Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States

Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamflow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948-1970 and 1971-2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human-induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e. g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate- and human-induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs

THE JOINT EFFECTS OF CLIMATE CHANGE AND URBANIZATION ON THE DISTRIBUTION OF STREAMFLOW MAGNITUDES IN THE MARYLAND PIEDMONT REGION

This thesis examines the effect of climate and land use change on streamflow distributions in six urbanizing watersheds in the Maryland Piedmont region, and produces future predictions under three proposed scenarios of future climate and land use varying individually and then jointly. Future climate is modeled using precipitation and temperature predictions from the CCC and Hadley models. Two approaches are used to predict future streamflows: a regression model approach, and a continuous streamflow model approach. Trend tests at a 5% level of significance are used to statistically quantify emerging trends in the simulated climate and streamflow time series. Precipitation is the dominant factor and generally controls the directionality of trends in streamflows. Temperature has less influence on low flows and no apparent effect on peak flows. Land use change has caused low flows to be slightly smaller and peak flows slightly larger, but no significant trends were detected

Prediction of weekly nitrate-N fluctuations in a small agricultural watershed in Illinois

Agricultural nonpoint source pollution has been identified as one of the leading causes of surface water quality impairment in the United States. Such an impact is important, particularly in predominantly agricultural areas, where application of agricultural fertilizers often results in excessive nitrate levels in streams and rivers. When nitrate concentration in a public water supply reaches or exceeds drinking water standards, costly measures such as well closure or water treatment have to be considered. Thus, having accurate nitrate-N predictions is critical in making correct and timely management decisions. This study applied a set of data mining tools to predict weekly nitrate-N concentrations at a gauging station on the Sangamon River near Decatur, Illinois. The data mining tools used in this study included artificial neural networks, evolutionary polynomial regression and the naive Bayes model. The results were compared using seven forecast measures. In general, all models performed reasonably well, but not all achieved best scores in each of the measures, suggesting that a multi-tool approach is needed. In addition to improving forecast accuracy compared with previous studies, the tools described in this study demonstrated potential for application in error analysis, input selection and ranking of explanatory variables, thereby designing cost-effective monitoring networks

Hydrologic and Hydraulic Modeling for the Restoration of the Calumet Marshes: Assessment of Runoff Scenarios

Lake Calumet is located south of Lake Michigan. It is a site of former landfills and abandoned industrial facilities, yet a place of economical and ecological significance for the future development of the area. The marshes surrounding Lake Calumet are ecologically significant to the Black-crowned Night Heron but the hydrology in the area has been greatly impacted by the large amount of landfilling and the constantly changing land use and drainage of the surrounding uplands. In order to save threatened species, to prevent ecosystem degradation, and recreate a local economic base, the City of Chicago’s Department of Environment has been leading community groups and other agencies to develop plans to restore the region to a recreational area. Millions of dollars will be invested for the effort. To support the development plan for the Calumet Region to become an ecological park, hydrologic and hydraulic models have been developed for the region. These models serve as a basis for determining the best water management strategies for the Lake Calumet Cluster Sites and the adjacent open spaces, namely the Indian Ridge Marsh (IRM). An integrated hydrologic and hydraulic model was used to evaluate the hydrologic impacts of different remedial options proposed for the Cluster Sites and other upland properties in the marsh watersheds, and to assess the adequacy of the existing marsh outlets in terms of long-range ecological goals. This report evaluates six proposed management scenarios to cope with flooding and to establish a more suitable environment for Black-crowned Night Heron nests in the marsh areas by controlling water level fluctuations. For Black-crowned Night Heron nests, the maximum fluctuation is ten inches. Our study showed that diverting surface runoff from the Cluster Sites appeared to be the best option for limiting water level fluctuations to around six inches in the IRM.Illinois Sustainable Technology Center Sponsored Research Program ; HWR06-202Ope

Efficacy of Polypropylene Mesh Coated with Bioresorbable Membrane (Sepramesh) for the Repair of Abdominal Wall Defects in Horses

Abstract Objective: The aim of this study was to compare the use of Polypropylene mesh (Prolene) and Sepramesh, a coated Polypropylene mesh with a protective layer of Seprafilm on its visceral side, for the repair of abdominal wall defects in horses. We also aimed to quantify the consequent visceral adhesion and tissue inflammation. Study Design: Experimental study. Animal Population: Ten horses. Methods: The horses were divided into the control group, where a 4×8 cm defect was created through the midline of the abdomen and repaired with polypropylene mesh, and the experimental group, where the same defect was made and closed sepramesh. Both meshes were placed intraperitoneally and sutured to the cut margins of peritoneum and the opponeurosis of external abdominal oblique muscle contacting in viscera in a tensionfree technique. Results: The severity and extent of adhesions were significantly lower in the experimental group (B) than the control group (A) (P<0.05). Horses that received a * Corresponding author: 46 Polypropylene mesh experienced higher levels of inflammation, both on the day of operation and at two weeks, but significant differences were not apparent after 4 weeks. Conclusions: This study confirmed the advantages of Sepramesh over Polypropylene mesh in the repair of abdominal wall defects in horses. Clinical Relevance: There are many causes of abdominal wall defects in horses, including congenital and traumatic. This experiment suggests that the use of Sepramesh could strengthen the healing of abdominal wounds, prevent incisional hernias, and reduce intraabdominal adhesions

Reconstruction of global gridded monthly sectoral water withdrawals for 1971-2010 and analysis of their spatiotemporal patterns

