Special Issue "Application of Climatic Data in Hydrologic Models"

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Mohammad Valipour
grade E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering & Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: remote sensing; climate change; sustainable development; irrigation and drainage; big data; best management practices; hydrometeorology; hydroclimatology; hydroinformatics; hydrological forecasting
Special Issues and Collections in MDPI journals
Prof. Dr. Sayed M. Bateni
E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering & Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI, USA
Interests: earth remote sensing; land–atmosphere Interactions; data assimilation; optimization techniques andthods parameter estimation in hydrology; application of artificial intelligence methods
Special Issues and Collections in MDPI journals

Special Issue Information

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

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.

Keywords

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

Published Papers (5 papers)

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Editorial

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Editorial
Global Surface Temperature: A New Insight
Climate 2021, 9(5), 81; https://doi.org/10.3390/cli9050081 - 12 May 2021
Cited by 14 | Viewed by 1017
Abstract
This paper belongs to our Special Issue “Application of Climate Data in Hydrologic Models” [...] Full article
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
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Research

Jump to: Editorial

Article
Hydro-Meteorological Trends in an Austrian Low-Mountain Catchment
Climate 2021, 9(8), 122; https://doi.org/10.3390/cli9080122 - 29 Jul 2021
Viewed by 582
Abstract
While ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when [...] Read more.
While ongoing climate change is well documented, the impacts exhibit a substantial variability, both in direction and magnitude, visible even at regional and local scales. However, the knowledge of regional impacts is crucial for the design of mitigation and adaptation measures, particularly when changes in the hydrological cycle are concerned. In this paper, we present hydro-meteorological trends based on observations from a hydrological research basin in Eastern Austria between 1979 and 2019. The analyzed variables include air temperature, precipitation, and catchment runoff. Additionally, the number of wet days, trends for catchment evapotranspiration, and computed potential evapotranspiration were derived. Long-term trends were computed using a non-parametric Mann–Kendall test. The analysis shows that while mean annual temperatures were decreasing and annual temperature minima remained constant, annual maxima were rising. Long-term trends indicate a shift of precipitation to the summer, with minor variations observed for the remaining seasons and at an annual scale. Observed precipitation intensities mainly increased in spring and summer between 1979 and 2019. Catchment actual evapotranspiration, computed based on catchment precipitation and outflow, showed no significant trend for the observed time period, while potential evapotranspiration rates based on remote sensing data increased between 1981 and 2019. Full article
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
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Article
Performance Evaluation and Comparison of Satellite-Derived Rainfall Datasets over the Ziway Lake Basin, Ethiopia
Climate 2021, 9(7), 113; https://doi.org/10.3390/cli9070113 - 08 Jul 2021
Viewed by 1160
Abstract
Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived [...] Read more.
Consistent time series rainfall datasets are important in performing climate trend analyses and agro-hydrological modeling. However, temporally consistent ground-based and long-term observed rainfall data are usually lacking for such analyses, especially in mountainous and developing countries. In the absence of such data, satellite-derived rainfall products, such as the Climate Hazard Infrared Precipitations with Stations (CHIRPS) and Global Precipitation Measurement Integrated Multi-SatellitE Retrieval (GPM-IMERG) can be used. However, as their performance varies from region to region, it is of interest to evaluate the accuracy of satellite-derived rainfall products at the basin scale using ground-based observations. In this study, we evaluated and demonstrated the performance of the three-run GPM-IMERG (early, late, and final) and CHIRPS rainfall datasets against the ground-based observations over the Ziway Lake Basin in Ethiopia. We performed the analysis at monthly and seasonal time scales from 2000 to 2014, using multiple statistical evaluation criteria and graphical methods. While both GPM-IMERG and CHIRPS showed good agreement with ground-observed rainfall data at monthly and seasonal time scales, the CHIRPS products slightly outperformed the GPM-IMERG products. The study thus concluded that CHIRPS or GPM-IMERG rainfall data can be used as a surrogate in the absence of ground-based observed rainfall data for monthly or seasonal agro-hydrological studies. Full article
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
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Article
Modeling the Soil Response to Rainstorms after Wildfire and Prescribed Fire in Mediterranean Forests
Climate 2020, 8(12), 150; https://doi.org/10.3390/cli8120150 - 17 Dec 2020
Cited by 5 | Viewed by 1091
Abstract
The use of the Soil Conservation Service-curve number (SCS-CN) model for runoff predictions after rainstorms in fire-affected forests in the Mediterranean climate is quite scarce and limited to the watershed scale. To validate the applicability of this model in this environment, this study [...] Read more.
The use of the Soil Conservation Service-curve number (SCS-CN) model for runoff predictions after rainstorms in fire-affected forests in the Mediterranean climate is quite scarce and limited to the watershed scale. To validate the applicability of this model in this environment, this study has evaluated the runoff prediction capacity of the SCS-CN model after storms at the plot scale in two pine forests of Central-Eastern Spain, affected by wildfire (with or without straw mulching) or prescribed fire and in unburned soils. The model performance has been compared to the predictions of linear regression equations between rainfall depth and runoff volume. The runoff volume was simulated with reliability by the linear regression only for the unburned soil (coefficient of Nash and Sutcliffe E = 0.73–0.89). Conversely, the SCS-CN model was more accurate for burned soils (E = 0.81–0.97), also when mulching was applied (E = 0.96). The performance of this model was very satisfactory in predicting the maximum runoff. Very low values of CNs and initial abstraction were required to predict the particular hydrology of the experimental areas. Moreover, the post-fire hydrological “window-of-disturbance” could be reproduced only by increasing the CN for the storms immediately after the wildfire. This study indicates that, in Mediterranean forests subject to the fire risk, the simple linear equations are feasible to predict runoff after low-intensity storms, while the SCS-CN model is advisable when runoff predictions are needed to control the flooding risk. Full article
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
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Article
Development of a Parametric Regional Multivariate Statistical Weather Generator for Risk Assessment Studies in Areas with Limited Data Availability
Climate 2020, 8(8), 93; https://doi.org/10.3390/cli8080093 - 11 Aug 2020
Cited by 3 | Viewed by 966
Abstract
Risk analysis of water resources systems can use statistical weather generators coupled with hydrologic models to examine scenarios of extreme events caused by climate change. These require multivariate, multi-site models that mimic the spatial, temporal, and cross correlations of observed data. This study [...] Read more.
Risk analysis of water resources systems can use statistical weather generators coupled with hydrologic models to examine scenarios of extreme events caused by climate change. These require multivariate, multi-site models that mimic the spatial, temporal, and cross correlations of observed data. This study developed a statistical weather generator to facilitate bottom-up approaches to assess the impact of climate change on water resources systems for cases of limited data. While existing weather generator models have impressive features, this study suggested a simple weather generator which is straightforward to implement and can employ any distribution function for variables such as precipitation or temperature. It is based on (1) a first-order, two-state Markov chain to simulate precipitation occurrences; (2) the use of Wilks’ technique to produce correlated weather variables at multiple sites with the conservation of spatial, temporal, and cross correlations; (3) the capability to vary the statistical parameters of the weather variables. The model was applied to studies of the Diyala River basin in Iraq, which is a case with limited observed records. Results show that it exhibits high values (e.g., over 0.95) for the Nash–Sutcliffe and Kling–Gupta metric tests, preserves the statistical properties of the observed variables, and conserves the spatial, temporal, and cross correlations among the weather variables in the meteorological stations. Full article
(This article belongs to the Special Issue Application of Climatic Data in Hydrologic Models)
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Planned Papers

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.

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