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The Hydrological Cycle and Its Relations with Climate: Latest Advances and Prospects

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 11289

Special Issue Editors


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Guest Editor
Department of General Physics, University of Turin, Via Pietro Giuria, 1, 10125 Torino, Italy
Interests: physical exchange processes (momentum, heat, water vapour and carbon) in the surface boundary layer; land surface and meteorological modelling; crop models
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Guest Editor
Department of General Physics, University of Turin, Via Pietro Giuria, 1, 10125 Torino, Italy
Interests: atmospheric physics and meteorology; climate physics

Special Issue Information

Dear Colleagues,

The hydrological cycle describes the transfer of water, a primary component for life of all living beings, in all its three phases between the Earth (including the snow/ice components, the groundwater and the exchange with vegetation) and the atmosphere. Meteorological events can produce strong accelerations in some of those transfers, while climate regulates the balance at regional scale. As a result of the balance, some areas can be rich in water and other poor, determining the water resources available for that region. Sometimes, events of intermediate scale between meteorology and climate (weeks to seasons) produce an alteration of regional balance, causing events of drought or floods that are able to produce severe damages to persons and infrastructures, and/or to have negative economic consequences. Climatic changes, occurring at present and projected for the near or far future, pose a further challenge, as they have an impact on several components of the hydrological cycle.

Prof. Claudio Cassardo
Prof. Silvia Ferrarese
Guest Editors

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Keywords

  • rainfall
  • snowfall
  • evapotranspiration
  • soil moisture
  • runoff
  • statistics
  • temperature
  • global warming
  • climatic components

Published Papers (4 papers)

