Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
References

Hydrology: Modeling an Uncertainty

Profile image of Boris ShmaginBoris Shmagin
https://doi.org/10.13140/2.1.4610.3689
visibility

…

description

1 page

link

1 file

Abstract

Vit Klemeš (1932-2010) wrote: “The unsatisfactory state of hydrology is … the result of the dichotomy between the theoretical recognition of hydrology as a science in its own right and the practical impossibility of studying it as a primary discipline, but only as an appendage of hydraulic engineering, geography, geology, etc. …” (1986) and “… the consequence of the fact that, in the absence of hydrology as a full-fledged science, engineers themselves had to supply the hydrological inputs they needed and they did so by constructing simple empirical models from the data - after all, it was not their business to do 'scientific hydrology'”(1999). These were last century articles; the recent publication, “Joint Editorial – On the Future of Journal Publications in Hydrology” in five international hydrological journals shows the presence of a similar situation now. Two topics considered in the presentation are Logic and Ontology, to present Hydrology in a wider multi-disciplinary ...

Related papers

Hydrological models: mathematics or science?
Denis Hughes

Hydrological Processes, 2010

Within the last 40 years or so there have been a large number of contributions to the scientific literature (journal articles, conference presentations and books) on various aspects of catchment hydrology (rainfall-runoff) models. The focus of these contributions has been on the structure of the models (both mathematical and hydrological), the software used to implement them, methods of calibration (both manual and automatic), estimation of parameters in ungauged situations, their practical application for solving problems and, with increasing frequency in recent years, the uncertainties associated with their outputs. These models are generally defined as mathematical representations of the hydrological cycle at the catchment scale and it is interesting to note how many of the contributions focus on the 'mathematical' part of that definition and how many on the 'hydrological' part. More than 20 years ago, Klemeš (1986) contested that for '.. . a good hydrological model it is not enough to work well. It must work well for the right reasons'. The main focus of this commentary is whether or not that message is true, whether active researchers and practitioners recognize it as an issue and whether or not it is possible to decide if a model is working for the right reasons in ungauged (i.e. no observed streamflow data) catchments. Does the level of complexity influence whether models can work well for the right reasons? The argument for so-called 'physics-based' models arose partly from the desire to include hydrological processes more explicitly (Abbott et al., 1986). However, there seems to be a point at which added complexity in the model structure is not matched by our ability to quantify the model parameters realistically, given typically available data resources. This is an important consideration in data scarce areas which are often the very areas where hydrological models have the potential to provide valuable information for the purposes of water resources planning and management. Beven (1989) questioned the use of algorithms based on small-scale process observations in models that are used at much larger scales. At the other end of the complexity scale, it is perhaps difficult to imagine how it is possible to assess whether highly simplified models are doing the right thing for the right reason. Simple models may be easier to calibrate, particularly when automatic calibration approaches (Vrugt et al., 2003) are being used, but this then becomes a largely mathematical fitting exercise. The inevitable process lumping that occurs in simple models implies that the parameters have little physical meaning, are just mathematical constants and are difficult to extrapolate to ungauged catchments. The desire for parsimonious models (Jakeman and Hornberger, 1993; Perrin et al., 2001), expressed by some contributions to the literature, must surely emanate from the school of hydrological modellers who are looking for elegant mathematical solutions to a complex input (i.e. climate signals)-output (streamflow) problem. It has been claimed in the past (Loague and Freeze, 1985) that more complex models have too many parameter interactions and are fraught with problems of equifinality and lack of parameter identifiability (Beven, 2006), whereby different parameter sets can result in very similar streamflow

View PDFchevron_right
The Void in Hydro Ontology
Boyan Brodaric

Voids are extremely important to water science, because their size and connectivity determines the storage and flow of water both above and below the ground surface. While previous formal theories about voids strictly consider holes hosted inside objects, we generalize voids to also include spaces between objects, and distinguish voids in macroscopic objects from those occurring microscopically in an object's matter. These notions are axiomatized in first-order logic as an extension of the DOLCE ontology, and are applied to key aspects of hydrology and hydrogeology, laying the groundwork for a foundational hydro ontology.

