{"id":1567,"date":"2023-06-14T14:42:33","date_gmt":"2023-06-14T21:42:33","guid":{"rendered":"https:\/\/ahssymposium.org\/2026\/?page_id=1567"},"modified":"2026-07-10T13:58:04","modified_gmt":"2026-07-10T20:58:04","slug":"posters","status":"publish","type":"page","link":"https:\/\/ahssymposium.org\/2026\/program\/posters\/","title":{"rendered":"Posters"},"content":{"rendered":"[et_pb_section fb_built=”1″ custom_padding_last_edited=”on|tablet” _builder_version=”4.27.6″ _module_preset=”default” background_color=”#184d68″ background_image=”https:\/\/ahssymposium.org\/2026\/wp-content\/uploads\/Poster-Ses.png” background_size=”contain” background_position=”top_right” custom_padding=”25px||25px||false|false” custom_padding_tablet=”0px||0px||false|false” custom_padding_phone=”15px||15px||false|false” hover_enabled=”0″ background_color_tablet=”#7fabcb” background_last_edited=”on|tablet” background_enable_color_tablet=”on” use_background_color_gradient_tablet=”on” background_color_gradient_stops_tablet=”#7fabcb 64%|rgba(127,171,203,0) 85%” background_enable_image_tablet=”off” da_disable_devices=”off|off|off” global_colors_info=”{}” theme_builder_area=”post_content” title_text=”Poster Ses” sticky_enabled=”0″ da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.27.5″ _module_preset=”default” width=”90%” max_width=”90%” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}” theme_builder_area=”post_content”][et_pb_text _builder_version=”4.27.4″ _dynamic_attributes=”content” _module_preset=”default” header_font=”|700||on|||||” header_text_color=”#FFFFFF” header_font_size=”5em” background_enable_color=”off” header_font_size_tablet=”3em” header_font_size_phone=”2em” header_font_size_last_edited=”on|phone” custom_css_main_element=”display:inline-block;||padding:20px;” global_colors_info=”{}” theme_builder_area=”post_content”]@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90aXRsZSIsInNldHRpbmdzIjp7ImJlZm9yZSI6IjxoMT4iLCJhZnRlciI6IjwvaDE+In19@[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.16″ da_disable_devices=”off|off|off” global_colors_info=”{}” custom_padding=”53px|||||” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”]

Poster Sessions at the 2026 Symposium<\/h2>\n

<\/p>\n

<\/p>\n

Posters will be displayed in Fremont. Information about poster setup, take-down, and judging \u00bb<\/a><\/p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_3,1_3,1_3″ _builder_version=”4.27.6″ background_size=”initial” background_position=”top_left” background_repeat=”repeat” global_colors_info=”{}”][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Stormwater Recharge and Site Prioritization for Net Zero Urban Water in Tucson<\/h3>\n

Bhushan Rash, UofA<\/h4>\n

Water-stressed regions like Tucson, Arizona, face mounting pressure from groundwater depletion and a heavy reliance on imported Colorado River supplies. To achieve Net Zero Urban Water (NZUW) goals, these regions must expand local, sustainable water sources, including stormwater. Stormwater-based managed aquifer recharge (MAR) offers a promising opportunity, but its effectiveness in arid regions is uncertain due to highly variable rainfall, flashy runoff, deep groundwater tables, and subsurface constraints.<\/span><\/p>\n

This study evaluates stormwater recharge potential and addresses two key questions: how much stormwater can contribute to aquifer recharge, and which sites should be prioritized for development? A three-stage modeling framework with an integrated cost-benefit approach is developed. GIS-based multi-criteria analysis is used to identify candidate recharge sites, followed by watershed-scale hydrologic modeling in HEC-HMS to estimate runoff generation, routing, and capture volumes. Vadose zone modeling with HYDRUS-2D is then applied to estimate the fraction of infiltrated water that reaches the aquifer.<\/span><\/p>\n

The approach is applied to urban watersheds in Eastern Pima County. Results provide site-specific recharge estimates and enable comparison of recharge performance across locations. A cost-based framework is used to estimate cost per unit recharge (e.g., $\/acre-foot) and support prioritization of projects based on both recharge potential and cost-effectiveness.<\/span><\/p>\n

This work provides a practical framework for quantifying stormwater recharge and identifying high-impact recharge projects to strengthen water security in arid regions.<\/span><\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Using Alluvial Mapping and Ecohydrology to Identify Drivers of Gully Erosion and Support Restoration Planning at Santa Margarita Ranch, Arizona<\/span><\/h3>\n

