Catchment Classification Research Articles

Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification

Understanding Catchments’ Hydrologic Response Similarity of Upper Blue Nile (Abay) basin through catchment classification

Affinity propagation approach for catchment classification applied to arid catchments

One of the major issues in the arid region is the availability of hydrological data for hydrological studies of the catchments for water resources projects. Since the Kingdom of Saudi Arabia (KSA) is a huge country and contains many arid catchments it is awfully expensive and time-consuming to make hydrological networks for studying all these catchments. Therefore, the Affinity Propagation (AP) clustering technique is proposed to cluster catchments into groups that are similar in morphological, hydrological, and land cover characteristics and defining an exemplar (a representative catchment) to each group. This catchment is utilized for the installation of a detailed hydrological network. The hydrological response of that catchment can be transferred and scaled appropriately to other catchments in the cluster since they are hydrologically and morphologically similar. A pilot study is performed on 18 sub-catchments in the southwestern part of KSA. GIS software is used to extract catchment attributes and the clustering process is performed using the AP cluster packages in R software. The results show that four clusters are obtained based on the morphological attributes (twenty-eight attributes), five clusters based on hydrological attributes (twelve attributes), and three clusters based on land cover and CN (three kinds of land cover as attributes). The AP clustering technique was evaluated by the construction of a correlation matrix that shows a high correlation of 0.817–0.999. This study provides a robust technique that is effective and efficient to identify the similarity of catchments and can help hydrologists to develop a catchment management application in arid regions.

ГЕОДЕЗІЯ, КАРТОГРАФІЯ І АЕРОФОТОЗНІМАННЯ

Aim of the work. To develop a method of automated allocation of catchment basins and obtaining their hydrological and morphometric characteristics, which is based on digital terrain models. Methods and results of work. A necessary condition for the correct filling of the terrain is the presence of points of true flow at the edge of the settlement area (if the river flows into the lake, it should not enter the calculated area completely, otherwise incorrect results will be obtained). By performing the operation of filling the relief of the terrain, a new dem is created, which does not contain fictitious depressions and is used in the next step as input data to calculate the flow direction according to the algorithm d8. According to the proposed technological scheme it is necessary to process step by step the following six blocks: filling of closed depressions, calculation of runoff direction, calculation of total runoff, creation of point vector data set of closing points (mouth points), creation of watershed boundaries, raster-vector data conversion. Theoretical research tested the method of automated allocation of watersheds, namely the determination of hydrological and morphometric parameters of the terrain. The pools were ranked according to these parameters according to the existing classifications, a series of relevant thematic electronic maps was compiled. It should be noted that in Skole district of lviv region there are 590 catchment areas, and their area is 1407 km2. Watersheds are classified by outcrop, namely low-mountain basins in the region of 6, their area is 7 km2, medium-mountain 360, area 755 km2, high-mountain 224, area 645 km2. Pools are classified according to the average slope: the first category from 0-3 degrees, very gentle slopes - pools 27, area 7 km2; the second category from 9-12 degrees, sloping slopes-pools of 128, the area 303 km2; the third category from 12-15> degrees, steep slopes - pools of 225, the area 648 km2. The accuracy between the reference and the original relief model was evaluated. We can say that sle = 0.63 (m) slope, sle = 5.43 (m) height. Scientific novelty and practical significance. The technological scheme of automated separation of catchment basins according to digital relief models for Skoliv district of lviv region is proposed and the method of separation of catchment basins is worked out. According to the developed method, maps of watercourses of different orders and their catchment basins and classification of basins by area on the territory of Skole administrative district, which can be used by local organizations on water resources, are constructed.

Regional Patterns and Physical Controls of Streamflow Generation Across the Conterminous United States

AbstractThis study unveils regional patterns of dominant streamflow generation mechanisms across the conterminous United States and identifies their climatic and physiographic controls. Six signatures describing the characteristics of rainfall‐stormflow responses and the characteristics of base flow response are extracted from continuous rainfall‐streamflow time series for 432 US catchments. These study catchments are classified into eight classes based on similarities of the six signatures, and dominant streamflow generation mechanisms for each class are inferred based on the magnitudes of the six signatures. This catchment classification reveals geographically coherent regional patterns across the conterminous United States. Catchment climatic and physiographic characteristics most relevant to this catchment classification are identified using techniques such as correlation analysis and random forest modeling. The most relevant characteristics are climate aridity, median event rainfall volume, topographic slope, soil permeability, rock type, and vegetation density, which are found to be the main controlling factors governing the dominant streamflow generation mechanisms. The linkages between the inferred dominant streamflow generation mechanisms and specific combinations of the climatic and catchment physiographic characteristics identified above are synthesized in a conceptual diagram, as an extension of the classical Dunne diagram. The new findings, while largely consistent with the results of previous empirical and modeling studies, add rich process detail to the transferable understanding of regional patterns of dominant streamflow generation mechanisms and their climatic and physiographic controls.

