Streamflow indices to identify catchment drivers of hydrograph

Streamflow indices are flow descriptors that quantify the streamflow dynamics, which are usually determined for a specific basin and are distinct from other basin features. The flow descriptors are appropriate for large-scale and comparative hydrology studies, independent of statistical assumptions and can distinguish signals that indicate basin behavior over time. In this paper, the characteristic features of the hydrograph's temporal asymmetry due to its different underlying hydrologic processes are primarily highlighted. Streamflow indices linked to each limb of the hydrograph within the time-irreversibility paradigm are distinguished with respect to its processes driving the rising and falling limbs. Various streamflow indices relating the rising and falling limbs, and the catchment attributes such as climate, topography, vegetation, geology and soil are then correlated. Finally, the key attributes governing rising and falling limbs are identified. The novelty of the work is on differentiating hydrographs by their time irreversibility property and offering an alternative way to recognize primary drivers of streamflow hydrographs. A set of streamflow indices at the catchment scale for 671 basins in the Contiguous United States (CONUS) is presented here. These streamflow indices complement the catchment attributes provided earlier (Addor et al., 2017) for the CAMELS data set. A series of spatial maps describing the streamflow indices and their regional variability over the CONUS is illustrated in this study.

Location, location, location – Considering local neighborhood when analyzing large samples of UK catchments

<p>Studying large samples of catchments has been an effective means for comparative hydrology as it provides a wide range of hydrological conditions which can be used to learn similarities and differences between places. Such analyses typically include an attempt to organize catchments along some gradient (e.g. climate) or in clusters (e.g. geology) using catchment descriptors (e.g. an aridity index). Various past studies have pointed to the problem that available catchment descriptors are often not sufficient to capture hydrologically relevant catchment behaviours. It is further widely acknowledged that the water balance of many catchments is not closed. Several hypotheses for the causes of this lack of closed water balance are stated in literature.</p><p>If we assume that the dominant control on water balance is climate, then catchments’ water balances should change smoothly in space (since the climate varies smoothly). If they do not, then something else must be controlling this behaviour. We expect that size, location and geology might play important role in the water balances of UK catchments. We aim to study the differences in water balance between catchments to understand the role of catchment location. We test different hypotheses while considering the local neighborhood of 669 UK catchments from the CAMELS-GB dataset.</p>

The CAMELS data set: catchment attributes and meteorology for large-sample studies

We present a new data set of attributes for 671 catchments in the contiguous United States (CONUS) minimally impacted by human activities. This complements the daily time series of meteorological forcing and streamflow provided by Newman et al. (2015b). To produce this extension, we synthesized diverse and complementary data sets to describe six main classes of attributes at the catchment scale: topography, climate, streamflow, land cover, soil, and geology. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of the attributes we extracted, makes this new data set well suited for large-sample studies and comparative hydrology. In comparison to the similar Model Parameter Estimation Experiment (MOPEX) data set, this data set relies on more recent data, it covers a wider range of attributes, and its catchments are more evenly distributed across the CONUS. This study also involves assessments of the limitations of the source data sets used to compute catchment attributes, as well as detailed descriptions of how the attributes were computed. The hydrometeorological time series provided by Newman et al. (2015b, https://doi.org/10.5065/D6MW2F4D) together with the catchment attributes introduced in this paper (https://doi.org/10.5065/D6G73C3Q) constitute the freely available CAMELS data set, which stands for Catchment Attributes and MEteorology for Large-sample Studies.

Understanding hydrologic variability across Europe through catchment classification

This study contributes to better understanding the physical controls on spatial patterns of pan-European flow signatures – taking advantage of large open datasets for catchment classification and comparative hydrology. Similarities in 16 flow signatures and 35 catchment descriptors were explored for 35 215 catchments and 1366 river gauges across Europe. Correlation analyses and stepwise regressions were used to identify the best explanatory variables for each signature. Catchments were clustered and analyzed for similarities in flow signature values, physiography and the combination of the two. We found the following. (i) A 15 to 33 % (depending on the classification used) improvement in regression model skills when combined with catchment classification versus simply using all catchments at once. (ii) Twelve out of 16 flow signatures were mainly controlled by climatic characteristics, especially those related to average and high flows. For the baseflow index, geology was more important and topography was the main control for the flashiness of flow. For most of the flow signatures, the second most important descriptor is generally land cover (mean flow, high flows, runoff coefficient, ET, variability of reversals). (iii) Using a classification and regression tree (CART), we further show that Europe can be divided into 10 classes with both similar flow signatures and physiography. The most dominant separation found was between energy-limited and moisture-limited catchments. The CART analyses also separated different explanatory variables for the same class of catchments. For example, the damped peak response for one class was explained by the presence of large water bodies for some catchments, while large flatland areas explained it for other catchments in the same class. In conclusion, we find that this type of comparative hydrology is a helpful tool for understanding hydrological variability, but is constrained by unknown human impacts on the water cycle and by relatively crude explanatory variables.