Rowan Huang<\/span>, UofA<\/h4>\n

The 89,000\u2011acre Santa Margarita Ranch (SMR), located in the headwaters of the Altar Valley, Arizona, exhibits extensive gully erosion, channel incision, and vegetation loss that threaten ecohydrologic function and downstream conditions in Brawley Wash. To support implementation of Natural Infrastructure in Dryland Streams (NIDS), we identified priority restoration reaches within paired watersheds and characterized baseline geomorphic conditions through mapping of alluvial units, high\u2011resolution topography, and multi\u2011temporal aerial imagery. Gully formation is concentrated within low\u2011gradient, sandy alluvial deposits situated between older fan surfaces. These units exhibit shallow relief and, where not actively eroding, support localized vegetation and dispersed surface flow visible in NAIP imagery and 1\u2011m lidar DEMs, which are indicators of favorable subsurface storage potential under enhanced detention. Analysis of NAIP imagery from 2007\u20132023 shows that gully headcut migration is dominated by deepening and lengthening at knickpoints, consistent with subsurface or near\u2011surface piping as a major erosional driver. Despite interannual variability in precipitation, headcut activity has generally declined since 2015, suggesting delayed hydrologic responses that may improve long\u2011term restoration effectiveness if active erosion is stabilized. A possible explanation for this is that subsurface transmission results in a delay between streamflow and rainfall, which would make the unit a good candidate for longer-term water detention\/storage if gully erosion is arrested.<\/span><\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Forest Ecohydrology Pilot Study in the Czech Republic<\/span><\/h3>\n

Sharon Masek Lopez<\/span>, NAU and ASU<\/h4>\n
\n
\n
\n

As global climate change prompts extreme weather events and makes watersheds worldwide more susceptible to the ravages of droughts and floods, land management practices that improve soil water infiltration and retention will become increasingly important to mitigate stress on hydrologic systems. One third of land in the Czech Republic is forest. In the humid southern Czech Republic, a pilot study is being conducted to develop a novel set of simple and inexpensive methods for forest managers to evaluate tree cover effects on soil hydraulics. Hypothetically, a forest soil\u2019s capacity to infiltrate, store, and convey water is strongly influenced by the symbiotic relationship between vegetative cover and the soil microbiome, which largely determines soil structure through soil aggregation. To test this hypothesis, data have been collected to relate explanatory variables (canopy cover, ground cover, soil color, soil texture, and soil aggregation) and a response variable (infiltration rate) that is used as a proxy for saturated soil hydraulic conductivity. Preliminary results indicate that soil aggregation and water infiltration rates respond to complex interactions of tree species, ground cover type, and forest management practices. During phase 3 of the pilot study in 2027, data will be collected for a selected few variables and at many more points for more robust statistical modeling. Also, the ecohydrology methods under development will be replicated in semi-arid Arizona to test their universal applicability for low-cost evaluation of forest management effects on soil hydraulics.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Natural tracer study to identify sources of water supporting spring-fed wetlands and Santa Cruz headwaters in the San Rafael Valley<\/span><\/h3>\n

Fernanda Munari<\/span>, UofA<\/h4>\n
\n
\n
\n
\n
\n
\n

Wetlands in southern Arizona are hotspots of plant and wildlife diversity, yet vulnerable to groundwater extraction, drought, and woody vegetation encroachment, dependent on their sources of water. Spring-fed wetlands (ci\u00e9negas) and baseflow in streams fed by relatively old regional groundwater (e.g., basin-fill aquifers) may be impacted by groundwater pumping and lowering of regional water tables. Surface waters and shallow groundwater recharged by more recent, local precipitation (e.g., alluvial aquifers) may be more sensitive to drought, shifts in precipitation patterns, and increases in evapotranspiration (ET). The San Rafael Valley (SRV) is one of the last untouched grassland ecosystems in the United States and contains the headwaters of the Santa Cruz River with adjacent ci\u00e9negas, serving as a critical wildlife corridor along the U.S.-Mexico border. Despite its ecological and hydrologic importance, few studies have characterized the SRV\u2019s regional and riparian aquifer systems. Previous water stable isotope results of select wells show dominance of summer monsoon recharge, compared to other basins across southern AZ, leading to questions about the fate of winter precipitation. This study is collecting spatially-distributed water chemistry, isotope, and age tracer data from wells and springs across the valley, and the Santa Cruz River to better characterize locations, seasonality, and extent of groundwater recharge and interactions with surface waters. Results of this study will inform how these biologically important and rare riparian ecosystems may respond to shifts in climate, vegetation, and groundwater pumping.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Toward a Unified, Extreme-Aware Diffusion Framework for Kilometer-Scale Precipitation Downscaling over Complex Terrain<\/span><\/h3>\n

Hossein Yousefi Sohi, Andrew Bennett, and Ali Behrangi<\/span>, UofA<\/h4>\n
\n
\n
\n
\n
\n
\n