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Research

22 pages, 7068 KiB  
Article
Weather Simulation of Extreme Precipitation Events Inducing Slope Instability Processes over Mountain Landscapes
by Alessio Golzio, Irene Maria Bollati, Marco Luciani, Manuela Pelfini and Silvia Ferrarese
Appl. Sci. 2020, 10(12), 4243; https://doi.org/10.3390/app10124243 - 20 Jun 2020
Cited by 2 | Viewed by 2483
Abstract
Mountain landscapes are characterised by a very variable environment under different points of view (topography, geology, meteorological conditions), and they are frequently affected by mass wasting processes. A debris flow that occurred along the Croso stream, located in the Italian Lepontine Alps in [...] Read more.
Mountain landscapes are characterised by a very variable environment under different points of view (topography, geology, meteorological conditions), and they are frequently affected by mass wasting processes. A debris flow that occurred along the Croso stream, located in the Italian Lepontine Alps in the Northern Ossola Valley, during summer 2019, was analysed from a geological/geomorphological and meteorological point of view. The debris flow was triggered by an intense precipitation event that heavily impacted a very restricted area over the course of three hours. A previous debris flow along the same stream occurred in Autumn 2000, but it was related to an intense and prolonged rainfall event. The slope was characterised in terms of sediment connectivity, and data were retrieved and elaborated from the Web-GIS (Web-Geographic Information System) database of the IFFI-Italian Landslide Inventory and historical archives of landslides. Both the events were analysed through the weather research and forecasting (WRF) model applying a very high horizontal grid spacing with the aim of catching the precipitation patterns and timings. The obtained results are compared with the observed precipitation at a selection of weather stations in the area. The simulation of WRF that measured the timing in total precipitation and in its minor steps could be considered reliable. Moreover, it reveals to be appropriate for detecting in advance the meteorological conditions potentially triggering mass-wasting processes affecting slopes featuring high connectivity conditions and lithotypes characterised by a high Landslide Susceptibility Index. Full article
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20 pages, 5864 KiB  
Article
Enhancing the Prediction Accuracy of Data-Driven Models for Monthly Streamflow in Urmia Lake Basin Based upon the Autoregressive Conditionally Heteroskedastic Time-Series Model
by Nasrin Fathollahzadeh Attar, Quoc Bao Pham, Sajad Fani Nowbandegani, Mohammad Rezaie-Balf, Chow Ming Fai, Ali Najah Ahmed, Saeed Pipelzadeh, Tran Duc Dung, Pham Thi Thao Nhi, Dao Nguyen Khoi and Ahmed El-Shafie
Appl. Sci. 2020, 10(2), 571; https://doi.org/10.3390/app10020571 - 13 Jan 2020
Cited by 24 | Viewed by 3604
Abstract
Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like [...] Read more.
Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonality, periodicities, anomalies, and lack of data. As streamflow is one of the most important components in the hydrological cycle, modeling and estimating streamflow is a crucial aspect. In this study, two models, namely, Optimally Pruned Extreme Learning Machine (OPELM) and Chi-Square Automatic Interaction Detector (CHAID) methods were used to model the deterministic parts of monthly streamflow equations, while Autoregressive Conditional Heteroskedasticity (ARCH) was used in modeling the stochastic parts of monthly streamflow equations. The state of art and innovation of this study is the integration of these models in order to create new hybrid models, ARCH-OPELM and ARCH-CHAID, and increasing the accuracy of models. The study draws on the monthly streamflow data of two different river stations, located in north-western Iran, including Dizaj and Tapik, which are on Nazluchai and Baranduzchai, gathered over 31 years from 1986 to 2016. To ascertain the conclusive accuracy, five evaluation metrics including Correlation Coefficient (R), Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), the ratio of RMSE to the Standard Deviation (RSD), scatter plots, time-series plots, and Taylor diagrams were used. Standalone CHAID models have better results than OPELM methods considering sole models. In the case of hybrid models, ARCH-CHAID models in the validation stage performed better than ARCH-OPELM for Dizaj station (R = 0.96, RMSE = 1.289 m3/s, NSE = 0.92, MAE = 0.719 m3/s and RSD = 0.301) and for Tapik station (R = 0.94, RMSE = 2.662 m3/s, NSE = 0.86, MAE = 1.467 m3/s and RSD = 0.419). The results remarkably reveal that ARCH-CHAID models in both stations outperformed all other models. Finally, it is worth mentioning that the new hybrid “ARCH-DDM” models outperformed standalone models in predicting monthly streamflow. Full article
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20 pages, 4366 KiB  
Article
Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model
by Mohammad Ehteram, Amr H. El-Shafie, Lai Sai Hin, Faridah Othman, Suhana Koting, Hojat Karami, Sayed-Farhad Mousavi, Saeed Farzin, Ali Najah Ahmed, Mohd Hafiz Bin Zawawi, Md Shabbir Hossain, Nuruol Syuhadaa Mohd, Haitham Abdulmohsin Afan and Ahmed El-Shafie
Appl. Sci. 2019, 9(19), 3960; https://doi.org/10.3390/app9193960 - 20 Sep 2019
Cited by 8 | Viewed by 2697
Abstract
Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. [...] Read more.
Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam. Full article
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21 pages, 262 KiB  
Article
On the Representativeness of UTOPIA Land Surface Model for Creating a Database of Surface Layer, Vegetation and Soil Variables in Piedmont Vineyards, Italy
by Claudio Cassardo and Valentina Andreoli
Appl. Sci. 2019, 9(18), 3880; https://doi.org/10.3390/app9183880 - 16 Sep 2019
Cited by 2 | Viewed by 1961
Abstract
The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the [...] Read more.
The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the atmosphere/biosphere interface, with a particular focus on heat and hydrologic transfers, over an area of the Piemonte (Piedmont) region, NW Italy, which is characterized by the presence of many vineyards. Another equally important aim is to understand how much the quality of the simulation outputs was influenced by the input data, whose measurements are often unavailable for long periods over country areas at an hourly basis. Three types of forcing data were used: observations from an experimental campaign carried out during the 2008, 2009, and 2010 vegetative seasons in three vineyards, and values extracted from the freely available Global Land Data Assimilation System (GLDAS, versions 2.0 and 2.1). Since GLDAS also contains the outputs of the simulations performed using the Land Surface Model NOAH, an additional intercomparison between the two models, UTOPIA and NOAH, both driven by the same GLDAS datasets, was performed. The intercomparisons were performed on the following micro-meteorological variables: net radiation, sensible and latent turbulent heat fluxes, and temperature and humidity of soil. The results of this study indicate that the methodology of employing land surface models driven by a gridded database to evaluate variables of micro-meteorological and agronomic interest in the absence of observations is suitable and gives satisfactory results, with uncertainties comparable to measurement errors, thus, allowing us to also evaluate some time trends. The comparison between GLDAS2.0 and GLDAS2.1 indicates that the latter generally produces simulation outputs more similar to the observations than the former, using both UTOPIA and NOAH models. Full article
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