Hydrological Modelling

Hydrological modelling involves the use of mathematical models to simulate the movement, distribution, and quality of water on Earth’s surface and within its subsurface layers. Through these models, scientists and water managers can predict water availability, assess the impacts of climate change, and make informed decisions about managing water resources more effectively.

Hydrological models serve as essential tools in a variety of applications, from planning for agricultural irrigation to forecasting floods, managing droughts, and even assessing the health of ecosystems. These models can also aid in understanding how water interacts with other environmental factors, such as temperature, soil composition, and vegetation.

Types of Hydrological Models

Within hydrological modelling, a broad range of models exists, each suited to specific needs. These models range from conceptual models, which provide simplified representations of the hydrological cycle, to more intricate distributed models, such as AD2 and DREAM, which offer detailed insights into water distribution and flow patterns over a given area.

Conceptual models focus on capturing the core components of a hydrological system, often using average or assumed values for certain variables. These models are generally simpler and faster to run, making them suitable for scenarios where a general overview is sufficient. In contrast, distributed models break down the landscape into smaller units, allowing for a more accurate representation of how water moves and interacts across different areas. These models are particularly useful in complex studies, such as assessing the effects of climate change on large river basins or predicting flood patterns in urban areas.

Choosing the Right Model

The choice of an appropriate hydrological model depends on the specific objectives of the study or project. For example, if the goal is to estimate water availability for a small agricultural area, a conceptual model may be adequate due to its simplicity and lower data requirements. However, for more detailed analysis, such as understanding how changing precipitation patterns could impact a river basin, a distributed model would be preferable, as it can account for variations in topography, land cover, and soil characteristics.

Model Calibration and Its Challenges

Beyond choosing a model, calibrating it is critical to ensure that it accurately reflects real-world conditions. Calibration involves adjusting model parameters to make the model’s output align as closely as possible with observed data. This process can be challenging, especially in regions where data is limited or inconsistent. Nevertheless, effective calibration is essential for enhancing the reliability of hydrological predictions and making the models more applicable to real-world scenarios.

Advances in Hydrological Modelling through Remote Sensing

In recent years, remote sensing technology has revolutionized hydrological modelling. By collecting data from satellites, we can gain valuable information on factors like soil moisture, land cover, and rainfall patterns, even in areas that are difficult to access. Integrating remote sensing data into hydrological models enhances their precision, providing a clearer and more comprehensive picture of water dynamics across large and varied landscapes.

Current Hydrological Modelling Initiatives

Within the realm of hydrological modelling various innovative projects and research initiatives are underway, including:

1. Improvement of Existing Hydrological Models: Enhancing models by incorporating new datasets and refining parameters for increased accuracy.
2. Development of New Models for Specific Case Studies: Crafting models tailored to address unique challenges or features in specific geographic locations.
3. Innovative Calibration Techniques: Developing advanced methods to better calibrate models, especially for use in data-scarce regions.
4. Integration of Remote Sensing Data: Leveraging satellite and aerial data to provide a more robust foundation for model predictions.
5. Water Balance Assessments: Applying models to calculate water budgets on a large scale, essential for sustainable resource planning.
6. Using Models for Nature-Based Solutions: Creating modules that utilize hydrological models to design natural interventions, like restoring wetlands to mitigate flooding.

Conclusion / Target Outcomes:

Hydrological modelling is a powerful and indispensable tool in our efforts to understand and manage Earth’s water resources more effectively. The ongoing development of new models, along with the refinement and calibration of existing ones, helps to improve the accuracy and usefulness of these tools. Incorporating data from remote sensing further enhances their ability to deliver precise insights, contributing to sustainable and resilient water management practices. By advancing hydrological modelling, we build a stronger foundation for addressing the challenges of water scarcity, environmental conservation, and adaptation to climate change, paving the way for a future where water resources are managed more wisely and equitably.

