Common Ground Over Common Water: Defining the Public Interest in the Milwaukee Watershed

My dissertation examines government and nongovernment entities’ attempts to restore and protect the use and health of the Milwaukee River and its watershed from 1960 to 2000. Under Mayor Henry Maier’s leadership, Milwaukee worked to reclaim the urban riverway to stimulate economic growth. However, state and federal representatives, after the passage of the 1965 Water Quality Act, demanded that the city government prioritize updating the combined storm and sewer system to lessen pollution in the Milwaukee River. At the same time, other groups worked to save rural areas from unplanned development and further degradation of the waterway. Influential groups included the Riveredge Nature Center members, the Southeastern Wisconsin Regional Planning Commission (SEWRPC), the Milwaukee River Restoration Council, and the Milwaukee River Revitalization Council. As these groups debated the best course of action, they recognized the benefits of a watershed approach to restoring the riverway’s health. However, arguments continued as the communities that purported a public interest in the waterway were often identified by boundaries that did not coincide with the watershed’s area. My research contributes to historical scholarship by investigating how these groups came to recognize the importance of a watershed approach to addressing water pollution problems and protecting private property from flood damage. However, searching for a shared public interest that reflected urban, suburban, and rural perspectives of the watershed’s future was more elusive as economic, social, and historical understandings of the watershed continued to divide people.Primary source materials were gathered through newspaper articles and archival sources. The Milwaukee Public Library funds the online storage of the Milwaukee Journal and Milwaukee Sentinel. Archival material was located through the University of Wisconsin-Milwaukee (UWM) Archives Department and the Wisconsin Historical Center. Also, I utilized materials stored by SEWRPC, Riveredge Nature Center, and the River Revitalization Foundation

Emergent relation between surface vapor conductance and relative humidity profiles yields evaporation rates from weather data

The ability to predict terrestrial evapotranspiration (E) is limited by the complexity of rate-limiting pathways as water moves through the soil, vegetation (roots, xylem, stomata), canopy air space, and the atmospheric boundary layer. The impossibility of specifying the numerous parameters required to model this process in full spatial detail has necessitated spatially upscaled models that depend on effective parameters such as the surface vapor conductance (Csurf). Csurf accounts for the biophysical and hydrological effects on diffusion through the soil and vegetation substrate. This approach, however, requires either site-specific calibration of Csurf to measured E, or further parameterization based on metrics such as leaf area, senescence state, stomatal conductance, soil texture, soil moisture, and water table depth. Here, we show that this key, rate-limiting, parameter can be estimated from an emergent relationship between the diurnal cycle of the relative humidity profile and E. The relation is that the vertical variance of the relative humidity profile is less than would occur for increased or decreased evaporation rates, suggesting that land–atmosphere feedback processes minimize this variance. It is found to hold over a wide range of climate conditions (arid–humid) and limiting factors (soil moisture, leaf area, energy). With this relation, estimates of E and Csurf can be obtained globally from widely available meteorological measurements, many of which have been archived since the early 1900s. In conjunction with precipitation and stream flow, long-term E estimates provide insights and empirical constraints on projected accelerations of the hydrologic cycle

Harmonic propagation of variability in surface energy balance within a coupled soil-vegetation-atmosphere system

International audienceThe response of a soil-vegetation-atmosphere continuum model to incoming radiation forcing is investigated in order to gain insights into the coupling of soil and atmospheric boundary layer (ABL) states and fluxes. The response is characterized through amplitude and phase propagation of the harmonics in order to differentiate between the response of the system to forcing at different frequencies (daily to hourly to near instantaneous). Stochastic noise is added to the surface energy balance. The amplitude of the noise is maximum at midday when the incoming radiative forcing is also at its peak. The temperatures and turbulent heat fluxes are shown to act as low-pass filters of the incoming radiation or energy budget noise variability at the surface. Conversely, soil heat flux is shown to act as a high-pass filter because of the strong contrast in the soil and air heat capacities and thermal conductivities. As a consequence, heat diffusion formulations that numerically dampen such forcing are potentially incapable of representing rapid fluctuations in soil heat flux (=30 min) and therefore introduce errors in the land-surface energy partitioning. The soil-vegetation-ABL continuum model and an electrical analogy for it are used to explain the frequency-dependent differences in the relative effectiveness of turbulent heat fluxes versus ground heat flux in dissipating noise in radiative forcing. Copyright 2011 by the American Geophysical Union

Deep learning to represent sub-grid processes in climate models

The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly. The trained neural network then replaces the traditional sub-grid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multi-year simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth System Model development could play a key role in reducing climate prediction uncertainty in the coming decade.Comment: View official PNAS version at https://doi.org/10.1073/pnas.181028611

Systematic errors in ground heat flux estimation and their correction

Incoming radiation forcing at the land surface is partitioned among the components of the surface energy balance in varying proportions depending on the time scale of the forcing. Based on a land-atmosphere analytic continuum model, a numerical land surface model, and field observations we show that high-frequency fluctuations in incoming radiation (with period less than 6 h, for example, due to intermittent clouds) are preferentially partitioned toward ground heat flux. These higher frequencies are concentrated in the 0–1 cm surface soil layer. Subsequently, measurements even at a few centimeters deep in the soil profile miss part of the surface soil heat flux signal. The attenuation of the high-frequency soil heat flux spectrum throughout the soil profile leads to systematic errors in both measurements and modeling, which require a very fine sampling near the soil surface (0–1 cm). Calorimetric measurement techniques introduce a systematic error in the form of an artificial band-pass filter if the temperature probes are not placed at appropriate depths. In addition, the temporal calculation of the change in the heat storage term of the calorimetric method can further distort the reconstruction of the surface soil heat flux signal. A correction methodology is introduced which provides practical application as well as insights into the estimation of surface soil heat flux and the closure of surface energy balance based on field measurements