Channel planform patterns arise from internal dynamics of sediment transport and fluid flow in rivers and are affected by external controls such as valley confinement. Understanding whether these channel patterns are preserved in the rock record has critical implications for our ability to constrain past environmental conditions. Rivers are preserved as channel belts, which are one of the most ubiquitous and accessible parts of the sedimentary record, yet the relationship between river and channel-belt planform patterns remains unquantified. We analyzed planform patterns of rivers and channel belts from 30 systems globally. Channel patterns were classified using a graph theory-based metric, the Entropic Braided Index (eBI), which quantifies the number of river channels by considering the partitioning of water and sediment discharge. We find that, after normalizing by river size, channel-belt width and wavelength, amplitude, and curvature of the belt edges decrease with increasing river channel number (eBI). Active flow in single-channel rivers occupies as little as 1% of the channel belt, while in multichannel rivers it can occupy >50% of the channel belt. Moreover, we find that channel patterns lie along a continuum of channel numbers. Our findings have implications for studies on river and floodplain interaction, storage timescales of floodplain sediment, and paleoenvironmental reconstruction.Rivers display a diverse set of planform channel patterns on planetary surfaces, which are typified by meandering and braided morphologies (Leopold and Wolman, 1960). These patterns emerge from internal dynamics of sediment transport and fluid flow and external controls such as vegetation cover and confinement (Parker, 1976; Limaye and Lamb, 2013; Naito and Parker, 2020). Thus, channel patterns—if preserved in, and accurately interpreted from, the rock record—have the possibility of recording crucial paleoenvironmental information through a planet's history, helping to constrain the past climate, carbon cycling, and habitability (e.g., Ganti et al., 2019).Through channel migration and avulsion, rivers move laterally away from their present courses. Over time, this movement forms channel belts as the amalgamation of many river courses, recording environmental signals in the stratigraphy (Hajek and Straub, 2017). In planform view, channel-belt deposits (a widely accessible sedimentary record across planets) are observed via a range of imaging techniques, such as seismic, hyperspectral, and lidar (e.g., Cardenas et al., 2018; Durkin et al., 2018; Hayden et al., 2019; Zaki et al., 2021). Channel-belt geometries may thus provide readily accessible constraints for paleoenvironmental reconstructions. Previous work has established an empirical relationship between channel-belt width and the thickness of channel deposits (Gibling, 2006). Results of numerical and physical experiments also suggest that channel-belt width grows logarithmically, while the growth rate and stable width are sensitive to internal and external controls, such as water discharge and regional slope (Howard, 1996; Jobe et al., 2016; Limaye, 2020).However, empirical relationships between river and channel-belt planform patterns remain elusive. Previous work on channel belts has often studied single-channel and multichannel rivers separately (Limaye, 2020; Yan et al., 2021), while in nature, planform channel patterns are unlikely to conform to this binary classification (Galeazzi et al., 2021). Furthermore, while morphodynamic models have the capacity to allow rivers to self-form channel patterns, computation costs prevent these models from simulating deposits over geologic timescales (Nicholas, 2013). We explored the connections between river and channel-belt planform patterns across a range of natural systems. We hypothesize that for multichannel rivers, the ratio of channel belt to channel width will approach unity, while for single-channel rivers this ratio will greatly exceed unity (Fig. 1A). To test our hypothesis, we conducted remote sensing analysis on 30 river reaches globally (see Fig. S1 in the Supplemental Material1). Results of this study inform future work on the interactions between rivers and their deposits, storage timescales of floodplain material, and paleoenvironmental reconstruction.To study channel-belt planform patterns, we mapped 30 river reaches globally, spanning a range of scales and hydrology (see the Supplemental Material for detail). The main remotely sensed data sets used for mapping include European Space Agency Sentinel-2 hyperspectral images and the National Aeronautics and Space Administration Shuttle Radar Topography Mission digital elevation model. Using these data, channel-belt edges were mapped manually in ArcGIS based on changes in topography, ground texture, and vegetation (see the Supplemental Material for detail). For example, channel-belt edges are delineated using elevation difference between the inferred alluvial channel belt and terraces (Fig. 1B), ground texture differences between regions with and without abundant thermokarst lakes near the channel belt (Fig. 1C), and abrupt changes in vegetation from trees/shrubs to bare earth (Fig. 1D). Channel-belt planform metrics were measured mainly using a graph theory-based mapping package called Riv-Graph (Schwenk and Hariharan, 2021; see the Supplemental Material for detail). A channel-belt centerline was generated automatically from the