The impacts of rising vapour pressure deficit in natural and managed ecosystems.

An exponential rise in the atmospheric vapour pressure deficit (VPD) is among the most consequential impacts of climate change in terrestrial ecosystems. Rising VPD has negative and cascading effects on nearly all aspects of plant function including photosynthesis, water status, growth and survival. These responses are exacerbated by land-atmosphere interactions that couple VPD to soil water and govern the evolution of drought, affecting a range of ecosystem services including carbon uptake, biodiversity, the provisioning of water resources and crop yields. However, despite the global nature of this phenomenon, research on how to incorporate these impacts into resilient management regimes is largely in its infancy, due in part to the entanglement of VPD trends with those of other co-evolving climate drivers. Here, we review the mechanistic bases of VPD impacts at a range of spatial scales, paying particular attention to the independent and interactive influence of VPD in the context of other environmental changes. We then evaluate the consequences of these impacts within key management contexts, including water resources, croplands, wildfire risk mitigation and management of natural grasslands and forests. We conclude with recommendations describing how management regimes could be altered to mitigate the otherwise highly deleterious consequences of rising VPD.

The forest, the cicadas and the holey fluxes: Periodical cicada impacts on soil respiration depends on tree mycorrhizal type.

The emergence of billions of periodical cicadas affects plant and animal communities profoundly, yet little is known about cicada impacts on soil carbon fluxes. We investigated the effects of Brood X cicadas (Magicicada septendecim, M. cassinii and M. septendeculain) on soil CO2 fluxes (RS ) in three Indiana forests. We hypothesized RS would be sensitive to emergence hole density, with the greatest effects occurring in soils with the lowest ambient fluxes. In support of our hypothesis, RS increased with increasing hole density and greater effects were observed near AM-associating trees (which expressed lower ambient fluxes) than near EcM-associating trees. Additionally, RS from emergence holes increased the temperature sensitivity (Q10 ) of RS by 13%, elevating the Q10 of ecosystem respiration. Brood X cicadas increased annual RS by ca. 2.5%, translating to an additional 717 Gg of CO2 across forested areas. As such, periodical cicadas can have substantial effects on soil processes and biogeochemistry.

Confronting the water potential information gap.

Water potential directly controls the function of leaves, roots, and microbes, and gradients in water potential drive water flows throughout the soil-plant-atmosphere continuum. Notwithstanding its clear relevance for many ecosystem processes, soil water potential is rarely measured in-situ, and plant water potential observations are generally discrete, sparse, and not yet aggregated into accessible databases. These gaps limit our conceptual understanding of biophysical responses to moisture stress and inject large uncertainty into hydrologic and land surface models. Here, we outline the conceptual and predictive gains that could be made with more continuous and discoverable observations of water potential in soils and plants. We discuss improvements to sensor technologies that facilitate in situ characterization of water potential, as well as strategies for building new networks that aggregate water potential data across sites. We end by highlighting novel opportunities for linking more representative site-level observations of water potential to remotely-sensed proxies. Together, these considerations offer a roadmap for clearer links between ecohydrological processes and the water potential gradients that have the 'potential' to substantially reduce conceptual and modeling uncertainties.

Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US.

Severe droughts can impart long-lasting legacies on forest ecosystems through lagged effects that hinder tree recovery and suppress whole-forest carbon uptake. However, the local climatic and edaphic factors that interact to affect drought legacies in temperate forests remain unknown. Here, we pair a dataset of 143 tree ring chronologies across the mesic forests of the eastern US with historical climate and local soil properties. We found legacy effects to be widespread, the magnitude of which increased markedly in diffuse porous species, sites with deep water tables, and in response to late-season droughts (August-September). Using an ensemble of downscaled climate projections, we additionally show that our sites are projected to drastically increase in water deficit and drought frequency by the end of the century, potentially increasing the size of legacy effects by up to 65% and acting as a significant process shaping forest composition, carbon uptake and mortality.

Embedding co-production and addressing uncertainty in watershed modeling decision-support tools: successes and challenges.

