Anatomy of Hydrology

Water

Quantity Quality

Phase of water

Liquid Vapor Snow + Ice 4th phase

Domain

Space Time Frequency

Processes

Occurrence Distribution Movement Storage

Scale

Meso Macro MegaMicro

Place of occurrence

Land surface Unsaturated zone Saturated zone

Historical Perspective

The Beginning Years: Development of Component Models

• Surface Runoff Modeling Rational Method (Mulvany, 1850; Imbeau, 1892)

Unit Hydrograph Method (Sherman, 1932)

Overland Flow Analysis (Keulegan, 1944; Izzard, 1944)

Unit Hydrograph Theory (Nash, 1957; Dooge, 1959)

Geomorphologic Unit Hydrograph (Rodriguez-Iturbe and Valdez, 1979)

Kinematic Wave Theory (Lighthill and Whitham, 1955)

Snow Modeling

Snow Hydrology (U.S. Army Corps of Engineers, 1956)

Kinematic Wave Theory (Colback, 1972, 1974)

Historical Perspective (contd.) Subsurface Flow Modeling

Subsurface Flow Mechanism (Lowdermilk, 1934; Hursh, 1936; Hursh and Brater, 1944; Hoover and Hursh, 1944; Hursh, 1944; Roessel, 1950; Hewlett, 1961; Nielsen et al., 1959; Remson, et al., 1960)

Determination of Storm Runoff Amount SCS-CN Method (1956)

Theory of Infiltration Green-Ampt Model (1911)

Kostiakov Model (1932)

Horton Model (1933)

Theory of Evaporation Energy Method (Richardson, 1931; Cummings, 1935)

Combination Method (Penman, 1948)

Historical Perspective (contd.) Determination of Abstractions

Interception (Horton, 1919)

Detention and Depression Storage (SCS, 1956)

Groundwater and Base Flow

Darcy Equation (1854)

Hydraulic Conductivity Relation (Fair and Hatch, 1933)

Well Response to Pumping (Theis, 1935)

Correlation between Ground Water and Precipitation (Jacob, 1943, 1944)

Streamflow Generation

Horton Mechanism-Rainfall Excess (Horton, 1933)

Subsurface flow mechanism (Lowdermilk, 1934; Hursh, 1934)

Saturation excess: Dune-Black Mechanism (Dunne and Black, 1970)

Variable Area concept (Betson, 1964; Hewlett and Hibbert, 1967)

Macropore and preferential flow paths (Beven and Germann, 1982)

Historical Perspective (Contd.)• Reservoir Routing

Puls Method (USACOE, 1928)

Modified Puls Method (USBR, 1949)

• Channel Routing

Muskingum Method (McCarthy, 1934-35)

Modified Puls Method (USBR, 1949)

Diffusion wave method (Hayami, 1951; Lighthill and Whitham, 1955)

• Watershed Sediment yield

Universal Soil Loss Equation (Wischmeier and Smith, 1960)

Delivery Ratio (Dendy, 1968)

Sediment Rating Curve (Campbell and Bauder, 1940)

Sediment Unit Graph (Rendon-Herrero, 1974; Williams, 1978)

Kinematic Wave Theory (Hjelmfelt, 1975; Singh, 1983; Singh and Regl, 1983)

•

Historical Perspective (Contd.) Sediment transport

Duboys Bed Load Equation (DuBoys, 1879)

Einstein’s Bed Load Formula (Einstein, 1950)

Schoklitsch equation (Schoklitsch, 1962)

Yalin’s Transport Capacity Equation (Yalin, 1963)

Yang’s Total Load Formula (Yang, 1972)

Solute Transport

Fick’s Law (in Jacob, 972)

Isotherm (Langmuir, 1915, 1918; Freundlich, 1926)

Advection-Dispersion Equation

Historical Perspective (Contd.) Basin geomorphology

Law of Geometric Similarity (Strahler, 1958)

Horton-Strahler ordering scheme (Horton, 1945; Strahler, 1952)

Horton’s laws of stream numbers and lengths (Horton, 1945)

