"The interplay of climate variability and change, global water crisis, and human impact on the water cycle pose the most significant challenge for hydrology today. Our inter-disciplinary research deals with Hydrocomplexity, the quantitative understanding and prediction of emergent patterns of form and function that arise from complex non-linear multi-scale interactions between soil, water, climate, vegetation and human systems, and how this understanding can be used for innovative solutions to water and sustainability challenges. We use deterministic and stochastic modeling, computer simulations, informatics, and field data and investigations for these studies".
Intensively managed landscapes, regions of significant land use change, serve as a cradle for economic prosperity. However, the intensity of change is responsible for unintended deterioration of our land and water environments. By understanding present day dynamics in the context of long-term co-evolution of the landscape, soil and biota, IML-CZO aims to support the assessment of short- and long-term resilience of the crucial ecological, hydrological and climatic services. An observational network of three sites in Illinois, Iowa, and Minnesota that capture the geological diversity of the low relief glaciated and tile-drained landscape will drive novel scientific and technological advances. IML-CZO will provide leadership in developing the next generation of work force and informing management strategies aimed at reducing the vulnerability of the system to present and emerging trends in human activities such as the expansion of bioenergy crops.
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Often, individuals and small groups collect scientific data that are targeted to address specific scientific issues and have limited geographic or temporal range. However, a large number of such collections together constitute a large database that is of immense value to the scientific community. Such data are complex in that they encompass a heterogeneous collection with many dimensions, coordinate systems, scales, variables, providers, users and scientific contexts. These data have been defined as long-tail data.
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Water security is about ensuring the short-term and long-term provision of adequate, affordable, accessible and safe freshwater supply for the growing human population needs and ecosystems services. Water security is being threatened through a number of stressors such as climate change, expansion of urban and peri-urban development, groundwater withdrawal at non-replenishable rates, large transmission and quality losses through water distribution systems, degradation of environmental and water quality, emerging landuse practices such as expansion of water intensive bioenergy crops, etc. As a result, ensuring water security for the present and future generations is becoming a formidable task. Water as a resource is no longer replenished by the hydrologic cycle due to the intensity of human use.
Resilience and vulnerability of co-evolving systems such as soils and vegetation, vegetation and climate, etc. are strongly dependent on the balance of the positive and negative feedbacks of interactions. Under stress, these feedbacks breakdown and result in runaway dynamics until new equilibrium is established. While these notions are now well-understood, quantitative characterization of both resilience and vulnerability remain elusive, or are only available for deterministic systems. Our study focus takes a broad look at the concepts of resilience and vulnerability from a stochastic framework to develop general principles to guide sustainable decision-making.
For more information see:
Emergent and divergent resilience behavior in catastrophic shift systems
The terrestrial biosphere that encompasses vegetation and the adjacent soil and atmosphere, is a veritable biogeochemical crossroads, consisting of complex interactions between energy, water, carbon and nutrient transfers. Changes in one component, e.g., water, energy, carbon or nutrient cycles, has a feedback effect on all other components, with the result that the connection between cause and effect is not easily understood and is hard to predict with confidence. How can we predict the acclimation response of vegetation to increase in atmospheric CO2 and associated impact on the hydrologic cycle? How can we model the mutualistic and competitive dynamics between different vegetation species that co-exist? How do root water uptake patterns through hydraulic redistribution affect biogeochemical dynamics? These are some of the questions that we are exploring through this research focus.
Mosquito-borne diseases are particularly responsive to changing environmental conditions, yet the impact of global warming on the spread of mosquito-borne disease has been heavily debated. Despite efforts on climate modeling and epidediological research, little is known about the potential link between the associated feedbacks of climate change and mosquito-borne disease transmission.
Our research activities in HydroEpidemiology aim to understand and predict the complexity of mosquito-borne disease transmission, such as malaria and dengue, under changing climate and projected socio-economic scenarios. We propose an interdisciplinary approach to gain a better understanding of the way in which hydrological modeling and water management methods can affect the distribution of mosquito vectors and thus reduce prevalence of infectious diseases that are vectored by mosquitoes.
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Hyperspectral remote sensing is an efficient tool for studying various ecosystems. The surface soil characteristics are an important component in ecosystem functioning and processes. Our research is targeted at the development of methodological frameworks for the interpretation and quantification of different physical and biogeochemical properties of landscapes which help us to better understand the eco-hydrology of the landscape. The focus of this research is also to understand the geochemical impacts of large scale hydrologic disaster events such as floods on landscapes using a combination of high spectral (AVIRIS) and spatial (LiDAR) resolution datasets.
We are looking for solutions for sustainable energy through bioenergy crops.
Energy researchers and environmental advocates are excited about the prospect of gaining more efficient large-scale biofuel production by using large grasses like miscanthus or switchgrass rather than corn in the Midwest United States.
However, the dependence of these crops on water gets ignored, and water can be a significant limiting factor.
The cost of water use for new generation energy crops should be factored in to the decision making.
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During extreme flooding in the Ohio and Mississippi Rivers in May 2011, the U.S. Army Corps of Engineers intentionally breached several levees to activate the Bird's Point-New Madrid Floodway and also activated the Morganza and Bonnet Carré Spillway. Extensive data from field surveys and areal lidar and hyper-spectral mapping were obtained to assess flood impact and landscape vulnerability. These events, when seen as a once in a scientific-lifetime experiment provide a unique opportunity to study the impact of hydrologic extremes and develop adaptation and mitigation strategies for future events in the context of both agricultural land use and flood protection.
For more information see:
Assessment of Floodplain Vulnerability during Extreme Mississippi River Flood 2011
Mitigating land loss in coastal Louisiana by controlled diversion of Mississippi River sand
Engineering at Illinois News, ScienceDaily
Ecohydrologic systems are emergent self-organizing systems, which evolve to exploit the variable gradients of carbon, water, nutrients, and energy at the land surface. Emergence due to self-organization is difficult to define and observe, but the concept requires the presence of feedbacks. Feedback and control in a coupled, self-organized system may be studied using complex network theory. This research uses entropy-based statistics of information flow to render the eco-hydrological system as a process network of weighted couplings between the measured variables, which is empirically derived from multivariate timeseries datasets consisting of observations, simulations or both. The statistical tool used is the Transfer Entropy, which measures directional coupling strength between variables as an information flow at a given time lag. Directional, asymmetric information flow allows us to identify and characterize feedback cycles in the resulting process network. Our research explores how complex network approaches can be developed and used for understanding and predicting interaction between processes and emergent behavior.