Decision-support tools (DSTs) are often produced from collaborations between technical experts and stakeholders to address environmental problems and inform decision making. Studies in the past two decades have provided key insights on the use of DSTs and the importance of bidirectional information flows among technical experts and stakeholders - a process that is variously referred to as co-production, participatory modeling, structured decision making, or simply stakeholder participation. Many of these studies have elicited foundational insights for the broad field of water resources management; however, questions remain on approaches for balancing co-production with uncertainty specifically for watershed modeling decision support tools. In this paper, we outline a simple conceptual model that focuses on the DST development process. Then, using watershed modeling case studies found in the literature, we discuss successful outcomes and challenges associated with embedding various forms of co-production into each stage of the conceptual model. We also emphasize the "3 Cs" (i.e., characterization, calculation, communication) of uncertainty and provide evidence-based suggestions for their incorporation in the watershed modeling DST development process. We conclude by presenting a list of best practices derived from current literature for achieving effective and robust watershed modeling decision-support tools.

Natural and managed watersheds show similar responses to recent climate change.

Changes in climate are driving an intensification of the hydrologic cycle and leading to alterations of natural streamflow regimes. Human disturbances such as dams, land-cover change, and water diversions are thought to obscure climate signals in hydrologic systems. As a result, most studies of changing hydroclimatic conditions are limited to areas with natural streamflow. Here, we compare trends in observed streamflow from natural and human-modified watersheds in the United States and Canada for the 1981-2015 water years to evaluate whether comparable responses to climate change are present in both systems. We find that patterns and magnitudes of trends in median daily streamflow, daily streamflow variability, and daily extremes in human-modified watersheds are similar to those from nearby natural watersheds. Streamflow in both systems show negative trends throughout the southern and western United States and positive trends throughout the northeastern United States, the northern Great Plains, and southern prairies of Canada. The trends in both natural and human-modified watersheds are linked to local trends in precipitation and reference evapotranspiration, demonstrating that water management and land-cover change have not substantially altered the effects of climate change on human-modified watersheds compared with nearby natural watersheds.

MOESHA: A Genetic Algorithm for Automatic Calibration and Estimation of Parameter Uncertainty and Sensitivity of Hydrologic Models.

Characterization of the uncertainty and sensitivity of model parameters is an essential facet of hydrologic modeling. This article introduces the multi-objective evolutionary sensitivity handling algorithm (MOESHA) that combines input parameter uncertainty and sensitivity analyses with a genetic algorithm calibration routine to dynamically sample the parameter space. This novel algorithm serves as an alternative to traditional static space-sampling methods, such as stratified sampling or Latin hypercube sampling. In addition to calibrating model parameters to a hydrologic model, MOESHA determines the optimal distribution of model parameters that maximizes model robustness and minimizes error, and the results provide an estimate for model uncertainty due to the uncertainty in model parameters. Subsequently, we compare the model parameter distributions to the distribution of a dummy variable (i.e., a variable that does not affect model output) to differentiate between impactful (i.e., sensitive) and non-impactful parameters. In this way, an optimally calibrated model is produced, and estimations of model uncertainty as well as the relative impact of model parameters on model output (i.e., sensitivity) are determined. A case study using a single-cell hydrologic model (EXP-HYDRO) is used to test the method using river discharge data from the Dee River catchment in Wales. We compare the results of MOESHA with Sobol's global sensitivity analysis method and demonstrate that the algorithm is able to pinpoint non-impactful parameters, demonstrate the uncertainty of model results with respect to uncertainties in model parameters, and achieve excellent calibration results. A major drawback of the algorithm is that it is computationally expensive; therefore, parallelized methods should be used to reduce the computational burden.

Climate change impacts on streamflow and subbasin-scale hydrology in the Upper Colorado River Basin.