Generative diffusion models have rapidly become state-of-the-art for kilometer-scale precipitation downscaling, yet current architectures carry structural limitations that restrict their operational utility in hydrology. Our recent evaluation of the residual corrective diffusion framework (CorrDiff), developed by NVIDIA, over Arizona’s monsoon-dominated terrain identified three coupled bottlenecks: (i) a two-stage cascaded design that propagates mean-stage bias into the generative step through exposure bias; (ii) a UNet backbone whose local receptive field struggles with orographically organized convective extremes; and (iii) a mean-squared-error objective that is blind to spatial organization and can actively degrade extreme-precipitation skill below the raw coarse input. Inference cost \u2014 32 ensemble members through a two-stage pipeline \u2014 further limits multi-decadal application.<\/p>\n

This study develops a new generative downscaling framework that redesigns all three components jointly. We unify the deterministic and stochastic stages into a single diffusion model conditioned end-to-end on coarse-resolution inputs, replace the convolutional backbone with an operator-based or attention-augmented architecture capable of capturing long-range atmospheric dependencies, and introduce a spatially-aware composite training objective that explicitly penalizes errors in extremes, spectral content, and neighborhood structure. The proposed framework will be evaluated against NVIDIA’s CorrDiff to quantify improvements in extreme-precipitation skill, spatial fidelity, and computational efficiency.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.27.6″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Process-Aware AI for Rainfall\u2013Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints<\/span><\/h3>\n

Mohammad Farmani<\/span>, UofA<\/h4>\n

Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The\u00a0<\/span>Mass-Conserving Perceptron<\/em>\u00a0<\/span>(MCP) provides a physics-aware artificial intelligence (AI) framework that enforces conservation principles while allowing hydrological process relationships to be learned from data.<\/p>\n

In this study, we investigate how progressively embedding physically meaningful representations of hydrological processes within a single MCP storage unit improves predictive skill and interpretability in rainfall\u2013runoff modeling. Starting from a minimal MCP formulation, we sequentially introduce bounded soil storage, state-dependent conductivity, variable porosity, infiltration capacity, surface ponding, vertical drainage, and nonlinear water-table dynamics.<\/p>\n

The resulting hierarchy of process-aware MCP models is evaluated across 15 catchments spanning five hydroclimatic regions of the continental United States using daily streamflow prediction as the target. Results show that progressively augmenting the internal physical structure of the MCP unit generally improves predictive performance. The influence of boundary conditions is strongly hydroclimate dependent: vertical drainage substantially improves model skill in arid and snow-dominated basins but reduces performance in rainfall-dominated regions, while surface ponding has comparatively small effects.<\/p>\n

The best-performing MCP configurations approach the predictive skill of a Long Short-Term Memory benchmark while maintaining explicit physical interpretability. These results demonstrate that embedding hydrological process constraints within AI architectures provides a promising pathway toward interpretable and process-aware rainfall\u2013runoff modeling.<\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Robustness of the Western U.S. Networked Water Supply System under Drought and Demand Scenarios<\/span><\/h3>\n

Kyungmin Kim<\/span>, ASU<\/h4>\n
\n
\n
\n

Large-scale water infrastructure, from reservoirs to aqueducts, has enabled a reliable water supply across the western United States. The Western U.S. water network is centered on the Colorado River, supplying water eastward to the Rio Grande Basin and westward to Southern California to mitigate water stress of each region. While such infrastructure systems are effective in reducing local water shortage risks during most drought events, their interconnectedness can introduce unintended vulnerabilities under prolonged or widespread drought conditions, including cascading failures. This is particularly concerning because these systems have been historically optimized based on past streamflow variability, without accounting for the possibility of unprecedented infrastructure failures driven by conditions beyond historical experience. Therefore, there is a growing need to prepare these systems for future uncertainties, including climate change and shifting water demand. However, our understanding remains limited regarding how future changes in climate and demand may affect such networked water supply systems, and how adjustments in operating rules could help mitigate these impacts. To address this gap, this study evaluates the robustness of the networked water supply system in the western U.S. under various drought and demand scenarios. Using a simplified yet representative modeling approach that incorporates key infrastructure, water demand, and major operational rule sets, we address two key research questions: to what extent can the system tolerate changing hydroclimate and demand conditions, and which operating rules might improve system robustness under such conditions. We propose performance metrics at both local and system levels to assess robustness and identify potential vulnerabilities and critical weak points within the network. The findings are intended to inform the design of more resilient operating strategies and support decision-makers in preparing interconnected water systems for future climate uncertainty.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Evaluating the Influence of Precipitation Forcing and Vegetation Dynamics on Hydrologic Simulations in Semi-Arid Snow-Dominated Catchments Using Noah-MP and RAPID<\/span><\/h3>\n

Sadaf Moghisi<\/span>, UofA<\/h4>\n
\n
\n
\n

The Salt and Verde River basins in central Arizona provide water to more than two million people, yet predicting streamflow in these snow-influenced, semi-arid watersheds remains challenging due to uncertainties in precipitation inputs and land surface processes. In this study, we use the Noah-MP land surface model coupled with the RAPID routing model to simulate hydrologic processes at 1 km spatial and hourly temporal resolution over the period 1981\u20132020.<\/p>\n

