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Advances and Challenges in Hydro-Climatological Modeling and Uncertainty Analysis
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Advances and Challenges in Hydro-Climatological Modeling and Uncertainty Analysis

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3798

Special Issue Editors

Dr. Md Shahid Latif

E-Mail Website
Guest Editor
Canada Research Chair in Statistical Hydro-Climatology, National Institute of Scientific Research (INRS-ETE), Quebec City, QC G1K 9A9, Canada
Interests: statistical and stochastic hydrology; compounded event modelling; hydrology and water resources; copula functions in different hydro-climatological studies; risk and uncertainity analysis; extreme value analysis; climate change; statistical linking between meteorological extremes with community wellness and public health; time-series forecasting and modelling
Prof. Dr. Taha B. M. J. Ouarda

E-Mail Website
Guest Editor
Canada Research Chair in Statistical Hydro-Climatology, National Institute of Scientific Research (INRS-ETE), Quebec City, QC G1K 9A9, Canada
Interests: statistical hydrometeorology; water resource management; environmental impact assessment; water quality; climate–health links; statistical models of the environment; climate variability and change; time series; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change (CC) and anthropogenic land use land cover (LULC) activities have heightened the risk that hydro-climatological extremes pose to the ecosystem, society (including human health/mortality), and the economy (e.g., infrastructure/agriculture). When multiple hydro-climatological extreme or nonextreme events (such as floods; drought; precipitation; extreme temperatures, humidity, and wind speed; heatwaves; etc.) occur simultaneously or successively, the compound effect amplifies the overall stress level compared to their individual consideration. Also, because of CC and/or LULC, the methods used to identify, attribute, and measure their frequency of occurrence or severity must be reviewed, as these processes are no longer stationary or time-invariant. Statistical methods have a long history of analysing such complex hydro-climatological data for designing, planning, infilling, and forecasting. Time–frequency analysis, hydrological modelling, multivariate statistical techniques, uncertainty analysis, artificial intelligence, etc., are powerful tools for the stationary and nonstationary modelling of hydro-climatological events in static and dynamic settings. We are particularly interested in and welcome contributions from a diverse community of studies related to innovative stochastic and statistical model development in climatology and hydrology, as well as their uncertainty analysis using historical and/or future projected datasets for different climate models (i.e., the CIMP5 and CIMP6 datasets under different scenarios). We focus on both the local and regional estimation of extreme hydro-climatological events, hydro-climatological forecasting, the design of measurement networks, analyses of the spatial and temporal variability of hydro-climatological datasets, modelling the impact of climate change on hydro-climatological variables, the homogenization of data-series flood damage estimation, and the evolution of hydrological regimes. Potential topics include, but are not limited to, the following:

  • Developing statistical, numerical, and physical modelling tools as well as deterministic and stochastic hydro-climatological models;
  • Modelling compounded hydro-climatological events;
  • Applications of time-invariant and time-varying copulas or extreme dependence models with historical or future projected datasets for hydro-climatic extremes;
  • Applications of Bayesian approaches in hydro-climatological modelling;
  • Advances in nonparametric function estimation in developing multidimensional models for hydro-climatological events;
  • Statistical downscaling, water quality management, and thermal modelling in rivers;
  • Statistical model development in investigating the impact of hydro-climatological extremes on agriculture crop yield, risks of forest fires, health-related issues, etc.;
  • The interplay between climate variability and changes;
  • Complicated interactions between precipitation, temperature, groundwater, surface water, wind speed, temperature, and other climatic variables;
  • Hydrological design and risk assessment under static and dynamic frameworks;
  • Stationary and nonstationary, local and regional, and univariate and multivariate frequency analyses of hydro-climatological variables;
  • Uncertainty assessments in hydrological and climatological projections and observations;
  • Adaption and mitigation strategies;
  • Innovative methods for hazard risk assessments;
  • Climate models (regional and global scales);
  • Regional flood and drought frequency analyses;
  • The spatio-temporal variation and characterization of recent extreme hazards, i.e., floods, drought, wildfire risks, etc.;
  • Addressing uncertainty in understanding hydro-climatology and hydrology across scales;
  • Nonparametric time-series models and stochastic generation;
  • Advancements in urban inundation forecasting techniques;
  • Reviews of previous studies on hydroclimatological hazards and risk assessment.