In the Upper Colorado River Basin (UCRB), the principal source of water in the southwestern U.S., demand exceeds supply in most years, and will likely continue to rise. While General Circulation Models (GCMs) project surface temperature warming by 3.5 to 5.6°C for the area, precipitation projections are variable, with no wetter or drier consensus. We assess the impacts of projected 21(st) century climatic changes on subbasins in the UCRB using the Soil and Water Assessment Tool, for all hydrologic components (snowmelt, evapotranspiration, surface runoff, subsurface runoff, and streamflow), and for 16 GCMs under the A2 emission scenario. Over the GCM ensemble, our simulations project median Spring streamflow declines of 36% by the end of the 21(st) century, with increases more likely at higher elevations, and an overall range of -100 to +68%. Additionally, our results indicated Summer streamflow declines with median decreases of 46%, and an overall range of -100 to +22%. Analysis of hydrologic components indicates large spatial and temporal changes throughout the UCRB, with large snowmelt declines and temporal shifts in most hydrologic components. Warmer temperatures increase average annual evapotranspiration by ∼23%, with shifting seasonal soil moisture availability driving these increases in late Winter and early Spring. For the high-elevation water-generating regions, modest precipitation decreases result in an even greater water yield decrease with less available snowmelt. Precipitation increases with modest warming do not translate into the same magnitude of water-yield increases due to slight decreases in snowmelt and increases in evapotranspiration. For these basins, whether modest warming is associated with precipitation decreases or increases, continued rising temperatures may make drier futures. Subsequently, many subbasins are projected to turn from semi-arid to arid conditions by the 2080 s. In conclusion, water availability in the UCRB could significantly decline with adverse consequences for water supplies, agriculture, and ecosystem health.

Assessment of climate change impacts on hydrology and water quality with a watershed modeling approach.

The assessment of hydrologic responses to climate change is required in watershed management and planning to protect water resources and environmental quality. This study is designed to evaluate and enhance watershed modeling approach in characterizing climate change impacts on water supply and ecosystem stressors. Soil and Water Assessment Tool (SWAT) was selected as a base model, and improved for the CO2 dependence of potential evapotranspiration and stream temperature prediction. The updated model was applied to quantify the impacts of projected 21st century climate change in the northern Coastal Ranges and western Sierra Nevada, which are important water source areas and aquatic habitats of California. Evapotranspiration response to CO2 concentration varied with vegetation type. For the forest-dominated watersheds in this study, only moderate (1-3%) reductions on evapotranspiration were predicted by solely elevating CO2 concentration under emission scenarios A2 and B1. Modeling results suggested increases in annual average stream temperature proportional to the projected increases in air temperature. Although no temporal trend was confirmed for annual precipitation in California, increases of precipitation and streamflow during winter months and decreases in summers were predicted. Decreased streamflow during summertime, together with the higher projected air temperature in summer than in winter, would increase stream temperature during those months and result in unfavorable conditions for cold-water species. Compared to the present-day conditions, 30-60 more days per year were predicted with average stream temperature >20°C during 2090s. Overall, the hydrologic cycle and water quality of headwater drainage basins of California, especially their seasonality, are very sensitive to projected climate change.

Sensitivity of agricultural runoff loads to rising levels of CO2 and climate change in the San Joaquin Valley watershed of California.

The Soil and Water Assessment Tool (SWAT) was used to assess the impact of climate change on sediment, nitrate, phosphorus and pesticide (diazinon and chlorpyrifos) runoff in the San Joaquin watershed in California. This study used modeling techniques that include variations of CO(2), temperature, and precipitation to quantify these responses. Precipitation had a greater impact on agricultural runoff compared to changes in either CO(2) concentration or temperature. Increase of precipitation by +/-10% and +/-20% generally changed agricultural runoff proportionally. Solely increasing CO(2) concentration resulted in an increase in nitrate, phosphorus, and chlorpyrifos yield by 4.2, 7.8, and 6.4%, respectively, and a decrease in sediment and diazinon yield by 6.3 and 5.3%, respectively, in comparison to the present-day reference scenario. Only increasing temperature reduced yields of all agricultural runoff components. The results suggest that agricultural runoff in the San Joaquin watershed is sensitive to precipitation, temperature, and CO(2) concentration changes.