The Future of Indiana’s Water Resources: A Report from the Indiana Climate Change Impacts Assessment

This report from the Indiana Climate Change Impacts Assessment (IN CCIA) applies climate change projections for the state to explore how continued changes in Indiana’s climate are going to affect all aspects of water resources, including soil water, evaporation, runoff, snow cover, streamflow, drought, and flooding. As local temperatures continue to rise and rainfall patterns shift, managing the multiple water needs of communities, natural systems, recreation, industry, and agriculture will become increasingly difficult. Ensuring that enough water is available in the right places and at the right times will require awareness of Indiana’s changing water resources and planning at regional and state levels

Lunch and Learn: Garett Pignotti Predicting Urban Air Temperatures Using Land Cover Type and Satellite Observations of Surface Temperatures

A 45 minute talk expanding on Garett\u27s presentation from the 2022 UERC symposium on Predicting Urban Air Temperatures Using Land Cover Type and Satellite Observations of Surface Temperature

Evaluating Impacts of Remote Sensing Soil Moisture Products on Water Quality Model Predictions in Mixed Land Use Basins

A critical consequence of agriculturally managed lands is the transport of nutrients and sediment to fresh water systems, which is ultimately responsible for a range of adverse impacts on human and environmental health. In the U.S. alone, over half of streams and rivers are classified as impaired, with agriculture as the primary contributor. To address deterioration of water quality, there is a need for reliable tools and mathematical models to monitor and predict impacts to water quantity and quality. Soil water content is a key variable in representing environmental systems, linking and driving hydrologic, climate, and biogeochemical cycles; however, the influence of soil water simulations on model predictions is not well characterized, particularly for water quality. Moreover, while soil moisture estimation is the focus of multiple remote sensing missions, defining its potential for use in water quality models remains an open question. The goal of this research is to test whether updating model soil water process representation or model soil water estimates can provide better overall predictive confidence in estimates of both soil moisture and water quality. A widely-used ecohydrologic model, the Soil and Water Assessment Tool (SWAT), was used to evaluate four objectives: 1) investigate the potential of a gridded version of the SWAT model for use with similarly gridded, remote sensing data products, 2) determine the sensitivity of model predictions to changes in soil water content, 3) implement and test a more physically representative soil water percolation algorithm, and 4) perform practical data assimilation experiments using remote sensing data products, focusing on the effects of soil water updates on water quality predictions. With the exception of the first objective, model source code was modified to investigate the relative influence and effect of soil water on overall model predictions. Results suggested that use of the SWAT grid model was currently not viable given practical computational constraints. While the advantages provided by the gridded approach are likely useful for small scale watersheds (\u3c 500 km2), the spatial resolution necessary to run the simulation was too coarse, such that many of the benefits of the gridded approach are negated. Sensitivity tests demonstrated a strong response of model predictions to perturbations in soil moisture. Effects were highly process dependent, where water quality was particularly sensitive to changes in both transport and transformation processes. Model response was reliant upon a default thresholding behavior that restricts subsurface flow and redistribution processes below field capacity. An alternative approach that removed this threshold and keyed processes to relative saturation showed improvement by allowing a more realistic range of soil moisture and a reduction of flushing behavior.This approach was further extended to test against baseline satellite data assimilation experiments; however, did not conclusively outperform the original model simulations. Nevertheless, overall, data assimilation experiments using a remote sensing surface soil moisture data product from the NASA Soil Moisture Active/Passive (SMAP) mission were able to correct for a dry bias in the model simulations and reduce error. Data assimilation updates significantly impacted flow predictions, generally by increasing the dominant contributing flow process. This led to substantial differences between two test sites, where landscape and seasonal characteristics moderated the impact of data assimilation updates to hydrologic, water quality, and crop yield predictions. While the findings illustrate the potential to improve predictions, continued future efforts to refine soil water process representation and optimize data assimilation with longer time series are needed. The dependence of ecohydrologic model predictions on soil moisture highlighted by this research underscores the importance and challenge of effectively representing a complex, physically-based process. As essential decision support systems rely on modeling analyses, improving prediction accuracy is vital

