoronaScreen code (Wilson et al. 2005) 
 +  * Electron balance model 
 +  * Analytic model 
 +  * Moving 1D 
 + 
 +CoronaScreen does not overestimates biodegradation because it does not overestimates mixing of contaminated ​ water (donors of electrons) with pristine water (electron acceptors). 
 + 
 +=== Upgradient: Limited pumping === 
 +Pumping is limited not to draw pollution from the area that could be contaminated. 
 +{{ :​en:​simple5.png?​direct&​350 |}} 
 + 
 + 
 +\\  
 +===== Comparison of the Case study 1 and Case study 2 ===== 
 +Table: Comparison of the approaches used in the case studies 1 and 2. 
 +^ ^  Case study 1 –  Hypotheses testing ^  Case study 2 –  Prediction for water resources management ​ ^^ 
 +^ ^ ^  Steady state plume  ^  Limited pumping ​ ^ 
 +^Processes |Darcy flux |Natural attenuation |Water balance | 
 +^Domain |Different depth horizons |Downgradient |Upgradient | 
 +^Apparatus |Closed-form solution |Closed-form solution, numerical model PHREEQC |Closed-form solution | 
 +^Spatial resolution |Lumped |Lumped |“Distributed” | 
 +^Resulting quantity |Water flux |Area that could be contaminated |Maximal permitted pumping | 
 + 
 + 
 +Tenability of the models in case study 1 and 2 is semi-quantitatively evaluated by the pedigree matrix of Refsgaard et al. (2006). 
 + 
 +^Score ^Supporting empirical evidence ^^Theoretical understanding ^Representation of understood underlying mechanisms ^Plausibility ^Colleague consensus ^ 
 +^ ::: ^Proxy ^Quality and quantity ^ ::: ^ ::: ^ ::: ^ ::: ^ 
 +|15 |Not correlated and not clearly related (0) |Historical/​field data uncontrolled experiments small sample direct measurements (3) |Well-established theory (4) |Aggregated parameterized meta model (2) |Highly plausible (4) |Competing schools (2) | 
 + 
 +Keywords: ground water; multiple model ensemble; simplicity; equifinality 
 + 
 +===== References ===== 
 +REFSGAARD, Jens Christian, Jeroen P. VAN DER SLUIJS, James BROWN and Peter VAN DER KEUR, 2006. A\_framework for dealing with uncertainty due to model structure error. //Advances in Water Resources//​. **29**(11), 1586–1597. ISSN 0309-1708. DOI:​\_[[https://​doi.org/​10.1016/​j.advwatres.2005.11.013|10.1016/​j.advwatres.2005.11.013]] 
 + 
 +WILSON, R.D., S.F. THORNTON, A. HUETTMANN, M. GUTIERREZ-NERI and H. SLENDERS, 2005. //​CoronaScreen :​ Process-based models for natural attenuation assessment: guidance for the application of NA assessment screening models//. 2005. University of Sheffield, UK; TNO Institute of Environmental Sciences, The Netherlands. Dostupné z: https://​www.sheffield.ac.uk/​polopoly_fs/​1.521443!/​file/​CORONA-Guidance-Document-v1.0.pdf 
 + 
 + 
 +\\ \\ \\  
 +====== External resources ====== 
 +Interesting resource:​\\ 
 +https://​darcylecture2016.wordpress.com/​2015/​08/​21/​references/​ 
 + 
 +Simplic: [[wp>​Spherical cow]]:\\ 
 +{{:​spot_the_cow.gif?​direct|Spherical cow}} 
 + 
 +===== Quotations about simplicity and complexity of groundwater models=====
  
 “Jamieson (2000) pointed out that scientists live in a highly competitive environment where funding for research is limited. Involvement in policy-modeling projects helps scientists present themselves as real-world problem solvers, which helps secure funding for their scientific pursuits.” (Clement 2011, p. 626) “Jamieson (2000) pointed out that scientists live in a highly competitive environment where funding for research is limited. Involvement in policy-modeling projects helps scientists present themselves as real-world problem solvers, which helps secure funding for their scientific pursuits.” (Clement 2011, p. 626)
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 “Leavesley et al. (2002) proposed a new modelling paradigm: ‘this concept requires that we change the question of ‘which model is most appropriate for a specific set of criteria?​’ to ‘what combination of process conceptualisations is most appropriate?​’” (Branger et al. 2010, p. 1673) “Leavesley et al. (2002) proposed a new modelling paradigm: ‘this concept requires that we change the question of ‘which model is most appropriate for a specific set of criteria?​’ to ‘what combination of process conceptualisations is most appropriate?​’” (Branger et al. 2010, p. 1673)
  