mapped belt edges (Fig. 2A). Channel-belt width was measured every ~10 m along the centerline using perpendicular transects. Three planform metrics, including wavelength, amplitude, and curvature, were measured from the channel-belt centerline and edges. These metrics were then normalized by the total active channel width to compare rivers across scales and to test our hypothesis (Fig. 1A).A binary water mask was generated for each river using a mosaic of Sentinel-2 data during the wettest month and a modified version of the normalized difference water index (Fig. 2A; Yan et al., 2020; see the Supplemental Material for detail). The channel centerline, width, and planform patterns were extracted automatically using RivGraph from the binary mask. We quantified the channel planform pattern using the channel number calculated from the Entropic Braided Index (eBI), a method that weights each channel by the amount of water/sediment discharge it conveys (Tejedor et al., 2019; Fig. 2B):where H is Shannon Entropy and is used to approximate the probability of a tracer particle entering a particular channel at a given cross section. Ideally, H is calculated using water/sediment discharge data, but such data at multiple cross sections along a river are scarce and challenging to collect. However, channel width has been shown to effectively predict water and sediment discharge under steady and uniform flow conditions (Dong et al., 2020). Thus, H can be expressed in terms of channel width (bi; Schwenk and Hariharan, 2021):where i is the ith channel at a sampling cross section, N is the total number of active channels, and B is the sum of individual channel widths at the cross-section (Fig. 2B). As eBI approaches 1, most of the water and sediment discharge is conveyed in one flow path, and thus a river is considered a single-channel system. Alternatively, when eBI is much larger than 1, a river is considered a multichannel system.To show the variability in our results, we report the median and interquartile ranges of the normalized channel-belt metrics and eBI along a single reach (Fig. 3A). Quantitative relationships between normalized channel-belt metrics and eBI are evaluated via linear least squares regression in logarithmic space. Note that the resulting empirical functions are used solely as a straightforward way to illustrate correlations. We find a relationship between normalized channel-belt width and eBI (Fig. 3A). In general, normalized channel-belt width decreases with increasing eBI, consistent with our hypothesis. Said another way: as eBI increases, the active river occupies a larger fraction of the channel belt. This relationship is also found after binning the data by quartiles of eBI (Fig. 3B). For the endmember cases, single-channel rivers (quartile 1, ) occupy of the channel-belt width, while multichannel rivers (quartile 4, ) occupy of the channel-belt width (Table S1). We also find relationships between normalized channel-belt wavelength, amplitude, and curvature, measured from both the channel-belt edges and centerline, and eBI (Fig. 4). However, metrics measured from the channel-belt centerline are nearly one order of magnitude larger than those measured from the channel-belt edges, and they have consistently lower R2 values (Fig. 4).We find that channel-belt width, wavelength, amplitude, and curvature, normalized by total channel width, decrease with increasing eBI (i.e., channel number). These findings indicate that channel belts, which are the amalgamation of individual river courses, retain scaling relationships with their formative channel patterns. Our results are also consistent with recent work showing similar curvature-to-width ratios for channels and channel belts (Hayden et al., 2021).We also find that river planform patterns lie along a spectrum of channel numbers, as quantified by eBI (Figs. 3 and 4). We argue that this is intuitive, because, in essence, channel patterns are a planform expression of barforms in rivers, such that point bars are found in meandering rivers, while alternating bars are found in braided rivers (Ikeda, 1984; Sylvester et al., 2019). Theoretically, barform types are them-selves well predicted by continuous hydraulic parameters, such as the Froude number, and sediment transport metrics, such as the particle Reynolds number (Ohata et al., 2017). Further-more, because eBI measures channel number based on water and sediment discharge, this index is expected to describe channel pattern in a continuum (Tejedor et al., 2019), as shown for natural systems here (Figs. 3 and 4). Compared to previous qualitative classifications, eBI offers a more physics-based description of channel patterns (Galeazzi et al., 2021).The linkage between barforms and channel patterns can also help explain the differences in the strength of the relationships between wave-length, amplitude, and curvature measured from channel-belt edges and the centerline (Fig. 4). Channel-belt edges are formed over time by the action of multiple river courses and thus record the cumulative history of these rivers and their barform dimensions (Gibling, 2006). Planform metrics measured from channel-belt edges are thus expected to contain scaling relationships with channel patterns (Galeazzi et al., 2021). Conversely, the channel-belt centerline can be viewed as a long-wavelength filtered belt edge and hence is instead expected to display a muted version of information about the channel