Accurate soil moisture and streamflow data are an aspirational need of many hydrologically-relevant fields. Model simulated soil moisture and streamflow hold promise but numerical models require calibration prior to application to ensure sufficient model performance. Manual or automated calibration methods require iterative model runs and hence are computationally expensive. In this study, we leverage the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to help constrain soil parameter uncertainties in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. After calibration, WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations. Compared to four well-established soil moisture datasets including Soil Moisture Active Passive Level 4 data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-calibrated WRF-Hydro produces the highest mean KGE (0.67) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases especially in the case where the model domain is set up with an SSURGO-informed total soil thickness. Both the magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean Nash-Sutcliffe Efficiency across seven of the nine gages increases from 0.19 in default WRF-Hydro to 0.63 after calibration. Our soil data-informed calibration approach, which is transferable to other spatially-distributed hydrological models, uses open-access data and non-iterative steps to improve model performance and is thus operationally and computationally attractive.

Characterizing changes in precipitation patterns over time is critical for hydrologically-dependent fields like water resource management and agriculture. Here, we explore observed trends in interannual precipitation variability using a suite of metrics that describe changes in precipitation over time. We analyze daily in-situ Global Historical Climatology Network precipitation data from 1970 to present over seventeen internally consistent sub-national United States domains using a regional Mann-Kendall trend test. We find robustly increasing trends in annual mean precipitation and wet day frequency for most of the central and eastern U.S., but decreasing trends in the western U.S. Importantly, we identify widespread significant trends in interannual precipitation variability, with increasing variability in the southeast, decreasing variability in the far west, and mixed signals in the Rocky Mountains and north-central U.S. Our results provide important context for water resource managers and a new observational standard for climate model performance assessments.

The characterization of changes over the full distribution of precipitation intensities remains an overlooked and underexplored subject, despite their critical importance to hazard assessments and water resource management. Here, we aggregate daily in situ Global Historical Climatology Network precipitation observations within seventeen internally consistent domains in the United States for two time periods (1951-1980 and 1991-2020). We find statistically significant changes in wet day precipitation distributions in all domains – changes primarily driven by a shift from lower to higher wet day intensities. Patterns of robust change are geographically consistent, with increases in the mean (4.5-5.7%) and standard deviation (4.4-8.7%) of wet day intensity in the eastern U.S., but mixed signals in the western U.S. Beyond their critical importance to the aforementioned impact assessments, these observational results can also inform climate model performance evaluations.

In steep wildfire-burned terrains, intense rainfall can produce large volumes of runoff that can trigger highly destructive debris flows. The ability to accurately characterize and forecast debris-flow hazards in burned terrains, however, remains limited. Here, we augment the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) to simulate both overland and channelized flows and assess postfire debris-flow hazards over a regional domain. We perform hindcast simulations using high-resolution weather radar-derived precipitation and reanalysis data to drive non-burned baseline and burn scar sensitivity experiments. Our simulations focus on January 2021 when an atmospheric river triggered numerous debris flows within a wildfire burn scar in Big Sur – one of which destroyed California’s famous Highway 1. Compared to the baseline, our burn scar simulation yields dramatic increases in total and peak discharge, and shorter lags between rainfall onset and peak discharge. At Rat Creek, where Highway 1 was destroyed, discharge volume increases eight-fold and peak discharge triples relative to the baseline. For all catchments within the burn scar, we find that the median catchment-area normalized discharge volume increases nine-fold after incorporating burn scar characteristics, while the 95th percentile volume increases 13-fold. Catchments with anomalously high hazard levels correspond well with post-event debris flow observations. Our results demonstrate that WRF-Hydro provides a compelling new physics-based tool to investigate and potentially forecast postfire hydrologic hazards at regional scales.

Localized and severe storms can cause citywide flooding, leading drainage systems to surcharge and overflow to nearby water courses. Urban catchments feature high degrees of imperviousness and heterogeneity, often resulting in highly nonlinear hydrologic responses with shorter time of concentration, lag times, and sharper peak flows. Additionally, due to population and economic growth, urban drainage systems have attempted to evolve to more efficiently drain surface waters and reduce vulnerabilities. A critical outcome of this evolution is the need for finer spatio-temporal resolution rainfall measurements and hydrological modeling. As the major driving mechanism, the spatio-temporal variability in rainfall is acknowledged as a key source of uncertainty for urban hydrological modeling. The objective of this research is to revisit the impact of the temporal and spatial resolution of rainfall measurements on urban hydrological applications. We first provide a quantitative analysis of the spatiotemporal structure and variability of rainfall using both a 9-member hourly rain gauge network spaced ~10 km apart and a single WSR-88D dual-polarimetric weather radar with precipitation resolved every 5 minutes at ~500 m. Precipitation data from each observing system extracted at different time steps is aggregated within urban catchments and compared for three typical intense storms over a set of urban catchments located in Chicago Metropolitan area. Then the rain-runoff dynamics for 9 geographically-diverse (relative to the underneath sewer system) and differently-sized catchments are examined utilizing MetroFlow – a coupled hydrologic and hydraulic modeling system. Finally, city-wide flooding risks are simulated by routing the predicted surface runoff through the as-built sewer system. Additional mitigating storage capacity is also considered by numerical modeling the deep tunnel and reservoir in construction. The sensitivity of urban flood variables (i.e., mean and peak depth as well as duration) to rainfall spatiotemporal resolution is analyzed. Our results complement and advance the limited literature attempting to resolve the ideal resolution of rainfall data relevant for urban hydrology and stormwater management.

