Aridity is expressed in river topography globally.
TL;DR: A global dataset of river longitudinal profiles is presented and it is shown that river profiles become straighter with increasing aridity and numerical modelling suggests that this can be explained by rainfall–runoff regimes in different climate zones.
Abstract: It has long been suggested that climate shapes land surface topography through interactions between rainfall, runoff and erosion in drainage basins1,2,3,4. The longitudinal profile of a river (elevation versus distance downstream) is a key morphological attribute that reflects the history of drainage basin evolution, so its form should be diagnostic of the regional expression of climate and its interaction with the land surface5,6,7,8,9. However, both detecting climatic signatures in longitudinal profiles and deciphering the climatic mechanisms of their development have been challenging, owing to the lack of relevant global data and to the variable effects of tectonics, lithology, land surface properties and human activities10,11. Here we present a global dataset of 333,502 river longitudinal profiles, and use it to explore differences in overall profile shape (concavity) across climate zones. We show that river profiles are systematically straighter with increasing aridity. Through simple numerical modelling, we demonstrate that these global patterns in longitudinal profile shape can be explained by hydrological controls that reflect rainfall–runoff regimes in different climate zones. The most important of these is the downstream rate of change in streamflow, independent of the area of the drainage basin. Our results illustrate that river topography expresses a signature of aridity, suggesting that climate is a first-order control on the evolution of the drainage basin.
Summary (2 min read)
- Having confirmation from two climatic indices (K-G and AI), which are computed in distinct ways (e.g., AI represents the balance between PET and P), gives us confidence that the authors have captured real climate influences on long profile development.
- This was performed using an algorithm 34 which minimizes the topographic change required to ensure all DEM cells flow to the DEM base level.
- This is the channel that was extracted in their analysis for this area and which is included in GLoPro.
- Given their source in SRTM data, the extracted profiles represent the water surface profile for perennial rivers and the bed topography profile for ephemeral rivers.
Normalized Concavity Index (NCI).
- The authors define the endpoints of the longitudinal profile (L0, E0) and (Ln, En) where L is distance downstream, E is elevation, and where the subscripts 0 and n indicate the most upstream and downstream points, respectively.
- Then, at each measured point along the profile, the vertical offset between the river profile and the fitted straight line is calculated as EL -YL.
- The authors then calculate the median value of all offsets, normalized by the total topographic relief along the profile (E0 -En) to enable comparison across scales (Extended Data Fig. 1b ).
NCI = median[(EL-YL)/(E0-En)]
- (1) There have been previous concavity indices developed in the literature, such as Stream Concavity Index (SCI) 7 , Concavity Index (θ) 40 , and Chi (χ) transformation 41 .
- SCI, for example, calculates the area between channel elevation and the straight line connecting the endpoints of channel, similar to NCI.
- SCI is sensitive to local variations along the profile (e.g., knickpoints) and requires smoothing.
- Since their goal was to explore conditions where the relationship between area and channel discharge are weak for complete river profiles, the authors opted for a different metric.
as an example).
- The river extraction methods and concavity calculation result in an internally consistent NCI dataset.
- The impact of channel head location on NCI is minimal because only the longest river of each basin or sub-basin was analyzed (not smaller tributaries).
- The authors confirmed that NCI for extracted rivers in GLoPro are not correlated with key river metrics, such as river length, gradient, relief, or basin area (Extended Data Fig. 4 ).
- Therefore, the authors were confident in using it to compare rivers of different sizes and across climate zones.
- The unique name given to each river record in GLoPro.
- Comprises the K-G climate zone that the river is within and a unique alphanumeric string.
- Used to identify the associated data for the river recorded in rivers.
SELECT elevation, length FROM profiles WHERE riverid like 'Aw_75_river_72';
- Note that due to the size of the profiles table, queries can take a few minutes to complete.
- To learn more about using SQL databases in a research context, the authors recommend the training materials provided by Software Carpentry: http://swcarpentry.github.io/sql-novice-survey.
Kernel density estimation (KDE).
- In several figures in the paper, the authors present plots generated based on kernel density estimation (KDE).
- Statistical differences of the NCI distributions were analyzed using the Kolmogorov-Smirnov test (K-S test) between distribution pairs across climate zones.
- K-S test is a nonparametric test for checking whether two continuous, one-dimensional data samples, X1 and X2, come from the same distribution.
- The authors simulated variations in downstream discharge and their impact on long profile evolution.
- Since other model parameters can also affect long profile concavities, the authors conducted sensitivity analyses to discharge (Qmax), median grain size , tectonic uplift, and base level change.
Calculation of α values from real rivers.
- To develop a real-world understanding of α and its variation in different climate zones, the authors downloaded multidecadal mean daily streamflow data for rivers from the US Geological Survey's National Water Information System (https://waterdata.usgs.gov/nwis).
- For each main K-G climate zone, the authors selected 5 rivers, spanning a range of river lengths, with at least three gauging stations along the same river (a total of 20 rivers), ensuring via Google Earth satellite imagery that there are no obvious anthropogenic factors that could influence the downstream variation in discharge.
- Then the authors extracted α for each power law fit from equation (3) (Extended Data Table 2 ).
- The exponent for arid channels is closest to zero for small floods and increases slightly for higher flood recurrence intervals.
- The analysis of α values was not exhaustive.
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Cites background from "Aridity is expressed in river topog..."
...These examples, along with similar recent work quantifying global fluvial geomorphic patterns (e.g., Chen et al., 2019; Frasson, Pavelsky, et al., 2019; Lin et al., 2020), suggest that RS is coming of age in its ability to provide global‐scale data that honors local differences in rivers....
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