scholarly journals Flood timescales: Understanding the interplay of climate and catchment processes through comparative hydrology

2012 ◽  
Vol 48 (4) ◽  
Author(s):  
Ladislav Gaál ◽  
Ján Szolgay ◽  
Silvia Kohnová ◽  
Juraj Parajka ◽  
Ralf Merz ◽  
...  
2021 ◽  
Author(s):  
Jeenu Mathai ◽  
Pradeep Mujumdar

Abstract. 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.


2012 ◽  
Vol 9 (12) ◽  
pp. 14231-14271
Author(s):  
C. B. Graham ◽  
H. S. Lin

Abstract. 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.


2021 ◽  
Author(s):  
Melike Kiraz ◽  
Thorsten Wagener ◽  
Gemma Coxon

<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>


2011 ◽  
Vol 47 (10) ◽  
Author(s):  
S. E. Thompson ◽  
C. J. Harman ◽  
A. G. Konings ◽  
M. Sivapalan ◽  
A. Neal ◽  
...  

2017 ◽  
Vol 21 (10) ◽  
pp. 5293-5313 ◽  
Author(s):  
Nans Addor ◽  
Andrew J. Newman ◽  
Naoki Mizukami ◽  
Martyn P. Clark

Abstract. 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.


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