Complexity of hydrologic basins: A chaotic dynamics perspective

The objective of this study is to examine the dynamic complexity of hydrologic basins using techniques of phase space reconstruction. These non-parametric, model-free techniques which have their basis in the theory of chaotic dynamical systems are advantageous in resurrecting the features of multivariable dynamics solely from one-dimensional timeseries. Dynamic complexity is characterized by two measures directly interpretable as number of active degrees of freedom (dimensionality) and strength of nonlinearity. The analysis is conducted on daily streamflow observations of 408 basins from the Model Parameter Estimation Project (MOPEX) dataset spanning a wide range of climatic, topographic and land surface properties. The results show that the two complexity measures vary considerably across basins; however, their distributions are positively skewed, indicating that most basins exhibit low dimensionality and moderate nonlinearity. By examining how dynamic complexity relate to a set of 15 basin properties, it was found that dynamics dimensionality is primarily related to basin size; precisely larger basins exhibit lower dimensional dynamics. On the other hand, strength of nonlinearity is found to be regulated by the extent of vegetation in land cover. Specifically, basins with limited vegetation cover tend to have more linear dynamics. Furthermore, some observations regarding the presence of chaotic dynamics and the relationship between dynamic complexity and accuracy of prediction are presented. The results of this study have implications and potential relevance to catchment similarity and classification frameworks as well as model selection and extrapolation of model parameters to ungauged basins.

Community structure concept for catchment classification: A modularity density-based edge betweenness (MDEB) method

Catchment classification is useful for a variety of purposes in hydrologic, environmental, and ecosystem studies. In the context of classification, the concept of community structure, within the realm of complex networks, is particularly attractive and gaining attention in catchment classification studies. Among the many community structure methods, the edge betweenness (EB) method, which applies a hierarchical clustering concept, is one of the most widely used. The EB method, however, is susceptible to the issue of scale (or size) of the network, essentially due to the modularity function that is used to measure the strength of the community structure. To overcome this limitation, the present study proposes an improvement to the EB method. The proposed method, termed as the Modularity Density-based Edge Betweenness (MDEB) method, uses a modularity density function (or D value) by maximization, to obtain the best split of the network. The effectiveness of the MDEB method is evaluated through its application for catchment classification using streamflow data from two large networks: 218 stations from Australia and 639 stations from the United States (US). For each network, three different scenarios in network sizes are studied: (1) the entire network; (2) smaller network sizes based on 100 random realizations, with each realization having 100 and 300 stations for Australia and the US, respectively; and (3) smaller network sizes based on nine different drainage division regions in Australia and 18 different hydrologic units in the US. The classification outcomes from the MDEB method for these three scenarios are compared with those from the EB method. The results indicate that the MDEB method generally performs better than the EB method, for both Australia and the US. The superiority of the MDEB method is evaluated in terms of the number of communities identified and the number of stations that change communities when different network sizes are considered. The catchment communities are also interpreted in terms of the distance-correlation relationship. The results from the present study offer further evidence as to the usefulness and effectiveness of the community structure concept for catchment classification, especially the proposed MDEB method.

A Canberra distance-based complex network classification framework using lumped catchment characteristics

Hydrological prediction in ungauged catchments remains a challenge despite numerous attempts in the past. The well-known solution to this challenge is transfer of information from gauged catchments to ‘hydrologically-similar’ ungauged catchments, an approach known as ‘regionalization.’ The basis of regionalization is, thus, classification of catchments into hydrologically-similar groups. A major limitation of the traditional classification methods, such as the K-means clustering algorithm, is that they are not very suitable when the classes are not well separated from each other. Additionally, they cannot determine the number of classes in a dataset automatically. To overcome these limitations, some recent studies have used complex networks-based classification algorithms, widely known as community structure algorithms, for catchment classification. However, such studies have applied the community structure algorithms only to time series of hydrological variables (e.g. streamflow) and have not so far used lumped information (e.g. mean rainfall and mean slope). In this short communication, we propose a Canberra distance-based metric that can enable a community structure algorithm to exploit lumped information. For demonstration, the proposed metric is used to compute link weights for the multilevel modularity optimization algorithm. The proposed classification method is applied to lumped data from 494 basins situated in the CONtiguous United States (CONUS) for their classification, and its performance is compared with that of the K-means clustering algorithm. By and large, the proposed classification framework opens up an alternative avenue towards prediction in ungauged catchments.

Catchment classification using community structure concept: application to two large regions

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.

Future streamflow regime changes in the United States: assessment using functional classification

Abstract. Streamflow regimes are changing and expected to further change under the influence of climate change, with potential impacts on flow variability and the seasonality of extremes. However, not all types of regimes are going to change in the same way. Climate change impact assessments can therefore benefit from identifying classes of catchments with similar streamflow regimes. Traditional catchment classification approaches have focused on specific meteorological and/or streamflow indices, usually neglecting the temporal information stored in the data. The aim of this study is 2-fold: (1) develop a catchment classification scheme that enables incorporation of such temporal information and (2) use the scheme to evaluate changes in future flow regimes. We use the developed classification scheme, which relies on a functional data representation, to cluster a large set of catchments in the conterminous United States (CONUS) according to their mean annual hydrographs. We identify five regime classes that summarize the behavior of catchments in the CONUS: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Our results show that these spatially contiguous classes are not only similar in terms of their regimes, but also their flood and drought behavior as well as their physiographical and meteorological characteristics. We therefore deem the functional regime classes valuable for a number of applications going beyond change assessments, including model validation studies or predictions of streamflow characteristics in ungauged basins. To assess future regime changes, we use simulated discharge time series obtained from the Variable Infiltration Capacity hydrologic model driven with meteorological time series generated by five general circulation models. A comparison of the future regime classes derived from these simulations with current classes shows that robust regime changes are expected only for currently melt-influenced regions in the Rocky Mountains. These changes in mountainous, upstream regions may require adaption of water management strategies to ensure sufficient water supply in dependent downstream regions. Highlights. Functional data clustering enables formation of clusters of catchments with similar hydrological regimes and a similar drought and flood behavior. We identify five streamflow regime clusters: (1) intermittent regime, (2) weak winter regime, (3) strong winter regime, (4) New Year's regime, and (5) melt regime. Future regime changes are most pronounced for currently melt-dominated regimes in the Rocky Mountains. Functional regime clusters have widespread utility for predictions in ungauged basins and hydroclimate analyses.

Classification of Urban Bus-Stop Catchments for Selecting Appropriate Sidewalk Facility on Access Roads

Classification of Urban Bus-Stop Catchments for Selecting Appropriate Sidewalk Facility on Access Roads

Contribution to the classification of small catchments according to the drainage area

ABSTRACT The adoption of an area value to define large, medium or small river basins should take into account the hydrological processes involved in the basin. Changes in runoff are identified with greater sensitivity in small catchments, as in large catchments it is not possible to disregard factors such as infiltration and evaporation, which directly interfere with the rainfall-runoff conversion phenomenon. Thus, the objective of this study is to classify the small catchments of the Amazon Region by drainage area using a simple linear model (SLM). The SLM is based on a linear and time-invariant system that establishes a cause-and-effect relationship between the data. The model was applied to rainfall and runoff data from test catchments to verify the linearity between these variables. The performance of the model was evaluated through the Root Mean Square Error (RMS) and metric distance . The results indicated that in the catchments with drainage areas less than to 500 km², the adjustment between the observed and simulated flow was good, presenting RMS and metric distance values less than 3 m³/s and 2000, respectively. In basins of the dimensions above 500 km², the RMS and results began to show nonlinearity from 4 m³/s and 3000, respectively, showing that SLM should not be applied to medium and large river basins. Thus, the small catchments of the Amazon were classified as drainage areas less than 500 km².

Intra-catchment comparison and classification of long-term streamflow variability in the Alps using wavelet analysis

Understanding the temporal and spatial variability of river discharge of alpine hydrological systems is of particular interest due to their relevance for water uses including water provisioning, hydropower production and touristic activities. Streamflow variability is highly heterogeneous both in time and space due to several reasons, such as a differentiated response to climate change, differences in catchment morphology and geographic location. Therefore, catchment classification for these systems is challenging. A suitable tool to determine the crucial scales of variability of a non-stationary time series is wavelet transform. In this work, we compute the wavelet coherence between fifty selected gauging stations located within the Inn River catchment to classify them by runoff behavior, focusing only on long term variability, between one and eight years scales. This choice allows us to filter out the effect of local meteorological patterns and the effects of hydropower production. In addition, we decompose the streamflow signals in three levels (256 days, three years, six years) using Discrete Wavelet Transform to further understand the detected alterations in the streamflow signal. Three main runoff behaviors (referred as three classes) are found at the yearly scale. Focusing on two-four years scales a loss of coherence between time series located at different elevations becomes significant in the 1980s. Prior to 1980 we detect four different behaviors, while after 1980 we detect eleven different classes. At larger scales the stations are clustered in four classes. Our analysis highlights that catchment classification may depend on the scale of the analyzed signal and it may vary both in time and space. This research contributes to the development of new methods for catchment classification, which is highly relevant for many hydrological applications such as prediction in ungauged basins, model parameterization, understanding the potential impact of environmental and climatic changes, and transferring information from gauged catchments to the ungauged ones.

Hydrological signatures describing the translation of climate seasonality into streamflow seasonality

Abstract. Seasonality is ubiquitous in nature, and it is closely linked to water quality, ecology, hydrological extremes, and water resources management. Hydrological signatures aim at extracting information about certain aspects of hydrological behaviour. Commonly used seasonal hydro-climatological signatures consider climate or streamflow seasonality, but they do not consider how climate seasonality translates into streamflow seasonality. In order to analyse the translation of seasonal climate input (precipitation minus potential evapotranspiration) into seasonal catchment output (streamflow), we represent the two time series by their seasonal (annual) Fourier mode, i.e. by sine waves. A catchment alters the input sine wave by reducing its amplitude and by shifting its phase. We propose to use these quantities, the amplitude ratio and the phase shift, as seasonal hydrological signatures. We present analytical solutions describing the response of linear reservoirs to periodic forcing to interpret the seasonal signatures in terms of configurations of linear reservoirs. Using data from the UK and the US, we show that the seasonal signatures exhibit hydrologically interpretable patterns and that they are a function of both climate and catchment attributes. Wet, rather impermeable catchments hardly attenuate the seasonal climate input. Drier catchments, especially if underlain by a productive aquifer, strongly attenuate the input sine wave leading to phase shifts up to several months. As an example application, we test whether two commonly used hydrological models (Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Streamflow – IHACRES; modèle du Génie Rural à 4 paramètres Journalier – GR4J) can reproduce the observed ranges of seasonal signatures in the UK. The results show that the seasonal signatures have the potential to be useful for catchment classification, predictions in ungauged catchments, and model building and evaluation. The use of potential evapotranspiration in the input restricts the applicability of the signatures to energy-limited (humid) catchments.

Seasonality of mean flows as a potential tool for the assessment of ecological processes: Mountain rivers, Polish Carpathians.

The classification of river catchments according to their hydrological regime is crucial elements of regionalisation. In absence of hydrological data, the regionalisation of catchment method may be used to asses many flows characteristics like regime or design flow and thus provide help in the analysis of hydrological and ecological processes and also in the management of water resources. Correct clarification of catchments requires knowledge about the main factors that influence on river regime, like meteorologic conditions, land cover/land use, geology, soil properties terrain features, human activities. The aim of the study was to analyse the relationship between selected catchment attributes along with precipitation climatology and seasonality of mean flows (MQ) in the mountainous rivers in the Upper Vistula basin (the biggest and the most important river in Poland) and regionalisation catchments based on seasonality index. To achieve the objective of the study, we concentrated on the mountain stream and river catchments that are regionalised to the Upper Vistula basin (all of which are Vistula tributaries) and we employed the Colwell's seasonality index in an attempt to clear up the said ecohydrological measures. The study confirmed that in mountainous catchments, where response time to rainfall is shorter due to larger slopes, higher seasonality of mean monthly discharges, as expressed by the seasonality index M, is observed. In this case, variability of seasonal rainfall affected seasonality of MQ. In case of smaller slopes and large forest cover and catchment areas, seasonality of flows was lower. The innovative aspect of the presented study is the attempt to correlate the Colwell's seasonality index with the physiographic and meteorological characteristics of the catchment. Until now, the characteristics of the catchments have been used as factors differentiating the hydrological regime of the catchments, thus allowing for agglomeration of similar catchments. Our results foster better understanding of the natural processes in the river basin, which definitely would help in better management of the environment and its relationship with huge number of people living there and depend on it. These results show that the regression tree methods based on CART algorithm can be used as effective tool for classification of catchments.

Factors affecting dominant peak‐flow runoff‐generation mechanisms among five neighbouring granitic headwater catchments

AbstractWe examined how and why dominant peak‐flow runoff‐generation mechanisms differ among neighbouring headwater catchments. We monitored runoff and groundwater levels and performed terrain analyses in a granitic second‐order catchment and its four neighbouring subcatchments in the Kiryu Experimental Watershed in Japan. Our analysis of lag times from peak rainfall to peak runoff suggests differences in the dominant peak‐flow runoff‐generation mechanisms among the five catchments. For two of the three zero‐order catchments, with few perennial groundwater bodies, subsurface flow from hillslopes was the dominant mechanism at some events. However, the dominant mechanisms were channel precipitation and riparian runoff at almost all events in first‐ and second‐order catchments and in the third zero‐order catchment, which has a large perennial groundwater body over a bedrock depression in the riparian zone. In this zero‐order catchment, the quick‐flow ratio was the smallest of the five catchments because subsurface flow from the hillslope was buffered at the riparian zone. These facts suggest that the channel length, riparian buffering, and hillslope connectivity were the factors governing the different dominant peak‐flow runoff‐generation mechanisms among the catchments. Riparian buffering was affected, not only by surface topography, but also by bedrock topography and bedrock groundwater (BGW) dynamics. Our findings indicate that both of BGW dynamics and topography are important for catchment classification, and the relative importance of topography increases with the change from baseflow to stormflow. Furthermore, mismatching between a geographic source and a flow path resulted in different catchment classifications depending on the approach. Therefore, multiple approaches during both baseflow and stormflow periods are necessary for catchment classification to apply information obtained from one headwater catchment to other headwater catchments within the same region.

Catchment classification in a transboundary river using runoff and sub-basin characteristics

ABSTRACTTransboundary rivers that run through separate water management jurisdictions with distinct hydrological characteristics sometimes experience competitive water usage. A system of classification of catchments along such river basins could help ease hydrological modelling on subbasin scales and hopefully refine the spatial and temporal resolution of information about adjacent catchments especially under changing natural and anthropogenic influences. However, there exists no universally accepted basis for hydrological classification of catchments. Some studies have applied hydrological models to catchment classification based upon criteria like streamflow regimes, hydrologic similarity, and hydroclimatic factors. But some constraints have included geographic or situational limitations of models, and large numbers of model parameters and data involved. The latter is counterproductive because the main purpose of catchment classification is to decrease the complexity of water systems by grouping them into several like categories. This study performed catchment classification in four different ways (quasi-runoff coefficients, empirical runoff equations, Muskingum-Cunge routing, and exceedance probabilities) in a major transboundary river whose basins sometimes experience water security issues. Using predefined catchment classification criteria and thirteen initial hydrological zones, the results across all four methods revealed five distinct catchment areas and suggest that a combination of different methodology may be needed for reliably classifying catchments. The methodology and information gathered could be used by stakeholders for applications in irrigation planning, river discharge modelling, navigation, flood risk management, improving dam storage and release policies, and optimizing food-water-energy nexus in the region.

Identification of homogeneous regions in terms of flood seasonality using a complex network approach

Catchment classification aids in the identification of homogeneous regions in which catchments have similar flood timing and corresponding climatic drivers. As a new classification scheme from complex network theory, the community detection method is introduced to classify 242 catchments in the United States based on flood seasonality. The robustness of this method is tested by calculating the Adjusted Rank Index between the classification results from different random subsets of the 242 catchments and different flood sampling methods. In addition, three network metrics (network density, centrality, and k-core nucleus) are used to further unravel the hydrological connections within each community of catchments. Catchments with similar flood seasonality cluster into six large communities, while catchments in sparsely gauged areas or with unique physiographic properties are isolated from large communities. High values of the Adjusted Rank Index show the robustness of the community detection method for catchment classification. The results indicate that the complex network is valid for use in classifying catchments based on flood seasonality. Moreover, for each community, the network metrics are potential descriptors of: (a) the degree of homogeneity, (b) the representativeness of any catchment, and (c) the spatial scale of synchronized floods. Therefore, the complex network is able to characterize the interconnections between catchments within the community.

Impact of catchment classification on streamflow regionalization in ungauged catchments

Hydrological parameter transfer between dissimilar catchments is bound to be associated with errors. As a result of that, hydrological prediction at ungauged sites gets affected. If the catchments are similar to each other in some way, then it may reduce the error in prediction. The logic mentioned above is applied in the current study, where three regionalization approaches, i.e., Inverse Distance Weighted, Kriging and global mean based on spatial proximity between gauged and ungauged catchments, were applied on 32 catchments in India. Before application of regionalization techniques, 32 catchments were categorized into four homogeneous groups using the self-organizing map method. The improvement in streamflow regionalization was checked for both calibration (1995–2006) and validation (2007–2011) while moving from unclassified to classified catchments. Results suggest that prior classification of catchments into homogeneous groups helps in improving the regionalization output. Fifty per cent of the total catchments displayed 10% improvement, while 30% of the catchments showed more than 10% improvement. The results imply that an appropriate combination of a hydrological model (Soil and Water Assessment Tool), regionalization technique and classification method will yield better results for ungauged catchments in streamflow prediction.

Assessing the Catchment Storage Function Through a Dual‐Storage Concept

AbstractCatchment storage controls most ecohydrological processes by modulating, among other things, the hydrological connectivity between landscape units. However, little is known about the internal dynamics of storage and its feedback with hydraulic connectivity. Here we evaluate the capability of a combined lumped approach (i.e., combining catchment water balance and recession analysis) to partition total dynamic storage (from the water balance) into hydraulically connected (the part feeding stream estimated from recession analysis) and hydraulically disconnected components (i.e., the remaining storage driven by precipitation and evapotranspiration). Results from our combined approach were compared to a point measurement‐based estimation (using wells and soil moisture data) from the experimental Weierbach catchment in Luxembourg (0.45 km2). We found good agreement for the hydraulically connected storage but contrasted results for the hydraulically disconnected storage. During wet periods, the latter were characterized by opposite dynamics and our lumped approach showed step‐by‐step deviations. Overall, these results indicated a good capability of our approach to partition storage, as well as its sensitivity to systematic and random deviations. Considering these possible deviations, this new conceptualization of storage provides relevant and quantifiable information about catchment state (i.e., storage dominated or release dominated). This is further highlighted when represented in a dual‐storage plot (hydraulically disconnected vs. connected storage). Compared to classic storage‐discharge relationships, it allows catchment efficiency (capacity of the catchment to convert storage into streamflow) to be assessed without the need for additional data. We expect that this mechanistic conceptualization of the storage function will provide new opportunities for assessing catchment functioning and catchment classification.

Influence of relief characteristics and landscape connectivity on sediment redistribution in small agricultural catchments in the forest-steppe landscape zone of the Russian Plain within European Russia

Sheet, rill and gully erosion occurring in the snowmelt period (March–April) and rainfall season (May–September) is the main factor of soil degradation and mobilized hillslope sediment redistribution within cultivated lands of the Russian Plain. The evaluation of sediment redistribution for the period since 1986 within catchment sediment cascades was done based on an integrated approach for some representative dry valley catchments located in the western (the Plava River basin, Tula Oblast, Russian Federation) and eastern (the Temeva Rechka Creek catchment, the Myosha River basin, Republic of Tatarstan, Russian Federation) sectors of the forest-steppe landscape zone of the Russian Plain. All the catchments studied are characterized by a high proportion (within the range of 60–80%) of cultivated lands. The modified version of the Universal Soil Loss Equation (USLE), the Russian State Hydrological Institute's erosion model, and the LandSoil erosion model were applied to calculate soil losses within the cultivated lands. The morphological classification of interfluve slopes and hollow slope catchments, in combination with the sediment delivery ratio (SDR) assessment for slopes and hollow slope catchments of different configuration, were used to assess sediment transfer from the cultivated lands. The Chernobyl-derived 137Cs isotope was applied as a chronomarker for sediment dating in different sediment sinks located along pathways from cultivated slopes to river valley bottoms. We found that the morphological features of the dry valley catchments, including a pattern of the dry valleys of different Hortonian orders, dry valley and hollow density, dry valley incision depth, and proportion of slopes and hollow slope catchments of different configurations are the main parameters that determined a proportional input of the different sediment sinks to the sediment interception along the pathways from the cultivated slopes to the river valley bottoms. The land use/cover features are mostly responsible for the pattern of buffer zones within the interfluve parts of the catchments. The quantitative assessment of the sediment budget allowed us to conclude that the mean SDR coefficients for the dry valley catchments of second, third, and fourth Hortonian orders are 0.56, 0.33, and 0.07 respectively. The extrapolation of the study results of sediment redistribution evaluation, obtained from the studied catchments located within the Plava River basin, to the entire basin of the river also allowed us to evaluate the mean value of hillslope-to-river-valley-bottom SDR = 0.27.