The CAMELS data set: catchment attributes and meteorology for large-sample studies

We present a new data set of attributes for 671 catchments in the contiguous USA (CONUS). This complements the daily hydrometeorological time series provided by Newman et al. (2015b) and opens new opportunities to explore how the interplay between landscape attributes shapes hydrological processes and catchment behavior. To produce this extension, we synthesized diverse and complementary data sets to describe topography, climate, hydrology, soil and vegetation characteristics at the catchment scale. The spatial variations among basins over the CONUS are discussed and compared using a series of maps. The large number of catchments, combined with the diversity of their geophysical characteristics, makes this new data well suited for large-sample studies and comparative hydrology. An essential feature, that differentiates this data set from similar ones, is that it both provides quantitative estimates of diverse catchment attributes, and involves assessments of the limitations of the data and methods used to compute those attributes. This data set will be publicly available and we encourage the community to further extend it. The hydrometeorological time series provided by Newman et al. (2015b) together with the catchment attributes introduced in this paper constitute the CAMELS data set: Catchment Attributes and MEteorology for Large-sample Studies.

Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case

The scientific initiative Prediction in Ungauged Basins (PUB) (2003–2012 by the IAHS) put considerable effort into improving the reliability of hydrological models to predict flow response in ungauged rivers. PUB's collective experience advanced hydrologic science and defined guidelines to make predictions in catchments without observed runoff data. At present, there is a raised interest in applying catchment models to large domains and large data samples in a multi-basin manner, to explore emerging spatial patterns or learn from comparative hydrology. However, such modelling involves additional sources of uncertainties caused by the inconsistency between input data sets, i.e. particularly regional and global databases. This may lead to inaccurate model parameterisation and erroneous process understanding. In order to bridge the gap between the best practices for flow predictions in single catchments and multi-basins at the large scale, we present a further developed and slightly modified version of the recommended best practices for PUB by Takeuchi et al. (2013). By using examples from a recent HYPE (Hydrological Predictions for the Environment) hydrological model set-up across 6000 subbasins for the Indian subcontinent, named India-HYPE v1.0, we explore the PUB recommendations, identify challenges and recommend ways to overcome them. We describe the work process related to (a) errors and inconsistencies in global databases, unknown human impacts, and poor data quality; (b) robust approaches to identify model parameters using a stepwise calibration approach, remote sensing data, expert knowledge, and catchment similarities; and (c) evaluation based on flow signatures and performance metrics, using both multiple criteria and multiple variables, and independent gauges for "blind tests". The results show that despite the strong physiographical gradient over the subcontinent, a single model can describe the spatial variability in dominant hydrological processes at the catchment scale. In addition, spatial model deficiencies are used to identify potential improvements of the model concept. Eventually, through simultaneous calibration using numerous gauges, the median Kling–Gupta efficiency for river flow increased from 0.14 to 0.64. We finally demonstrate the potential of multi-basin modelling for comparative hydrology using PUB, by grouping the 6000 subbasins based on similarities in flow signatures to gain insights into the spatial patterns of flow generating processes at the large scale.

Comparative hydrology: relationships among physical characteristics, hydrological behavior, and results of the SWAT model in different regions of Brazil (Hidrologia comparativa: relações entre características físicas, comportamento hidrológico e ....)

Comparative hydrology studies, either by the similarities or the differences in the obtained data and results, represent an important tool for advancing knowledge of cause-effect relationships between the physical characteristics of the basins and their hydrological behavior. The objective of this study was to present a comparative analysis of measured and simulated characteristics of experimental and representative basins in different regions of Brazil. The SWAT model was used. Four catchments were evaluated: Alto Ipanema, located in the Caatinga biome, with semi-arid climate; Tapacurá, in the transition zone between the Caatinga and Atlantic Forest biomes, with hot and humid tropical climate; and Lago Descoberto and Alto Jardim, both in the Cerrado biome and with tropical altitude climate. The catchments were compared with respect to their physical characteristics (climate, soil, altitude, and land use). Using sensitivity analysis, it was found which of the SWAT model parameters best explain the hydrological behavior of the study regions. Considering its characteristics, the parameters values obtained in each catchment after model calibration were analyzed and compared, indicating the possibility of using these values as reference for their regions. The results indicate a clear relationship between the physical characteristics of watersheds, their respective hydrological behavior, and the values of two SWAT model parameters, CN2 and SOL_K. For other parameters, the relationship between the obtained values do not reflected adequately the characteristics of the catchment, indicating a need for improvement in the physical basis of the calibrated model.

The Hydropedograph Toolbox and its application

The Hydropedograph Toolbox has been developed to provide a set of standardized tools for analyzing soil moisture time series in an efficient and consistent manner. This toolbox contains various modules that permit the exploration and visualization of key soil hydrological parameters and processes using multi-depth real-time soil moisture monitoring datasets. This includes statistical summary, soil water release curve, preferential flow occurrence, hydraulic redistribution, and the relationship between soil moisture and soil temperature. After describing this toolbox, this paper demonstrates the utility of this toolbox in a case study from the Shale Hills Critical Zone Observatory in USA. The case study illustrates the topographic impacts on soil moisture dynamics along a hillslope transect, and quantifies the frequency of the occurrence of preferential flow, diel fluxes of water, and seasonal storage dynamics. It is expected that such a toolbox, with continued enhancements in the future and wide applications across diverse landscapes, can facilitate the advancement of comparative hydrology and hydropedology.