Alpine regions are changing rapidly due to loss of snow and ice in response to ongoing climate change. While studies have documented ecological responses in alpine | 6645 ELSER Et aL. We propose four rules of life necessary for obtaining a fundamental and thus predictive understanding of how aquatic biota and ecosystems in alpine environments will respond to a changing cryosphere under ongoing climate change. Key rule 1: Temperature. Temperature has a fundamental effect on nearly all biological activities due to the underlying physics of biochemical processes (red arrows in Figure 1, center). Key rule 2: Wavelength dependence. Biological systems are differentially affected by photosynthetically active radiation (PAR) and ultraviolet radiation (UVR) from molecules to ecosystems (Figure 1, top). Key rule 3: Biological stoichiometry. Earth's species comprise a nonrandom assemblage of chemical elements that reflect their evolved life histories and shape their distribution and dynamics (Figure 1, gray bars).

Inland lakes constitute an important global freshwater resource and are often defining features of local and regional landscapes. While coupled surface water (SW) and groundwater (GW) models are increasingly available, there is a clear need for spatially explicit yet computationally parsimonious modeling frameworks to explore the impacts of climate, land use, and other drivers on lake hydrologic and biogeochemical processes. To address this need, we developed a new method to simulate daily water budgets for many individual lakes at large spatial scales. By integrating SW, GW, and lake water budget models in a simple manner, we created a modeling framework capable of simulating the historical and future hydrologic dynamics of lakes with varying hydrologic characteristics. By extension, the model output enables ecological modeling in response to hydrologic drivers. As a case study, we applied the model to a large, lake‐rich region in northern Wisconsin and Michigan, simulating daily water budgets for nearly 4,000 lakes over a 36‐year period. Despite minimal calibration efforts, our simulated results compared reasonably well with observations and more sophisticated modeling approaches. Our integrated modeling requires very limited information, can be run on readily available computer resources, such as a desktop PC, and can be applied at regional, continental, or global scales, where necessary model setup and forcing data are available.

Lakes are areas of intense biogeochemical processing in the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. However, current large‐scale estimates of lake biogeochemical fluxes are all generated by multiplying a mean observed areal rate by regional or global lake surface area, which ignores important heterogeneous spatial and temporal processes that regulate lake carbon cycling. We have developed a process‐based model that integrates core scientific knowledge in hydrology, biogeochemistry, and ecology that is specifically designed to be applied over large geographic regions to hindcast or forecast regional lake carbon fluxes. We used our model to simulate daily carbon fluxes and pools for 3,675 lakes in the Northern Highlands Lake District from 1980–2010 and produced spatial and seasonal patterns consistent with observations. Variabilities in lake carbon fluxes were well predicted by relatively simple hydrologic metrics, such as the fraction of hydrologic export as evaporation (FHEE). Overall, lakes with a high FHEE processed a greater percentage of carbon inputs in the simulations than lakes with a low FHEE, but low‐FHEE lakes ultimately processed more total carbon because of greater carbon inputs. Large lakes with low FHEE and high external loading of dissolved inorganic carbon contributed most to total CO2 emissions for the Northern Highlands Lake District, and our model estimated that 78% of total CO2 emissions from lakes to the atmosphere originated from external loads of dissolved inorganic carbon. By better characterizing the unique biogeochemical processes for each individual lake, regional estimates of carbon fluxes are more accurately determined.

C arbon (C) is the building block of life. Global photosynthesis generates approximately 100 terawatts (TW) of energy each year by converting solar radiation into stored chemical energy (Barber 2009). Photosynthesis also represents the largest global annual C flux, of ~125 petagrams (Pg; where 1 Pg equals 10 15 grams [g] and 1 Pg C is roughly equivalent to 0.47 parts per million [ppm] of CO 2), with the second greatest flux consisting of the subsequent release of CO 2 via respiration (~122 Pg C/year). Both of these fluxes are an order of magnitude greater than fossil-fuel emissions (Ballantyne et al. 2015). The atmospheric CO 2 that is fixed during photosynthesis is subsequently stored and transferred as chemical energy, which in turn fuels the metabolic reactions of most autotrophs and heterotrophs. Although C is the most common element in the terrestrial biosphere, representing approximately 50 parts per hundred (%) of all organic matter, CO 2 represents only a very small fraction of the atmosphere and is therefore measured in ppm (~415 ppm in 2020). Given the abundance of C in the terrestrial biosphere and the massive fluxes of C occurring between the biosphere and the atmosphere, it is no surprise that scientists have developed a myriad of innovative ways for measuring and simulating C-cycle processes across a range of scales in time and space. For example, chloroplast CO 2 fluxes are estimated over millimeters per second, whereas biome CO 2 fluxes may be estimated over thousands of kilometers per year. There have been many advances in C-cycle science over the past 60 years at leaf, plant, ecosystem, and global scales, but both challenges to and opportunities for scientific advancement remain. Progress is necessary, however, especially at the macrosystem scale, where human management and ecological processes are often at odds and create interesting interactions of C dynamics. One of the greatest impediments to accurate predictions of future climate is the uncertain response of the terrestrial C cycle to impending changes in temperature, precipitation, and atmospheric CO 2 concentrations (Friedlingstein et al. 2013). Even though land-surface models have become increasingly realistic in their mechanistic representation of C-cycle processes by including nutrient limitation (Thornton et al. 2007),

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