pattern. Thus, as observed, the relationships derived from the belt centerline are expected to be weaker (Fig. 4).As eBI approaches 1 (single-channel rivers), the variability in normalized channel-belt width increases, weakening the overall quantitative relationship (Fig. 3A). We hypothesize that this increased variability is due to confinement by bedrock valleys/fluvial terraces. In confined systems, shear stress near the river banks is often insufficient to overcome the strength of the valley wall material, which limits a river's ability to expand laterally and forces water and sediment flow into a single pathway, driving incision (Larsen and Lamb, 2016). Thus, normalized channel-belt width would approach unity, even as eBI remains low (Fig. 3A). For unconfined systems, rivers can self-organize to form planform patterns based on water and sediment discharge (Parker, 1976).To test this hypothesis, we parsed our data of normalized channel-belt width and eBI based on confinement of the channel belt, defined as the elevation difference between the channel belt and its surrounding valley, normalized by the standard deviation of channel-belt elevation (inspired by Limaye and Lamb, 2013; see the Supplemental Material for detail). A subset of unconfined channel belts shows a stronger relationship between channel-belt width and eBI (R2 = 0.84; Fig. S6B), while a subset of confined systems shows no correlation between these two metrics (R2 = 0.00; Fig. S6D), which confirms our hypothesis.As an alternative hypothesis, the observed variability in normalized channel-belt width at low eBI could also be due to age differences among the mapped river systems, where older rivers have developed a larger channel belt. However, the exact ages of mapped channel belts are unknown, and the timescale for channel-belt width to reach a stable value remains an open question. Furthermore, it is unclear why this age trend would be observed for low eBI (single-channel) rivers but not for high eBI (multichannel) rivers. Despite the causes of variability, like most geomorphic systems, channel-belt width is subject to external impacts of valley confinement while also retaining signals of internal dynamics of sediment transport and fluid flow (Parker, 1976; Hajek and Straub, 2017).Unconfined, single-channel rivers occupy as little as 1% of the channel-belt width (Fig. 3A), implying limited interaction between the active river channel and the channel belt. While single-channel rivers migrate/avulse laterally, the timescale for a river to visit everywhere in a channel belt is on the order of centuries to millennia (Jerolmack and Mohrig, 2007), which implies that significant portions of the channel belt have limited fluvial sedimentation and thus can remain as topographic lows. Meanwhile, areas of the channel belt adjacent to the active river aggrade to become topographic highs, which promotes compensational stacking (Hajek and Straub, 2017; Jobe et al., 2020). In contrast, unconfined, multichannel systems can occupy over 50% of the channel-belt width (Fig. 3A), implying a greater interaction between the river and the channel belt. Thus, the overall deposits likely contain a greater fraction of channel deposits with smaller topographic variability, consistent with previous findings that braided systems contain more spatially connected channels in the stratigraphy (Bridge and Leeder, 1979).Interaction between the active river and channel-belt deposits also has important implications for sediment storage timescales, which affect the terrestrial component of the organic carbon cycle. Previous studies have found that sediment storage timescale is well described by heavy-tailed distributions in unconfined meandering rivers, and this indicates the preferential erosion of young floodplain material (e.g., Torres el., 2017). This conclusion is consistent with our observations that indicate unconfined single-channel rivers occupy a small fraction of the channel-belt width, decreasing the probability that the active river could interact with older deposits. However, for confined or multichannel rivers, the active river occupies a much larger fraction of the channel belt, which likely reduces the age bias in fluvial erosion. It is thus reasonable to hypothesize that for these types of systems, the probability distribution of sediment storage timescale would be light tailed (Wohl, 2011; Huffman et al., 2021). In particular, confined or multichannel rivers may export a greater amount of black carbon to the ocean, affecting residence timescales of organic carbon in the ocean (Masiello, 2004).Given the prominence of channel-belt deposits in the rock record, the relationships developed herein can be used to inform studies on past environmental changes. For example, our findings could be readily applied to analysis of commonly observed channel-belt deposits from subsurface data (Gibling, 2006) and across Mars (Cardenas et al., 2020; Dickson et al., 2021), to reconstruct past channel patterns and environments.This material is based upon work supported by the U.S. National Science Foundation under award no. 1952814 to T. Dong. We are grateful for insightful reviews by editor William Clyde, Jason Muhlbauer, Zane Jobe, and an anonymous reviewer. T. Dong thanks C. Qiu and W. Liu for their love and support during the writing of this work. T. Dong also thanks J. Hariharan for the helpful discussion regarding the application of RivGraph.

