University of New Hampshire

About: University of New Hampshire is a education organization based out in Durham, New Hampshire, United States. It is known for research contribution in the topics: Population & Solar wind. The organization has 9379 authors who have published 24025 publications receiving 1020112 citations. The organization is also known as: UNH.

Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources

Abstract: . To sustain growing food demand and increasing standard of living, global water withdrawal and consumptive water use have been increasing rapidly. To analyze the human perturbation on water resources consistently over large scales, a number of macro-scale hydrological models (MHMs) have been developed in recent decades. However, few models consider the interaction between terrestrial water fluxes, and human activities and associated water use, and even fewer models distinguish water use from surface water and groundwater resources. Here, we couple a global water demand model with a global hydrological model and dynamically simulate daily water withdrawal and consumptive water use over the period 1979–2010, using two re-analysis products: ERA-Interim and MERRA. We explicitly take into account the mutual feedback between supply and demand, and implement a newly developed water allocation scheme to distinguish surface water and groundwater use. Moreover, we include a new irrigation scheme, which works dynamically with a daily surface and soil water balance, and incorporate the newly available extensive Global Reservoir and Dams data set (GRanD). Simulated surface water and groundwater withdrawals generally show good agreement with reported national and subnational statistics. The results show a consistent increase in both surface water and groundwater use worldwide, with a more rapid increase in groundwater use since the 1990s. Human impacts on terrestrial water storage (TWS) signals are evident, altering the seasonal and interannual variability. This alteration is particularly large over heavily regulated basins such as the Colorado and the Columbia, and over the major irrigated basins such as the Mississippi, the Indus, and the Ganges. Including human water use and associated reservoir operations generally improves the correlation of simulated TWS anomalies with those of the GRACE observations.

Thawing sub-arctic permafrost - effects on vegetation and methane emissions

Abstract: Ecosystems along the 0degreesC mean annual isotherm are arguably among the most sensitive to changing climate and mires in these regions emit significant amounts of the important greenhouse gas methane (CH4) to the atmosphere. These CH4 emissions are intimately related to temperature and hydrology, and alterations in permafrost coverage, which affect both of those, could have dramatic impacts on the emissions. Using a variety of data and information sources from the same region in subarctic Sweden we show that mire ecosystems are subject to dramatic recent changes in the distribution of permafrost and vegetation. These changes are most likely caused by a warming, which has been observed during recent decades. A detailed study of one mire show that the permafrost and vegetation changes have been associated with increases in landscape scale CH4 emissions in the range of 22-66% over the period 1970 to 2000.

Ecosystem engineering in space and time.

Abstract: The ecosystem engineering concept focuses on how organisms physically change the abiotic environment and how this feeds back to the biota. While the concept was formally introduced a little more than 10 years ago, the underpinning of the concept can be traced back to more than a century to the early work of Darwin. The formal application of the idea is yielding new insights into the role of species in ecosystems and many other areas of basic and applied ecology. Here we focus on how temporal, spatial and organizational scales usefully inform the roles played by ecosystem engineers and their incorporation into broader ecological contexts. Two particular, distinguishing features of ecosystem engineers are that they affect the physical space in which other species live and their direct effects can last longer than the lifetime of the organism – engineering can in essence outlive the engineer. Together, these factors identify critical considerations that need to be included in models, experimental and observational work. The ecosystem engineering concept holds particular promise in the area of ecological applications, where influence over abiotic variables and their consequent effects on biotic communities may facilitate ecological restoration and counterbalance anthropogenic influences.

Uncertainty in eddy covariance measurements and its application to physiological models

Abstract: Summary Flux data are noisy, and this uncertainty is largely due to random measurement error. Knowledge of uncertainty is essential for the statistical evaluation of modeled and measured fluxes, for comparison of parameters derived by fitting models to measured fluxes and in formal data-assimilation efforts. We used the difference between simultaneous measurements from two towers located less than 1 km apart to quantify the distributional characteristics of the measurement error in fluxes of carbon dioxide (CO2) and sensible and latent heat (H and LE, respectively). Flux measurement error more closely follows a double exponential than a normal distribution. The CO2 flux uncertainty is negatively correlated with mean wind speed, whereas uncertainty in H and LE is positively correlated with net radiation flux. Measurements from a single tower made 24 h apart under similar environmental conditions can also be used to characterize flux uncertainty. Uncertainty calculated by this method is somewhat higher than that derived from the two-tower approach. We demonstrate the use of flux uncertainty in maximum likelihood parameter estimates for simple physiological models of daytime net carbon exchange. We show that inferred model parameters are highly correlated, and that hypothesis testing is therefore possible only when the joint distribution of the model parameters is taken into account.