An Najah National University

Faculty of Graduate Studies

Hydrologic Simulation
Models
Prepared by
Majeda Alhaj-Hussein
Submitted to
Dr. Anan Jayyousi

This report submitted as a semester-research project for

Hydrological systems and processes
course (461641),
in Master of Water and Environmental Engineering
Abstract

The conception of modeling in hydrology is involved with relationships of water,

climate, soil and land use. Moreover, hydrological models include temporal and spatial
features. Behavior of each feature controlled by its own and therefore it makes a vast
variety for types of hydrological models. Hydrological models are the main tools for
hydrologists with different purposes to use such as water resource management, ground
water modeling, urban and rural watershed management and so on. Many hydrological
models have been developed and refined during the past four decades and it is required
to fully understand their characteristics to effortlessly employ them. Therefore,
hydrologists need to familiarize themselves with the classification of hydrological
models and understand the theoretical definition behind them. However, in regard to
this issue, only a few discrete studies had been done. Classification of hydrological
models is not exact and different hydrologist may give different definitions. The reason
is that the nature of models is often the same but many models have overlapping
characteristics. Thus, this study was aimed at showing the dominant classifications for
hydrological models alongside the different views from past to present but generally,
they have common meaning even though they may be classified under different
categories. In addition, although there are overlapping features in different hydrological
models, their nature is not that hard to understand.

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Introduction

Advances in computer science combined with larger and more extensive hydrologic
data-monitoring efforts allowed for the development and application of a number of
models in hydrology. These computer models can be used for a variety of purposes in
simulating hydrologic response under a number of assumptions within a watershed
area.
Since the core of the management of water resources is the development and
implementation of optimal measures, it is quite essential to predict the impact of these
measures to identify the optimal ones Impact prediction implies modeling. These
predictions of what will happen are either made based on very qualitative information
and beliefs in peoples’ heads or at least in part, on quantitative information provided by
mathematical or computer-based models. The quantitative mathematical models are
considered essential for carrying out assessment pertaining to the different aspects of
the management of water resources. Thus, the mathematical simulation models provide
means by which decision makers, planners, and managers can predict the behavior of
the water resources system design and related policies before implementation. These
models used several equations to describe hydrologic transport processes, storages, and
to account for water balances in space and time. Complex rainfall patterns and
heterogeneous basins can be easily simulated if watershed and hydrologic information
are sufficient, and various design and control schemes can be tested with hydrologic
models such as HEC, HEC-HMS. Hydrologic models allow for hydrologic prediction
in space and time through the use of well-known numerical methods.

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What is the Hydrologic Simulation Models?
The conception of modeling in hydrology is involved with relationships of water,
climate, soil and land use. Moreover, hydrological models include temporal and spatial
features.

A hydrologic model is a simplification of a real-world system (e.g., surface water, soil

water, wetland, groundwater, estuary) that helps in understanding, predicting, and
managing water resources. Both the flow and quality of water are commonly studied
using hydrologic models, another way hydrologic model can be defined as: "The
characterization of real hydrologic features and system by the use of small-scale
physical models, mathematical analogues, and computer simulations".

Figure1:Hydrological models

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Why To Model?

Hydrologic modeling is used to answer environmental transport questions where

water excess, scarcity, or dissolved or solid content is of primary importance (Burges,
1986). Because of the nature of environmental predictions, there is no single best
model. Rather, there are many plausible solutions, depending on purpose and needed
complexity. For this reason and others, the practice of hydrologic modeling has, in
general, included too much reliance on mathematics at the expense of true knowledge,
and suffers from a need for more evaluation of appropriateness. Models are essential
in performing complex analyses and in making informed predictions, and allows more
effective use of the available data; more complexities can be accounted for; and the
implications of the management decisions can be evaluated.

So, one might ask the question: Why model at all? Flowing water has shaped the
surface of Earth. As a liquid or a solid, the motion of water across the land surface
causes erosion and sedimentation, lowers the height of mountains, and adds new land
in river deltas. As a liquid, water reacts chemically with rock to produce soil through
the process of weathering. Liquid water carries dissolved elements, nutrients,
contaminants, and sediment in ways that affect all life on earth. Those interested in
predicting the effects of the motion of water, particularly liquid water on or under the
earth's surface will typically seek to gain insight and understanding through use of a
hydrologic model. [Table 1] provides a partial list of reasons why hydrologic models
are developed and applied.