中文翻译：

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河流与河道带平面格局之间的定量关系

河道平面模式源于河流中泥沙输送和流体流动的内部动态，并受到山谷限制等外部控制的影响。了解这些通道模式是否保留在岩石记录中对我们限制过去环境条件的能力具有重要意义。河流被保存为河道带，这是沉积记录中最普遍和最容易接近的部分之一，但河流与河道带平面形态之间的关系仍未量化。我们分析了全球 30 个系统的河流和河道带的平面格局。使用基于图论的指标熵编织指数 (eBI) 对河道模式进行分类，该指标通过考虑水和沉积物排放的划分来量化河道的数量。我们发现，通过河流大小归一化后，河道带宽度和波长、幅度和带边缘的曲率随着河道数量（eBI）的增加而减小。单河道河流的主动水流只占河道带的1%，而多河道河流的活跃流可以占据河道带的50%以上。此外，我们发现通道模式位于通道数的连续统一体上。我们的研究结果对河流和洪泛区相互作用、洪泛区沉积物的储存时间尺度和古环境重建的研究具有重要意义。河流在行星表面上显示出一系列不同的平面河道模式，以蜿蜒和编织形态为代表（Leopold 和 Wolman，1960 年） . 这些模式来自沉积物迁移和流体流动的内部动态以及植被覆盖和限制等外部控制（Parker，1976；Limaye 和 Lamb，2013；Naito 和 Parker，2020）。因此，通道模式——如果保存在岩石记录中并从岩石记录中准确解释——就有可能通过行星的历史记录关键的古环境信息，帮助限制过去的气候、碳循环和可居住性（例如，Ganti 等人。 , 2019)。通过河道迁移和撕脱，河流横向远离当前河道。随着时间的推移，这种运动形成了许多河道的汇合带，在地层中记录了环境信号（Hajek 和 Straub，2017 年）。在平面图中，通过地震、高光谱和激光雷达等一系列成像技术（例如，Cardenas 等人，2018 年；Durkin 等人，2018 年；Hayden 等人.，2019 年；Zaki 等人，2021 年）。因此，通道带几何形状可以为古环境重建提供易于访问的约束。以前的工作已经建立了通道带宽度和通道沉积物厚度之间的经验关系（Gibling，2006）。数值和物理实验结果也表明，河道带宽度呈对数增长，而增长率和稳定宽度对内外部控制敏感，例如排水量和区域坡度（霍华德，1996；乔布等，2016；利马耶，2020）。然而，河流和河道带平面模式之间的经验关系仍然难以捉摸。以往关于河道带的工作通常分别研究单河道和多河道河流（Limaye，2020；Yan 等人，2021 年），而在自然界中，平面河道模式不太可能符合这种二元分类（Galeazzi 等人，2021 年） ）。此外，虽然形态动力学模型能够让河流自行形成河道模式，但计算成本使这些模型无法模拟地质时间尺度上的沉积物（Nicholas，2013 年）。我们探索了一系列自然系统中河流和河道带平面图模式之间的联系。我们假设对于多河道河流，河道带与河道宽度的比率将接近统一，而对于单河道河流，该比率将大大超过统一（图1A）。为了检验我们的假设，我们对全球 30 条河流进行了遥感分析（参见补充材料 1 中的图 S1）。这项研究的结果为未来关于河流与其沉积物之间的相互作用、洪泛区物质的储存时间尺度和古环境重建的工作提供了信息。为了研究河道带平面图模式，我们绘制了全球 30 条河流河段，涵盖了一系列尺度和水文（见有关详细信息的补充材料）。用于测绘的主要遥感数据集包括欧洲航天局 Sentinel-2 高光谱图像和美国国家航空航天局航天飞机雷达地形任务数字高程模型。使用这些数据，根据地形、地面纹理、和植被（详见补充材料）。例如，使用推断的冲积河道带和阶地之间的高程差异（图 1B）、河道带附近有和没有丰富的热岩溶湖区域之间的地面纹理差异（图 1C）和突变来描绘河道带边缘。从树木/灌木到裸地的植被中（图 1D）。通道带平面度量主要使用基于图论的映射包 Riv-Graph 进行测量（Schwenk 和 Hariharan，2021；详见补充材料）。从映射的带边缘自动生成通道带中心线（图 2A）。