13.1 Types of hydrologic models and their applications

Types of Hydrologic Models

Types of hydrologic models

Hydrologic models simulate how water moves through the environment. Different models exist because water problems vary widely in scale, complexity, and purpose. Choosing the right model type depends on how much spatial detail you need, what time period you're looking at, and how deeply you want to represent the underlying physics.

Spatial scale determines how a model represents the landscape:

• Lumped models treat the entire watershed as a single unit with averaged properties and inputs (precipitation, evapotranspiration). There's no internal spatial variation; the whole basin gets one set of parameters.
• Semi-distributed models divide the watershed into sub-units called hydrologic response units (HRUs), grouped by similar land use, soil type, and slope. This captures some spatial variability without modeling every individual cell.
• Fully-distributed models discretize the watershed into a grid or mesh of cells, representing spatial heterogeneity at fine resolution. They rely on spatially explicit data like digital elevation models and remote sensing imagery.

Temporal scale determines what time period the model covers:

• Event-based models simulate individual storm events with short time steps (minutes to hours), focusing on peak flows and storm hydrographs.
• Continuous models simulate hydrologic processes over extended periods (months to years), capturing the long-term water balance including baseflow and seasonal low flows.

Process representation describes how the model actually calculates water movement:

• Empirical models rely on observed data and statistical relationships between inputs and outputs. Think regression equations or unit hydrographs. They don't attempt to describe the physics of water movement.
• Conceptual models simplify complex processes using conceptual storage elements (like imaginary reservoirs) connected by flow pathways governed by linear or nonlinear equations. They approximate physical behavior without solving the full governing equations.
• Physically-based models solve the governing equations of fluid mechanics and conservation laws (mass, momentum, energy) using partial differential equations. They attempt to represent the actual physics at each point in the domain.

Intended applications shape which combination of the above characteristics you need:

• Flood forecasting requires event-based models with short time steps to predict peak flows and flood timing for early warning systems.
• Water resources management uses continuous models with longer time steps to assess water availability, allocation, and infrastructure planning for reservoirs and irrigation systems.
• Climate change impact assessment employs physically-based models coupled with climate models to evaluate long-term shifts in hydrologic regimes driven by changing temperature and precipitation patterns.

Application of hydrologic models

Each spatial model type fits different situations depending on watershed size, data availability, and the questions you're trying to answer.

Lumped models:

1. Treat the watershed as a single unit with averaged properties and inputs
2. Best suited for small watersheds with relatively homogeneous characteristics (uniform geology, consistent land cover)
3. Require minimal input data and computational resources

Semi-distributed models:

1. Divide the watershed into sub-units or HRUs based on similar hydrologic properties
2. Capture spatial variability of processes like runoff generation and groundwater recharge across the watershed
3. Require moderate input data and computational resources

Fully-distributed models:

1. Discretize the watershed into a grid or mesh of cells at fine spatial resolution
2. Represent complex processes such as surface water-groundwater interactions and unsaturated zone flow, along with their spatial variability
3. Require extensive input data (spatially distributed soil, land use, topography) and significant computational resources

Model Selection and Evaluation

Strengths vs limitations of models

No single model type is universally best. The trade-off generally runs along a spectrum: simpler models need less data but capture less detail, while complex models capture more physics but demand more data and computing power.

• Empirical models
  • Strengths: simplicity, low data requirements, computational efficiency, and ease of use
  • Limitations: no physical basis, limited ability to extrapolate beyond the conditions used to build them, and results are site-specific
• Conceptual models
  • Strengths: balance between simplicity and physical representation, moderate data requirements, computational efficiency, and ability to capture dominant hydrologic processes
  • Limitations: heavy dependence on calibration, limited spatial representation, simplified process descriptions, and difficulty in estimating parameters that don't correspond directly to measurable quantities
• Physically-based models
  • Strengths: detailed representation of hydrologic processes and spatial heterogeneity, grounded in physical laws, and able to simulate complex interactions and feedbacks
  • Limitations: high data requirements, computational intensity, challenges in estimating parameters at the model's grid scale, and uncertainty in process descriptions and boundary conditions

Selection of appropriate models

Matching a model to a problem involves thinking about what outputs you need, what data you have, and what computational constraints exist.

Flood forecasting:

1. Use event-based models with short time steps to capture rapid watershed response and peak flows
2. Lumped or semi-distributed models are preferred for computational efficiency, especially when the model must run in real time
3. Incorporate weather radar and satellite data for improved spatial rainfall estimates

Water resources management:

1. Use continuous models with longer time steps to assess long-term water availability and demand
2. Semi-distributed or fully-distributed models provide the spatial detail needed to represent water resources and infrastructure across a basin
3. Often integrated with water allocation models and decision support systems

Climate change impact assessment:

1. Use physically-based models coupled with global or regional climate models to evaluate long-term changes in hydrologic regimes
2. Fully-distributed models provide the spatial detail needed to assess how climate change affects different parts of a watershed differently
3. Incorporate statistical or dynamical downscaling techniques and bias correction methods to translate coarse climate model outputs to the watershed scale