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Abstract The mean transit time (MTT) is an important descriptor of water storage and release dynamics in watersheds. Although MTT studies are numerous for many regions around the world, they are rare for prairie watersheds where seasonally cold or dry conditions require adequate methodological choices towards MTT estimation, especially regarding the handling of sparse data records and tracer selection. To examine the impact of such choices, we used timeseries of δ 18 O and δ 2 H from two contrasted years (2014 and 2015) and relied on two metrics and two modelling methods to infer MTTs in prairie watersheds. Our focus was on nested outlets with different drainage areas, geologies, and known run‐off generation mechanisms. The damping ratio and young water fraction (i.e., the fraction of streamflow with transit times lesser than 3 months) metrics, as well as the sine‐wave modelling and time‐based convolution modelling methods, were applied to year‐specific data. Results show that young water fractions and modelled MTT values were, respectively, larger and smaller in 2014, which was a wet year, compared with that in 2015. In 2014, most outlets had young water fractions larger than 0.5 and MTT values lesser than 6 months. The damping ratio, young water fraction, and sine‐wave modelling methods led to convergent conclusions about watershed water storage and release dynamics for some of the monitored sites. Contrasting results were, however, obtained when the same method was applied using δ 2 H instead of δ 18 O, due to differing evaporation fractionation, or when the time‐based convolution modelling method was used. Some methods also failed to provide any robust results during the dry year (i.e., 2015), highlighting the difficulty in inferring MTTs when data are sparse due to intermittent streamflow. This study therefore allowed the formulation of empirical recommendations for MTT estimation in prairie environments as a function of data availability and antecedent wetness conditions.

Abstract Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.

Abstract This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km 2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave‐one‐out cross‐validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven. , Key Points Uncertainty can be limited in regionalization Physical similarity method is best, followed by spatial proximity Regression‐augmented methods can yield better performance

La quatrième de couverture indique : "L'hydrologie est la science qui étudie les eaux terrestres, leur origine, leur mouvement et leur répartition sur notre planète, leurs propriétés physiques et chimiques, leurs interactions avec l'environnement physique et biologique, et leur influence sur les activités humaines. Au sens plus strict, c'est la science qui étudie le cycle de l'eau dans la nature. Elle examine la distribution géographique et temporelle de l'eau dans l'atmosphère, en surface et dans le sol et le-sous-sol. Hydrologie - Cheminements de l'eau, deuxième édition, permet à l'hydrologue moderne d'explorer les volets scientifique et technique de l'hydrologie. Une description scientifique des phénomènes hydrologiques est offerte afin de proposer une motivation à leur étude, d'identifier les observations requises et d'assurer une compréhension de chaque étape du cycle de l'eau. Les éléments de chacune des situations d'apprentissage sont intégrés dans des modèles théoriques et d'application, et de nombreuses méthodes et techniques pour la résolution de problèmes hydrologiques sont présentées. En plus de fournir une description universelle de l'hydrologie, il couvre de multiples sujets dont l'estimation statistique des débits, l'exploitation des eaux, les systèmes d'information géographique et la télédétection. Il comporte, en outre, de nombreuses figures qui permettent d'en illustrer le propos, une bibliographie substantielle et quelque cent cinquante exercices. Ce livre s'adresse particulièrement aux étudiants de premier cycle universitaire en génie civil, forestier ou agricole, ainsi qu'à ceux de géographie physique, de géologie ou des sciences de l'environnement, mais aussi aux ingénieurs-conseils, au personnel des agences gouvernementales confronté à différents aspects de l'hydrologie et aux professeurs."

Compound dry-hot events enlarge homogenously due to teleconnected land-atmosphere feedbacks. , Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.