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79:
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39:
367:"Application of machine learning techniques for rainfall-runoff modelling" by Solomatine and Siek (2004), and "Data-driven modelling approaches for hydrological forecasting and prediction" by Valipour et al. (2021). These models are commonly used for predicting rainfall, runoff, groundwater levels, and water quality, and have proven to be valuable tools for optimizing water resource management strategies.
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data-driven models leverage techniques from artificial intelligence, machine learning, and statistical analysis, including correlation analysis, time series analysis, and statistical moments, to learn complex patterns and dependencies from historical data. This allows them to make more accurate predictions and provide insights into the underlying processes.
98:
Scale models commonly use physical properties that are similar to their natural counterparts (e.g., gravity, temperature). Yet, maintaining some properties at their natural values can lead to erroneous predictions. Properties such as viscosity, friction, and surface area must be adjusted to maintain
350:
are a mathematical technique for determine the probability of a state or event based on a previous state or event. The event must be dependent, such as rainy weather. Markov Chains were first used to model rainfall event length in days in 1976, and continues to be used for flood risk assessment and
336:
is a mathematical operation on two different functions to produce a third function. With respect to hydrologic modeling, convolution can be used to analyze stream discharge's relationship to precipitation. Convolution is used to predict discharge downstream after a precipitation event. This type of
118:
Groundwater flow can be visualized using a scale model built of acrylic and filled with sand, silt, and clay. Water and tracer dye may be pumped through this system to represent the flow of the simulated groundwater. Some physical aquifer models are between two and three dimensions, with simplified
1334:
Specialized software may also be used to solve sets of equations using a graphical user interface and complex code, such that the solutions are obtained relatively rapidly and the program may be operated by a layperson or an end user without a deep knowledge of the system. There are model software
94:
Scale models offer a useful approximation of physical or chemical processes at a size that allows for greater ease of visualization. The model may be created in one (core, column), two (plan, profile), or three dimensions, and can be designed to represent a variety of specific initial and boundary
433:
as boxes with arrows pointing toward a box that represents the main river. The conceptual model would then specify the important watershed features (e.g., land use, land cover, soils, subsoils, geology, wetlands, lakes), atmospheric exchanges (e.g., precipitation, evapotranspiration), human uses
343:
analysis is used to characterize temporal correlation within a data series as well as between different time series. Many hydrologic phenomena are studied within the context of historical probability. Within a temporal dataset, event frequencies, trends, and comparisons may be made by using the
366:
Since their inception in the latter half of the 20th century, data-driven models have gained popularity in the water domain, as they help improve forecasting, decision-making, and management of water resources. A couple of notable publications that use data-driven models in hydrology include
362:
in hydrology emerged as an alternative approach to traditional statistical models, offering a more flexible and adaptable methodology for analysing and predicting various aspects of hydrological processes. While statistical models rely on rigorous assumptions about probability distributions,
434:(e.g., agricultural, municipal, industrial, navigation, thermo- and hydro-electric power generation), flow processes (e.g., overland, interflow, baseflow, channel flow), transport processes (e.g., sediments, nutrients, pathogens), and events (e.g., low-, flood-, and mean-flow conditions).
1378:
to characterize the unique aspects of the system being studied. These parameters can be obtained using laboratory and field studies, or estimated by finding the best correspondence between observed and modelled behavior. Between neighbouring catchments which have physical and hydrological
797:
are used to mathematically define the behavior of the system. Algebraic equations are likely often used for simple systems, while ordinary and partial differential equations are often used for problems that change in space in time. Examples of governing equations include:
194:
An early process analog model was an electrical network model of an aquifer composed of resistors in a grid. Voltages were assigned along the outer boundary, and then measured within the domain. Electrical conductivity paper can also be used instead of resistors.
425:
of interest, and are often constructed using entities (stores of water) and relationships between these entitites (flows or fluxes between stores). The conceptual model is coupled with scenarios to describe specific events (either input or outcome scenarios).
34:
is a simplification of a real-world system (e.g., surface water, soil water, wetland, groundwater, estuary) that aids in understanding, predicting, and managing water resources. Both the flow and quality of water are commonly studied using hydrologic models.
1107:
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Many real-world mathematical models are too complex to meet the simplifying assumptions required for an analytic solution. In these cases, the modeler develops a numerical solution that approximates the exact solution. Solution techniques include the
253:
The frequency of extremal events, such as severe droughts and storms, often requires the use of distributions that focus on the tail of the distribution, rather than the data nearest the mean. These techniques, collectively known as
1186:
1291:
995:
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The model combines continuity and storage-discharge equations, which yields an ordinary differential equation that describes outflow from each reservoir. The continuity equation for tank models is:
344:
statistical techniques of time series analysis. The questions that are answered through these techniques are often important for municipal planning, civil engineering, and risk assessments.