Groundwater Surface water (quantity/quality) Land-atmosphere interactions

(quantity/quality)
Groundwater/surface
Geochemistry/weathering Soil erosion/deposition
water interactions
Earth system/landscape evolution
Contaminant transport Land use/land cover change effects
and geomorphology
Riparian/hyporheic flow Channel hydraulics/floodplain
Water management
analysis interactions and flood inundation
Water
Drought forecasting Aquatic habitat analysis
availability/budget/census
Compound coastal/inland
Saltwater intrusion Flood risk prediction
flooding

Table 1. A partial list of analytical or predictive uses for hydrologic models

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A hydrologic model simulates a flux, flow, or change of water storage with time within
one or more components of the natural hydrologic cycle. The typical hydrological
cycle is described using diagrams such as the one shown by (Fig.2).

Fig. 2. Conceptualization of the hydrologic cycle showing relevant fluxes and storages (public domain from US Geological Survey).

A major advantage of simulation models is the insight gained by gathering and

organizing data required as input to the mathematical algorithms that comprise the
overall model system. This exercise can often guide the collection of additional data or
direct the improvement of mathematical formulations to better represent watershed
behavior. Another advantage is that many alternative schemes for water supply systems,
for urban development, or flood control options can be quickly tested and compared
with simulation models. The major limitation of simulation models is the inability to
properly calibrate and verify applications in which input data are lacking. Current
practice assumes that the simplest model that will satisfactorily describe the system for
the given input data should be used. Model accuracy is largely determined by available
input data and observed input and output time series at various locations in a watershed.
Modern radar rainfall, hydrologic, and topographic datasets are now available for many
areas, and model accuracy has increased accordingly. Despite their limitations,
simulation models still provide the most logical and scientifically advanced approach
to understanding the hydrologic behavior of complex watershed and water resources
systems.

Model Calibration

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Complexity in the hydrologic modeling over large space and long times has prompted
a significant need for model calibration or parameter optimization. Model calibration is
a demonstration that the model is capable of reproducing field-observed values of
various hydrologic variables (e.g., streamflow, soil moisture, and well-observed
groundwater level) (Figure 3).

Prediction of various hydrologic variables based on an uncalibrated flow model are

sterile and indefensible. Generally, the goodness-of-fit between simulated and
measured variables is not satisfactory based on the initial values of hydrologic and
hydraulic parameters used in the model. The goodness-of-fit can be improved by the
adjustment or optimization of these parameter values until the difference between
simulated and measured variables is satisfactory during this model practice. The
adjustment process most commonly is based on trial-and-error changes in a parameter
while other parameter values are held constant. Some numerical models are now
equipped with a semi-automated or automated procedure to optimize one or multiple
parameters. The range of adjustment to values of hydrologic and hydraulic parameters
must be constrained by plausible site-specific field data such as streamflow, water
levels, hydraulic conductivity, and so on. The difficulty in achieving a good calibration
is that boundary conditions and values of hydrologic and hydraulic parameters are
always known with uncertainty. Goodness-of-fit calibration can be evaluated through
visual comparison and statistical measures. Visual comparison includes scatterplot of
simulated versus measured variables, simulated and field-based temporal and spatial
distribution, and spatial distribution of residuals. Statistical measures consist of mean
error, absolute mean error, and root mean-squared error, between simulated and
observed variables. For any model calibration, objective functions need to be set up for
variables (e.g., streamflow, groundwater level) that will be optimized during the
calibration process.

Figure 3. Procedure of model calibration.

STEPS IN MODELING

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With so many hydrologic models available to the hydrologist or civil engineer, very
little new model development is currently being supported. Rather, one must select one
of the available simulation models based on characteristics of the system to be studied,
the objectives to be met, and the available budget for data collection and analysis. Once
the model is selected, the steps involved in watershed simulation analysis generally
follow the sequence of [Table 2]

Table 2: Steps in Watershed Modeling

Step 5 in Table 2 , model calibration and verification, is important in fitting the model
parameters and producing accurate and reliable results in steps 6 and 7. Model
calibration involves selecting a measured set of input data (rainfall, channel routing,
land use, and so on) and measured output hydrographs for model application. The
controlling parameters in the model are adjusted until a “best fit” is obtained for this set
of data. The model should then be “verified” by simulating a second or third event (i.e.,
different rainfall) and keeping all other parameters unchanged to produce a comparison
of predicted and measured hydrographs.

Classification of Hydrological Models

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Classification of hydrological models is not exact and different hydrologist may give
different definitions. The reason is that the nature of models is often the same but many
models have overlapping characteristics. Some classification methods are shown next.

1. Stochastic or deterministic model: models that always provide the same result for
a given set of parameters are deterministic. If the model accounts of uncertainty in these
quantities and provides a measure of the distribution of possible outcomes, it is
stochastic.

