Exploring mechanisms for the decrease of atmospheric CO₂ during the last glacial maximum using a four-box ocean model

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 2001.Includes bibliographical references (leaf 33).A record of carbon dioxide for the last 400,000 years revealed that atmospheric CO2 decreased from a pre-industrial concentration of 280 ppmV to approximately 200 ppmV during the last glacial maximum (Petit et al., 1999). Several hypotheses as to why this happened have been suggested yet no one explanation has been able to fully account for this decrease. Deep water is the main sink for carbon in the ocean through the biological pump, where the organic matter and CaCO3 shells of dead surface biota sink. The stored carbon in the deep ocean is 'aired' in the southern polar ocean, where large amounts of deep water are upwelled to the surface. Stephens and Keeling (2000) have proposed that if the southern polar ocean was covered with ice during the last glacial maximum, this would have prevented carbon stored in the deep ocean to be released into the atmosphere, thereby reducing the concentration of atmospheric CO2. Stephens and Keeling (2000) created a six-box ocean to test this hypothesis and were able to produce a 67 ppmV decrease of atmospheric CO2 from the pre-industrial concentration when only the gas exchange between the southern polar ocean and the atmosphere was limited. Based on the Toggweiler and Sarmiento (1985) three-box ocean model, a four-box ocean model that splits the Toggiweiler and Sarmiento polar ocean box in to a northern and southern component was created. The four-box ocean model examined the sensitivity of atmospheric CO2 to limitations in the airsea gas exchange for the southern polar ocean. The four-box ocean was able to produce seventy percent of Stephens and Keeling's decrease in atmospheric CO2 when the air-sea gas exchange was limited in the southern polar ocean. In addition, the four-box ocean model calculated carbon-14 concentrations in the ocean, which provide a useful constraint on model results that was not presented in the Stephens and Keeling model. The atmospheric carbon dioxide in the four box model was found to be more sensitive to increasing biological productivity in the southern polar ocean than to the growth of the Antarctic ice sheet.by Stacey A. Archfield.S.M

A logistic regression equation for estimating the probability of a stream flowing perennially in Massachusetts /

"Prepared in cooperation with the Massachusetts Executive Office of Environemental Affairs, and the Massachusetts Department of Environmental Protection."Includes bibliographical references (p. 23).Mode of access: Internet

Monthly river temperature trends across the US confound annual changes

Climate variations and human modifications of the water cycle continue to alter the Earth’s surface water and energy exchanges. It is therefore critical to ascertain how these changes impact water quality and aquatic ecosystem habitat metrics such as river temperatures. Though river temperature trend analyses exist in the literature, studies on seasonal trends in river temperatures across large spatial extents, e.g. the contiguous United States (US), are limited. As we show through both annual and monthly trend analyses for 20 year ( n = 138 sites) and 40 year ( n = 40 sites) periods, annual temperature trends across the US mask extensive monthly variability. While most sites exhibited annual warming trends, these annual trends obscured sub-annual cooling trends at many sites. Monthly trend anomalies were spatially organized, with persistent regional patterns at both reference and human-impacted sites. The largest warming and cooling anomalies happened at human impacted sites and during summer months. Though our analysis points to coherence in trends as well as the overall impact of human activity in driving these patterns, we did not investigate the impact of river temperature observation accuracy on reported trends, an area needed for future work. Overall, these patterns emphasize the need to consider sub-annual behavior when managing the ecological impacts of river temperature throughout lotic networks

A bootstrap method for estimating uncertainty of water quality trends

AbstractEstimation of the direction and magnitude of trends in surface water quality remains a problem of great scientific and practical interest. The Weighted Regressions on Time, Discharge, and Season (WRTDS) method was recently introduced as an exploratory data analysis tool to provide flexible and robust estimates of water quality trends. This paper enhances the WRTDS method through the introduction of the WRTDS Bootstrap Test (WBT), an extension of WRTDS that quantifies the uncertainty in WRTDS-estimates of water quality trends and offers various ways to visualize and communicate these uncertainties. Monte Carlo experiments are applied to estimate the Type I error probabilities for this method. WBT is compared to other water-quality trend-testing methods appropriate for data sets of one to three decades in length with sampling frequencies of 6–24 observations per year. The software to conduct the test is in the EGRETci R-package

Topological and canonical kriging for design-flood prediction in ungauged catchments: An improvement over a traditional regional regression approach?

In the United States, estimation of flood frequency quantiles at ungaged locations has been largely based on regional regression techniques that relate measurable catchment descriptors to flood quantiles. More recently, spatial interpolation techniques of point data have been shown to be effective for predicting streamflow statistics (i.e. flood flows and low-flow indices) in ungauged catchments. Literature reports successful applications of two techniques, Canonical kriging, CK, (or physiographical-space based interpolation, PSBI) and Topological kriging, TK, (or Top-kriging). CK performs the spatial interpolation of the streamflow statistic of interest in the two-dimensional space of catchment descriptors. TK predicts the streamflow statistic along river networks taking both the catchment area and nested nature of catchments into account. It is of interest to understand how these spatial interpolation methods compare with generalized-least squares (GLS) regression, one of the most common approaches to estimate flood quantiles at ungauged locations. By means of a leave-one-out cross validation procedure, the performance of CK and TK was compared to GLS regression equations developed for the prediction of 10-, 50-, 100- and 500-year floods for 61 streamgauges in the southeast United States. TK substantially outperforms GLS and CK for the study area, particularly for large catchments. The performance of TK over GLS highlights an important distinction between the treatment of spatial correlation when using regression-based versus spatial interpolation methods to estimate flood quantiles at ungauged locations. The analysis also shows that coupling TK with CK slightly improves the performance of TK; however, the improvement is marginal when compared to the improvement in performance over GLS.JRC.H.5-Land Resources Managemen

Regional flow duration curves: Geostatistical techniques versus multivariate regression

A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98

Predictions of Runoff Hydrographs in Ungauged Basins

Predicting water runoff in ungauged water catchment areas is vital to practical applications such as the design of drainage infrastructure and flooding defences, runoff forecasting, and for catchment management tasks such as water allocation and climate impact analysis. This book synthesises decades of international research, forming a holistic approach to catchment hydrology and providing a one-stop resource for hydrologists in both developed and developing countries. Chapter 10 deals with predicting the entire runoff hydrograph in ungauged basins. Runoff hydrographs form the basis for a wide range of hydrologic investigations and water resources management tasks. From a scientific perspective one may be interested in predicting hydrographs in ungauged basins in order to understand how the individual processes combine to produce catchment response. From a practical perspective one may be interested in obtaining design characteristics for spillways, culverts and embankments. One may also be interested in water resources management applications such as water allocation for irrigation, industry and human use, hydropower operation and environmental flow estimation. Predictions of hydrographs in ungauged basins are also essential for risk management such as in flood and drought forecasting. Both statistical and process based methods have been used for predictions, although the trend is for increasing use of process based methods. Nevertheless, in data-rich regions of the world, geostatistical methods that account for the network structure are seen to work much better in ungauged basins than process based (rainfall-runoff) methods.JRC.H.5-Land Resources Managemen

Accelerating advances in continental domain hydrologic modeling

In the past, hydrological modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity amongst the catchment, global water security, and land surface modeling communities such that these communities are converging towards continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community towards this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or re-analysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behaviour; (3) maintaining a balance between model complexity and data availability as well as uncertainties and (4) quantifying and communicating significant advancements towards the modeling goals