312:
diagrams are the most commonly used statistical regression model in the physical sciences, but there are a variety of models available from simplistic to complex. In a bivariate diagram, a
874:
1009:
537:
927:
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Abrahart, R. P., See, L. M., & Solomatine, D. P. (2008). Practical
Hydroinformatics: Computational Intelligence and Technological Developments in Water Applications. Springer.
657:
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Solomatine, D. P., & Siek, M. B. (2004). Application of machine learning techniques for rainfall-runoff modeling. Hydroinformatics: A wide range of technologies, 333-342.
730:
595:
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Valipour, M., Shahsavani, D., & Choubin, B. (2021). Data-driven modeling approaches for hydrological forecasting and prediction. Journal of
Hydrology, 597, 125996.
390:. The conceptual model is used as the starting point for defining the important model components. The relationships between model components are then specified using
2089:
Ouarda, Taha B. M. J.; Girard, Claude; Cavadias, George S.; Bobée, Bernard (2001-12-10). "Regional flood frequency estimation with canonical correlation analysis".
453:(or Nash model) is widely used for rainfall-runoff analysis. The model uses a cascade of linear reservoirs along with a constant first-order storage coefficient,
2877:
337:
model would be considered a “lag convolution”, because of the predicting of the “lag time” as water moves through the watershed using this method of modeling.
2159:
Marshall, R.J. (1980). "The estimation and distribution of storm movement and storm structure, using a correlation analysis technique and rain-gauge data".
2124:
Ribeiro-Corréa, J.; Cavadias, G.S.; Clément, B.; Rousselle, J. (1995). "Identification of hydrological neighborhoods using canonical correlation analysis".
437:
Model scope and complexity is dependent on modeling objectives, with greater detail required if human or environmental systems are subject to greater risk.
1125:
1739:
Zaharia, L. "L-MOMENTS AND THEIR USE IN MAXIMUM DISCHARGES'ANALYSIS IN CURVATURE CARPATHIANS REGION." Aerul si Apa. Componente ale
Mediului (2013): 119.
1306:
Exact solutions for algebraic, differential, and integral equations can often be found using specified boundary conditions and simplifying assumptions.
1200:
67:
1386:
is used to determine the ability of the calibrated model to meet the needs of the modeler. A commonly used measure of hydrologic model fit is the
2744:"Understanding Catchment-Scale Forest Root Water Uptake Strategies Across the Continental United States Through Inverse Ecohydrological Modeling"
1387:
604:
is a constant that indicates how quickly the reservoir drains; a smaller value indicates more rapid outflow. Combining these two equation yields
1580:
258:, provide a methodology for identifying the likelihood and uncertainty of extreme events. Examples of extreme value distributions include the
2801:"Assessment of spatial transferability of process-based hydrological model parameters in two neighbouring catchments in the Himalayan Region"
210:
that are commonly used in hydrology to describe data, as well as relationships between data. Using statistical methods, hydrologists develop
59:
that use equations to describe, predict, and manage hydrologic systems, analog models use non-mathematical approaches to simulate hydrology.
2237:
Larocque, M. (1998). "Contribution of correlation and spectral analyses to the regional study of a large karst aquifer (Charente, France)".
542:
which indicates that the change in storage over time is the difference between inflows and outflows. The storage-discharge relationship is:
270:. The standard method for determining peak discharge uses the log-Pearson Type III (log-gamma) distribution and observed annual flow peaks.
3196:
3156:
2870:
1913:
1646:
Wallis, James R. (1965-12-01). "Multivariate statistical methods in hydrology—A comparison using data of known functional relationship".
804:
is an algebraic equation that predicts stream velocity as a function of channel roughness, the hydraulic radius, and the channel slope:
1605:
298:. These techniques may be used in the identification of flood dynamics, storm characterization, and groundwater flow in karst systems.
941:
1525:
Lee, S.S.; Kim, J.S.; Kim, D.J. (2001). "Monitoring of drawdown pattern during pumping in an unconfined physical aquifer model".
1003:
describes solute movement in steady, one-dimensional flow using the solute dispersion coefficient and the groundwater velocity:
3324:
2863:
2689:"Do Energy-Based PET Models Require More Input Data than Temperature-Based Models? — An Evaluation at Four Humid FluxNet Sites"
1689:
Hamed, Khaled H. (2008-02-01). "Trend detection in hydrologic data: The Mann–Kendall trend test under the scaling hypothesis".
