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Editorial Board Members’ Collection Series: Projecting Aleatoric Uncertainty of Temperature—Precipitation and Flows into the Future to Assess Climate Change Impacts and Deciding on Adaptation Measures

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A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Hydrology–Climate Interactions".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 3609

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Special Issue Editors

Dr. Arona Diedhiou

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Guest Editor

Institute of Environmental Geosciences (IGE), Université Grenoble Alpes/National Research Institute for Sustainable Development (IRD), 38000 Grenoble, France
Interests: African climate system; hydroclimatic extremes and drivers; hydrometeorology; West African monsoon dynamics and teleconnections; atmospheric dynamic and climate change in the tropics
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ali A. Assani

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Guest Editor

Département des Sciences de l’Environnement, University of Quebec at Trois-Rivières, Trois-Rivières, QC G9A 5H7, Canada
Interests: hydrology; hydroclimatology; fluvial geomorphology; climatology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce a new Special Issue titled “Editorial Board Members’ Collection Series: Projecting Aleatoric Uncertainty of Temperature—Precipitation and Flows into the Future to Assess Climate Change Impacts and Deciding on Adaptation Measures”, which will collect papers invited by the Editorial Board Members.

Indeed, as uncertainty management is an integral part of risk management, decision-makers need to know the degree of uncertainty associated with specific data sources or climate projections in order to be able to take them into account in the development of plausible adaptation options. These uncertainties should not prevent adaptation decisions from being made and their importance depends on factors such as the time horizon and the reversibility of the decision, etc. However, emerging and future climate change makes traditional conceptions of uncertainty and risk insufficient to adapt to and potentially counteract the potential harm (risk) that can be caused by increasing uncertainty.

The aim of this Special Issue is to contribute to discussions of methods for assessing aleatoric uncertainties in global and regional climate projections, and to show how differences in the understanding of uncertainties (and risks) inform and determine adaptation policies and actions at different scales. This Special Issue will provide a venue for networking and communication between the community of hydro-climatologists, scholars in the field of impacts of climate change on runoff, flow, water resources, and adaptation practitioners in the fields related to water.

All papers will be published in open access following peer review.

Dr. Arona Diedhiou
Prof. Dr. Ali A. Assani
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. Hydrology 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.

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Published Papers (2 papers)

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Research

27 pages, 13234 KiB

Open AccessArticle

How Do CMIP6 HighResMIP Models Perform in Simulating Precipitation Extremes over East Africa?

by Hassen Babaousmail, Brian Odhiambo Ayugi, Kenny Thiam Choy Lim Kam Sian, Herijaona Hani-Roge Hundilida Randriatsara and Richard Mumo

Hydrology 2024, 11(7), 106; https://doi.org/10.3390/hydrology11070106 - 20 Jul 2024

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Abstract

This work assesses the ability of nine Coupled Model Intercomparison Project phase 6 (CMIP6) High-Resolution Model Intercomparison Project (HighResMIP) models and their ensemble mean to reproduce precipitation extremes over East Africa for the period 1995–2014. The model datasets are assessed against two observation datasets: CHIRPS and GPCC. The precipitation indices considered are CDD, CWD, R1mm, R10mm, R20mm, SDII, R95p, PRCPTOT, and Rx1day. The overall results show that HighResMIP models reproduce annual variability fairly well; however, certain consistent biases are found across HighResMIP models, which tend to overestimate CWD and R1mm and underestimate CDD and SDII. The HighResMIP models are ranked using the Taylor diagram and Taylor Skill Score. The results show that the models reasonably simulate indices, such as PRCPTOT, R1mm, R10mm, R95p, and CDD; however, the simulation of SDII CWD, SDII, and R20mm is generally poor. They are CMCC-CM2-VHR4, HadGEM31-MM, HadGEM3-GC31-HM, and GFDL-CM4. Conversely, MPI-ESM1-2-XR and MPI-ESM1-2-HR show remarkable performance in simulating the OND season while underestimating the MAM season. A comparative analysis demonstrates that the MME has better accuracy than the individual models in the simulation of the various indices. The findings of the present study are important to establish the ability of HighResMIP data to reproduce extreme precipitation events over East Africa and, thus, help in decision making. However, caution should be exercised in the interpretation of the findings based on individual CMIP6 models over East Africa given the overall weakness observed in reproducing mean precipitation. Full article

(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Projecting Aleatoric Uncertainty of Temperature—Precipitation and Flows into the Future to Assess Climate Change Impacts and Deciding on Adaptation Measures)

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30 pages, 1637 KiB

Open AccessFeature PaperArticle

Enhancing Monthly Streamflow Prediction Using Meteorological Factors and Machine Learning Models in the Upper Colorado River Basin

by Saichand Thota, Ayman Nassar, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi and Pouya Hosseinzadeh

Hydrology 2024, 11(5), 66; https://doi.org/10.3390/hydrology11050066 - 1 May 2024

Cited by 1 | Viewed by 2216

Abstract

Streamflow prediction is crucial for planning future developments and safety measures along river basins, especially in the face of changing climate patterns. In this study, we utilized monthly streamflow data from the United States Bureau of Reclamation and meteorological data (snow water equivalent, temperature, and precipitation) from the various weather monitoring stations of the Snow Telemetry Network within the Upper Colorado River Basin to forecast monthly streamflow at Lees Ferry, a specific location along the Colorado River in the basin. Four machine learning models—Random Forest Regression, Long short-term memory, Gated Recurrent Unit, and Seasonal AutoRegresive Integrated Moving Average—were trained using 30 years of monthly data (1991–2020), split into 80% for training (1991–2014) and 20% for testing (2015–2020). Initially, only historical streamflow data were used for predictions, followed by including meteorological factors to assess their impact on streamflow. Subsequently, sequence analysis was conducted to explore various input-output sequence window combinations. We then evaluated the influence of each factor on streamflow by testing all possible combinations to identify the optimal feature combination for prediction. Our results indicate that the Random Forest Regression model consistently outperformed others, especially after integrating all meteorological factors with historical streamflow data. The best performance was achieved with a 24-month look-back period to predict 12 months of streamflow, yielding a Root Mean Square Error of 2.25 and R-squared (

R^{2}

) of 0.80. Finally, to assess model generalizability, we tested the best model at other locations—Greenwood Springs (Colorado River), Maybell (Yampa River), and Archuleta (San Juan) in the basin. Full article