View PDFchevron_right
Hydrology: The interdisciplinary science of water
Richard Vogel

Water Resources Research, 2015

We live in a world where biophysical and social processes are tightly coupled. Hydrologic systems change in response to a variety of natural and human forces such as climate variability and change, water use and water infrastructure, and land cover change. In turn, changes in hydrologic systems impact socioeconomic, ecological, and climate systems at a number of scales, leading to a coevolution of these interlinked systems. The Harvard Water Program, Hydrosociology, Integrated Water Resources Management, Ecohydrology, Hydromorphology, and Sociohydrology were all introduced to provide distinct, interdisciplinary perspectives on water problems to address the contemporary dynamics of human interaction with the hydrosphere and the evolution of the Earth's hydrologic systems. Each of them addresses scientific, social, and engineering challenges related to how humans influence water systems and vice versa. There are now numerous examples in the literature of how holistic approaches can provide a structure and vision of the future of hydrology. We review selected examples, which taken together, describe the type of theoretical and applied integrated hydrologic analyses and associated curricular content required to address the societal issue of water resources sustainability. We describe a modern interdisciplinary science of hydrology needed to develop an in-depth understanding of the dynamics of the connectedness between human and natural systems and to determine effective solutions to resolve the complex water problems that the world faces today. Nearly, every theoretical hydrologic model introduced previously is in need of revision to accommodate how climate, land, vegetation, and socioeconomic factors interact, change, and evolve over time.

View PDFchevron_right
What is the role of the model in socio-hydrology? Discussion of " Prediction in a socio-hydrological world "
Rutgerd Boelens

Srinivasan et al. provide an interesting overview of the challenges for long-term socio-hydrological predictions. Although agreeing with most of the statements made, we argue for the need to take socio-hydrological analysis a step further and add some fundamental considerations, especially concerning the crucial importance of many (conscious and unconscious) assumptions made upfront of the modelling exercise. Eventual assumptions of technological determinism need correction: Models are not “value-free”, but uncertain, subjective and a product of the society in which they were shaped. It is important to acknowledge this uncertainty and bias when making decisions based on socio-hydrological models, considering also that these models are “social and political actors” in and by themselves. Furthermore, socio-hydrological models require a transdisciplinary approach, since physical water availability is only one of the boundary conditions for society. Last but not least, interaction with stakeholders remains important to enable understanding of what the variable of interest is.

View PDFchevron_right
Editorial: Challenging hydrological theory and practice
Chris Bradley

Hydrology Research, 2016

View PDFchevron_right
The future of hydrology: An evolving science for a changing world
Thorsten Wagener

2010

Human activities exert global-scale impacts on our environment with significant implications for freshwater-driven services and hazards for humans and nature. Our approach to the science of hydrology needs to significantly change so that we can understand and predict these implications. Such an adjustment is a necessary prerequisite for the development of sustainable water resource management strategies and to achieve long-term water security for people and the environment.

View PDFchevron_right
Seeking parsimony in hydrology and water resources technology
Demetris Koutsoyiannis

The principle of parsimony, also known as the principle of simplicity, the principle of economy and Ockham's razor, advises scientists to prefer the simplest theory among those that fit the data equally well. In this, it is an epistemic principle but reflects an ontological characterization that the universe is ultimately parsimonious. Is this principle useful and can it really be reconciled with, and implemented to, our modelling approaches of complex hydrological systems, whose elements and events are extraordinarily numerous, different and unique? The answer underlying the mainstream hydrological research of the last two decades seems to be negative. Hopes were invested to the power of computers that would enable faithful and detailed representation of the diverse system elements and the hydrological processes, based on merely "first principles" and resulting in "physically-based" models that tend to approach in complexity the real world systems. Today the...

View PDFchevron_right
Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023-2032
Dhruv Sehgal

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) scientific decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology”, “Intensification of the water cycle”, and “Water services under pressure”. Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other Early-Career Scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initia...

