Special Issue "Application of Climatic Data in Hydrologic Models"
A special issue of Climate (ISSN 2225-1154).
Deadline for manuscript submissions: 31 March 2022.
Interests: remote sensing; climate change; sustainable development; irrigation and drainage; big data; best management practices; hydrometeorology; hydroclimatology; hydroinformatics; hydrological forecasting
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Interests: earth remote sensing; land–atmosphere Interactions; data assimilation; optimization techniques andthods parameter estimation in hydrology; application of artificial intelligence methods
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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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- climatic data
- hydrologic modelling
- hydrologic forecasting
- data mining
- artificial intelligence
- big data
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Spatial and temporal variability of weather data for modeling urban hydrology. Application to Madrid (Spain)
Authors: Galan, V.; Matendo, S.; Zubelzu, S.
Affiliation: Technical University of Madrid
Abstract: Watershed based hydrological phenomena are affected by extreme spatial and temporal variability. This complicates the accurate modelling and forecasting particularly in urban environments were specific local conditions add further complexity. In this paper we address the spatial and temporal relationships of a set of weather variables with data collected from a weather station network located in a highly urbanized environmental as Madrid city. We mainly focus on precipitation seeking to give an accurate insight on valuable information for hydrology analysis. We address this study with data-driven models by analysing both precipitation spatial and temporal self-correlations and causal relationships between precipitation and primary variables.
Title: Heterogeneous Future Snowpacks Changes from WRF-SnowModel Simulations
Authors: Alison P. Kingston; Steven R. Fassnacht,; Graham A. Sexstone; Kristen L. Rasmussen; Daniel McGrath; Glen Liston
Affiliation: Colorado State University
Abstract: We used meteorological simulations of the Weather Research and Forecasting (WRF) Model to drive SnowModel for an mountainous region in Northern Colorado, in the western United States. The 4-km convection-permitting WRF runs are from October 2000 through September 2013 and a pseudo-global warming future scenario for 2080-2093. We compare the simulated snowpack across the domain and examine several latitudinal transects to investigate where and why the snowpack varies between future and recent meteorological inputs. When examining the details for the two time periods, the snowpack decreases and starts to melt earlier in much of the domain, as expected. However, there is an increase in peak snow accumulation in some portions of domain. The snowpack decreases are only partially driven by more precipitation falling as rain than as snow.