267:
17:
2489:
1841:
Bobee, B.; Perreault, L.; Ashkar, F. (1993-03-01). "Two kinds of moment ratio diagrams and their applications in hydrology".
1753:. International Series in Operations Research & Management Science. Springer International Publishing. pp. 149–164.
1335:
packages for hundreds of hydrologic purposes, such as surface water flow, nutrient transport and fate, and groundwater flow.
242:) are used to describe the information content of data. These moments can then be used to determine an appropriate frequency
3360:
3406:
305:
749:
2925:
2194:
Nathan, R. J.; McMahon, T. A. (1990-07-01). "Evaluation of automated techniques for base flow and recession analyses".
2589:"Multi-Step Calibration Approach for SWAT Model Using Soil Moisture and Crop Yields in a Small Agricultural Catchment"
1776:
1000:
1102:{\displaystyle D{\partial ^{2}C \over \partial x^{2}}-v{\partial C \over \partial x}={\partial C \over \partial t}}
295:
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810:
418:
279:
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describes steady, one-dimensional groundwater flow using the hydraulic conductivity and the hydraulic gradient:
466:
1409:
399:
395:
1749:
Vargo, Erik; Pasupathy, Raghu; Leemis, Lawrence M. (2017-01-01). Glen, Andrew G.; Leemis, Lawrence M. (eds.).
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1112:
83:
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describes time-varying, multidimensional groundwater flow using the aquifer transmissivity and storativity:
3257:
3211:
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3173:
3001:
1191:
888:
323:
457:, to predict the outflow from each reservoir (which is then used as the input to the next in the series).
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between observed variables, find trends in historical data, or forecast probable storm or drought events.
3201:
2042:"Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information"
1379:
similarities, the model parameters varies smoothly suggesting the spatial transferability of parameters.
879:
2799:
Nepal, Santosh; FlĂĽgel, Wolfgang-Albert; Krause, Peter; Fink, Manfred; Fischer, Christian (2017-07-30).
1984:
Helsel, Dennis R., and Robert M. Hirsch. Statistical methods in water resources. Vol. 49. Elsevier, 1992
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Archibald, J.A.; Buchanan, B.P.; Fuka, D.R.; Georgakakos, C.B.; Lyon, S.W.; Walter, M.T. (2014-07-01).
243:
87:
42:
MODFLOW, a computational groundwater flow model based on methods developed by the US Geological Survey.
2640:"A simple, regionally parameterized model for predicting nonpoint source areas in the northeastern US"
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transform methods are widely used to find analytic solutions to differential and integral equations.
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can be used for building conceptual models that are then populated using mathematical relationships.
421:) that relate hydrologic inputs to outputs. These components describe the important functions of the
1759:
99:
appropriate flow and transport behavior. This usually involves matching dimensionless ratios (e.g.,
3232:
3006:
1513:
Experimental design of physical aquifer models for evaluation of groundwater remediation strategies
450:
70:
that use comparable physics (e.g., electricity, heat, diffusion) to mimic the system of interest.
3086:
2506:
2410:
Haan, C. T.; Allen, D. M.; Street, J. O. (1976-06-01). "A Markov Chain Model of daily rainfall".
548:
327:
180:
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Matalas, N. C.; Reiher, Barbara J. (1967-03-01). "Some comments on the use of factor analyses".
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Salas, Jose D. Applied modeling of hydrologic time series. Water
Resources Publication, 1980.
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223:
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127:
Process analogs are used in hydrology to represent fluid flow using the similarity between
1794:"The utility of L-moment ratio diagrams for selecting a regional probability distribution"
1477:
Beven, Keith (1989). "Changing ideas in hydrology – The case of physically-based models".
8:
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The degree and nature of correlation may be quantified, by using a method such as the
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1973:
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Sharma, T. C. (1998-03-30). "An analysis of non-normal
Markovian extremal droughts".
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767:(Alpha), to indicate the dependence of this factor on storage (S) and discharge (q).
359:
313:
247:
52:
1827:
1181:{\displaystyle {\partial P \over \partial x}=-\mu {\partial \tau \over \partial y}}
330:
statistical procedures used to identify relationships between hydrologic variables.
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2708:
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Enemark, Trine; Peeters, Luk J.M.; Mallants, Dirk; Batelaan, Okke (February 2019).
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2014:
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290:. The degree of randomness or uncertainty in the model may also be estimated using
136:
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1710:
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Conceptual models are commonly used to represent the important components (e.g.,
319:
283:
100:
2855:
3370:
3334:
3181:
2983:
2664:
2639:
2352:
The London, Edinburgh, and Dublin
Philosophical Magazine and Journal of Science
2272:
Geological Survey (U.S.) (1950-01-01). "Geological Survey water-supply paper".