We evaluate four precipitation datasets (AORC, NLDAS2, CONUS404, and IMERG) and assess the role of both static and dynamic vegetation representations. Model performance is evaluated against multiple observational datasets, including streamflow records, snow water equivalent (SNOTEL, SNODAS, and UA SWE), and leaf area index (LAI). We also examine precipitation biases and their impacts on hydrologic simulations.<\/p>\n

Our results show that precipitation forcing strongly controls the magnitude and variability of both streamflow and snow water equivalent, while vegetation dynamics primarily influence snow processes through canopy interception. Dynamic vegetation improves the representation of seasonal vegetation changes and affects evapotranspiration and runoff timing.<\/p>\n

These findings highlight the importance of accurate precipitation inputs and realistic vegetation representation\u2014particularly canopy\u2013snow interactions\u2014for improving hydrologic predictions in snow-affected, semi-arid basins.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Can Antecedent Groundwater Storage Improve Prediction of Seasonal Flow Volumes in Warm Snowpack Regions, as Observed in Cold Snowpack Regions?<\/span><\/h3>\n

Andrew Pfaff<\/span>, ASU<\/h4>\n
\n
\n
\n

Cold, seasonal snowpacks store winter precipitation and sustain spring streamflow, soil moisture, and ecosystem health. Climate warming is reducing snow persistence, shifting precipitation phase, altering melt timing, and increasing mid-winter melt events. These changes are especially important in arid and semi-arid regions, where even shallow or short-duration snowpacks can influence runoff, reservoir inflows, and water availability.<\/span><\/p>\n

Previous studies in cold snowpack regions show that snowmelt dynamics can be inferred from long-term streamflow records and that antecedent groundwater storage can improve seasonal flow prediction. However, it remains unclear whether these relationships also apply to warmer, more ephemeral snowpack regions, where runoff may depend more strongly on watershed storage conditions.<\/span><\/p>\n

This study evaluates whether antecedent groundwater storage improves seasonal flow prediction across warm and cold snowpack basins in the western United States. We use 36 years of hydrologic data from six minimally disturbed catchments, including cold snowpack basins in Utah and Colorado and warm snowpack basins in Arizona\u2019s Verde River and Salt River watersheds. Snowpack and melt dynamics are evaluated using SNOTEL SWE and precipitation, gridded SWE datasets, and streamflow records.<\/span><\/p>\n

Seasonal forecasting skill is assessed using the Multi-method Machine Learning Metasystem, or M4, from 1990 to 2025. M4 compares multiple machine learning approaches, including regression-based models, neural networks, random forests, support vector machines, and ensemble methods, to determine whether antecedent groundwater storage improves forecasts beyond snowpack, precipitation, and streamflow predictors alone.<\/span><\/p>\n

By comparing streamflow-derived melt signals with SNOTEL and gridded SWE observations, this work examines how snowpack persistence, melt timing, and groundwater memory influence seasonal runoff predictability. Results will support improved water-supply forecasting under increasingly variable snow conditions.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Relationships between ephemeral snowpack and soil moisture in semi-arid forest revealed by PlanetScope imagery and machine learning<\/span><\/h3>\n

Julia Tatum<\/span>, NAU<\/h4>\n
\n
\n
\n
\n
\n
\n

Ephemeral snow is a critical water source in the North American Southwest, particularly in the semi-arid Ponderosa pine forests of northern Arizona. Furthermore, there is a global trend towards seasonal snowpack converting to ephemeral snow under climate change. Therefore, better understanding and timely detection of ephemeral snow dynamics is important for forest management and climate adaptation. Unfortunately, studying ephemeral snow presents significant challenges because of high heterogeneity over small spatial and temporal extents. Leveraging novel CubeSat imaging technology, we created a dense time series of PlanetScope imagery to monitor ephemeral snow dynamics across three winters (2022-2024) in thinned and non-thinned ponderosa pine forests in northern Arizona to evaluate forest structure effects on snow cover and persistent snow patches. We paired these observations with a very large network of soil water potential sensors (n=126), facilitating unique insights into the ecologically relevant connections between ephemeral snowpack dynamics, forest structure, and soil water availability across the full root zone. A random forest classifier applied to PlanetScope imagery and validated against imagery from an uncrewed aerial vehicle was effective at classifying snow-covered area, with a balanced accuracy of 0.92 and F-score of 0.90. Forest that had been thinned under 4FRI had greater snow cover and more persistent snow patches as compared to non-thinned forest. Importantly, the dense PlanetScope time series revealed that the more-prevalent snow patches in thinned forest were associated with higher root-zone soil moisture, likely reducing water stress in subsequent dry seasons. The excellent performance of PlanetScope in this application highlights its novel potential for monitoring important ecohydrological processes under climate change and informs directions for future research.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_3″ _builder_version=”4.16″ custom_padding=”|||” global_colors_info=”{}” custom_padding__hover=”|||”][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Monitoring the Hydrogeological response in the Kaibab Plateau karst groundwater aquifer on the North Rim of Grand Canyon National Park to the Dragon Bravo Wildfire<\/span><\/h3>\n