References

1. Perrini, P. , L. Cea, F. Chiaravalloti, S. Gabriele, S. Manfreda, M. Fiorentino, A. Gioia, V. Iacobellis, A Runoff-On-Grid Approach to Embed Hydrological Processes in Shallow Water Models, Water Resources Research, https://doi.org/10.1029/2023WR036421, 2024. [pdf]
2. Bancheri, M., R. Rigon and S. Manfreda, The GEOframe-NewAge modelling system applied in a data scarce environment. Water, 12, 86, (doi: 10.3390/w12010086) 2019. [pdf]
3. Baldwin, D., S. Manfreda, H. Lin, and E.A.H. Smithwick, Estimating root zone soil moisture across the Eastern United States with passive microwave satellite data and a simple hydrologic model. Remote Sensing, 11, 2013, (doi: 10.3390/rs11172013), 2019. [pdf]
4. Manfreda, S., Mita, L., S. F. Dal Sasso, C. Samela, L. Mancusi, Exploiting the Use of Physical Information for the Calibration of a Lumped Hydrological Model. Hydrological Processes, 32(10), 1420-1433, (doi: 10.1002/hyp.11501), 2018.  [pdf]
5. Ruiz-Pérez, G., J. Koch, S. Manfreda, K.K. Caylor, F. Francés, Calibration of a parsimonious distributed ecohydrological daily model in a data scarce basin using exclusively the spatio-temporal variation of NDVI. Hydrology and Earth System Sciences, 21, 6235-6251, (doi: 10.5194/hess-21-6235-2017) 2017. [pdf]
6. Manfreda, S., L. Brocca, T. Moramarco, F. Melone, and J. Sheffield, A physically based approach for the estimation of root-zone soil moisture from surface measurements. Hydrology and Earth System Sciences, 18, 1199-1212, (doi:10.5194/hess-18-1199-2014), 2014. [pdf] 
7. Manfreda, S., T. Lacava, B. Onorati, N. Pergola, M. Di Leo, M. R. Margiotta, and V. Tramutoli, On the use of AMSU-based products for the description of soil water content at basin scale. Hydrology and Earth System Sciences, 15, 2839-2852, (doi:10.5194/hess-15-2839-2011), 2011. [pdf] 
8. Manfreda, S., T.M. Scanlon, K.K. Caylor, On the importance of accurate depiction of infiltration processes on modelled soil moisture and vegetation water stress. Ecohydrology, 3, 155-165, (doi: 10.1002/eco.79), 2010. [pdf]
9. Gigante V., P. Milella, V. Iacobellis, S. Manfreda, and I. Portoghese, Influences of Leaf Area Index estimations on the soil water balance predictions in Mediterranean regions. Natural Hazard and Earth System Sciences, 9, 979-991, (doi:10.5194/nhess-9-979-2009), 2009. [pdf] 
10. Manfreda, S., Runoff Generation Dynamics within a Humid River Basin. Natural Hazard and Earth System Sciences, 8, 1349-1357, (doi:10.5194/nhess-8-1349-2008), 2008. [pdf]
11. Manfreda, S., M. Fiorentino, A Stochastic Approach for the Description of the Water Balance Dynamics in a River Basin. Hydrology and Earth System Sciences, 12, 1189-1200, (doi:10.5194/hess-12-1189-2008), 2008.  [pdf]
12. Fiorentino, M., S. Manfreda, V. Iacobellis, Peak Runoff Contributing Area as Hydrological Signature of the Probability Distribution of Floods. Advances in Water Resources, 30(10), 2123-2134, (doi:10.1016/j.advwatres.2006.11.017), 2007.  [pdf]
13. Manfreda, S., M. Fiorentino, V. Iacobellis, DREAM: a Distributed model for Runoff, Evapotranspiration, and Antecedent Soil Moisture Simulation. Advances in Geosciences, 2, 31-39, (SRef-ID: 1680-7359/adgeo/2005-2-31), 2005. [pdf]