使用垂直横断面沿中心线每隔约 10 m 测量一次通道带宽度。三个平面度量，包括波长、幅度和曲率，从通道带中心线和边缘测量。然后通过总活跃河道宽度对这些指标进行归一化，以比较不同尺度的河流并检验我们的假设（图 1A）。在最潮湿的月份使用 Sentinel-2 数据的马赛克和归一化差异水指数的修改版本（图 2A；Yan 等人，2020；详见补充材料）。通道中心线、宽度和平面图案是使用 RivGraph 从二进制掩码中自动提取的。我们使用根据熵编织指数 (eBI) 计算的通道数对通道平面模式进行量化，该方法通过其输送的水/沉积物排放量对每个通道进行加权（Tejedor 等人，2019；图 2B）：其中 H 是香农熵，用于近似示踪粒子在给定横截面进入特定通道的概率。理想情况下，H 是使用水/泥沙排放数据计算的，但河流沿岸多个横截面的此类数据稀缺且难以收集。然而，河道宽度已被证明可以有效地预测稳定和均匀流动条件下的水和泥沙排放（Dong et al., 2020）。因此，H 可以用通道宽度表示（bi；Schwenk 和 Hariharan，2021）：其中 i 是采样横截面上的第 i 个通道，N 是活动通道的总数，B 是各个通道的总和横截面的宽度（图 2B）。当 eBI 接近 1 时，大部分水和泥沙排放在一条流道中输送，因此一条河流被认为是一个单通道系统。或者，当 eBI 远大于 1 时，河流被认为是一个多渠道系统。为了显示我们结果的可变性，我们报告了单一河段的归一化渠道带指标和 eBI 的中位数和四分位数范围（图 3A ）。通过对数空间中的线性最小二乘回归评估归一化通道带指标和 eBI 之间的定量关系。请注意，所得经验函数仅用作说明相关性的直接方式。我们发现归一化通道带宽度和 eBI 之间的关系（图 3A）。一般来说，归一化通道带宽度随着 eBI 的增加而减小，这与我们的假设一致。换句话说：随着 eBI 的增加，活跃的河流占据了河道带的大部分。在按 eBI 的四分位数对数据进行分箱后也发现了这种关系（图 3B）。对于端元情况，单河道河流（四分位数 1, ）占据河道带宽度，而多河道河流（四分位数 4, ）占据河道带宽度（表 S1）。我们还发现了从通道带边缘和中心线测量的归一化通道带波长、幅度和曲率与 eBI 之间的关系（图 4）。然而，从通道带中心线测量的指标几乎比从通道带边缘测量的指标大一个数量级，并且它们始终具有较低的 R2 值（图 4）。我们发现通道带宽度、波长、通过总通道宽度归一化的幅度和曲率随着eBI（即通道数）的增加而减小。这些发现表明，通道带，它们是各个河道的合并，与其形成的河道模式保持比例关系。我们的结果也与最近显示河道和河道带的曲率与宽度比相似的工作一致（Hayden 等人，2021 年）。我们还发现河流平面形态分布在一系列河道数上，如 eBI 量化的那样（图 3 和 4)。我们认为这是直观的，因为本质上，河道模式是河流中条形的平面表达，因此在蜿蜒的河流中发现了点条，而在辫状河流中发现了交替的条（Ikeda，1984；Sylvester 等人。 , 2019)。从理论上讲，barform 类型本身可以通过连续水力参数（例如弗劳德数）和沉积物输送指标（例如粒子雷诺数）很好地预测（Ohata 等人，2017）。此外，由于 eBI 根据水和沉积物排放量测量河道数量，因此该指数有望描述连续统一的河道模式（Tejedor 等人，2019 年），如此处自然系统所示（图 3 和图 4）。与之前的定性分类相比，eBI 对通道模式提供了更多基于物理的描述（Galeazzi 等人，2021 年）。条形和通道模式之间的联系也有助于解释波长之间关系强度的差异，从通道带边缘和中心线测量的幅度和曲率（图 4）。随着时间的推移，多条河道的作用形成了通道带边缘，从而记录了这些河流的累积历史及其条形尺寸（Gibling，2006）。因此，从通道带边缘测量的平面度量预计将包含与通道模式的比例关系（Galeazzi 等，2021）。相反，通道带中心线可以被视为长波长过滤带边缘，因此预计会显示有关通道模式的信息的静音版本。因此，正如所观察到的，预计来自带中心线的关系会更弱（图 4）。