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.

Lakes support globally important food webs through algal productivity and contribute significantly to the global carbon cycle. However, predictions of how broad‐scale lake carbon flux and productivity may respond to future climate are extremely limited. Here, we used an integrated modeling framework to project changes in lake‐specific and regional primary productivity and carbon fluxes under 21st century climate for thousands of lakes. We observed high uncertainty in whether lakes collectively were to increase or decrease lake CO2 emissions and carbon burial in our modeled region owing to divergence in projected regional water balance among climate models. Variation in projected air temperature influenced projected changes in lake primary productivity (but not CO2 emissions or carbon burial) as warmer air temperatures decreased productivity through reduced lake water volume. Cross‐scale interactions between regional drivers and local characteristics dictated the magnitude and direction of lake‐specific carbon flux and productivity responses to future climate.

Inland lakes are socially and ecologically important components of many regional landscapes. Exploring lake responses to plausible future climate scenarios can provide important information needed to inform stakeholders of likely effects of hydrologic changes on these waterbodies in coming decades. To assess potential climate effects on lake hydrology, we combined a previously published spatially explicit, processed-based hydrologic modeling framework implemented over the lake-rich landscape of the Northern Highlands Lake District within the United States with an ensemble of climate change scenarios for the 2050s (2041-2070) and 2080s (2071-2100). Model results quantify the effects of climate change on water budgets and lake stage elevations for 3692 lakes and highlight the importance of landscape and hydrologic setting for the response of specific lake types to climate change. All future climate projections resulted in loss of ice cover and snowpack as well as increased evaporation, but variability in climate projections (warmer conditions, wet winters combined with wet or dry summers) interacted with lake characteristics and landscape position to produce variable lake hydrologic changes. Water levels for drainage lakes (lakes with substantial surface water inflows and outflows) showed nearly no change, whereas minimum water levels for seepage lakes (minimal surface water fluxes) decreased by an average of up to 2.64 m by the end of the 21st century. Our physically based modeling approach is parsimonious and computationally efficient and can be applied to other lake-rich regions to investigate interregional variability in lake hydrologic response to future climate scenarios.