Schumm’s law of stream areas (Schumm, 1954, 1956)

Yang’s law of stream slope (Yang, 1971)

Length-area relation (Gray, 1961)

Law of basin relief (Maxwell, 1960)

Law of drainage density (Horton, 1945)

Hyposometric curve (Strahler, 1952)

Watershed Models

Models of Hydrologic Cycle

Stanford Watershed Model (Now BASIN) (Crawford and Linsley, 1966)

Examples of Models

HSPF-IV (Bicknell et al., 1993)

USDA-HL Model (Holtan et al., 1974)

PRMS (Leavesley et al., 1983)

NWS-RFS (Burnash et al., 1973)

Watershed Models (contd.)

SSARR (Rockwood, 1982)

SWMM (Metcalf and Eddy et al., 1971): Urban watersheds

HEC-HMS (U. S. Army Corps of Engineers, 1999)

KINEROS (Woolhiser et al, 1990)

ANSWERS (Beasley et al., 1977)

CREAMS (USDA, 1980)

EPIC (Williams, 1995)

SWRRB (Williams, 1995)

SPUR (Carlson et al., 1995)

AGNPS (Young et al., 1995)

WATFLOOD (Kouwen et al., 1993)

UBC (Quick, 1995)

SHE (Abbott et al., 1986)

TOPMODEL (Beven, 1995)

IHDM (Calver and Wood, 1995)

SHETRAN (Ewen et al., 2000)

Watershed Models (contd.) WBNM (Boyd et al., 1979)

RORB (Laurenson and Mein, 1995)

THALES (Grayson et al., 1995)

LASCAM (Sivapalan et al., 1996)

Tank Model (Sugawara, 1975)

Xinanjiang Model (Zhao et al., 1980)

HBV Model (Bergstrom, 1976)

ARNO Model (Todini, 1988)

TOPIKAPI Model (Todini, 1995)

HYDROTEL (Fortin et al., 2001)One of the limitations of many of these models (with the exception of a few) is that

they do not invoke the power of GIS technology. After all, watershed hydrology is aspatial science and GIS technology offers a tremendous power of combininganalytical or computational component of the modelling with spatially distributednature of the science which is at the very heart of hydrology of large areas.

Watershed Models (contd.)

WBNM (Boyd et al., 1979)

VIC-2L (Variable Infiltration Capacity – Two soil Layers) model (Wood et al., 1992; Liang et al., 1994).

ARNO model (Todini, 1996)

SWAT (Soil and Water Assessment Tool) model (Arnold et al., 1998; 1999; Arnold and Fohrer, 2005),

Macro-PDM (Arnell, 1999),

TOPKAPI (topographic kinematic approximations and integration) model (Liu and Todini, 2002), and

BTOP model (Takeuchi et al., 2007)

SWAM (Mukhopadhyay and Dutta, 2010)

Evolution of Hydrologic Models: Stanford Watershed Model (Donigian, 2002)

STANFORD WATERSHED MODEL

To Stream

Actual ET

Potential ET

Precipitation

Temperature

Radiation

Wind,Dewpoint

Snowmelt

Interception

Storage

Lower Zone

Storage

Groundwater

Storage

InterflowUpper Zone

Storage

Overland Flow

Deep or Inactive

Groundwater

CEPSC*

BASETP*

AGWETP*

DEEPFR*

LZSN*

INFILT*

INTFW*UZSN*

AGWRC*

NSUR*SLSUR*LSUR*

IRC*

Delayed Infiltration

Direct

Infiltration

PERC

1 ET

2 ET

3 ET

4 ET

5 ET

LZETP*

* Parameters

Output

Process

Input

Storage

ET - Evapotranspiration

n Order taken to

meet ET demand

Decision

Evolution in Mechanistic Modeling: Stanford Watershed Model (Donigian, 2002)

