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Modified Hydrological Cycle under Global Warming
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Modified Hydrological Cycle under Global Warming

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

Deadline for manuscript submissions: closed (30 April 2018) | Viewed by 33411

Special Issue Editors

Dr. Daniele Bocchiola

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Polytechnic of Milan, Leonardo da Vinci, 32, 20133 Milan, Italy
Interests: water resources; hydrology; climate change; avalanche risk
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Claudio Cassardo

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guglielmina Diolaiuti

E-Mail Website
Guest Editor
University Milano, Dept. Environmental Sciences and Policies, Celoria 2, 20133 Milano, Italy
Interests: glaciers morphology and dynamics; climate change; remote sensing of the cryosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Global warming is affecting water hydrological cycles worldwide, changing precipitation amounts and timing and hydrologic losses, and making previously extreme events more frequent. Hydrology and water resources in high altitudes are affected by cryospheric down wasting, and downstream desert areas may lose large amounts of water resources.

The modified water cycle under global warming will have fallout on water and food security, energy production, ecosystem services, and adaptation measures will be needed.

This Special Issue will, thus, welcome contributions tackling the broad range issue of hydrological changes, water availability, and adaptation in a broad array of conditions, such as:

  • Hydrological modeling under global warming;
  • Water resources prediction, sensitivity analysis, and adaptation measures;
  • Climatic and hydrological trends’ assessment;
  • Impact of climate change on cryospheric water;
  • Enhanced magnitude of extreme events;
  • Modified water needs for multipurpose use;
  • Effects of modified hydrology on riverine environment.

Dr. Daniele Bocchiola
Prof. Dr. Claudio Cassardo
Prof. Dr. Guglielmina Diolaiuti
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 submissions that pass pre-check are 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 1800 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

  • Global warming
  • Solid/liquid precipitation partitioning, and extremes
  • Hydrological losses
  • Hydrological changes and water resources availability
  • Mountain hydrology and cryosphere
  • Floods and Droughts

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Published Papers (5 papers)