-“Wolfram feels that science is far too ad hoc, in part because the models used are too complicated and/or unnecessarily organized around the limited primitives of traditional mathematics. Wolfram advocates using models whose variations are enumerable and whose consequences are straightforward to compute and analyze.” ​((https://​en.wikipedia.org/​wiki/​A_New_Kind_of_Science))+“Wolfram feels that science is far too ad hoc, in part because the models used are too complicated and/or unnecessarily organized around the limited primitives of traditional mathematics. Wolfram advocates using models whose variations are enumerable and whose consequences are straightforward to compute and analyze.” ​[[wp>A New Kind of Science]]
  
 “Booch et al. defined a model: ‘simplification of reality created to better understand the system being created’.” “Booch et al. defined a model: ‘simplification of reality created to better understand the system being created’.”
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 “…simple models are a good place to start because their transparent features provide clarity. A simple model is something to build on. In its sleek lines and limited assumptions,​ it can provide a base for elaboration while capturing the essence of a variety of more detailed possible explanations.” Levin (2007), citováno podle Fatichi et al. (2016). “…simple models are a good place to start because their transparent features provide clarity. A simple model is something to build on. In its sleek lines and limited assumptions,​ it can provide a base for elaboration while capturing the essence of a variety of more detailed possible explanations.” Levin (2007), citováno podle Fatichi et al. (2016).
  
 +A wise man has great power; and a knowledgeable man increases strength; for by wise guidance you wage your war; and victory is in many advisors. [[http://​www.obohu.cz/​bible/​index.php?​styl=WEB&​v=6&​kv=6&​k=Pr&​kap=24#​v6|Bible:​ Proverbs 24:5–6]]
 === References === === References ===
-CLEMENTT. Prabhakar2011Complexities in hindcasting ​models-when should we say enough is enough? //Ground Water//. **49**(5), 620–629. ISSN 1745-6584. DOI: [[http://dx.doi.org/​10.1111/​j.1745-6584.2010.00765.x|10.1111/j.1745-6584.2010.00765.x]]+BAKKERMark2013Are all models ​wrongAbsolutely not. //Groundwater//. **51**(3), 313. ISSN 1745-6584. DOI:\_[[https://​doi.org/​10.1111/​gwat.12037|10.1111/gwat.12037]]
  
-VOSSClifford I.2005The future of hydrogeology. //Hydrogeology Journal//. **13**(1), ​16. ISSN 1431-2174, 1435-0157. DOI: [[http://dx.doi.org/10.1007/s10040-005-0435-8|10.1007/s10040-005-0435-8]]+BEVENKeith1993Prophecy, reality and uncertainty in distributed hydrological modelling. //Advances in Water Resources//. **16**(1), ​4151. ISSN 0309-1708. DOI:\_[[https://​doi.org/​10.1016/0309-1708(93)90028-E|10.1016/0309-1708(93)90028-E]]
  
-VOSSClifford ​I., 2011aEditor'​s message: Groundwater modeling fantasies -part 1adrift in the details. //Hydrogeology Journal//. **19**(7), 1281-1284. ISSN 1431-2174, 1435-0157. DOI: [[http://dx.doi.org/10.1007/s10040-011-0789-z|10.1007/s10040-011-0789-z]]+BRANGER, F., I. BRAUDSDEBIONNEP. VIALLET, J. DEHOTIN, H. HENINE, Y. NEDELEC and S. ANQUETIN, 2010. Towards multi-scale integrated hydrological models using the LIQUID® framework. Overview of the concepts and first application examples. //Environmental Modelling & Software//. **25**(12), 1672–1681. ISSN 1364-8152. DOI:\_[[https://​doi.org/​10.1016/j.envsoft.2010.06.005|10.1016/j.envsoft.2010.06.005]]
  
-VOSSClifford I.2011bEditor'​s message: Groundwater modeling fantasies-part 2, down to earth. //Hydrogeology Journal//. **19**(8), 14551458. ISSN 1431-2174, 1435-0157. DOI: [[http://dx.doi.org/10.1007/s10040-011-0790-6|10.1007/s10040-011-0790-6]]+BREDEHOEFTJohn2010Models and model analysis. //Ground Water//. **48**(3), 328328. DOI:\_[[https://​doi.org/​10.1111/j.1745-6584.2009.00631.x|10.1111/j.1745-6584.2009.00631.x]]
  