1960

1965

1970

1975

1980

1985

1990

1995

2000

MODEL SCIENCECOMPUTER

TECHNOLOGY

MODELS &

SUPPORT TOOLS

WETLANDS

SEDIMENT

& NUTRIENTS

FOREST N

WATERSHED

HYDROLOGY

WATERSHED

WATER QUALITY

NEEDS &

RESEARCH

DEPOSITION

LEGISLATION

CWA

TMDLs

FWPCA

PL 92-500

DIGITAL

COMPUTER

PERSONAL

COMPUTER

RELATIONAL

DBs

WINDOWS 95

MINI-

COMPUTER

EXPERT SYSTEMS,

DSS & GIS

WATER

QUALITY ACT

EPA CREATED

BASINS 1.0

STANFORD

WATERSHED

MODEL

BASINS 2.0,GenScn,

HSPFParm, WDMUtil, WInHSPF

BASINS 3.0, HSPF 12.0

HSPF 8.0

HSPF 5.0, CREAMS

HSPEXP

PTR,

ARM,

NPS

ANNIE/WDM

HSP, SWMM

HSPX (WQ),

HSPX UTILITY

EPA

WATERSHED

APPROACH

CWA AMENDs

SS WQ MODELS

SEDIMENT

TRANSPORT

1st GIS/WATER

RESOURCES CONF

SW/GW

INTERACTION

3.0 GHz

1.0 GHz

200 MHz

120 MHz

5 MHz

20 MHz

40 MHz

80 MHz

0.2 MHz

WINDOWS XP

Watershed Models1. Singh, V.P., editor, Computer Models of Watershed Hydrology, Water Resources

Publications, Littleton, Colorado, 1130 pp., 1995.

2. Singh, V.P. and Frevert, D.K., editors, Mathematical Modeling of Small Watershed Hydrology and Applications. Water Resources Publications, Littleton, Colorado, 950 pp., 2002.

3. Singh, V.P. and Woolhiser, D. A., Mathematical Modeling of Watershed Hydrology. Journal of Hydrologic Engineering, ASCE, Vol. 7, No. 4, pp. 270-292, 2002

4. Singh, V.P., and Frevert, D.K., editors, Mathematical Modeling of Large Watershed Hydrology. Water Resources Publications, Littleton, Colorado, 891 pp., 2002.

5. Singh, V.P. and Frevert, D.K., Watershed Modeling. Proceedings, World Water & Environmental Resources Congress 2003, ASCE, June 23-26, Philadelphia, 2003.

6. Singh, V.P. and Frevert, D.K., editors, Watershed Models. CRC Press, Boca Raton, Florida, 2006.

5. Singh, V.P., Frevert, D.K., Rieker, J.D., Leverson, V., Meyer, S., and Meyer, S., The Hydrologic Modeling Inventory-A Cooperative Research Effort. Journal of Irrigation and Drainage Engineering, ASCE, Vol. 132, No. 2, pp. 98-103, 2006.

7. Singh, V.P., Hydrologic Modeling: Progress and Future Directions. Geoscience Letters, Vol.5, No. 15, pp. 5-15, 2018

Recent Advances

Hydrologic Data Needs Hydrometeorologic

Topographic

Geomorphologic

Pedologic

Land Use

Lithologic

Hydraulic

Hydrologic Data Acquisition

Remote Sensing

Satellite Technology

Radar Technology

Digital Terrain and Elevation Models

Chemical Tracers

Data Processing and Management Geographical Information Systems (GIS)

Data Base Management Systems (DBMS)

Storm Movement

Spatial Variability

Temporal Variability

Rainfall Field Description

Rainfall Forecasting

Precipitation Variability

Variability in Watershed Characteristics

Spatial Variability of Hydraulic Roughness Effect on Runoff Dynamics and Hydrograph

Formation of Shocks

Spatial Variability of Infiltration Hydraulic Conductivity

Steady Infiltration

Mean Infiltration

Effect on Runoff Hydrograph

Scaling and Variability

. Spatial Scaling. Spatial Heterogeneity in Watershed Characteristics

. Spatial Variability in Processes

. Physical Spatial Size . Representative Elementary Area

. Hydrologic Response Units

. Computational Grid Size

. Temporal Scaling. Time Interval of Observations

. Computational Grid Size

. Temporal Variability of Processes

Model Calibration Parameter Estimation Algorithm

Objective Function

Optimization Algorithm

Termination Criteria

Calibration Data

Handling Data Errors

Determination of Data Needs-quantity and Information-richness

Representation of Uncertainty of Calibrated Model

Artificial Neural Networks

Linking Hydrologic Models Geochemistry

Environmental Biology

Environmental Chemistry

Earth Sciences

Meteorology

Climatology

Oceanography

Social Sciences

Economics

Decision Making

Emerging Tools

Tools for Hydrologic Modeling, Water Resources System Analysis and Decision Making