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Research

27 pages, 15727 KiB  
Article
Modelling Hydrological Components of the Rio Maipo of Chile, and Their Prospective Evolution under Climate Change
by Daniele Bocchiola, Andrea Soncini, Antonella Senese and Guglielmina Diolaiuti
Climate 2018, 6(3), 57; https://doi.org/10.3390/cli6030057 - 25 Jun 2018
Cited by 22 | Viewed by 6194
Abstract
We used the Poly-Hydro model to assess the main hydrological components of the snow-ice melt driven Maipo River in Chile, and glaciers’ retreat under climate change therein until 2100. We used field data of ice ablation, ice thickness, weather and hydrological data, and [...] Read more.
We used the Poly-Hydro model to assess the main hydrological components of the snow-ice melt driven Maipo River in Chile, and glaciers’ retreat under climate change therein until 2100. We used field data of ice ablation, ice thickness, weather and hydrological data, and precipitation from TRMM. Snow cover and temperature were taken from MODIS. We forced the model using weather projections until 2100 from three GCMs from the IPCC AR5, under three different radiative concentration pathways (RCPs 2.6, 4.5, 8.5). We investigated trends of precipitation, temperature, and hydrology until 2100 in the projection period (PR, 2014–2100) and the whole period (CM 1980–2100, composite), against historical trends in control period (CP, 1980–2013). We found potentially increasing temperature until 2100, except for Spring (OND). In the PR period, yearly flow decreases significantly under RCP85, on average −0.25 m3·s−1·year−1, and down to −0.48 m3·s−1·year−1, i.e., −0.4% year−1 against CP yearly average (120 m3 s−1). In the long run (CM) significant flow decrease would, occur under almost all scenarios, confirming persistence of a historical decrease, down to −0.39 m3·s−1·year−1 during CM. Large flow decreases are expected under all scenarios in Summer (JFM) during PR, down to −1.6 m3·s−1·year−1, or −1% year−1 against CP for RCP8.5, due to increase of evapotranspiration in response to higher temperatures. Fall (AMJ) flows would be mostly unchanged, while Winter (JAS) flows would be projected to increase significantly, up to 0.7 m3·s−1·year−1 during 2014–2100, i.e., +0.9% year−1 vs. CP under RCP8.5, due to large melting therein. Spring (OND) flows would decrease largely under RCP8.5, down to −0.67 m3 s−1·year−1, or −0.4% year−1 vs. CP, again due to evapotranspiration. Glacier down wasting is projected to speed up, and increasingly so with RCPs. Until 2100 ice loss would range from −13% to −49% (−9%, and −39% at 2050) of the estimated volume at 2012, which changed by −24% to −56% (−21%, and −39% at 2050) vs. ice volume in 1982, thus with rapider depletion in the first half of the century. Policy makers will have to cope with modified hydrological cycle in the Maipo River, and greatly decreasing ice cover in the area. Full article
(This article belongs to the Special Issue Modified Hydrological Cycle under Global Warming)
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26 pages, 2845 KiB  
Article
Evaluation of Statistical-Downscaling/Bias-Correction Methods to Predict Hydrologic Responses to Climate Change in the Zarrine River Basin, Iran
by Farzad Emami and Manfred Koch
Climate 2018, 6(2), 30; https://doi.org/10.3390/cli6020030 - 20 Apr 2018
Cited by 15 | Viewed by 7070
Abstract
Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), firstly future daily temperatures [...] Read more.
Modeling the hydrologic responses to future changes of climate is important for improving adaptive water management. In the present application to the Zarrine River Basin (ZRB), with the major reach being the main inflow source of Lake Urmia (LU), firstly future daily temperatures and precipitation are predicted using two statistical downscaling methods: the classical statistical downscaling model (SDSM), augmented by a trend-preserving bias correction, and a two-step updated quantile mapping (QM) method. The general circulation models (GCM) input to SDSM are climate predictors of the Canadian Earth System Model (CanESM2) GCM under the representative concentration pathway (RCP) emission scenarios, RCP45 and RCP85, whereas that to the QM is provided by the most suitable of several Climate Model Intercomparison Project Phase 5 (CMIP5) GCMs under RCP60, in addition. The performances of the two downscaling methods are compared to each other for a past “future” period (2006–2016) and the QM is found to be better and so is selected in the subsequent ZR streamflow simulations by means of the Soil and Water Assessment Tool (SWAT) hydrological model, calibrated and validated for the reference period (1991–2012). The impacts of climate change on the hydrologic response of the river basin, specifically the inflow to the Boukan Reservoir, the reservoir-dependable water release (DWR), are then compared for the three RCPs in the near- (2020–2038), middle- (2050–2068) and far- (2080–2098) future periods assuming (1) the “current” consumptive demand to be continued in the future, and (2) a more conservative “recommended” demand. A systematic future shortage of the available water is obtained for case (1) which can be mitigated somewhat for (2). Finally, the SWAT-predicted ZRB outflow is compared with the Montana-based estimated environmental flow of the ZR. The latter can successfully be sustained at good and fair levels for the near- and middle-future periods, but not so for the summer months of the far-future period, particularly, for RCP85. Full article
(This article belongs to the Special Issue Modified Hydrological Cycle under Global Warming)
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40621 KiB  
Article
Cluster Analysis of Monthly Precipitation over the Western Maritime Continent under Climate Change
by Saurabh K Singh, Edmond Yat-Man Lo and Xiaosheng Qin
Climate 2017, 5(4), 84; https://doi.org/10.3390/cli5040084 - 8 Nov 2017
Cited by 7 | Viewed by 9324
Abstract