1286:{\displaystyle f(a)={\frac {1}{2\pi i}}\oint _{\gamma }{\frac {f(z)}{z-a}}\,dz}
128:
2575:
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1818:
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2439:
2332:
2281:
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2026:
1997:(1986-08-01). "Stochastic subsurface hydrology from theory to applications".
1921:
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104:
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2018:
1914:
10.1002/(sici)1099-1085(19980330)12:4<597::aid-hyp596>3.0.co;2-n
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PEEL, MURRAY C.; WANG, Q. J.; VOGEL, RICHARD M.; McMAHON, THOMAS A. (2001).
1667:
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2289:
2040:
Gupta, Hoshin Vijai; Sorooshian, Soroosh; Yapo, Patrice Ogou (1998-04-01).
1116:
347:
304:
is used in hydrology to determine whether a relationship may exist between
250:. Two common techniques include L-moment ratios and Moment-Ratio Diagrams.
132:
1936:
3044:
3016:
2768:
2743:
1994:
430:
379:
The Nash Model uses a cascade of linear reservoirs to predict streamflow.
340:
333:
164:
148:
110:
63:
2615:
2564:
2348:"LIII. On lines and planes of closest fit to systems of points in space"
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Instead of using a series of linear reservoirs, also the model of a
3123:
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1351:
239:
235:
2851:
http://drought.unl.edu/MonitoringTools/DownloadableSPIProgram.aspx
2587:
Musyoka, F.K; Strauss, P; Zhao, G; Srinivasan, R; Klik, A (2021).
1935:
Katz, Richard W; Parlange, Marc B; Naveau, Philippe (2002-08-01).
1370:
Observed and modelled runoff using the non-linear reservoir model.
735:
38:
3101:
2507:"Hydrogeological conceptual model building and testing: A review"
1343:
387:
160:
159:, respectively. The corresponding analogs to fluid potential are
156:
51:
Prior to the advent of computer models, hydrologic modeling used
2637:
1115:
describes laminar, steady, one-dimensional fluid flow using the
665:
1347:
422:
2504:
739:
The reaction factor Alpha increases with increasing discharge.
2943:
375:
2742:
Knighton, James; Singh, Kanishka; Evaristo, Jaivime (2020).
2586:
990:{\displaystyle T\nabla ^{2}h=S{\partial h \over \partial t}}
1355:
1194:
is an integral method for solving boundary value problems:
227:
152:
144:
429:
For example, a watershed model could be represented using
66:
that use miniaturized versions of the physical system and
2693:
JAWRA Journal of the
American Water Resources Association
2088:
2565:
Non-linear reservoir model for rainfall-runoff relations
2271:
669:
A non-linear reservoir used in rainfall-runoff modelling
119:
boundary conditions simulated using pumps and barriers.
1730:. Fort Collins, CO: Water resources publications, 1972.
2576:
Rainfall-runoff modelling using a non-linear reservoir
2798:
2741:
1430:"Physical models for classroom teaching in hydrology"
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Two general categories of analog models are common;
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1974:https://water.usgs.gov/osw/bulletin17b/dl_flow.pdf
1934:
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2687:Archibald, Josephine A.; Walter, M. Todd (2014).
316:or higher-order model may be fitted to the data.
3393:
2482:Environmental and Hydrological Systems Modelling
2686:
2409:
1636:. HYDROLOGIC ENGINEERING CENTER DAVIS CA, 1962.
759:in the above equation, that may also be called
55:to simulate flow and transport systems. Unlike
1569:https://books.google.com/books?isbn=0124200788
770:In the left figure the relation is quadratic:
763:, needs to be replaced by another symbol, say
3150:
2871:
2302:
2193:
1567:Principles of Soil and Plant Water Relations
114:A two-dimensional scale model of an aquifer.
2479:
869:{\displaystyle v={k \over n}R^{2/3}S^{1/2}}
95:conditions as needed to answer a question.