View PDFchevron_right

References (61)

  1. Beven, K. (2007) Towards integrated environmental models of everywhere: uncertainty, data & modelling as a learning process. Hydrology & Earth System Sciences 11:460-467.
  2. Beven, K., & P. Young (2013) A guide to good practice in modeling semantics for authors & referees. Water Resour. Res., 49:5092-5098.
  3. Blöschl, G., A. Bardossy, D. Koutsoyiannis, Z. W. Kundzewicz, I. Littlewood, A. Montanari, &
  4. H. Savenije (2014) On the future of journal publications in Hydrology. Water Resour. Res. 50:2795-2797.
  5. Bohm, D. (1952) A Suggested Interpretation of the Quantum Theory in terms of 'Hidden' Variables. Parts I & II. Physics Review 85:166-193.
  6. Bohm, D. (1982) Wholeness & the implicate order. Routledge & Kegan Paul, Boston. 224 p.
  7. Brugnach, M., A. Dewulf, C. Pahl-Wostl, & T. Taillieu. (2008) Toward a relational concept of uncertainty: about knowing too little, knowing too differently, & accepting not to know. Ecology & Society 13(2):30.
  8. Delgado, M., M. D. Ruiz, D. Sánchez, & M. A. Vila (2014) Fuzzy quantification: a state of the art. Fuzzy Sets & Systems 242:1-30.
  9. Dubois, D. & H. Prade (1988) Possibility theory. An approach to computerized processing of uncertainty. Plenum Press, New York, New York, USA, 263 p.
  10. Dubois, D. & H. Prade (2010). Formal representations of uncertainty. Chapter 3, in Decision-making Process: Concepts & Methods (Eds Denis Bouyssou, Didier Dubois, Marc Pirlot, Henri Prade), p. 85-156. Willey. Hoboken, NJ.
  11. Efron, B. & D. V. Hinkley (1978) Assessing the Accuracy of the Maximum Likelihood Estimator: Observed Versus Expected Fisher Information. Biometrika 65(3):457-482.
  12. Gleeson, T. & D. Paszkowski (2014) Perceptions of scale in hydrology: what do you mean by regional scale? Hydrological Sciences J. 59(1):99-107.
  13. Gruber, T. R. (1993) A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition 5(2):199-220.
  14. Freshwater: Supply Concerns Continue, & Uncertainties Complicate Planning. GAO-14-430: Published: May 20, 2014. Publicly Released: May 22, 2014.
  15. Klemeš, V. (1976) Geophysical time series & catastrophism, Catastrophist Geology, 1(1):43-48.
  16. Klemeš, V. (1978) Physically based stochastic hydrologic analysis. Advances in Hydroscience (V. T. Chow, editor). Vol. 11. Academic Press, NY, pp. 286-356.
  17. Klemeš, V. (1982) Empirical & causal models in hydrology. In: Scientific Basis of Water Resource Management. National Academy of Sciences, Washington, DC., pp. 391-403.
  18. Klemeš, V. (1983) Hydrological uncertainty in water management context. In "Scientific Procedures Applied to the Planning, Design & Management of Water Resources Systems" Proceedings of the Hamburg Symposium, August 1983. lAHS. Publ. 147:175-186.
  19. Klemeš, V. (1986) "Dilettantism in hydrology: Transition or destiny?" Water Resour. Res., 22(9):1775-1885.
  20. Klemeš, V. (1989) ''The improbable probabilities of extreme floods & droughts.'' Hydrology & disasters, O. Starosolszky & O. M. Melder, eds., James & James, London, 43-51.
  21. Klemeš, V. (1999) Keeping techniques, methods & models in perspective. Journal of Water Resources Planning & Management -ASCE, 125 (4):181-185, Victoria BC, Canada.
  22. Klemeš, V. (2007) Association Lecture by the President of IAHS in 1987-1991. 20 years later: What has changed -& what hasn't. The IAHS General Assembly in Perugia, Italy, 9 July 2007, 42 p. 22. Koutsoyiannis, D. (2011) Hurst-Kolmogorov dynamics & uncertainty, Journal of the American Water Resources Association 47(3):481-495.
  23. Koutsoyiannis, D. (2013) Hydrology & Change, Hydrological Sciences Journal 58(6):1177-1197.
  24. Krcho, J. (1978) The Spatial Organization of the Physical-Geographic Sphere as a Cybernetic System Expressed by Means of Measure of Entropy: Acta Facultatis Rerum Naturaium Universitatis Commenianae, Bratislava. Ceographica 16:57-147.
  25. Leopold, L. B. (1977) A reverence for rivers. Geology 5:429-430.
  26. Liberman, A. A. & Shmagin B. A. (1984) The case of research latent periodicity of groundwater flow. Moscow University Geology Bulletin, series 4, 1988, 6:85-88. (In Russian).
  27. Lindley, D. (2000) The Philosophy of Statistic. The Statistisian 49:293-337.
