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Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping‐based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow‐related hydrological signatures. Key Points A classification‐based training strategy is introduced for the regional long short‐term memory (LSTM) model The influence of static attributes on the performance of the regional LSTM model in ungauged basins is investigated There is a high level of consistency in the enhancement achieved by the two training strategies, either incorporated with static catchment attributes or based on classification

Despite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment‐scale relationships between drivers and streamflow are still poorly understood. Here, we used streamflow signatures and attributes of 735 catchments from the Catchment Attributes for Brazil data set to investigate the dominant hydrological processes for the catchments. We also assess how catchments group based on hydrologic behavior similarities and analyze which climatic/landscape attributes control the streamflow variability. To classify and group the catchments, we used the k‐means method optimized by the Elbow approach, along with a Principal Component Analysis. Uncertainty on catchment grouping was checked by k‐fold cross‐validation. Then, we used a recursive feature elimination using the random forest technique to assess the most influential catchment attributes to the hydrological signatures. Our results revealed six similarity groups, which followed mainly an aridity gradient ranging from the wettest to the driest, but also seasonality. The climate is the primary driver of hydrological behavior for the water‐limited groups, highlighting the influence and importance of the atmospheric demand in several Brazilian catchments. High soil storage capacity in energy‐limited catchments associated with high precipitation led to high discharge all year due to the subsurface fluxes' contribution. Our findings may be useful to improve streamflow predictability and hydrological behavior identification by further understanding hydrological similarities and their signatures due to catchment landscape characteristics. Further, by employing an easily reproducible methodology and clear metrics to weigh uncertainty, our study provides a significant step toward establishing a catchment‐scale common classification system. Key Points We analyzed catchment‐scale relationships between streamflow and its drivers by clustering the catchments based on hydrological similarity Six catchment groups emerged following mainly an aridity gradient, from the wettest to the driest, and seasonality Climate controls the hydrological behavior of water‐limited catchments, while landscape characteristics control energy‐limited catchments

The present study applies the concept of community structure to classify catchments in two large regions: Australia and the United States. Specifically, the edge betweenness method is applied to monthly streamflow data from a network of 218 stations across Australia and from a network of 639 stations across the United States. The influence of streamflow correlation threshold (i.e. spatial correlation in streamflow between streamflow stations) on catchment classification is examined, through use of different thresholds, suitable for each region, as appropriate. The results reveal that, for both regions, a very small number of communities have a large number of catchments within them (for instance, considering both regions as small as 16–18% of the largest communities combine to represent as much as 70–75% of the catchments), and a significantly large number of communities have only a very few catchments within them (for instance, almost 70% of the communities have only one or two stations within them, and thus represent only about 20% and 10% of the catchments in Australia and the US, respectively). An interpretation of the identified catchment communities in terms of catchment characteristics (station drainage area, station stream length, and station elevation) and flow properties (mean and coefficient of variation) is also made. The catchment classification is also explained using the correlation–distance relationship between the stations.

The landscape scaling relation challenges catchment ecological management; however, how the scaling relations change among naturally and anthropogenically differentiated catchments is still unknown. In this study, approximately 1500 soil samples were determined; more than 800 households were surveyed; and the landscape pattern was investigated in 120 sub-catchments of a subtropical Chinese urbanizing agricultural catchment. A scalogram and a coefficient of variation of the commonly used landscape metrics were estimated among various grain sizes, to quantify the Strength of Landscape Scale Effects (SSE) among sub-catchments. Natural and anthropogenic determinants for the SSE were determined. Then, the determinants incorporating landscape scaling relation were applied to classify the sub-catchments through the k-means clustering analysis. The SSE presented different spatial heterogeneity across the 120 sub-catchments and was not expectedly related to the scaling relation over the entire catchment, especially for the Contagion index and Shannon’s Evenness Index. The SSE were significantly related to natural and anthropogenic factors including the soil sand content, the population density, the relief ratio, and the ratio of arable land to woodland. The four factors combing with landscape scaling relations contributed to the four gratifying convergent categories for the 120 sub-catchments. Category I with a large relief and less anthropogenic disturbance had higher spatially non-stationary relationship, while categories II, III, and IV, with varying degrees of relatively small relief and strong intensities of anthropogenic disturbance, had a lower spatial heterogeneity of the landscape scaling relation. The results implied that category I was required to strengthen environmental protection of spatial differences, and categories II, III, and IV could ignore the landscape scale effects and even upscaling management to save management resources when carrying out ecological management within. Our findings could minimize uncertainty in ecological planning and provide opportunities for the application of multiple-scale management.