3157:
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2864:
532:{\displaystyle {dS(t) \over dt}=i(t)-q(t)}
3109:: Saltmod coupled to a groundwater model
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1728:Probability and statistics in hydrology
1338:Commonly used numerical models include
273:
27:Predicting and managing water resources
14:
3394:
3325:Construction and management simulation
2644:Journal of Hydrology: Regional Studies
2555:
1993:
1891:
1751:Computational Probability Applications
1645:
1604:: CS1 maint: archived copy as title (
1296:
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1937:"Statistics of extremes in hydrology"
1688:
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1427:
1388:Nash-Sutcliffe efficiency coefficient
922:{\displaystyle {\vec {q}}=-K\nabla h}
354:
198:
3361:List of computer simulation software
2274:Geological Survey Water-supply Paper
370:
143:. The analogs to fluid flow are the
1843:Stochastic Hydrology and Hydraulics
1301:
386:represent hydrologic systems using
306:independent and dependent variables
24:
2387:"Markov Chains explained visually"
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652:{\displaystyle K{dq \over dt}=i-q}
122:
25:
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2844:
1620:"Conductive Paper and Pen: PASCO"
406:. The model is then solved using
2926:Irrigation environmental impacts
1634:Statistical methods in hydrology
1362:Model calibration and evaluation
73:
46:
3289:Integrated assessment modelling
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1967:
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725:{\displaystyle q=i(1-e^{-t/k})}
419:features, events, and processes
280:Pearson correlation coefficient
1639:
1626:
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1561:
1518:
1505:
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1421:
1410:Soil and Water Assessment Tool
1259:
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898:
719:
689:
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400:partial differential equations
246:, which can then be used as a
13:
1:
2959:Drainage system (agriculture)
2886:Agricultural water management
2531:10.1016/j.jhydrol.2018.12.007
2259:10.1016/S0022-1694(97)00155-8
2111:10.1016/S0022-1694(01)00488-7
1961:10.1016/S0309-1708(02)00056-8
1798:Hydrological Sciences Journal
1711:10.1016/j.jhydrol.2007.11.009
1415:
1001:Advection-Dispersion equation
755:In such a model the constant
84:Mississippi River Basin Model
3258:Hydrological transport model
3212:Protein structure prediction
3207:Modelling biological systems
3002:Hydrological transport model
2748:Geophysical Research Letters
2181:10.1016/0022-1694(80)90063-3
2146:10.1016/0022-1694(95)02719-6
1769:10.1007/978-3-319-43317-2_12
1499:10.1016/0022-1694(89)90101-7
1331:methods, among many others.
324:principal component analysis
7:
3202:Metabolic network modelling
2480:Jayawardena, A. W. (2014).
1941:Advances in Water Resources
1393:
590:{\displaystyle q(t)=S(t)/K}
10:
3423:
3407:Water resources management
3315:Business process modelling
3030:Groundwater energy balance
2665:10.1016/j.ejrh.2014.06.003
217:
88:US Army Corps of Engineers
3348:
3302:
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3187:Chemical process modeling
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3080:Agro-hydro-salinity group
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2364:10.1080/14786440109462720
1819:10.1080/02626660109492806
1455:10.5194/hess-16-3075-2012
1400:Hydrological optimization
933:Groundwater flow equation
268:generalized extreme value
3233:Chemical transport model
3197:Infectious disease model
3107:SahysMod polygonal model
3102:SaltMod integrated model
3007:Runoff model (reservoir)
2412:Water Resources Research
2305:Water Resources Research
2196:Water Resources Research
2046:Water Resources Research
1999:Water Resources Research
1648:Water Resources Research
1515:(Doctoral dissertation).
1428:Rodhe, A. (2012-09-03).
3087:Hydrology (agriculture)
2432:10.1029/WR012i003p00443
2325:10.1029/WR003i001p00213
2216:10.1029/WR026i007p01465
2019:10.1029/WR022i09Sp0135S
1668:10.1029/WR001i004p00447
1434:Hydrol. Earth Syst. Sci
212:empirical relationships
181:electrical conductivity
3040:Hydraulic conductivity
2805:Hydrological Processes
1894:Hydrological Processes
1527:Hydrological Processes
1511:Humphrey, M.D., 1992.
1371:
1287:
1182:
1103:
991:
923:
870:
740:
726:
670:
662:and has the solution:
653:
591:
533:
451:linear-reservoir model
380:
256:extreme value analysis
177:hydraulic conductivity
115:
91:
43:
18:Hydrological modelling
3366:Mathematical modeling
3310:Biopsychosocial model
3097:Leaching model (soil)
3092:Soil salinity control
2921:Irrigation management
2916:Irrigation statistics
1369:
1288:
1183:
1104:
992:
924:
871:
738:
727:
668:
654:
592:
534:
378:
189:diffusion coefficient
113:
81:
41:
3320:Catastrophe modeling
3166:Scientific modelling
2984:Surface water/runoff
2769:10.1029/2019GL085937
2754:(1): e2019GL085937.