  28. Markonis, Y., & D. Koutsoyiannis, (2013) Climatic variability over time scales spanning nine orders of magnitude: Connecting Milankovitch cycles with Hurst-Kolmogorov dynamics. Surveys in Geophysics 34(2):181-207.
  29. Matalas, N. (2012) Comment on the Announced Death of Stationarity. J. Water Resour. Plann. Manage., 138(4):311-312.
  30. McDonald, R. I., K. Weber, J. Padowski, M. Flörke, C. Schneider, P. A. Green, T. Gleeson, S. Eckman, B. Lehner, D. Balk, T. Boucher, G. Grill, & M. Montgomery (2014) Water on an urban planet: Urbanization & the reach of urban water infrastructure. Global Environ. Change 27:96-105.
  31. Minsky, M. L. (1965, 1968, 1995) Matter, Mind & Models. Proc. International Federation of Information Processing Congress 1965, vol. 1: 45-49. Reprinted: "Semantic Information processing," (Marvin Minsky, Ed.) MIT Press, 1968.
  32. Minsky, M. (1975) A framework for representing knowledge. Pages 211-277 in P. H. Winston, editor. The psychology of computer vision. MC-Graw-Hill, New York, New York, USA.
  33. Montanari, A., G. Blöschl, X. Cai, D. S. Mackay, A. M. Michalak, H. Rajaram, & G. Sander (2013), Editorial : Toward 50 years of Water Resources Research, Water Resour. Res. 49:7841-7842.
  34. Mooers, H., R. Kanivetsky, J. Nieber, H. Peterson, C. Johnston, & B. Shmagin (2009) Geological Controls on Water Resource Variability in Minnesota, USA. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2009.3957.1>
  35. Nalimov, V. V. (1981) In the labyrinths of language: A mathematician's journey, Philadelphia, Pa., ISI Press, 246 p. 36. Newman, A. J., M. P. Clark, K. Sampson, A. Wood, L. E. Hay, A. Bock, R. Viger, D. Blodgett, L. Brekke, J. R. Arnold, T. Hopson, & Q. Duan (2014) Development of a large-sample watershed-scale hydrometeorological dataset for the contiguous USA: dataset characteristics & assessment of regional variability in hydrologic model performance. HESSD 11:5599-5631.
  36. Peterson, H. M., J. L. Nieber, R. Kanivetsky & B. Shmagin (2014) Regionalization of landscape characteristics to map hydrologic variables. J. of Hydroinformatics 16(3):633-648.
  37. Peterson, H. M., J. L. Nieber, R. Kanivetsky & B. Shmagin (2013) Water Resources Sustainability Indicator: Application of the Watershed Characteristics Approach. Water Resour. Manag. 27:1221-1234.
  38. Polanyi, M. (1958) Personal Knowledge, Routledge, London, & University of Chicago Press, Chicago. 428 p. 40. Polanyi, M. (1964) Science, Faith & Society, 2nd ed., University of Chicago Press, Chicago. 273 p. 41. Polanyi, M. (1966) The Tacit Dimension, University of Chicago Press: Chicago, 128 p. 42. Poliakov, A. S., V. N. Sokolov & B. A. Shmagin. (1988) Probabilistic Models for V. I. Vernadsky's Geospheres. Moscow University Geology Bulletin, series 4, 6:30-41. (In Russian).
  39. Salas, J.D., J. W. Delleur, V. Yevjevich, & W. L. Lane 1980, Applied Modeling of Hydrologic Time Series. Water Resources Publication, 484 pages 44. Schmandt, B., Jacobsen, S. D., Becker, T. W., Liu, Z., & Dueker, K. G. (2014) Dehydration melting at the top of the lower mantle. Science, 334:1265-1268.
  40. Searle, J. R., (1972) Chomsky's Revolution in Linguistics. The New York Review of Books, June 29, 1972. 46. Shannon, C. E. (1948) A Mathematical Theory of Communication. The Bell System Technical Journal. 27:379-423, 623-656, July, October 1948.
  41. Shmagin, B. A. (1997) The use of a runoff hydrosphere system's structure model as a basis of monitoring water resources. Third USA/CIS Joint Conference on Environmental Hydrology & Hydrogeology - Water: Sustaining a Critical Resource. A selection of papers presented at the Conference held in Tashkent, Uzbekistan, Sept. 22-27, 1996, (ed. J. D. Powell), pp. 107-111.
  42. Shmagin B. A. (2009) Precipitation in Aberdeen, SD: data analysis approach. Proceedings of SD Academy of Science 88:67-79.
  43. Shmagin, B. (2011a) Missouri River Watershed: the Object for Hydrological Study & the Uncertainty of Models. Available from Nature Precedings < http://dx.doi.org/10.1038/npre.2011.6537.1 > 50. Shmagin, B. (2011b). The Issue of Uncertainty for the River Watershed: Data Analysis of Scaled Space & Time Variability. Available from Nature Precedings < http://dx.doi.org/10.1038/npre.2011.6008.1 > 51. Shmagin, B. (2012) The Issue of Uncertainty of Hydrologic Events in the Missouri River Watershed & the Property of Coordinate System in Use. // Proceedings of the South Dakota Academy of Science 91:253-256.