Liam Houlgate<\/span>, NAU<\/h4>\n

Characterizing the response of groundwater to wildfires in the southwestern US is paramount for understanding related water resource responses. The Dragon Bravo fire burned from 4 July to 28 September 2025, impacting more than 140,000 acres of the Kaibab Plateau, situated on the north rim of Grand Canyon National Park and the adjoining Kaibab National Forest. Springs draining the North Rim are the principal source of water for the Transcanyon Waterline, supplying water for all NPS operations and for important aquatic ecosystems. To monitor the impact of the Dragon Bravo fire on Grand Canyon water resources, In-Situ brand water quality sondes have been installed at three spring sites known to be fed by the Kaibab Plateau hydrogeological system. Each site monitors pertinent hydrological parameters for assessing water quality and water quantity. Two distinct karst aquifers make up the broader North Rim hydrological system, the upper Coconino aquifer and lower Redwall-Mauv aquifer. Instrumentation has been placed at Robbers Roost Spring and North Canyon wash, upper Coconino aquifer sites, and Roaring Springs and Bright Angel Creek<\/span>,\u00a0<\/span><\/em>both lower Redwall Mauv aquifer sites. Real time hydrologic monitoring provides opportunities for remote aquifer analysis and rapid response on the part of resource managers. Additional sampling for geochemical variability in the upper aquifer monitoring sites is being conducted for pre- and post- fire analysis. The research improves the current understanding of hydrogeological processes on the North Rim of the Grand Canyon and the broader understanding of karst aquifers in arid and semi-arid regions. Findings from this research will assist National Park Service resource management in the Grand Canyon and provide a framework for studying and interpreting the impacts of wildfires on similar aquifer systems in the southwestern US.<\/span><\/p>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Spatio-temporal drought migration patterns in the Southwestern United States<\/span><\/h3>\n

Kyungmin Kim<\/span>, ASU<\/h4>\n
\n
\n
\n

Drought is a long-lasting hydrological phenomenon that can persist from weeks to years, causing significant environmental and economic impacts, including water deficits and crop failure. In recent decades, the southwestern United States has experienced prolonged droughts, highlighting the need to better understand regional drought dynamics to improve preparedness and mitigation. Drought typically begins with reduced precipitation, often coupled with higher temperatures and changes in snow dynamics. As it propagates through the hydrological cycle, it reduces soil moisture (agricultural drought) and eventually decreases streamflow and water availability (hydrological drought). This cascading process is driven by complex interactions among climate and land surface characteristics (e.g., topography, geology, etc.), making drought a highly dynamic spatiotemporal phenomenon. Despite previous research, gaps remain in understanding how these dynamics evolve across different drought stages and across space. This study analyzes the spatiotemporal migration of drought in the southwestern United States using three standardized indices: the Standardized Precipitation Index (SPI), the Standardized Soil Moisture Index (SSMI), and the Standardized Runoff Index (SRI), representing meteorological, agricultural, and hydrological drought, respectively. These indices are derived from land surface model outputs and remote sensing data. We apply the Spatio-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) algorithm to identify and track drought clusters at different stages from 1979 to 2025. The results show that drought events exhibit persistent spatial migration patterns and tend to concentrate in specific regions, indicating the existence of drought-prone hotspots. Drought clusters also persist over extended periods, reflecting strong temporal continuity. Our findings offer new insights into spatiotemporal drought dynamics in the southwestern United States, supporting more effective water resource management and drought mitigation in the region.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

GIS-based flood risk mapping using multi-criteria decision analysis in the Sebeya Catchment, Rwanda.<\/span><\/h3>\n