-BREDEHOEFTJohn2010. Models and model analysis. //Ground Water//. **48**(3), 328328. DOI: [[http://dx.doi.org/​10.1111/​j.1745-6584.2009.00631.x|10.1111/​j.1745-6584.2009.00631.x]]+CLEMENTT. Prabhakar2011Complexities in hindcasting models-when should we say enough is enough? ​//Ground Water//. **49**(5), 620629. ISSN 1745-6584. DOI:\_[[https://​doi.org/​10.1111/​j.1745-6584.2010.00765.x|10.1111/​j.1745-6584.2010.00765.x]]
  
-HUNT, Randall J., John DOHERTY a Matthew J. TONKIN2007Are models too simpleArguments for increased parameterization. ​//Ground Water//. **45**(3), 254–262. DOI: [[http://dx.doi.org/​10.1111/​j.1745-6584.2007.00316.x|10.1111/​j.1745-6584.2007.00316.x]]+DOHERTY, John, 2011Modeling: Picture perfect or abstract art? //Ground Water//. **49**(4), 455. ISSN 1745-6584. DOI:\_[[https://​doi.org/​10.1111/​j.1745-6584.2011.00812.x|10.1111/​j.1745-6584.2011.00812.x]]
  
-NARASIMHANT. N., 2005Hydrogeology in North Americapast and future. //Hydrogeology Journal//. **13**(1), 724. DOI: [[http://dx.doi.org/10.1007/s10040-004-0422-5|10.1007/s10040-004-0422-5]]+EBELBrian Aand Keith LOAGUE2006Physics-based hydrologic-response simulationSeeing through the fog of equifinality. //Hydrological Processes//. **20**(13), 28872900. ISSN 1099-1085. DOI:\_[[https://​doi.org/​10.1002/hyp.6388|10.1002/hyp.6388]]
  
-NARASIMHANTN., 2010Comment on guest editorial: "​Models ​and model analysis"​. //Ground Water//. **48**(6)785785. ISSN 1745-6584. DOI: [[http://dx.doi.org/10.1111/j.1745-6584.2010.00731.x|10.1111/j.1745-6584.2010.00731.x]]+FATICHISimone, Enrique RVIVONI, Fred LOGDENValeriy YIVANOV, Benjamin MIRUS, David GOCHIS, Charles W. DOWNER, Matteo CAMPORESE, Jason H. DAVISON, Brian EBEL, Norm JONES, Jongho KIM, Giuseppe MASCARO, Richard NISWONGER, Pedro RESTREPO, Riccardo RIGON, Chaopeng SHEN, Mauro SULIS and David TARBOTON, 2016. An overview of current applications,​ challenges, and future trends in distributed process-based models in hydrology. //Journal of Hydrology//. **537**, 4560. ISSN 0022-1694. DOI:\_[[https://​doi.org/​10.1016/j.jhydrol.2016.03.026|10.1016/j.jhydrol.2016.03.026]]
  
-BRANGERF., IBRAUDSDEBIONNEP. VIALLETJ. DEHOTINH. HENINE, Y. NEDELEC a S. ANQUETIN, 2010. Towards multi-scale integrated hydrological models using the LIQUID® framework. Overview of the concepts ​and first application examples. //​Environmental Modelling & Software//. **25**(12), 1672–1681ISSN 1364-8152DOI[[http://dx.doi.org/​10.1016/​j.envsoft.2010.06.005|10.1016/​j.envsoft.2010.06.005]]+HILLMary Cand Claire RTIEDEMAN2007//Effective groundwater model calibration?:​ with analysis of datasensitivitiespredictions, and uncertainty//. Hoboken N.J.: WileyISBN 978-0-471-77636-9
  
-DOHERTY, John, 2011Modeling: Picture perfect or abstract art? //Ground Water//. **49**(4), 455. ISSN 1745-6584. DOI: [[http://dx.doi.org/​10.1111/​j.1745-6584.2011.00812.x|10.1111/​j.1745-6584.2011.00812.x]]+HUNT, Randall J., John DOHERTY and Matthew J. TONKIN2007Are models too simpleArguments for increased parameterization. ​//Ground Water//. **45**(3), 254–262. DOI:\_[[https://​doi.org/​10.1111/​j.1745-6584.2007.00316.x|10.1111/​j.1745-6584.2007.00316.x]]
  
-PERLISAlan J.1982. Epigrams on programming. //SIGPLAN Notices//. **17**(9), 7–13ISSN 0362-1340. DOI: [[http://​dx.doi.org/​10.1145/​947955.1083808|10.1145/​947955.1083808]]+LEVINSimon2007. //Fragile Dominion//. Basic BooksISBN 978-0-465-01073-8.
  