2511:Journal of Hydrology
2239:Journal of Hydrology
2161:Journal of Hydrology
2126:Journal of Hydrology
2091:Journal of Hydrology
1691:Journal of Hydrology
1479:Journal of Hydrology
1405:Scientific modelling
1374:Physical models use
1201:
1126:
1010:
942:
889:
811:
750:non-linear reservoir
677:
611:
549:
467:
274:Correlation analysis
185:thermal conductivity
3263:Modular Ocean Model
3061:Acid sulphate soils
2936:Subsurface drainage
2817:2017HyPr...31.2812N
2760:2020GeoRL..4785937K
2705:2014JAWRA..50..497A
2656:2014JHyRS...1...74A
2523:2019JHyd..569..310E
2424:1976WRR....12..443H
2346:Pearson, K (1901).
2317:1967WRR.....3..213M
2251:1998JHyd..205..217L
2208:1990WRR....26.1465N
2173:1980JHyd...48...19M
2138:1995JHyd..173...71R
2103:2001JHyd..254..157O
2058:1998WRR....34..751G
2011:1986WRR....22R.135G
1953:2002AdWR...25.1287K
1947:(8–12): 1287–1304.
1906:1998HyPr...12..597S
1855:1993SHH.....7...41B
1810:2001HydSJ..46..147P
1703:2008JHyd..349..350H
1660:1965WRR.....1..447W
1539:2001HyPr...15..479L
1491:1989JHyd..105..157B
1446:2012HESS...16.3075R
1297:Solution algorithms
795:Governing equations
790:Governing equations
392:algebraic equations
302:Regression analysis
57:mathematical models
3356:Data visualization
3340:Input–output model
3253:Hydrological model
3243:Geologic modelling
2997:Hydrological model
2964:Watertable control
2901:Surface irrigation
2713:10.1111/jawr.12137
2391:Explained Visually
1863:10.1007/BF01581566
1372:
1283:
1178:
1099:
987:
919:
866:
802:Manning's equation
741:
722:
671:
649:
587:
529:
404:integral equations
381:
360:Data-driven models
355:Data-driven models
232:standard deviation
208:mathematical model
204:Statistical models
199:Statistical models
175:). The analogs to
173:chemical potential
116:
92:
44:
3389:
3388:
3268:Wildfire modeling
3248:Groundwater model
3228:Atmospheric model
3132:
3131:
3035:Groundwater model
2992:Contour trenching
2974:Drainage by wells
2969:Drainage research
2954:Drainage equation
2826:10.1002/hyp.11199
2811:(16): 2812–2826.
2606:10.3390/w13162238
2491:978-0-415-46532-8
2484:. US: CRC Press.
2067:10.1029/97WR03495
2005:(9S): 135S–145S.
1724:Yevjevich, Vujica
1325:finite-difference
1274:
1235:
1192:Cauchy's integral
1176:
1147:
1097:
1074:
1048:
985:
901:
828:
635:
497:
388:physical concepts
384:Conceptual models
371:Conceptual models
296:residual analysis
248:probability model
187:, and the solute
16:(Redirected from
3414:
3402:Hydrology models
3381:Visual analytics
3376:Systems thinking
3294:Population model
3159:
3152:
3145:
3136:
3135:
3025:Groundwater flow
2911:Tidal irrigation
2880:
2873:
2866:
2857:
2856:
2839:
2838:
2828:
2796:
2790:
2789:
2771:
2739:
2733:
2732:
2684:
2678:
2677:
2667:
2635:
2629:
2628:
2618:
2608:
2584:
2578:
2573:
2567:
2562:
2553:
2552:
2542:
2502:
2496:
2495:
2477:
2471:
2468:
2462:
2459:
2453:
2450:
2444:
2443:
2407:
2401:
2400:
2398:
2397:
2383:
2377:
2374:
2368:
2367:
2343:
2337:
2336:
2300:
2294:
2293:
2269:
2263:
2262:
2245:(3–4): 217–231.
2234:
2228:
2227:
2202:(7): 1465–1473.
2191:
2185:
2184:
2156:
2150:
2149:
2121:
2115:
2114:
2097:(1–4): 157–173.
2086:
2080:
2079:
2069:
2037:
2031:
2030:
1991:
1985:
1982:
1976:
1971:
1965:
1964:
1932:
1926:
1925:
1889:
1883:
1882:
1838:
1832:
1831:
1821:
1789:
1783:
1782:
1762:
1746:
1740:
1737:
1731:
1721:
1715:
1714:
1697:(3–4): 350–363.
1686:
1680:
1679:
1643:
1637:
1630:
1624:
1623:
1616:
1610:
1609:
1603:
1595:
1593:
1592:
1583:. Archived from
1577:
1571:
1565:
1559:
1558:
1522:
1516:
1509:
1503:
1502:
1485:(1–2): 157–172.