Human water withdrawal has increasingly altered the global water cycle in past decades, yet our understanding of its driving forces and patterns is limited. Reported historical estimates of sectoral water withdrawals are often sparse and incomplete, mainly restricted to water withdrawal estimates available at annual and country scale, due to a lack of observations at local and seasonal time scales. In this study, through collecting and consolidating various sources of reported data and developing spatial and temporal statistical downscaling algorithms, we reconstruct a global monthly gridded (0.5 degree) sectoral water withdrawal dataset for the period 1971–2010, which distinguishes six water use sectors, i.e. irrigation, domestic, electricity generation (cooling of thermal power plants), livestock, mining, and manufacturing. Based on the reconstructed dataset, the spatial and temporal patterns of historical water withdrawal are analyzed. Results show that global total water withdrawal has increased significantly during 1971–2010, mainly driven by the increase of irrigation water withdrawal. Regions with high water withdrawal are those densely populated or with large irrigated cropland production, e.g., the United States (US), eastern China, India, and Europe. Seasonally, irrigation water withdrawal in summer for the major crops contributes a large percentage of annual total irrigation water withdrawal in mid and high-latitude regions, and the dominant season of irrigation water withdrawal is also different across regions. Domestic water withdrawal is mostly characterized by a summer peak, while water withdrawal for electricity generation has a winter peak in high-latitude regions and a summer peak in low-latitude regions. Despite the overall increasing trend, irrigation in the western US and domestic water withdrawal in western Europe exhibit a decreasing trend. Our results highlight the distinct spatial pattern of human water use by sectors at the seasonal and annual scales. The reconstructed gridded water withdrawal dataset is open-access, and can be used for examining issues related to water withdrawals at fine spatial, temporal and sectoral scales

A Global Hydrologic Framework to Accelerate Scientific Discovery

With the ability to simulate historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degree, the Python package Xanthos version 1 provided a solid foundation for continuing advancements in global water dynamics science. The goal of Xanthos version 2 was to build upon previous investments by creating a Python framework where core components of the model (potential evapotranspiration (PET), runoff generation, and river routing) could be interchanged or extended without having to start from scratch. Xanthos 2 utilizes a component-style architecture which enables researchers to quickly incorporate and test cutting-edge research in a stable modeling environment prebuilt with diagnostics. Major advancements for Xanthos 2 were also achieved by the creation of a robust default configuration with a calibration module, hydropower modules, and new PET modules, which are now available to the scientific community. Funding statement: This research was supported by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. The Pacific Northwest National Laboratory is operated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830. The views and opinions expressed in this paper are those of the authors alone

Climate policy implications for agricultural water demand

Energy, water and land are scarce resources, critical to humans. Developments in each affect the availability and cost of the others, and consequently human prosperity. Measures to limit greenhouse gas concentrations will inevitably exact dramatic changes on energy and land systems and in turn alter the character, magnitude and geographic distribution of human claims on water resources. We employ the Global Change Assessment Model (GCAM), an integrated assessment model to explore the interactions of energy, land and water systems in the context of alternative policies to limit climate change to three alternative levels: 2.5 Wm-2 (445 ppm CO2-e), 3.5 Wm-2 (535 ppm CO2-e) and 4.5 Wm-2 (645 ppm CO2-e). We explore the effects of two alternative land-use emissions mitigation policy options—one which taxes terrestrial carbon emissions equally with fossil fuel and industrial emissions, and an alternative which only taxes fossil fuel and industrial emissions but places no penalty on land-use change emissions. We find that increasing populations and economic growth could be anticipated to almost triple demand for water for agricultural systems across the century even in the absence of climate policy. In general policies to mitigate climate change increase agricultural demands for water still further, though the largest changes occur in the second half of the century, under both policy regimes. The two policies examined profoundly affected both the sources and magnitudes of the increase in irrigation water demands. The largest increases in agricultural irrigation water demand occurred in scenarios where only fossil fuel emissions were priced (but not land-use change emission) and were primarily driven by rapid expansion in bioenergy production. In these scenarios water demands were large relative to present-day total available water, calling into question whether it would be physically possible to produce the associated biomass energy. We explored the potential of improved water delivery and irrigation system efficiencies. These could potentially reduce demands substantially. However, overall demands remained high under our fossil-fuel-only tax policy. In contrast, when all carbon was priced, increases in agricultural water demands were smaller than under the fossil-fuel-only policy and were driven primarily by increased demands for water by non-biomass crops such as rice. Finally we estimate the geospatial pattern of water demands and find that regions such as China, India and other countries in south and east Asia might be expected to experience greatest increases in water demands

Integrated Solutions for the Water-Energy-Land Nexus: Are Global Models Rising to the Challenge?

Increasing human demands for water, energy, food and materials, are expected to accentuate resource supply challenges over the coming decades. Experience suggests that long-term strategies for a single sector could yield both trade-offs and synergies for other sectors. Thus, long-term transition pathways for linked resource systems should be informed using nexus approaches. Global integrated assessment models can represent the synergies and trade-offs inherent in the exploitation of water, energy and land (WEL) resources, including the impacts of international trade and climate policies. In this study, we review the current state-of-the-science in global integrated assessment modeling with an emphasis on how models have incorporated integrated WEL solutions. A large-scale assessment of the relevant literature was performed using online databases and structured keyword search queries. The results point to the following main opportunities for future research and model development: (1) improving the temporal and spatial resolution of economic models for the energy and water sectors; (2) balancing energy and land requirements across sectors; (3) integrated representation of the role of distribution infrastructure in alleviating resource challenges; (4) modeling of solution impacts on downstream environmental quality; (5) improved representation of the implementation challenges stemming from regional financial and institutional capacity; (6) enabling dynamic multi-sectoral vulnerability and adaptation needs assessment; and (7) the development of fully-coupled assessment frameworks based on consistent, scalable, and regionally-transferable platforms. Improved database management and computational power are needed to address many of these modeling challenges at a global-scale