In hydrology study and modeling, watershed classification techniques are critical for finding groups of hydrologically similar catchments, and they are used to predict streamflow for ungauged catchments in data-scarce regions during regionalization. In addition, it improves our understanding of the interaction between climate variability, catchment characteristics, and the resulting hydrological response. The goal of this work was to understand the catchments' hydrologic response similarity by combining climatic, physiographic, and flow signature characteristics to highlight the regional pattern of Upper Blue Nile basin flow signatures. Similarities in nine (9) flow signatures and 15 catchment descriptors were explored for 32 catchments of the upper Blue Nile (Abay) basin. Correlation analysis was done using PCA for each catchment characteristic to decrease the multicollinearity problems between different catchment descriptors and flow signatures. The catchments were grouped together and analyzed for flow signature values and physiography characteristics using an advanced hierarchical k-means algorithm combined with Euclidean distance and Ward's linkage method. We found that catchments are classified into three types with both similar flow signatures and catchment descriptors. The most dominant physiographic characteristic in all clusters is the aridity index, which separates the energy-limited catchments from the moisture-limited catchments. In addition, explanatory variables such as higher mean annual precipitation ( P mean ), soil type, topography, and other aspects of the climate/weather had an impact on clustering. Moreover, the results of this study showed that catchment clustering patterns are mainly dependent on discharge characteristics, geographical proximity, and climatic factors of catchments.

The ability of a hydrological model to reproduce observed streamflow can be represented by a large variety of performance measures. Although these metrics may suit different purposes, it is unclear which of them is most appropriate for a given application. Our objective is to investigate various performance measures to assess model structures as tools for catchment classification. For this purpose, 12 model structures are generated using the SUPERFLEX modelling framework, which are then applied to 53 meso-scale basins in the Rhineland-Palatinate (Germany). Statistical and hydrological performance measures are compared with signature indices derived from the flow duration curve and combined into a new performance measure, the standardized signature index sum (SIS). The performance measures are evaluated in their ability to distinguish the relative merits of various model alternatives. In many cases, classical and hydrological performance measures assign similar values to different hydrographs. These measures, therefore, are not well suited for model comparison. The proposed SIS is more effective in revealing differences between model results. It allows for a more distinctive identification of a best performing model for individual basins. A best performing model structure obtained through the SIS can be used as basin classifier.

The transit time of water is an important indicator of catchment functioning and affects many biological and geochemical processes. Water entering a catchment at one point in time is composed of water molecules that will spend different amounts of time in the catchment before exiting. The next water input pulse can exhibit a totally different distribution of transit times. The distribution of water transit times is thus best characterized by a time‐variable probability density function. It is often assumed, however, that the variability of the transit time distribution is negligible and that catchments can be characterized with a unique transit time distribution. In many cases this assumption is not valid because of variations in precipitation, evapotranspiration, and catchment water storage and associated (de)activation of dominant flow paths. This paper presents a general method to estimate the time‐variable transit time distribution of catchment waters. Application of the method using several years of rainfall‐runoff and stable water isotope data yields an ensemble of transit time distributions with different moments. The combined probability density function represents the master transit time distribution and characterizes the intra‐annual and interannual variability of catchment storage and flow paths. Comparing the derived master transit time distributions of two research catchments (one humid and one semiarid) reveals differences in dominant hydrologic processes and dynamic water storage behavior, with the semiarid catchment generally reacting slower to precipitation events and containing a lower fraction of preevent water in the immediate hydrologic response. Key Points Water transit time distributions are highly irregular and variable in time Water transit time distributions differ from hydrologic response functions Differences between the two functions yield information on storage dynamics