1474:
1468:
1467:
1457:
1440:(9): 3075–3082.
1425:
1302:Analytic methods
1292:
1290:
1289:
1284:
1275:
1273:
1262:
1248:
1246:
1245:
1236:
1234:
1220:
1187:
1185:
1184:
1179:
1177:
1175:
1167:
1159:
1148:
1146:
1138:
1130:
1113:Poiseuille's law
1108:
1106:
1105:
1100:
1098:
1096:
1088:
1080:
1075:
1073:
1065:
1057:
1049:
1047:
1046:
1045:
1032:
1028:
1027:
1017:
996:
994:
993:
988:
986:
984:
976:
968:
957:
956:
928:
926:
925:
920:
903:
902:
894:
875:
873:
872:
867:
865:
864:
860:
847:
846:
842:
829:
821:
731:
729:
728:
723:
718:
717:
713:
658:
656:
655:
650:
636:
634:
626:
618:
596:
594:
593:
588:
583:
538:
536:
535:
530:
498:
496:
488:
471:
439:Systems modeling
351:dam management.
32:hydrologic model
21:
3422:
3421:
3417:
3416:
3415:
3413:
3412:
3411:
3392:
3391:
3390:
3385:
3344:
3298:
3284:Energy modeling
3272:
3216:
3192:Ecosystem model
3168:
3163:
3133:
3128:
3112:
3075:
3049:
3011:
2978:
2930:
2906:Drip irrigation
2887:
2884:
2847:
2842:
2797:
2793:
2740:
2736:
2685:
2681:
2636:
2632:
2585:
2581:
2574:
2570:
2563:
2556:
2503:
2499:
2492:
2478:
2474:
2469:
2465:
2460:
2456:
2451:
2447:
2408:
2404:
2395:
2393:
2385:
2384:
2380:
2375:
2371:
2358:(11): 559–572.
2344:
2340:
2301:
2297:
2270:
2266:
2235:
2231:
2192:
2188:
2157:
2153:
2122:
2118:
2087:
2083:
2038:
2034:
1995:Gelhar, Lynn W.
1992:
1988:
1983:
1979:
1972:
1968:
1933:
1929:
1890:
1886:
1839:
1835:
1790:
1786:
1779:
1760:10.1.1.295.9820
1747:
1743:
1738:
1734:
1722:
1718:
1687:
1683:
1644:
1640:
1631:
1627:
1618:
1617:
1613:
1597:
1596:
1590:
1588:
1581:"Archived copy"
1579:
1578:
1574:
1566:
1562:
1547:10.1002/hyp.162
1523:
1519:
1510:
1506:
1475:
1471:
1426:
1422:
1418:
1396:
1364:
1320:
1318:Numeric methods
1304:
1299:
1263:
1249:
1247:
1241:
1237:
1224:
1219:
1202:
1199:
1198:
1168:
1160:
1158:
1139:
1131:
1129:
1127:
1124:
1123:
1089:
1081:
1079:
1066:
1058:
1056:
1041:
1037:
1033:
1023:
1019:
1018:
1016:
1011:
1008:
1007:
977:
969:
967:
952:
948:
943:
940:
939:
893:
892:
890:
887:
886:
856:
852:
848:
838:
834:
830:
820:
812:
809:
808:
792:
761:reaction factor
709:
702:
698:
678:
675:
674:
627:
619:
617:
612:
609:
608:
579:
550:
547:
546:
489:
472:
470:
468:
465:
464:
373:
357:
320:Factor analysis
284:autocorrelation
276:
220:
201:
125:
123:Process analogs
101:Reynolds number
76:
68:process analogs
49:
28:
23:
22:
15:
12:
11:
5:
3420:
3410:
3409:
3404:
3387:
3386:
3384:
3383:
3378:
3373:
3371:Systems theory
3368:
3363:
3358:
3352:
3350:
3349:Related topics
3346:
3345:
3343:
3342:
3337:
3335:Economic model
3332:
3327:
3322:
3317:
3312:
3306:
3304:
3300:
3299:
3297:
3296:
3291:
3286:
3280:
3278:
3277:Sustainability
3274:
3273:
3271:
3270:
3265:
3260:
3255:
3250:
3245:
3240:
3235:
3230:
3224:
3222:
3218:
3217:
3215:
3214:
3209:
3204:
3199:
3194:
3189:
3184:
3182:Cellular model
3178:
3176:
3170:
3169:
3162:
3161:
3154:
3147:
3139:
3130:
3129:
3127:
3126:
3120:
3118:
3117:Related topics
3114:
3113:
3111:
3110:
3104:
3099:
3094:
3089:
3083:
3081:
3077:
3076:
3074:
3073:
3068:
3063:
3057:
3055:
3051:
3050:
3048:
3047:
3042:
3037:
3032:
3027:
3021:
3019:
3013:
3012:
3010:
3009:
3004:
2999:
2994:
2988:
2986:
2980:
2979:
2977:
2976:
2971:
2966:
2961:
2956:
2951:
2946:
2940:
2938:
2932:
2931:
2929:
2928:
2923:
2918:
2913:
2908:
2903:
2897:
2895:
2889:
2888:
2883:
2882:
2875:
2868:
2860:
2854:
2853:
2846:
2845:External links
2843:
2841:
2840:
2791:
2734:
2699:(2): 497–508.