Sifa Mumararungu<\/span>, Yeugnam Universities<\/span><\/h4>\n
\n
\n
\n

Flooding poses a recurring threat to lives, infrastructure, and sustainable development in the Sebeya Catchment, Rwanda. This study employed an integrated Geographic Information System (GIS)-based Multi-Criteria Decision Analysis framework using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess flood hazard, vulnerability, and risk. Seven flood-conditioning factors\u2014elevation, slope, drainage density, distance to river, rainfall, soil type, and land use\/land cover\u2014were incorporated to generate the flood hazard map. Socio-economic and infrastructural indicators, including population density, road proximity, built-up areas, and impervious surfaces, were used to evaluate vulnerability. The catchment was further analyzed across 20 administrative sectors to enhance spatial prioritization.<\/span>
Results indicate that 27.6%, 30.9%, 23.6%, 12.1%, and 5.7% of the catchment fall within very low, low, moderate, high, and very high flood risk zones, respectively, with approximately 45.4% of the area classified as flood-prone. High and very high-risk zones are concentrated along low-lying river corridors and densely populated sectors, particularly Nyundo, Rugerero, Gisenyi, Nyakiriba, Mushonyi, and Kanama. Validation using historical flood events (2018, 2020, and 2023) and ROC-AUC analysis demonstrated strong agreement and satisfactory predictive performance.<\/span>
The study confirms the robustness of the integrated GIS\u2013TOPSIS approach and provides a spatial decision-support framework to strengthen land-use planning, disaster risk reduction, and long-term climate resilience in the Sebeya Catchment and comparable data-limited regions.<\/span><\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][et_pb_text _builder_version=”4.27.6″ _module_preset=”default” text_font=”Arial||||||||” text_font_size=”13px” text_line_height=”1.4em” header_3_font_size=”23px” header_4_text_color=”rgba(145,145,145,0.94)” global_colors_info=”{}”]

Drought Cascading in Arizona: Exploring Propagation from Meteorological to Hydrological Stages using Machine Learning<\/span><\/h3>\n

Margaret Garcia, Sara Alonso Vicario, and Tejas Sharma<\/span>, ASU<\/span><\/h4>\n
\n
\n
\n

Drought is a major natural hazard in Arizona, where prolonged precipitation deficits and extreme heat can produce severe ecological, hydrological, and socioeconomic impacts. This study investigates how drought propagates across meteorological, agricultural, and hydrological stages within the state, with particular attention to the time lags that separate one stage from the next. Using historical climate records, satellite observations, and geospatial environmental data, we examine how both landscape characteristics and event specific drought conditions shape the timing of drought development and propagation.<\/p>\n

Our earlier analyses based on long term average lag values showed that spatial covariance explains a large share of the observed variation, while measured factors provide only limited additional explanatory power once spatial structure is accounted for. To address this limitation, we extend the analysis to an event level framework in which individual drought events at each grid cell are treated as separate observations. This approach allows us to evaluate whether meteorological drought characteristics such as timing, duration, and severity improve our ability to explain onset and termination lag beyond static spatial factors alone.<\/p>\n

To model these relationships, we compare machine learning approaches including random forests and neural networks, with and without explicit treatment of spatial dependence, and evaluate alternative strategies for combining flexible mean models with spatial correction. By moving from long term averages to event level propagation behavior, this work aims to provide a more interpretable view of how drought evolves across Arizona and which factors most strongly influence the speed and structure of drought propagation.<\/p>\n<\/div>\n<\/div>\n<\/div>\n

\n
<\/div>\n<\/div>\n

<\/p>\n

<\/p>\n

<\/p>\n

<\/p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]","protected":false},"excerpt":{"rendered":"

Poster Sessions at the 2026 Symposium Posters will be displayed in Fremont. Information about poster setup, take-down, and judging \u00bbStormwater Recharge and Site Prioritization for Net Zero Urban Water in Tucson Bhushan Rash, UofA Water-stressed regions like Tucson, Arizona, face mounting pressure from groundwater depletion and a heavy reliance on imported Colorado River supplies. To […]<\/p>\n","protected":false},"author":63,"featured_media":0,"parent":590,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"\r\n

Poster Sessions at the 2023 Symposium<\/h2>\r\n\r\n\r\n\r\n

Posters will be displayed in Fremont. Information about poster setup, take-down, and judging \u00bb<\/a><\/p>\r\n\r\n\r\n\r\n

<\/div>\r\n\r\n\r\n\r\n
\r\n
\r\n

An overview of groundwater well siting using geophysical methods<\/h3>\r\n\r\n\r\n\r\n

Austin Bergerson, hydroGEOPHYSICS Inc<\/strong><\/p>\r\n\r\n\r\n\r\n

Discovering new groundwater sources is integral to mines. However, many traditional methods for subsurface investigation require invasive techniques, such as drilling to obtain data at point\u00a0locations to infer what is happening on a broader scale. Geophysical methods \u2014 in this case, electrical resistivity \u2014 can provide a rapid, nonintrusive view of the subsurface up to the regional scale. This case study details a recent electrical resistivity survey that we conducted to characterize potential well locations for a mine site in Arizona.\u00a0A model highlighted a number of fault and fracture zones, which are good potential well sites since they tend to increase the porosity and transmissivity. It was unclear which fault zones would produce good yields, a condition that depends on the occurrence of clay-rich fault gouge that can reduce the porosity and transmissivity of these structures. The resistivity results were able to identify potentially productive fault zones. The use of the high-resolution, spatially continuous geophysical information, paired with tectonic and hydrology information, can help site productive wells.<\/p>\r\n\r\n\r\n\r\n

Identifying critical headwaters in the San Juan River basin: Characteristics and strategies for downstream water security in the arid southwestern U.S.<\/h3>\r\n\r\n\r\n\r\n

Eric Sj\u00f6stedt, NAU<\/strong><\/p>\r\n\r\n\r\n\r\n

Mountainous headwaters have been shown to disproportionately generate runoff flows compared to their downstream areas. Major Western metropolitan areas such as Phoenix, Tucson, Denver, Salt Lake City, Las Vegas, Los Angeles, and San Diego depend on the mountainous Colorado River basin for all or some of their water. Prior studies identifying critical runoff generation areas have lacked the fine-scale spatial resolution for adequate systems-level decision-making. As a significant tributary of the Colorado River, the San Juan River basin (SJRB) is an essential water source for the arid southwestern U.S., particularly for Arizona. The goal of this study was to identify the critical runoff generation areas in the SJRB using the relative water yield (RWY) equation developed by Viviroli et al. (2007) and subsequent adaptations. We used high-resolution runoff, land cover, and topographic data to investigate the contributions of each component. We also examined changes in the variability of runoff generation and land cover composition over time to identify controls..<\/p>\r\n\r\n\r\n\r\n

Tailoring hydrologic modeling for improved water resources decision support: A mixed ensemble approach<\/h3>\r\n\r\n\r\n\r\n

Abigail Kahler, UA<\/strong><\/p>\r\n\r\n\r\n\r\n

One of the challenges of hydrologic modeling is quantifying uncertainty; initially small uncertainties magnify over time, increasing the costs of miscalculated decisions. Hydrologic models help predict consequences but are limited by sparse data and uncertainty. This suggests a need for multiple models, and it is worthwhile to pay special attention to less probable, still plausible models that predict consequential outcomes. We call these models of concern (MOCs). We propose a method of combining two ensembles: one comprising the best-fitting calibrated models, and another entirely of MOCs. The usefulness of each model\u2019s prediction to the stakeholder is defined through a utility function: utility may be low or high, depending on the associated consequences, and its threshold can be set based on risk tolerance. The mixed ensemble involves an iterative process that allows the stakeholder to reconsider their willingness to accept risk according to the likelihood of a consequential outcome. This process represents stakeholder concerns more fully than a single ensemble, which only considers goodness-of-fit.<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n

\r\n

The Arizona Streamgage Catalog (AZStreamCAT): A comprehensive compilation of locations and metadata for streamgages throughout Arizona<\/h3>\r\n\r\n\r\n\r\n

Martha Whitaker, UA<\/strong><\/p>\r\n\r\n\r\n\r\n

Throughout Arizona, streamgages are used for various purposes by different entities. However, a lack of collaboration among the entities means it is difficult to identify the locations and other information about these gages. This drastically reduces the efficiency and accuracy of gathering information for streamflow data. The Arizona Streamgage Catalog (AZStreamCAT) was initiated in February 2023 to develop an online, publicly available catalog that provides an overview of the quantity and location of non-USGS streamgages. By coalescing pre-existing data, the catalog can inform stakeholders, improve resource management, and help establish protocols for future data collection. Data for AZStreamCAT were collected by direct communication with operators and via online surveys. Respondents were asked to provide latitude, longitude, and elevation coordinates for each gage location. The resulting map includes layers showing county boundaries, watershed boundaries, and rivers. It will aggregate virtually every streamgage in Arizona and will ultimately be published on ArcGIS Online as a hosted feature layer for public use.<\/p>\r\n\r\n\r\n\r\n

Preliminary watershed water budget analysis for forest restoration sites in Coconino National Forest, northern Arizona<\/h3>\r\n\r\n\r\n\r\n

Cole Denver, NAU<\/strong><\/p>\r\n\r\n\r\n\r\n

Climate change and European settlement have altered the natural structures of large portions of the Coconino National Forest in northern Arizona.\u00a0The increased occurrence of high-severity wildfires due to these changes has led to the establishment of various restoration programs to protect the region\u2019s forests and their watersheds. Several studies have looked at the impact of forest restoration on watershed hydrology; however, information on how these project affect the large-scale hydrology of the forests is still minimal. To better assess these effects, a study was initiated to compare the impacts of differing levels of restoration in six subwatersheds within the Upper Lake Mary watershed. Its goal is to accurately describe pretreatment hydrologic conditions to provide a baseline for assessing the efficacy of future restoration projects. This study synthesizes legacy precipitation, discharge, groundwater recharge, soil moisture, and runoff data \u2014 combined with novel evapotranspiration (ET) data \u2014 to create a holistic water balance for each subwatershed, uncover pretreatment hydrologic trends that correlate with climate, and devise a reliable method for calculating ET. The results will provide crucial information for policy and decision-making as the region plans for future water availability.<\/p>\r\n\r\n\r\n\r\n