Localized and severe storms can cause citywide flooding, leading drainage systems to surcharge and overflow to nearby water courses. Urban catchments feature high degrees of imperviousness and heterogeneity, often resulting in highly nonlinear hydrologic responses with shorter time of concentration, lag times, and sharper peak flows. Additionally, due to population and economic growth, urban drainage systems have attempted to evolve to more efficiently drain surface waters and reduce vulnerabilities. A critical outcome of this evolution is the need for finer spatio-temporal resolution rainfall measurements and hydrological modeling. As the major driving mechanism, the spatio-temporal variability in rainfall is acknowledged as a key source of uncertainty for urban hydrological modeling. The objective of this research is to revisit the impact of the temporal and spatial resolution of rainfall measurements on urban hydrological applications. We first provide a quantitative analysis of the spatiotemporal structure and variability of rainfall using both a 9-member hourly rain gauge network spaced ~10 km apart and a single WSR-88D dual-polarimetric weather radar with precipitation resolved every 5 minutes at ~500 m. Precipitation data from each observing system extracted at different time steps is aggregated within urban catchments and compared for three typical intense storms over a set of urban catchments located in Chicago Metropolitan area. Then the rain-runoff dynamics for 9 geographically-diverse (relative to the underneath sewer system) and differently-sized catchments are examined utilizing MetroFlow – a coupled hydrologic and hydraulic modeling system. Finally, city-wide flooding risks are simulated by routing the predicted surface runoff through the as-built sewer system. Additional mitigating storage capacity is also considered by numerical modeling the deep tunnel and reservoir in construction. The sensitivity of urban flood variables (i.e., mean and peak depth as well as duration) to rainfall spatio-temporal resolution is analyzed. Our results complement and advance the limited literature attempting to resolve the ideal resolution of rainfall data relevant for urban hydrology and stormwater management.

Electric vehicle (EV) adoption promises potential air pollutant and greenhouse gas (GHG) reduction co-benefits. As such, China has aggressively incentivized EV adoption, however much remains unknown with regard to EVs’ mitigation potential, including optimal vehicle type prioritization, power generation contingencies, effects of Clean Air regulations, and the ability of EVs to reduce acute impacts of extreme air quality events. Here, we present a suite of scenarios with a chemistry-climate model that assess the potential co-benefits of EVs during an extreme winter air quality event. We find that regardless of power generation source, heavy-duty vehicle (HDV) electrification consistently improves air quality in terms of NO2 and fine particulate matter (PM2.5), potentially avoiding 562 deaths due to acute pollutant exposure during the infamous January 2013 pollution episode (~1% of total premature mortality). However, HDV electrification does not reduce GHG emissions without enhanced emission-free electricity generation. In contrast, due to differing emission profiles, light-duty vehicle (LDV) electrification in China consistently reduces GHG emissions (~2 Mt CO2), but results in fewer air quality and human health improvements (145 avoided deaths). The calculated economic impacts for human health endpoints and CO2 reductions for LDV electrification are nearly double those of HDV electrification in present-day (155M vs. 87M US$), but are within ~25% when enhanced emission-free generation is used to power them. Overall we find only a modest benefit for EVs to ameliorate severe wintertime pollution events, and that continued emission reductions in the power generation sector will have the greatest human health and economic benefits.

The U.S. and much of the world sit on the cusp of an electrification revolution – a moment driven largely by the need to reduce the emission of greenhouse gases to limit the impacts of anthropogenic climate change. The electrify everything movement aims to transition combustion-powered sectors into technologies powered solely by electricity, with the idea that the electric grid – currently comprised of a mix of combustion, renewable, and nuclear generation units – will become cleaner and greener over time. Studies indicate that electrifying high-efficiency devices, appliances, and vehicles will reduce greenhouse gas emissions regardless of the grid’s composition, however ancillary air quality benefits and tradeoffs remain poorly resolved, particularly at impact- and equity-relevant scales. