The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL) - Mai, Juliane; Shen, Hongren; Tolson, Bryan A.; Gaborit, Étienne; Arsenault, Richard; Craig, James R.; Fortin, Vincent; Fry, Lauren M.; Gauch, Martin; Klotz, Daniel; Kratzert, Frederik; O'Brien, Nicole; et al. (Hydrology and Earth System Sciences, 2022)
Abstract. Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×106 km2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basin-wise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed. Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways – either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model. While the locally calibrated models lead to good performance in calibration and temporal validation (even outperforming several regionally calibrated models), they lose performance when they are transferred to locations that the model has not been calibrated on. This is likely to be improved with more advanced strategies to transfer these models in space. The regionally calibrated models – while losing less performance in spatial and spatiotemporal validation than locally calibrated models – exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i.e., HYMOD2-lumped) and regionally calibrated models (i.e., MESH-SVS-Raven and GEM-Hydro-Watroute) due to varying reasons. The machine-learning-based model was not included here as it is not set up to simulate AET, SSM, and SWE. All basin-aggregated model outputs and observations for the model variables evaluated in this study are available on an interactive website that enables users to visualize results and download the data and model outputs.

Convolutional neural network and long short-term memory models for ice-jam predictions - Madaeni, Fatemehalsadat; Chokmani, Karem; Lhissou, Rachid; Homayouni, Saeid; Gauthier, Yves; Tolszczuk-Leclerc, Simon (The Cryosphere, 2022)
In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.
Publisher: Copernicus GmbH
Keywords: Infolettre; Infolettre_Juillet_2022; Site_internet_RIISQ

Modélisation locale et régionale du régime thermique des rivières. - Souaissi, Zina (Doctorat en sciences de l'eau, 2022)
La température extrême de l’eau influence de nombreuses propriétés physiques, chimiques et biologiques des rivières. l ’ évaluation de l ’ Une prédiction précise de la température de l’eau est importante pour impact environnemental. Dans ce cadre, différents modèles ont été utilisés pour estimer les températures de l ’ linéaires simp eau à différentes échelles spatiales et temporelles, allant des méthodes les pour déterminer l’incertitude à des modèles sophistiqués non linéaires. Cependant, cette variable primordiale n’a pas été traitée dans un contexte probabiliste (ou fréquentiste). Donc, l’estimation des évènements extrêmes thermiques à l’aide des approc hes d’analyse fréquentielle locale (AFL) est importante. Lors de l’estimation des extrêmes thermiques, il est crucial de tenir compte de la forme de la distribution de fréquences considérée. Dans la première partie de la thèse , nous nous concentrons sur la sélection de la distribution de probabilité la plus appropriée des températures des rivières. Le critère d critère d ’ ’ information d ’ Akaike (AIC) et le information bayésien (BIC) sont utilisés pour évaluer la qualité de l distributions statis ’ ajustement des tiques. La validation des distributions candidates appropriées est également effectuée en utilisant l ’ approche de diagramme de rapport des L obtenus montrent que la distribution de Weibull (W2) moments (MRD). Les résultats est celle qui semble s’ajuster le données provenant des stations de haute altitude, tandis que les mieux aux séries d’extrêmes provenant des stations situées dans les régions de basse altitude sont bien adaptées avec la distribution normale (N). Ceci correspond au premier article. L a ’ couverture spatiale des données de température des cours d ’ eau est limitée dans de nombreuses régions du monde. Pour cette raison, une analyse fréquentielle régionale (AFR) permettant d estimer les extrêmes de température des rivières sur des sites non jau gés ou mal surveillés est nécessaire. En général, l’AFR inclut deux étapes principales, la délimitation des régions homogènes (DRH) qui vise à déterminer les sites similaires, et l’estimation régionale (ER) qui transfère l’information depuis les sites déte rminés dans la première étape vers le site cible. Par conséquent, le modèle d’indice thermique (IT) est introduit dans le contexte d’AFR pour estimer les extrêmes du régime thermique. Cette méthode est analogue au modèle d ’ indice de crue (IF) largement uti lisé en hydrologie. Le modèle IT incorpore l’homogénéité de la distribution de fréquence appropriée pour chaque région, ce qui offre une plus grande flexibilité. Dans cette étude, le modèle IT est comparé avec la régression linéaire multiple (MLR). Les rés ultats indiquent que le modèle IT fournit la meilleure performance (Article 2) . Ensuite, l’approche d’analyse canonique des corrélations non linéaires (ACCNL) est intégrée dans la DRH, présentée dans le Chapitre 4 de ce manuscrit (Article 3). Elle permet de considérer la complexité des phénomènes thermiques dans l’étape de DRH. Par la suite, dans le but d’identifier des combinaisons (DRH-ER) plus prometteuses permettant une meilleure estimation, une étude comparative est réalisée. Les combinaisons considérées au niveau des deux étapes de la procédure de l’AFR sont des combinaisons linéaires, semi-linéaires et non linéaires. Les résultats montrent que la meilleure performance globale est présentée par la combinaison non linéaire ACCNL et le modèle additif généralisé (GAM). Finalement, des modèles non paramétriques tels que le foret aléatoire (RF), le boosting de gradient extrême (XGBoost) et le modèle régression multivariée par spline adaptative (MARS) sont introduits dans le contexte de l’AFR pour estimer les quantiles thermiques et les comparer aux quantiles estimés à l’aide du modèle semi-paramétrique GAM. Ces modèles sont combinés avec des approches linéaires et non linéaires dans l’étape DRH, telles que ACC et ACCNL, afin de déterminer leur potentiel prédictif. Les résultats indiquent que ACCNL+GAM est la meilleure, suivie par ACC+MARS. Ceci correspond à l’article 4. Extreme water temperatures have a significant impact on the physical, chemical, and biological properties of the rivers. Environmental impact assessment requires accurate predictions of water temperature. The models used to estimate water temperatures within this framework range from simple linear methods to more complex nonlinear models. However, w ater temperature has not been studied in a probabilistic manner. It is, therefore, essential to estimate extreme thermal events using local frequency analysis (LFA). An LFA aims to predict the frequency and amplitude of these events at a given gauged locat ion. In order to estimate quantiles, it is essential to consider the shape of the frequency distribution being considered. The first part of our study focuses on selecting the most appropriate probability distribution for river water temperatures. The Akai ke information criteria (AIC) and the Bayesian information criteria (BIC) are used to evaluate the goodness of fit of statistical distributions. An Lmoment ratio diagram (MRD) approach is also used to validate sui table candidate distributions. The results good fit for extremes data from the highindicate that the Weibull distribution (W2) provides a altitude stations, while the normal distribution (N) is most appropriate for lowaltitude stations. This corresponds to the first article. In many parts of the world, river temperature data are limited in terms of spatial coverage and size of the series. Therefore, it is necessary to perform a regional frequency analysis (RFA) to estimate river temperature extremes at ungauged or poorly monitored sites. Generall y, RFA involves two main steps: delineation of homogenous regions (DHR), which identifies similar sites, and regional estimation (RE), which transfers information from the identified sites to the target site. The thermal index (TI) model is introduced in t he context of RFA to estimate the extremes of the thermal regime. This method is analogous to the index flood (IF) model commonly used in hydrology. The TI model considers the homogeneity of the appropriate frequency distributions for each region, which pr ovides larger flexibility. This study compares the TI model with multiple linear regression (MLR) approach. Results indicate that the TI model leads to better performances (Article 2). Then, the nonlinear canonical correlations analysis (NLCCA) approach is integrated into the DHR, as presented in Chapter 4 of this manuscript (Article 3). It allows considering the complexity of the thermal phenomena in the DHR step. A comparative study is then conducted to identify more promising combinations (DHR RE), that RFA procedure, linear, semilead to best estimation results. In the two stages of the linear, and nonlinear combinations are considered. The results of this study indicate that the nonlinear combination of the NLCCA and the generalized additive model (GAM ) produces the best overall performances. Finally, nonparametric models such as random forest (RF), extreme gradient boosting (XGBoost), and multivariate adaptive regression splines (MARS) are introduced in the context of RFA in order to estimate thermal q uantiles and compare them to quantiles estimated using the semiparametric GAM model. The predictive potential of these models is determined by combining them with linear and nonlinear approaches, such as CCA and NLCCA, in the DHR step. The results indicat e that NLCCA+GAM is the best, followed by CCA+MARS. This corresponds to article 4.

Climate change and the nonlinear impact of precipitation anomalies on income inequality - Palagi, Elisa; Coronese, Matteo; Lamperti, Francesco; Roventini, Andrea (Proceedings of the National Academy of Sciences, 2022)
Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids - Li, Mingke; McGrath, Heather; Stefanakis, Emmanuel (ISPRS International Journal of Geo-Information, 2022)
Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.