Spatially distributed base-flow index analysis of Arizona streams<\/h3>\r\n\r\n\r\n\r\n

Caelum Mroczek, NAU<\/strong><\/p>\r\n\r\n\r\n\r\n

Base flow is important for identifying the groundwater contribution to streams, especially in arid environments. One metric, the base-flow index (BFI) \u2014 the ratio of groundwater-derived flow to total flow \u2014 provides a normalized measure of groundwater dependence. Historically, variable BFI values can indicate watersheds that may be enhanced by future managed aquifer recharge. This study examines BFI throughout Arizona, based on data from instrumented streams in federal, state, and city managed lands, at a range of elevations. It involved using runoff data to estimate the natural recharge volume, runoff values, and base-flow contribution and then using BFI and geophysical watershed properties with a random forest regression analysis to\u00a0estimate the base-flow of ungauged rivers. Since many natural channels in Arizona are intermittent or ephemeral, we focused our BFI characterization on non-perennial streams.<\/p>\r\n<\/div>\r\n\r\n\r\n\r\n

\r\n

Arizona\u2019s abandoned mines: Remediation efforts to improve watershed health<\/h3>\r\n\r\n\r\n\r\n

Adam Stratman, ADEQ<\/strong><\/p>\r\n\r\n\r\n\r\n

Arizona has numerous abandoned mines, with estimates ranging from 1,300 to 200,000. These mines threaten human health and the environment by causing safety risks, degrading streams, and decreasing biodiversity. Through collaborative partnerships, ADEQ\u2019s Watershed Improvement Unit has been remediating abandoned mines since 2018. This poster highlights some of the remediated mines and details the process used to identify and prioritize sites for future remediation.<\/p>\r\n\r\n\r\n\r\n

Investigating the seasonal variability of metals concentrations and their transport mechanisms in intermittent streams<\/h3>\r\n\r\n\r\n\r\n

J<\/strong>ustin Headley, UA<\/strong><\/p>\r\n\r\n\r\n\r\n

In the 1960s, anomalously high metal concentrations in southeastern Arizona streams led to the discovery of large porphyry deposits of copper and molybdenum in upstream drainage areas of the Santa Rita Mountains. No large-scale mining operation has yet to occur in the Santa Ritas; however, tailings at one proposed mine site would have the potential to negatively impact downstream water quality in Lower Cienega Creek and Davidson Canyon \u2014 one of Arizona\u2019s \u201coutstanding waters.\u201d Water infiltrating through tailings can decrease pH through sulfide oxidation, the hydrolysis of iron (III) minerals, and other mechanisms. Additionally, the chemical weathering of ore brought to the surface can mobilize toxic metals that were previously sequestered underground. Assessing any future mine-related contamination requires characterizing the baseline levels and seasonal variability of metals in the surface waters of Davidson Canyon and Lower Cienega Creek. We hypothesize that these metals concentrations follow a seasonal cycle, with see higher concentrations in groundwater-fed baseflow during the dry seasons (spring and fall) and lower concentrations during winter and summer, when recent precipitation makes up a larger component of surface flow. To test this hypothesis, we collected samples in Davidson Canyon and Lower Cienega Creek under both base flow and high flow conditions; we also analyzed major ion and isotope chemistry.<\/p>\r\n\r\n\r\n\r\n

Using GIS mobile applications to streamline field data collection and data management<\/h3>\r\n\r\n\r\n\r\n

Tristan Dicke, Montgomery & Associates<\/strong><\/p>\r\n\r\n\r\n\r\n

Collecting surface water data in the field is a time-consuming process, with large amounts of metadata that can take a tedious amount of time to digitize. The use of map- and form-based mobile applications \u2014 ESRI\u2019s Survey123, ArcGIS Field Maps, and VuSitu \u2014 can streamline and automate data collection and reduce the amount of time required to manually entering data. These applications integrate QA\/QC and help reduce paper-to-digital data entry errors. Field data can be compiled and uploaded to a viewable data management system within hours of collection. These applications also can give users the tools they need to visualize the data distribution and can minimize the risk of injury to field personnel in remote locations.<\/p>\r\n<\/div>\r\n<\/div>\r\n","_et_gb_content_width":"","footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-1567","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/pages\/1567","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/users\/63"}],"replies":[{"embeddable":true,"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/comments?post=1567"}],"version-history":[{"count":11,"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/pages\/1567\/revisions"}],"predecessor-version":[{"id":4799,"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/pages\/1567\/revisions\/4799"}],"up":[{"embeddable":true,"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/pages\/590"}],"wp:attachment":[{"href":"https:\/\/ahssymposium.org\/2026\/wp-json\/wp\/v2\/media?parent=1567"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}