Application of Climatic Data in Hydrologic Models

Over the past few decades, global warming and climate change have impacted the hydrologic cycle. Many models (e.g., the Variable Infiltration Capacity (VIC) model, Mosaic, Noah, Sacramento (SAC), Soil and Water Assessment Tool (SWAT), MODFLOW, Weather Research and Forecasting-Hydrology (WRF-Hydro), and European Hydrological System Model (MIKE SHE)) have been developed to simulate hydrologic processes. The performance of these models partly depends on the accuracy of their input climatic data. Obtaining accurate climatic data on local, meso, and global scales is essential for the realistic simulation of hydrologic processes. However, the limited availability of climatic data often poses a challenge to hydrologic modeling efforts.

Hydrologic science is currently undergoing a revolution in which the field is being transformed by the multitude of new available data streams. Historically, hydrologic models that have been developed to answer basic questions about the rainfall–runoff relationship, surface water and groundwater storage and fluxes, land–atmosphere interactions, and so forth, have been optimized for previously data-limited conditions. Largely due to the lack of historical data, the mean states and fluxes in the terrestrial water cycle remain poorly characterized. With the advent of remote sensing technologies and increased computational resources, the environment for water cycle researchers has fundamentally changed to one where there is now a flood of spatially distributed and time-dependent data. However, the remotely sensed climate data are biased. The bias in the climatic data is propagated through models and can yield estimation errors. Therefore, the bias in climatic data should be removed before their use in hydrologic models.

Climatic data have been a core component of the science of hydrology. Their intrinsic role in understanding and managing water resources and developing sound water policies dictates their vital importance. During the 20th century, attempts were focused mainly on establishing and maintaining in situ observing networks to understand and predict water resources.

The recent discussions of big data and emerging efforts associated with the shaping of “data science” are crucial concerns for the future of hydrology and should be explored. In addition, a number of concerns dealing with retrospective investigations are data-dependent, with particular worries related to data archiving and data rescue.

Hydrologic data are typically obtained through a combination of observations and computational algorithms. For example, the discharge from a river is often estimated from its water level via a rating curve. Multi-spectrum analysis of satellite data is frequently combined with multiple information sources to produce a variety of Earth observation products. Observed hydrologic time-series are used to estimate parameters in complex dynamic hydrologic models. As a result, the boundary between observed and computed data is often vague and, considering the degree to which such data are shared, re-used, and cited, it can be difficult to trace their provenance.

Notably, a strong and vigorous debate on data could be critical to the development of new policies regarding in situ observing networks as well as air- and spaceborne sensors. This include their density, quality, sustainability, investment, modernization, etc. Such a debate may also serve as an important contribution to the development of science data.

This Special Issue aims to present recent advances concerning climatic data and their applications in hydrologic models. For this Special Issue, we invite studies on the following main themes:

• Application of machine learning and soft computing approaches in hydrology
• Estimation of reference evapotranspiration by climatic data
• Time series and hydrologic forecasting
• Role of precipitation and evapotranspiration data on soil moisture estimates
• Bias correction methods for climatic data
• Evaluation of hydrologic models in limited data conditions
• Application of remote sensing and big data in hydrologic modeling
• Performance of Statistical Downscaling Models (SDSMs) in simulating climatic data
• Impact of climate change on climatic data
• Interpolation/extrapolation and filling data gaps in hydrology
• Analyzing climatic data for simulating groundwater level

Dr. Mohammad Valipour
Prof. Dr. Sayed M. Bateni
Guest Editors

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• climatic data
• hydrologic modelling
• hydrologic forecasting
• data mining
• artificial intelligence
• big data