  44. Shmagin, B. & D. Chen (2006). Understanding & mapping water resources by multidimensional statistics & fuzzy logic: Missouri River Basin case. 2006 Western SD hydrology conference. April 18, 2006. Rapid City, SD. Program & Abstracts, p. 28. Available from Nature Precedings < http://precedings.nature.com/documents/1071/version/1 > 53. Shmagin, B.A., & C. A. Johnston (2004).The hydroclimatology of the central territory of conterminous U.S. & stream flow regime in upper Missouri River basin. Abstract. Proceedings of the South Dakota Academy of Science 83: 291-292.
  45. Shmagin, B., Johnston, C., & Bridgham, S. The spatial temporal regime of stream flow of the conterminous U.S. in connection with indices of global atmospheric circulation. Eos Trans. AGU, 85(17), Jt.
  46. Assem. Suppl., Abstract H33A-06, American Geophysical Union Joint Assembly, Montreal Canada, May. Available from Nature Precedings <http://dx.doi.org/10.1038/npre.2007.1382.1> (2007) 55. Shmagin, B. & R. Kanivetsky (2006). Regional Hydrology: Tools vs. Ideas. In: "Coastal Hydrology & Processes". Proceedings of the AIH 25th Anniversary Meeting & International Conference on "Challenges in Coastal Hydrology & Water Quality". Water Resources Publications, LLC. (Eds: Vijay P. Singh & Y. Jun Xu). Chapter 15, pp. 183-196.
  47. Shmagin B. & D. Todey (2007). South Dakota Diversity of Temperature: Pictures from Statistical Analysis. Proceedings of SD Academy of Science 86:115-158.
  48. Shmagin, B. & M. Trizna (1992). A Long-Term Prognosis of Average Annual Flows of Danube River in Orsova Station. Acta Faculitatis Rerum Naturalium Universitatis Comenianae. Bratislava. Geographica 33:233- 243.
  49. Singh, V., Byrd, A., & Cui, H. (2014). "Flow Duration Curve Using Entropy Theory." J. Hydrol. Eng., 19(7), 1340-1348.
  50. Sivapalan, M., Savenije, H. H. G., & Blöschl, G. (2012). Socio-hydrology: A new science of people & water. Hydrol. Process. 26(8):1270-1276.
  51. Slack, J. R., & Landwehr, J. M. (1992) Hydro-Climatic Data Network (HCDN): A U.S. Geological Survey streamflow data set for the United States for the study of climate variations, 1874-1988, U.S. Geol. Surv. Open File Rep. 92-129.
  52. UNESCO (1964) International Hydrological Decade. Intergovernmental Meeting of Experts. Paris. (unesdoc.unesco.org/images/0001/000170/017099EB.pdf)
  53. Vapnik, V. (1982) Estimation of Dependencies Based on Empirical Data. Springer, 506 p. Second edition (2006). Estimation of Dependencies Based on Empirical Data; Empirical inference science: afterword of 2006. New York, N.Y. Springer.
  54. Vapnik, V. (2006) Empirical Inference Science. Video: "Foundations of Statistical Learning Theory: Empirical Inference in high-dimension spaces". Léon Bottou & Vladimir Vapnik. Published on the Web: Dec. 3, 2007, recorded: September 2007. (http://videolectures.net/mmdss07_bottou_fslt/). Slide show for the lecture: http://www1.cs.columbia.edu/~jebara/6998/NSF-2006.pdf.
  55. Vernadsky, V. I. (1998) The Biosphere, A Peter A. Nevraumont Book, N.Y.: pp. 41-150.
  56. Weaver, W. (1949) Recent contributions to the mathematical theory of communication in Weaver, W. & C. E. Shannon. The Mathematical Theory of Communication, Urbana, Illinois: University of Illinois Press, 1949, pp. 1-28.
  57. Wilson, D. S. (2002) Darwin's Cathedral: Evolution, Religion, & the Nature of Society, University of Chicago Press. 268 p. 67. Wittgenstein, L. (1922) Tractatus Logico-Philosophicus. First published by Kegan Paul (London), 1922. 68. Zadeh, L. A. (1975). The concept of a linguistic variable & its application to approximate reasoning, I-III, Information Sciences 8:199-251, 9:301-357, 9:43-80.
  58. Zadeh, L. A. (2005). Toward a generalized theory of uncertainty (GTU) -an outline. Information Sciences, 172(1-2), 1-40.
  59. Zaltsberg, E. 2013. Development of regional hydrogeological mapping in Russia. Hydrogeology Journal 21: 525-528.
  60. Zachos J, Pagani M, Sloan L, Thomas E, Billups K (2001) Trends, rhythms, & aberrations in global climate 65 Ma to present. Science 292:686-693. Words Clouds (Math Models)
  61. Five models represent the Abstract (left), List of References (l. center), Text of the Poster (center & r. center) & Quotations (right) (images created on Wordel.com)

