Toward catchment hydro‐biogeochemical theories
Toward catchment hydro‐biogeochemical theories
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流域水文生物地球化学理论
DOI：
10.1002/wat2.1495
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发表时间：
2020
期刊：
影响因子：
8.2
通讯作者：
Seibert, Jan
中科院分区：
文献类型：
--
作者：
Li, Li;Sullivan, Pamela L.;Benettin, Paolo;Cirpka, Olaf A.;Bishop, Kevin;Brantley, Susan L.;Knapp, Julia L. A.;van Meerveld, Ilja;Rinaldo, Andrea;Seibert, Jan
水源集水区是连接陆地与海洋的基本单位。水文流动和生物地球化学过程错综复杂地耦合在一起，但它们各自的科学在没有太多整合的情况下取得了进步。规定速率依赖于环境变量（例如温度和水含量）的反应动力学理论已经取得了长足的进步，主要是在充分混合的反应器、塔和升温实验中，没有考虑流域尺度的水文流量特征。这些理论与自然系统中的观察结果存在显着差异。另一方面，包括渡越时间理论在内的水文理论已经取得了长足的进步，但尚未被纳入对流域尺度反应的理解中。在这里，我们提倡发展跨越气候、植被和地质条件梯度的综合水文生物地球化学理论。缺乏此类理论给理解人类和气候引起的扰动下的机制和预测关键区的未来带来了障碍。尽管集成已经开始，并且同地测量也在顺利进行，但仍然存在巨大的挑战。特别是，即使在这个“大数据”时代，我们仍然受到数据的限制，需要（1）加强河道以外的测量，表征垂直连通性以及广泛的浅层和深层地下； (2) 扩展到超出稳定水同位素反映的时间尺度的较旧的水年代测定； (3) 结合使用反应性溶质、非反应性示踪剂和同位素； (4) 增强正在经历快速变化的环境中的测量。为了发展综合理论，有必要（1）在各个阶段使用模型来开发基于模型的数据收集策略并最大限度地利用数据； (2) 采用“简单但不简单化”或适合目的的方法，将基本流程纳入基于流程的模型中； （3）在“理论指导的数据科学”框架中混合使用基于过程和数据驱动的模型。在假设检验的框架内，模型数据融合可以推进综合理论，将流域的内部结构和外部驱动因素与其功能机械地联系起来。它不仅可以推进水文生物地球化学领域的发展，还可以进行事后预测并服务于整个社会。从广义上讲，未来的教育需要培养传统学科与整体方法交叉的思想家，以理解复杂地球系统中的相互作用过程。本文分类为：工程水 > 方法
Headwater catchments are the fundamental units that connect the land to the ocean. Hydrological flow and biogeochemical processes are intricately coupled, yet their respective sciences have progressed without much integration. Reaction kinetic theories that prescribe rate dependence on environmental variables (e.g., temperature and water content) have advanced substantially, mostly in well‐mixed reactors, columns, and warming experiments without considering the characteristics of hydrological flow at the catchment scale. These theories have shown significant divergence from observations in natural systems. On the other hand, hydrological theories, including transit time theory, have progressed substantially yet have not been incorporated into understanding reactions at the catchment scale. Here we advocate for the development of integrated hydro‐biogeochemical theories across gradients of climate, vegetation, and geology conditions. The lack of such theories presents barriers for understanding mechanisms and forecasting the future of the Critical Zone under human‐ and climate‐induced perturbations. Although integration has started and co‐located measurements are well under way, tremendous challenges remain. In particular, even in this era of “big data,” we are still limited by data and will need to (1) intensify measurements beyond river channels and characterize the vertical connectivity and broadly the shallow and deep subsurface; (2) expand to older water dating beyond the time scales reflected in stable water isotopes; (3) combine the use of reactive solutes, nonreactive tracers, and isotopes; and (4) augment measurements in environments that are undergoing rapid changes. To develop integrated theories, it is essential to (1) engage models at all stages to develop model‐informed data collection strategies and to maximize data usage; (2) adopt a “simple but not simplistic,” or fit‐for‐purpose approach to include essential processes in process‐based models; (3) blend the use of process‐based and data‐driven models in the framework of “theory‐guided data science.” Within the framework of hypothesis testing, model‐data fusion can advance integrated theories that mechanistically link catchments' internal structures and external drivers to their functioning. It can not only advance the field of hydro‐biogeochemistry, but also enable hind‐ and fore‐casting and serve the society at large. Broadly, future education will need to cultivate thinkers at the intersections of traditional disciplines with hollistic approaches for understanding interacting processes in complex earth systems.This article is categorized under:Engineering Water > Methods
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