2. Lumped or distributed model: models that treat the entire catchment as a single unit are
lumped model ( fig.4), while distributed models (fig.5) discretize a domain into small elements.
Both models have advantages and disadvantages.

Fig.4: Lumped Model Fig.5: Distributed Model

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3. Conceptual or physically-based model: a conceptual model (fig.6) relies on
storage volumes and fluxes that may only represent the catchment response and have
parameters that cannot be associated to measurements. Physically-based models (fig.7)
attempt to parameterize processes using equations for which parameter values can be
readily measured.

Fig.6: Conceptual Model Fig.7: Physiclly Model

Table 3 : Examples of Model types

* General guidelines for model selection:

1. Ease of use: skill required, ease of interpreting results, assumption required by model.

2. Availability of data: ability to use readily available data, ability to handle small and
variable time increments, data accuracy and data resolution.

3. Availability of models: cost to operate in terms of computing time and hardware

system.

4. Application to management activities: number of parameters predicted, sensitivity to

change in management activities.

5. Broad regional coverage: ability of a model to operate in various hydrological areas,

extrapolation of model.
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6. Accuracy of prediction: ability to predict relative change and absolute effects needed
to calibrate model, repeatability of model predictions, error between actual and
predicted values for volumes.

Table 4: Selected Simulation Models in Hydrology

* Hydrological Modeling Protocol :

Rainfall-Runoff (Hydrological Models)

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In general, rainfall-runoff models are the standard tools used for investigating
hydrological processes. A large number of models with different applications ranges
from small catchments to global models has been developed.

Each model has got its own unique characteristics and respective applications. Some of
them are comprehensive and uses the physics of basic hydrological processes and are
distributed in space and time. The models are used for the modelling of both gauged
and ungauged catchments, helps in flood forecasting, proper water resource
management and evaluation of water quality, erosion and sedimentation, nutrient and
pesticide circulation, land use and climate change etc. Each model has various
drawbacks like lack of user friendliness, large data requirements, absence of clear
statements of their limitations etc. In order to overcome these defects, it is necessary
for the models to include rapid advances in remote sensing technologies, risk analysis,
etc. By the application of new technologies, new distributed models can be developed
for modelling gauged and ungauged basins.

One of the challenges is regarding the use of large quantity of data and hence new
facilities are to be included for the efficient storing, managing and manipulation of
extensive data. Each model should give a clear statement of their limitations and must
provide a proper guidance and include require description of dominant physical
processes.

For accurate prediction, different means of model evaluation is required. Also it should
be taken into account that the calibrated parameter values will reflect the source of
errors in modelling. Both meteorological data and soil properties have got a large
influence on the performance of each model. A proper knowledge of subsurface flow
pathways and hydraulic characteristics is necessary otherwise it will create adverse
effect on model calibration.

Various researches are still going on to make better predictions and to face major
challenges. It is necessary to improve the existing theories or to develop new theories
in order to find the impact of climate change and land use changes on the system

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CONCLUSION

Development in the computing technology leads to new computation methods in

hydrological science and computer modeling. For many years, hydrological models
have been developed with different characteristics and purposes. In order to understand
the configuration and operation procedures of the models, researchers have tried to
define them through different classifications where most of them are based on
mathematical definition. However, a definite classification for hydrological models is
not possible as most models have overlapping characteristics (Gosain et al., 2009).
Different views make the variety of hydrological models classifications to come under
four basic terms, namely simulation basis, spatial presentation, temporal presentation
and method of solution (Dingman, 2002). These categories show the comprehensive
and explicit perspective of classifications for hydrological models. By comparing the
different classifications and reviewing these from different aspects, a deeper
understanding of the models’ characteristics can be obtained. All in all, it can be
concluded that proper classification helps experts to select and apply the desired
hydrological models for their researchers and works. Also, different types of
classifications for hydrological models actually have the same meaning in nature but
they are categorized differently due to different views and overlapping characteristics.

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References
1. Allaby & Allaby 1999: Journal of African Earth Sciences, 2016
2. Gayathri K Devia , Ganasri B Pa, Dwarakish G Sa: A Review on Hydrological
Models
3. Milad Jajarmizadeh, Sobri Harun, Mohsen Salarpour :Journal of Environmental
Science and Technology.
4. Philip Bedient. Wayne Huber. Baxter Vieux: Hydrology and Floodplain Analysis
5. Shadeed, S.: Up To Date Hydrological Modeling in Arid and Semi-arid Catchment
6. https://en.wikipedia.org/wiki/Hydrological_model
7. William H. Farmer1 and Richard M. Vogel2: On the deterministic and stochastic use
of hydrologic models

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