2679:
2630:
2579:
2568:
2554:
2497:
2490:
2472:
2463:
2454:
2445:
2418:(3): 443–449.
2402:
2378:
2369:
2338:
2311:(1): 213–223.
2295:
2264:
2229:
2186:
2167:(1–2): 19–39.
2151:
2132:(1–4): 71–89.
2116:
2081:
2052:(4): 751–763.
2032:
1986:
1977:
1966:
1927:
1900:(4): 597–611.
1884:
1833:
1804:(1): 147–155.
1784:
1777:
1741:
1732:
1716:
1681:
1654:(4): 447–461.
1638:
1632:Beard, Leo R.
1625:
1611:
1572:
1560:
1533:(3): 479–492.
1517:
1504:
1469:
1419:
1417:
1414:
1413:
1412:
1407:
1402:
1395:
1392:
1363:
1360:
1329:finite-element
1319:
1316:
1303:
1300:
1298:
1295:
1294:
1293:
1282:
1279:
1272:
1269:
1266:
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1040:
1036:
1031:
1026:
1022:
1015:
998:
997:
983:
980:
975:
972:
966:
963:
960:
955:
951:
947:
930:
929:
918:
915:
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909:
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897:
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863:
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791:
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569:
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492:
487:
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372:
369:
356:
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275:
272:
219:
216:
206:are a type of
200:
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124:
121:
82:Detail of the
75:
72:
48:
45:
26:
9:
6:
4:
3:
2:
3419:
3408:
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3400:
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3354:
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3336:
3333:
3331:
3330:Crime mapping
3328:
3326:
3323:
3321:
3318:
3316:
3313:
3311:
3308:
3307:
3305:
3301:
3295:
3292:
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3287:
3285:
3282:
3281:
3279:
3275:
3269:
3266:
3264:
3261:
3259:
3256:
3254:
3251:
3249:
3246:
3244:
3241:
3239:
3238:Climate model
3236:
3234:
3231:
3229:
3226:
3225:
3223:
3221:Environmental
3219:
3213:
3210:
3208:
3205:
3203:
3200:
3198:
3195:
3193:
3190:
3188:
3185:
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3141:
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3119:
3115:
3108:
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3098:
3095:
3093:
3090:
3088:
3085:
3084:
3082:
3078:
3072:
3069:
3067:
3064:
3062:
3059:
3058:
3056:
3054:Problem soils
3052:
3046:
3043:
3041:
3038:
3036:
3033:
3031:
3028:
3026:
3023:
3022:
3020:
3018:
3014:
3008:
3005:
3003:
3000:
2998:
2995:
2993:
2990:
2989:
2987:
2985:
2981:
2975:
2972:
2970:
2967:
2965:
2962:
2960:
2957:
2955:
2952:
2950:
2949:Tile drainage
2947:
2945:
2942:
2941:
2939:
2937:
2933:
2927:
2924:
2922:
2919:
2917:
2914:
2912:
2909:
2907:
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2902:
2899:
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2890:
2881:
2876:
2874:
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2827:
2822:
2818:
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2541:
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2532:
2528:
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2516:
2512:
2508:
2501:
2493:
2487:
2483:
2476:
2467:
2458:
2449:
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2421:
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2413:
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2155:
2147:
2143:
2139:
2135:
2131:
2127:
2120:
2112:
2108:
2104:
2100:
2096:
2092:
2085:
2077:
2073:
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167:, and solute
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137:Fourier's law
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105:Froude number
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292:stochastics
165:temperature
149:electricity
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3396:Categories
3174:Biological
3045:Watertable
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2540:2328/38835
2396:2017-04-21
1591:2017-05-01
1416:References
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1376:parameters
408:analytical
141:Fick's law
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236:skewness
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