Characterizing remotely sensed soil moisture over an agricultural catchment

As global population and subsequent demand on the provisioning services of natural systems continue to rise, it is increasingly important to identify creative, pragmatic solutions to meet these needs while minimizing environmental degradation. Of particular importance are agricultural ecosystems, among the most widespread anthropogenically driven and managed systems. Soil moisture is critical to characterizing many atmospheric, hydrologic, and land-surface interactions relevant to monitoring and modeling such systems. However, soil moisture is spatially and temporally highly variable, and is thus challenging to measure in a meaningful, representative manner with point based in situ sensors. Alternatively, remote sensing in the microwave region of the electromagnetic spectrum is capable of providing all weather, day/night gridded global soil moisture measurements. Unfortunately, the spatial resolution of standard passive microwave based products is too low (∼40 km) for most hydrologic applications (1–10 km). One approach to overcome this limitation is to combine soil moisture maps with fine scale ancillary remote sensing data to downscale soil moisture data products. This research focused on evaluating two variants of a downscaling algorithm applied to soil moisture products derived from data acquired by the SMOS (Soil Moisture and Ocean Salinity) satellite over a sensor network in the Upper Cedar Creek Watershed in northeastern Indiana. Results in this region indicated that while the accuracy of standard SMOS products does not meet the stated mission objectives (0.068 m3/m3 vs. 0.04 m3/m3), the derived downscaled data did not degrade either the root mean square difference or correlation with in situ measurements. Potential deficiencies in the downscaling models were identified and discussed. Overfitting was detected in one approach, and both models were inadequate for tracking temporal variations associated with vegetation data in the Midwest. A qualitative evaluation of within network variability and auxiliary soil moisture data sources was also performed. Comparison of remotely sensed data to ground networks provided insight into sensor derived response over a heterogeneous area and potential to exploit such relationships. By refining downscaling approaches, it should be possible to better resolve the scale at which soil moisture processes act. Specific to agricultural applications, accurate soil moisture estimation can improve yield prediction, forecasting of drought and floods, and water management practices

Predicting urban air temperatures using land cover type and satellite observations of surface temperatures

Extreme urban heat is known to adversely impact humans and the environment, where certain land use/land cover (LULC) types may amplify temperatures. However, sparsely available air temperature (Ta) data limits study of these impacts. Attempts to map air temperature from satellite land surface temperature (LST) data are often highly empirical and lack sufficient data for robust evaluation. In particular, we do not know: 1) how well do predictions perform across diverse land cover characteristics? And 2) what insights can we gain from predictions based on expected biophysical air surface temperature relationships? In this study, we derived an LST-Ta relationship from a surface energy balance to fit LULC-specific LST-Ta predictive relationships (biophysically-based), benchmarked against a simple linear regression fit. We used satellite LST data and spatially-extensive (\u3e 1 million samples) air temperature maps from sampling campaigns during heat wave days in five U.S. cities, including Portland. Results showed LULC had a large impact on LST and Ta values, e.g., more developed areas had higher temperatures than forested (10 ℃ LST and 1 ℃ Ta differences). Both the linear and biophysical models performed well in predicting air temperatures (RMSE 0.50 and 0.49 ℃, respectively); however, biophysical fitted model coefficients corresponded better to LULC characteristics (i.e. vegetation or imperviousness). Using this approach, this suggests some ability to resolve differences in underlying mechanisms of heat transfer among LULCs. Quantifying such relationships in urban landscapes is critical in adapting and managing cities that often face inequitable exposure to heat from historical disinvestment and segregation

UERC 2022 Symposium Day 2 Recording

Urban Ecosystem Research Consortium (UERC) Portland, OR - Vancouver, WA Metropolitan Region, 2022 Symposium Day 2 recording. Containing keynote and presentations given on March 8th 2022