Changes in climate because of global warming during the 20th and 21st centuries have a direct impact on the hydrological cycle as driven by precipitation. However, studying precipitation over the Western Maritime Continent (WMC) is a great challenge, as the WMC has a [...] Read more.
Changes in climate because of global warming during the 20th and 21st centuries have a direct impact on the hydrological cycle as driven by precipitation. However, studying precipitation over the Western Maritime Continent (WMC) is a great challenge, as the WMC has a complex topography and weather system. Understanding changes in precipitation patterns and their groupings is an important aspect of planning mitigation measures to minimize flood and drought risk as well as of understanding the redistribution of precipitation arising from climate change. This paper employs Ward’s hierarchical clustering on regional climate model (RCM)-simulated monthly precipitation gridded data over 42 approximately evenly distributed grid stations from the years 2030 to 2060. The aim was to investigate spatial and temporal groupings over the four major landmasses in the WMC and to compare these with historical precipitation groupings. The results showed that the four large-scale islands of Java, Sumatra, Peninsular Malaysia and Borneo would experience a significant spatial redistribution of precipitation over the years 2030 to 2060, as compared to historical patterns from 1980 to 2005. The spatial groups were also compared for two future forcing scenarios, representative concentration pathways (RCPs) 4.5 and 8.5, and different groupings over the Borneo region were observed. Full article
(This article belongs to the Special Issue Modified Hydrological Cycle under Global Warming)
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3189 KiB  
Article
Influence of Parameter Sensitivity and Uncertainty on Projected Runoff in the Upper Niger Basin under a Changing Climate
by Ganiyu Titilope Oyerinde and Bernd Diekkrüger
Climate 2017, 5(3), 67; https://doi.org/10.3390/cli5030067 - 27 Aug 2017
Cited by 6 | Viewed by 4709
Abstract
Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from [...] Read more.
Hydro-climatic projections in West Africa are attributed with high uncertainties that are difficult to quantify. This study assesses the influence of the parameter sensitivities and uncertainties of three rainfall runoff models on simulated discharge in current and future times using meteorological data from eight Global Climate Models (GCM). The IHACRES Catchment Moisture Deficit (IHACRES-CMD) model, the GR4J, and the Sacramento model were chosen for this study. During the model evaluation, 10,000 parameter sets were generated for each model and used in a sensitivity and uncertainty analysis using the Generalized Likelihood Uncertainty Estimation (GLUE) method. Out of the three models, IHACRES-CMD recorded the highest Nash-Sutcliffe Efficiency (NSE) of 0.92 and 0.86 for the calibration (1997–2003) and the validation (2004–2010) period, respectively. The Sacramento model was able to adequately predict low flow patterns on the catchment, while the GR4J and IHACRES-CMD over and under estimated low flow, respectively. The use of multiple hydrological models to reduce uncertainties caused by model approaches is recommended, along with other methods for sustainable river basin management. Full article
(This article belongs to the Special Issue Modified Hydrological Cycle under Global Warming)
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1308 KiB  
Article
Hydrologic Alterations Predicted by Seasonally-Consistent Subset Ensembles of General Circulation Models
by Aleksey Y. Sheshukov and Kyle R. Douglas-Mankin
Climate 2017, 5(3), 44; https://doi.org/10.3390/cli5030044 - 26 Jun 2017
Cited by 3 | Viewed by 4668
Abstract
Future climate forcing data at the temporal and spatial scales needed to drive hydrologic models are not readily available. Simple methods to derive these data from historical data or General Circulation Model (GCM) results may not adequately capture future hydrological variability. This study [...] Read more.
Future climate forcing data at the temporal and spatial scales needed to drive hydrologic models are not readily available. Simple methods to derive these data from historical data or General Circulation Model (GCM) results may not adequately capture future hydrological variability. This study assessed streamflow response to daily future climate forcing data produced by a new method using subsets of multi-model GCM ensembles for the mid-21st century period in northeast Kansas. Daily timeseries of precipitation and temperature were developed for six future climate scenarios: stationary, uniform 10% changes in precipitation; shifts based on a 15-GCM ensemble-mean; and shifts based on three seasonally-consistent subsets of GCMs representing Spring–Summer combinations that were wetter or drier than the historical period. The analysis of daily streamflow and hydrologic index statistics were conducted. Stationary 10% precipitation shifts generally bounded the monthly mean streamflow projections of the other scenarios, and the 15-GCM ensemble-mean captured non-stationary effects of annual and seasonal hydrological response, but did not identify important intra-annual shifts in drought and flood characteristics. The seasonally-consistent subset ensembles produced a range of distinct monthly streamflow trends, particularly for extreme low-flow and high-flow events. Meaningful water management and planning for the future will require hydrological impact simulations that reflect the range of possible future climates. Use of GCM ensemble-mean climate forcing data without consideration of the range of seasonal patterns among models was demonstrated to remove important seasonal hydrologic patterns that were retained in the subset ensemble-mean approach. Full article
(This article belongs to the Special Issue Modified Hydrological Cycle under Global Warming)
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