Modeling plant–water interactions: an ecohydrological overview from the cell to the global scale

Abstract

Vegetation and the water cycles are inherently coupled across a wide range of spatial and temporal scales. Water availability interacts with plant ecophysiology and controls vegetation functioning. Concurrently, vegetation has direct and indirect effects on energy, water, carbon, and nutrient cycles. To better understand and model plant–water interactions, highly interdisciplinary approaches are required. We present an overview of the main processes and relevant interactions between water and plants across a range of spatial scales, from the cell level of leaves, where stomatal controls occur, to drought stress at the level of a single tree, to the integrating scales of a watershed, region, and the globe. A review of process representations in models at different scales is presented. More specifically, three main model families are identified: (1) models of plant hydraulics that mechanistically simulate stomatal controls and/or water transport at the tree level; (2) ecohydrological models that simulate plot‐ to catchment‐scale water, energy, and carbon fluxes; and (3) terrestrial biosphere models that simulate carbon, water, and nutrient dynamics at the regional and global scales and address feedback between Earth's vegetation and the climate system. We identify special features and similarities across the model families. Examples of where plant–water interactions are especially important and have led to key scientific findings are also highlighted. Finally, we discuss the various data sources that are currently available to force and validate existing models, and we present perspectives on the evolution of the field. WIREs Water 2016, 3:327–368. doi: 10.1002/wat2.1125 This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Science of Water > Hydrological Processes

Inferring the effects of sink strength on plant carbon balance processes from experimental measurements

“…This hypothesis was rejected, with a storage pool being necessary to simulate growth, particularly for containerized seedlings (Sim A, Table 3). The approach of simulating growth from current-day photosynthate is commonly used in models, particularly for evergreen plants (e.g., Jain and Yang, 2005;Law et al, 2006;Thornton et al, 2007), but several authors have proposed the need for a storage pool to balance the C sources and sinks in the short term, as well as to simulate the effects of photosynthetic downregulation in the long term (Pugh et al, 2016;Richardson et al, 2013;Fatichi et al, 2016). Our results support the need for an NSC pool in CBMs.…”

Modelling carbon sources and sinks in terrestrial vegetation

“…Historically, the aforementioned observation‐driven focus on plant C assimilation rather than growth is reflected in terrestrial biosphere models (TBMs, here used in the broadest sense; e.g. Fatichi et al ., ), which mostly concentrate on photosynthesis and, in comparison, have simplified representation of plant respiration, sink activities and C turnover rates (Fatichi et al ., ; Pugh et al ., ). The C‐source perspective in models is further reinforced by the existence of an elegant, robust and mathematically tractable framework for modelling leaf‐level C assimilation (e.g.…”

Ecohydrological modelling with EcH2O‐iso to quantify forest and grassland effects on water partitioning and flux ages

“…For example, the impacts of vegetation dynamics on water fluxes—in terms of directional long‐term growth and seasonal phenology—are rarely well constrained (Huisman et al, ). Historically, most hydrological models conceptualize vegetation as a static element with prescribed constants that parameterize the physical processes of evapotranspiration, disregarding the strong coupling between evapotranspiration and the physiological processes that drive plant phenology and water use (Fatichi et al, ; Speich, Lischke, Scherstjanoi, & Zappa, ; Wegehenkel, ). Over the past 15 years, various ecohydrological models have explicitly included dynamic vegetation parameterization to overcome such limitations (e.g., RheSYSS [Tague & Band, ], EcH 2 O [Maneta & Silverman, ; Kuppel, Tetzlaff, Maneta, & Soulsby, ; Simeone et al, ], tRIBS‐VEGGIE [Ivanov, Bras, & Vivoni, ], Cathy [Niu et al, ], Tethys‐Chloris [Fatichi, Ivanov, & Caporali, ], and FLETCH2 [Mirfenderesgi et al, ]).…”