Classification is considered as a fundamental step towards improved science and management data. Introducing methods that describe the underlying dynamics of runoff could be a promising way for catchment classification. In this respect, chaos theory and correlation dimension were applied to test its ability to construct a concept to introduce a catchment classification framework in this study. The correlation dimension, as an indicator, was calculated for the daily river flow of sixty grouping stations in different catchments in Iran, ranging in size from 8 to 36500 km2. The results confirmed that applying this indicator to catchments in varied ranges, from low to high complexity, could also be classified. The results showed that Iran's catchments could be classified into four groups based on the complexity degree of runoff time series. The group is categorized as follows: low dimension (D2 <= 4) as Group 1, medium dimension (D2 = 5) as Group 2, high dimension (D2 => 6) as Group 3, and unidentifiable as Group 4. The spatial pattern classification of Iran's catchments indicates that catchments with different climate characteristics, which are located at a far distance from each other, might yield similar responses along with the same level of complexity.

During the last two decades or so, studies on the applications of the concepts of nonlinear dynamics and chaos to hydrologic systems and processes have been on the rise. Earlier studies on this topic focused mainly on the investigation and prediction of chaos in rainfall and river flow, and further advances were made during the subsequent years through applications of the concepts to other problems (e.g. data disaggregation, missing data estimation, and reconstruction of system equations) and other processes (e.g. rainfall-runoff and sediment transport). The outcomes of these studies are certainly encouraging, especially considering the exploratory stage of the concepts in hydrologic sciences. This paper discusses some of the latest developments on the applications of these concepts to hydrologic systems and the challenges that lie ahead on the way to further progress. As for their applications, studies in the important areas of scaling, groundwater contamination, parameter estimation and optimization, and catchment classification are reviewed and the inroads made thus far are reported. In regards to the challenges that lie ahead, particular focus is given to improving our understanding of these largely less-understood concepts and also finding ways to integrate these concepts with the others. With the recognition that none of the existing one-sided ‘extreme-view’ modeling approaches is capable of solving the hydrologic problems that we are faced with, the need for finding a balanced ‘middle-ground’ approach that can integrate different methods is stressed. To this end, the viability of bringing together the stochastic concepts and the deterministic concepts as a starting point is also highlighted.

A proactive sampling strategy was designed and implemented in 2000 to document changes in streams whose catchment land uses were predicted to change over the next two decades due to increased building density. Diatoms, macroinvertebrates, fishes, suspended sediment, dissolved solids, and bed composition were measured at two reference sites and six sites where a socioeconomic model suggested new building construction would influence stream ecosystems in the future; we label these “hazard sites.” The six hazard sites were located in catchments with forested and agricultural land use histories. Diatoms were species-poor at reference sites, where riparian forest cover was significantly higher than all other sites. Cluster analysis, Wishart's distance function, non-metric multidimensional scaling, indicator species analysis, and t-tests show that macroinvertebrate assemblages, fish assemblages, in situ physical measures, and catchment land use and land cover were different between streams whose catchments were mostly forested, relative to those with agricultural land use histories and varying levels of current and predicted development. Comparing initial results with other regional studies, we predict homogenization of fauna with increased nutrient inputs and sediment associated with agricultural sites where more intense building activities are occurring. Based on statistical separability of sampled sites, catchment classes were identified and mapped throughout an 8,600 km² region in western North Carolina's Blue Ridge physiographic province. The classification is a generalized representation of two ongoing trajectories of land use change that we suggest will support streams with diverging biota and physical conditions over the next two decades.