随着 eBI 接近 1（单河道河流），归一化河道带宽度的变异性增加，削弱了整体定量关系（图 3A)。我们假设这种增加的变异性是由于基岩山谷/河流阶地的限制。在受限系统中，河岸附近的剪应力通常不足以克服谷壁材料的强度，这限制了河流横向扩张的能力，并迫使水和沉积物流入单一路径，从而驱动切口（Larsen 和 Lamb，2016 年）。因此，即使 eBI 仍然很低，归一化的通道带宽度将接近统一（图 3A）。对于无约束系统，河流可以根据水和泥沙排放自组织形成平面模式（Parker，1976）。为了验证这一假设，我们解析了基于河道带约束的归一化河道带宽度和 eBI 数据，定义为渠道带与其周围山谷之间的海拔差异，由渠道带海拔的标准偏差标准化（受 Limaye 和 Lamb，2013 的启发；详见补充材料）。无侧限通道带的一个子集显示通道带宽度与 eBI 之间的关系更强（R2 = 0.84；图 S6B），而受限系统的一个子集显示这两个指标之间没有相关性（R2 = 0.00；图 S6D），这证实了我们的假设。作为替代假设，在低 eBI 下观察到的归一化通道带宽度的变异性也可能是由于绘制的河流系统之间的年龄差异，其中较老的河流发展了更大的河道带。然而，映射的通道带的确切年龄尚不清楚，通道带宽度达到稳定值的时间尺度仍然是一个悬而未决的问题。此外，尚不清楚为什么在低 eBI（单通道）河流中会观察到这种年龄趋势，而对于高 eBI（多通道）河流则不会。尽管有变异的原因，像大多数地貌系统一样，河道带宽度受河谷限制的外部影响，同时也保留了泥沙输送和流体流动的内部动态信号（Parker，1976；Hajek 和 Straub，2017）。无承压的单河道河流仅占 1%河道带宽度（图 3A），这意味着活跃河道和河道带之间的相互作用有限。虽然单河道河流横向迁移/撕裂，但一条河流在河道带中到处流淌的时间尺度是几百年到几千年（Jerolmack 和 Mohrig，2007 年），这意味着河道带的重要部分具有有限的河流沉降，因此可以保持为地形低点。同时，与活跃河流相邻的河道带区域变质成为地形高点，这促进了补偿叠加（Hajek 和 Straub，2017；Jobe 等人，2020）。相比之下，无侧限的多河道系统可以占据河道带宽度的 50% 以上（图 3A），这意味着河流与河道带之间的相互作用更大。因此，总体沉积物可能包含更大比例的具有较小地形变异性的河道沉积物，这与之前的发现一致，即编织系统在地层中包含更多空间连接的河道（Bridge 和 Leeder，1979）。 活跃河流与河道带之间的相互作用沉积物对沉积物储存时间尺度也有重要影响，这会影响有机碳循环的陆地部分。先前的研究发现，沉积物储存时间尺度可以通过无侧限曲流河流中的重尾分布很好地描述，这表明年轻洪泛区物质的优先侵蚀（例如，Torres el.，2017）。这一结论与我们的观察结果一致，即无侧限的单河道河流仅占河道带宽度的一小部分，从而降低了活跃河流与较老沉积物相互作用的可能性。然而，对于受限河流或多河道河流，活跃河流占据了河道带的大部分，这可能会减少河流侵蚀的年龄偏差。因此可以合理地假设，对于这些类型的系统，沉积物储存时间尺度的概率分布将是轻尾的（Wohl，2011；Huffman 等，2021）。特别是，受限制的或多渠道的河流可能会向海洋输出更多的黑碳，影响海洋中有机碳的停留时间尺度（Masiello，2004 年）。鉴于在岩石记录中通道带沉积物的突出地位，此处开发的关系可用于为过去环境变化的研究提供信息。例如，我们的研究结果可以很容易地应用于分析来自地下数据（Gibling，2006）和整个火星（Cardenas 等人，2020；Dickson 等人，2021）的常见河道带沉积物，以重建过去的河道模式和环境。本材料基于美国国家科学基金会资助的工作，奖号为。1952814给T. Dong。我们感谢编辑 William Clyde、Jason Muhlbauer、Zane Jobe 和一位匿名审稿人的深刻评论。T. Dong 感谢 C. Qiu 和 W. Liu 在本书写作过程中给予的爱与支持。T. Dong 也感谢 J.