-HILLMary Ca Claire RTIEDEMAN2007//Effective groundwater model calibration?​with analysis of data, sensitivities,​ predictions, ​and uncertainty//. Hoboken N.J.: WileyISBN 978-0-471-77636-9+NARASIMHANTN., 2005Hydrogeology in North Americapast and future. //​Hydrogeology Journal//. **13**(1), 7–24DOI:\_[[https://​doi.org/​10.1007/​s10040-004-0422-5|10.1007/​s10040-004-0422-5]]
  
-EBELBrian Aa Keith LOAGUE2006Physics-based hydrologic-response simulationSeeing through the fog of equifinality. //Hydrological Processes//. **20**(13), 28872900. ISSN 1099-1085. DOI: [[http://dx.doi.org/10.1002/hyp.6388|10.1002/hyp.6388]]+NARASIMHANT. N., 2010Comment on guest editorial"​Models and model analysis"​. //Ground Water//. **48**(6), 785785. ISSN 1745-6584. DOI:\_[[https://​doi.org/​10.1111/j.1745-6584.2010.00731.x|10.1111/j.1745-6584.2010.00731.x]]
  
-BEVENKeith, 1993Prophecyreality and uncertainty in distributed hydrological modelling. //Advances in Water Resources//. **16**(1), 4151. ISSN 0309-1708. DOI: [[http://dx.doi.org/10.1016/0309-1708(93)90028-E|10.1016/0309-1708(93)90028-E]]+PERLISAlan J., 1982. Epigrams on programming. //SIGPLAN Notices//. **17**(9), 713. ISSN 0362-1340. DOI:\_[[https://​doi.org/​10.1145/947955.1083808|10.1145/947955.1083808]]
  
-SAVENIJE, Hubert H. G., 2001. Equifinality,​ a blessing in disguise? //​Hydrological Processes//​. **15**(14), 2835–2838. ISSN 1099-1085. DOI: [[http://dx.doi.org/​10.1002/​hyp.494|10.1002/​hyp.494]]+SAVENIJE, Hubert H. G., 2001. Equifinality,​ a blessing in disguise? //​Hydrological Processes//​. **15**(14), 2835–2838. ISSN 1099-1085. DOI:\_[[https://​doi.org/​10.1002/​hyp.494|10.1002/​hyp.494]]
  
-BAKKERMark2013Are all models wrong? Absolutely not. //Groundwater//. **51**(3), 313. ISSN 1745-6584. DOI: [[http://dx.doi.org/10.1111/gwat.12037|10.1111/gwat.12037]]+VOSSClifford I.2005The future of hydrogeology. //Hydrogeology Journal//. **13**(1), 1–6. ISSN 1431-2174, 1435-0157. DOI:\_[[https://​doi.org/​10.1007/s10040-005-0435-8|10.1007/s10040-005-0435-8]]
  
-FATICHISimone, Enrique RVIVONIFred LOGDEN, Valeriy Y. IVANOV, Benjamin MIRUS, David GOCHIS, Charles W. DOWNER, Matteo CAMPORESE, Jason H. DAVISON, Brian EBEL, Norm JONES, Jongho KIM, Giuseppe MASCARO, Richard NISWONGER, Pedro RESTREPO, Riccardo RIGON, Chaopeng SHEN, Mauro SULIS a David TARBOTON, 2016. An overview of current applications,​ challengesand future trends ​in distributed process-based models in hydrology. //​Journal ​of Hydrology//. **537**, 45–60. ISSN 0022-1694. DOI: [[http://dx.doi.org/10.1016/j.jhydrol.2016.03.026|10.1016/j.jhydrol.2016.03.026]]+VOSSClifford I., 2011aEditor'​s message: Groundwater modeling fantasies -part 1adrift ​in the details. //Hydrogeology ​Journal//. **19**(7)1281-1284. ISSN 1431-2174, 1435-0157. DOI:\_[[https://​doi.org/​10.1007/s10040-011-0789-z|10.1007/s10040-011-0789-z]]
  
-LEVINSimon2007. //Fragile Dominion//. Basic BooksISBN 978-0-465-01073-8.+VOSSClifford I.2011b. Editor'​s message: Groundwater modeling fantasies-part 2, down to earth. //Hydrogeology Journal//. **19**(8), 1455–1458ISSN 1431-2174, 1435-0157. DOI:​\_[[https://​doi.org/​10.1007/​s10040-011-0790-6|10.1007/​s10040-011-0790-6]] 
 +> **[[cs:​simple|Czech version of this document is much more elaborate]]** because it references part of my  [[cs:​simplealt|updated dissertation]] that contains both theoretical part and several [[cs:​casestudies|case studies]].
en/simple.1527065401.txt.gz · Last modified: 2018-05-23