• Mechanistic models

• Data mining models

• Uncertainty analysis

• Entropy theory

• Risk analysis

• Multivariate stochastic analysis (copula theory)

• Intelligent systems (ANN, Fuzzy, etc.)

• Optimization algorithms

• Decision support systems

• GIS

• Data collection and mining

Hydrologic Modeling

Challenges• Hydrologic

• More data at finer spatial resolutions

• Regional scale models

• Uncertainty analysis

• Long-term forecasting (ahead of time)

• PMP and PMF

• Integration with biogeochemnical models

• Integration with climate models as well as with ecosystems models: Climate change and global warming

• Coupling for decision making issues (social, political, economic, environmental, etc.)

Model Calibration Parameter Estimation Algorithm

Objective Function

Optimization Algorithm

Termination Criteria

Calibration Data

Handling Data Errors

Determination of Data Needs-quantity and Information-richness

Representation of Uncertainty of Calibrated Model

Artificial Neural Networks

Grand Challenges

Life’s Security

Water Security

Food security

Energy security

Environmental security

Ecological security

Health security

Environmental Sustainability• Sustainability of environment• Sustainability of our way of life• Sustainability of society/human civilization• Constraints

• Population rise

• Demographic changes

• Climate change

• Land use change

• Urbanization

• Rising standard of living

• Rising human expectations

• Rising energy demand

• Food security

Problems in Development of River Basin Scale Hydrologic Models

The greatest impediment is the availability of high quality data on hydrometeorology, land use and land cover, soil types and their hydrologic characteristics, climatic variables, topography, and stream flows.

The impediments are present to various degrees in various regions.

For example, if one wants to develop a hydrologic model of any river basin in the United States most of the data that are required can be obtained (free and open access)

Problems in Development of River Basin Scale Hydrologic Models (contd.)

High quality means high spatial and temporal resolutions and with high reliability.

Reliability means data that can be verified for their accuracy from field and station data, which, in turn, again leads to the question of availability, open and free access to the data.

Some countries pose severe limitations to the dissemination of such information which has caused great hindrance to the development of reliable hydrologic models of large watersheds.

Prospects for Development of River Basin Scale Hydrologic Models (contd.) The impediments are being gradually removed with

the development and refinement of globally gridded data sets.

Such data sets are expected to evolve further in the future providing opportunities for researchers in various fields to investigate various issues related to water availability, water management, climate change, etc.

Such investigations will either support or challenge the various posits given in various agency and governmental reports whose peer-review process can sometimes be questioned

Hydrologic Modeling

Challenges

• User friendly modeling

• Model choices and credibility

• Scoping out hydrology

Where do We Go from Here?

Future Outlook

Increasing Societal Demand for Models

Increasing Emphasis on Linking Models to Environmental and Eco-systems models

Emphasis on User-friendliness

Incorporation of Information Technology, Computer-based Design, Artificial Intelligence, and Space Technology

Model Uncertainty and Reliability

Model Competitiveness

Reexamination/Reflection

Water-Energy-Food Security System

Partnership among academia, government sector, NGOs, and private sector

Educational System

Train the trainers

Teach the teachers

Where are we headed as a society? Development to management

Paradigm shift

Scientific and technological progress Social progress: Value system Changes in global demographic landscape Human nature

Conflicts and wars Competition Convergence or divergence

Integration of engineering, technology, socio-economic-political science

SUMMATION

Taking stock of current situation Recognition of social and cultural constraints Taking account of looming global changes Re-examination and re-evaluation of our social values

and value systems Future can be bright or dark-all in our hands