Dr. Md Shahid Latif
Prof. Dr. Taha B. M. J. Ouarda
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. Water is an international peer-reviewed open access semimonthly 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 2600 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

  • statistical and stochastic hydro-climatology
  • extreme events
  • water resources
  • precipitation
  • evapotranspiration
  • streamflow
  • drought
  • flood
  • downscaling
  • runoff
  • heat wave
  • wind speed
  • modelling extreme hydrological and meteorological events
  • uncertainty analysis
  • risk hazard assessments
  • climate change
  • anthropogenic land use
  • nonstationary hydro-climatological modelling
  • artificial intelligence
  • time-series analysis
  • water quality
  • non-parametric models

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Published Papers (2 papers)

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Research

38 pages, 11320 KiB  
Article
Assessing the Effect of Bias Correction Methods on the Development of Intensity–Duration–Frequency Curves Based on Projections from the CORDEX Central America GCM-RCM Multimodel-Ensemble
by Maikel Mendez, Luis-Alexander Calvo-Valverde, Jorge-Andrés Hidalgo-Madriz and José-Andrés Araya-Obando
Water 2024, 16(23), 3473; https://doi.org/10.3390/w16233473 - 2 Dec 2024
Viewed by 1335
Abstract
This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent [...] Read more.
This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-model ensemble (MME) under RCP 8.5 were used to represent future precipitation. Two stationary BC methods, empirical quantile mapping (EQM) and gamma-pareto quantile mapping (GPM), along with three non-stationary BC methods, detrended quantile mapping (DQM), quantile delta mapping (QDM), and robust quantile mapping (RQM), were selected to adjust daily biases between MME members and observations from the SJO weather station located in Costa Rica. The equidistant quantile-matching (EDQM) temporal disaggregation method was applied to obtain future sub-daily annual maximum precipitation series (AMPs) based on daily projections from the bias-corrected ensemble members. Both historical and future IDF curves were developed based on 5 min temporal resolution AMP series using the Generalized Extreme Value (GEV) distribution. The results indicate that projected future precipitation intensities (2020–2100) vary significantly from historical IDF curves (1970–2020), depending on individual GCM-RCMs, BC methods, durations, and return periods. Regardless of stationarity, the ensemble spread increases steadily with the return period, as uncertainties are further amplified with increasing return periods. Stationary BC methods show a wide variety of trends depending on individual GCM-RCM models, many of which are unrealistic and physically improbable. In contrast, non-stationary BC methods generally show a tendency towards higher precipitation intensities as the return period increases for individual GCM-RCMs, despite differences in the magnitude of changes. Precipitation intensities based on ensemble means are found to increase with the change factor (CF), ranging between 2 and 25% depending on the temporal scale, return period, and non-stationary BC method, with moderately smaller increases for short-durations and long-durations, and slightly higher for mid-durations. In summary, it can be concluded that stationary BC methods underperform compared to non-stationary BC methods. DQM and RQM are the most suitable BC methods for generating future IDF curves, recommending the use of ensemble means over ensemble medians or individual GCM-RCM outcomes. Full article
(This article belongs to the Special Issue Advances and Challenges in Hydro-Climatological Modeling and Uncertainty Analysis)
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27 pages, 9213 KiB  
Article
Seasonal WaveNet-LSTM: A Deep Learning Framework for Precipitation Forecasting with Integrated Large Scale Climate Drivers
by Muhammad Waqas, Usa Wannasingha Humphries, Phyo Thandar Hlaing and Shakeel Ahmad
Water 2024, 16(22), 3194; https://doi.org/10.3390/w16223194 - 7 Nov 2024
Cited by 2 | Viewed by 1815
Abstract
Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole [...] Read more.
Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Real-time Multivariate Madden–Julian Oscillation (MJO), and Multivariate ENSO Index (MEI)—and seasonal precipitation anomalies (rainy, summer, and winter) in Eastern Thailand, utilizing Pearson’s correlation coefficient. Following the establishment of these correlations, the most influential drivers were incorporated into the forecasting models. This study proposed an advanced SPF methodology for Eastern Thailand through a Seasonal WaveNet-LSTM model, which integrates Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) with Wavelet Transformation (WT). By integrating large-scale climate drivers alongside key meteorological variables, the model achieves superior predictive accuracy compared to traditional LSTM models across all seasons. During the rainy season, the WaveNet-LSTM model (SPF-3) achieved a coefficient of determination (R2) of 0.91, a normalized root mean square error (NRMSE) of 8.68%, a false alarm rate (FAR) of 0.03, and a critical success index (CSI) of 0.97, indicating minimal error and exceptional event detection capabilities. In contrast, traditional LSTM models yielded an R2 of 0.85, an NRMSE of 10.28%, a FAR of 0.20, and a CSI of 0.80. For the summer season, the WaveNet-LSTM model (SPF-1) outperformed the traditional model with an R2 of 0.87 (compared to 0.50 for the traditional model), an NRMSE of 12.01% (versus 25.37%), a FAR of 0.09 (versus 0.30), and a CSI of 0.83 (versus 0.60). In the winter season, the WaveNet-LSTM model demonstrated similar improvements, achieving an R2 of 0.79 and an NRMSE of 13.69%, with a FAR of 0.23, compared to the traditional LSTM’s R2 of 0.20 and NRMSE of 41.46%. These results highlight the superior reliability and accuracy of the WaveNet-LSTM model for operational seasonal precipitation forecasting (SPF). The integration of large-scale climate drivers and wavelet-decomposed features significantly enhances forecasting performance, underscoring the importance of selecting appropriate predictors for climatological and hydrological studies. Full article
(This article belongs to the Special Issue Advances and Challenges in Hydro-Climatological Modeling and Uncertainty Analysis)
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