Related papers

Reconciling hydrology with engineering
Demetris Koutsoyiannis

Hydrology Research, 2014

Hydrology has played an important role in the birth of science. Yet practical hydrological knowledge, related to human needs for water storage, transfer and management, existed before the development of natural philosophy and science. In contemporary times, hydrology has had strong links with engineering as its development has been related to the needs of the design and management of water infrastructures. In the 1980s these links were questioned and it was suggested that separating hydrology from engineering would be beneficial for both. It is argued that, thereafter, hydrology, instead of becoming an autonomous science, developed new dependencies, particularly on politically driven agendas. This change of direction in effect demoted the role of hydrology, for example in studying hypothetical or projected climate-related threats. Revisiting past experiences suggests that reestablishing the relationship of hydrology with engineering could be beneficial. The study of change and the implied uncertainty and risk could constitute a field of mutual integration of hydrology and engineering. Engineering experience may help hydrology to appreciate that change is essential for progress and evolution, rather than only having adverse impacts. While the uncertainty and risk cannot be eliminated they can be dealt with in a quantitative and rigorous manner.

View PDFchevron_right
An Ontological Framework for Characterizing Hydrological Flow Processes
Torsten Hahmann

2017

The spatio-temporal processes that describe hydrologic flow - the movement of water above and below the surface of the Earth -- are currently underrepresented in formal semantic representations of the water domain. This paper analyses basic flow processes in the hydrology domain and systematically studies the hydrogeological entities, such as different rock and water bodies, the ground surface or subsurface zones, that participate in them. It identifies the source and goal entities and the transported water (the theme) as common participants in hydrologic flow and constructs a taxonomy of different flow patterns based on differences in source and goal participants. The taxonomy and related concepts are axiomatized in first-order logic as refinements of DOLCE&#39;s participation relation and reusing hydrogeological concepts from the Hydro Foundational Ontology (HyFO). The formalization further enhances HyFO and contributes to improved knowledge integration in the hydrology domain.

View PDFchevron_right
Introductory Chapter: Hydrologic Science - Where We Stand Today?
Muhammad Salik Javaid

Hydrology - The Science of Water [Working Title]

View PDFchevron_right
Hydrologic Models
Richard Warner

2016

Water is a vital part of our everyday lives. Water is necessary for domestic activities such as drinking, cooking and cleaning; agricultural practices such as growing crops and livestock; and industrial uses such as manufacturing and transporting products (Figure 1). Some of our water use is seen, such as when we take a shower. However, most of our water use is hidden, meaning that water was used to make a product although little to no water may be in the product itself. Because water is so important, a branch of science is devoted to its study

View PDFchevron_right
Conceptual Models in Hydrogeology, Methodology and Results
Carlos A Palacio

2012

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved