Catchment classification by runoff behaviour with self-organizing maps (SOM)
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Abstract. Catchments show a wide range of response behaviour, even if they are adjacent. For many purposes it is necessary to characterise and classify them, e.g. for regionalisation, prediction in ungauged catchments, model parameterisation. In this study, we investigate hydrological similarity of catchments with respect to their response behaviour. We analyse more than 8200 event runoff coefficients (ERCs) and flow duration curves of 53 gauged catchments in Rhineland-Palatinate, Germany, for the period from 1993 to 2008, covering a huge variability of weather and runoff conditions. The spatio-temporal variability of event-runoff coefficients and flow duration curves are assumed to represent how different catchments "transform" rainfall into runoff. From the runoff coefficients and flow duration curves we derive 12 signature indices describing various aspects of catchment response behaviour to characterise each catchment. Hydrological similarity of catchments is defined by high similarities of their indices. We identify, analyse and describe hydrologically similar catchments by cluster analysis using Self-Organizing Maps (SOM). As a result of the cluster analysis we get five clusters of similarly behaving catchments where each cluster represents one differentiated class of catchments. As catchment response behaviour is supposed to be dependent on its physiographic and climatic characteristics, we compare groups of catchments clustered by response behaviour with clusters of catchments based on catchment properties. Results show an overlap of 67% between these two pools of clustered catchments which can be improved using the topologic correctness of SOMs.Keywords:
Regionalisation
Catchment hydrology
Abstract. Research on regionalisation in hydrology has been constantly advancing due to the need for prediction of streamflow in ungauged catchments. There are two types of studies that use regionalisation techniques for ungauged catchments. One type estimates parameters of streamflow statistics, flood quantiles in most cases. The other type estimates parameters of a rainfall-runoff model for simulating continuous streamflow or estimates continuous streamflow without using a model. Almost all methods applied to the latter can be applied to the former. This paper reviews all methods that are applied to continuous streamflow estimation for ungauged catchments. We divide them into two general categories: (1) distance-based and (2) regression-based. Methods that fall within each category are reviewed first and followed with a discussion on merits or problems associated with these various methods.
Regionalisation
Quantile
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Information on river flows is essential for water resources management. Most of the Brazilian small watersheds is ungauged. Regionalization of hydrologic information is an alternative way to get this lacking information. The regionalization models, however, are a simplified representation of natural phenomena, what could lead to miscalculation. Therefore, these models should be tested before being used as a management tool. This paper evaluates the performance of the regionalization models created for the Santa Barbara River Basin region, in Goias. This river basin is located between 17o45’ and 18o15’ South and 49o36’ and 50o03’ West and has a catchment area of 1371,16 km2. The models estimate average streamflow, maximum streamflow, minimum streamflow, the 95% duration streamflow, the seven day, ten years, minimum streamflow and the monthly-average streamflows. The tested streamflow regionalization models proved to be good enough for average flows, but not for maximum and minimum events. The models based on homogeneous regions were superior to those that ignored that aspect. The model that uses drainage area and density to estimate the 95% duration streamflow could be used in water resources management with little error. The use of this model would better represent the water availability at Santa Barbara River Basin than a few local measurements currently used.
Hydrological modelling
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Both sensitivity-based method and simulation method are used to analyze the streamflow response to climate variability and human activities in the upper catchment of the Yellow River Basin (UYRB) in this study. The separation regime of effects from climate variability and human activities is investigated. Results show that the changes of streamflow are more sensitive to precipitation than potential evapotranspiration (PET). Effect of climate variability on streamflow estimated using the sensitivity-based method is weak in the upper catchment of Jimai station, and strong in the upper catchment of Lanzhou station, where the climate effects accounted for about 50% of total streamflow changes. Effects of human activities on streamflow accounted for about 40% in the UYRB, with weaker effects in the upper catchment of Tangnaihai station than those in the upper catchment of Lanzhou station. Both climate variability and human activities are main factors to affect the changes of streamflow in the UYRB.
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Rainfall is the main input into the land phase of the hydrological cycle which greatly determines
the available water resources. Accurate precipitation information is critical for mountain
catchments as they are the main suppliers of usable water to the human population. Rainfall
received in mountain catchments usually varies with altitude due to the orographic influence on
the formation of rainfall. The Langrivier mountain catchment, a sub-catchment of the
Jonkershoek research catchment, was found to have a network of rain gauges that does not
accurately represent the catchment rainfall. As a result, this study aimed to improve the
estimation of catchment precipitation and evaluate how improving estimation catchment
precipitation affects the prediction of streamflows.
Catchment hydrology
Water cycle
Orography
Catchment area
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Abstract The current generation of catchment travel time distribution (TTD) research, integrating nearly three decades of work since publication of Water's Journey from Rain to Stream , seeks to represent the full distribution in catchment travel times and its temporal variability. Here, we compare conceptualizations of increasing complexity with regards to mixing of water storages and evaluate how these assumptions influence time‐variable TTD estimates for two catchments with contrasting climates: the Gårdsjön catchment in Sweden and the Marshall Gulch catchment in Arizona, USA. Our results highlight that, as long as catchment TTDs cannot be measured directly but need to be inferred from input‐output signals of catchments, the inferred catchment TTDs depend strongly on the underlying assumptions of mixing within a catchment. Furthermore, we found that the conceptualization of the evapotranspiration flux strongly influences the inferred travel times of stream discharge. For the wet and forested Gårdsjön catchment in Sweden, we inferred that evapotranspiration most likely resembles a completely mixed sample of the water stored in the catchment; however, for the drier Marshall Gulch catchment in Arizona, evapotranspiration predominantly contained the younger water stored in the catchment. For the Marshall Gulch catchment, this higher probability for young water in evapotranspiration resulted in older water in the stream compared to travel times inferred with assumptions of complete mixing. New observations that focus on the TTD of the evapotranspiration flux and the actual travel time of water through a catchment are necessary to improve identification of mixing and consequently travel times of stream water. Copyright © 2014 John Wiley & Sons, Ltd.
Catchment hydrology
Catchment area
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Several tools are available to simulate catchment streamflow with a higher level of accuracy. Validation of simulated streamflow of ungauged catchments is a challenge in hydrological science due to the non-availability of gauging data. Generally, complex linear and nonlinear mathematical approaches are used to generate regionalized streamflow for ungauged catchments with available observed hydrological data from a neighbouring catchment. Machine learning (ML) is broadly used to model complex nonlinear relationships between different variables. This study demonstrates how novel ML approaches such as support vector machine (SVM) and extreme gradient boosting (XGB) can be applied to generate regionalized streamflow to calibrate ungauged simulated flow from the existing hydrological model. This study was performed on two study areas and four catchments located in different climate zones. The Soil and Water Assessment Tool (SWAT) model was used for ungauged flow simulation, and ML was used for regionalization.
Regionalisation
Catchment hydrology
SWAT model
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Identifying a catchment’s streamflow generation mechanisms could inform the hydrologic functioning of the catchment, and how the catchment responds to the changes in climate and land-use. This study focuses on identifying the dominant streamflow generation mechanism and its drivers at more than 2,000 natural catchments located in North and South America, Europe, and Oceania. First, in a given catchment, we use a suite of diagnostic tools to infer the relative contribution of different streamflow generation mechanisms from precipitation and streamflow observations and simulated time series of subsurface storage. Then, in a large sample hydrology framework, we explore the major physical and climatic drivers of streamflow generation mechanisms. In this study, we made progress in differentiation among, seemingly similar, but naturally different subsurface mechanisms of streamflow generation (e.g., subsurface stormflow, transmissivity feedback, groundwater flow) as well as in identifying the drivers of these mechanisms. Our study extracts generalizable process understanding by combining conventional hydrologic science tools with modern data learning techniques.
Catchment hydrology
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In this paper,measured data of precipitation,evaporation,temperature and runoff from meteorological and hydrometric stations in the studied basins were used for researching the changing trend and features of the runoff from the mountain areas of some main rivers in the region.The result shows that there is a decreasing trend of mean annual runoff in Shiyang river drainage basin,in easten and middle reaches,there is a decreasing trend,but in weasten reaches is tend to increase.The average distribution of the runoff due to the conditions of supply,the runoff of season in turn for the summer,autumn and spring and winter.There is a decreasing trend in the observations on runoff in four seasons,most significant decreases occur in summer,the least in winter;It is also found that the vibration periods of 6~7 a,9~10 a long period and 2~3 a short period are displayed very clear in Shiyang river drainage basin.The probability of mean water is the biggest in different age in Shiyang river drainage basin.On the attribution,we calculated the correlation coefficients of climate factors with runoff,positive correlation between runoff and regional precipitation is significant,and negative correlation between runoff and regional evaporation is also significant,indicating that the variations of regional precipitation and evaporation have predominant effect on the runoff variations,and maximum temperature is important factor.And the regional mean and minimum temperature also have different influences on the runoff variations.Synthetic effect of regional climatic elements is the prime cause for runoff variations in the source region of the Shiyang river drainage basin.
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The ability to predict flows at gauged and ungauged catchments is an important goal in hydrology. The reason why prediction is of importance is for instance the ability to estimate impacts of climate or land use change on the discharge regime. For such purposes hydrological models are generally used all over the world. However, in order to be able to predict discharge values for concerning model parameters have to be determined. In general, this is done by calibrating the model against observed discharge using efficiency criteria which evaluate model performance. Yet, with respect to the ungauged catchment topographic and climatic properties are available, but no observed discharge data. Hence, the ungauged catchment can not be calibrated and model parameter values have to be determined using other sources of information. The
objective of this study contributing to this issue is as follows: Contribute to reducing uncertainty in the prediction of discharge regime at the ungauged catchments through application of the method
of regionalisation based on establishing relationships between model parameters of the hydrological model HBV and climatic and physiographic data using 61 well gauged catchments in the United Kingdom.
Regionalisation
Discharge
Catchment hydrology
Hydrological modelling
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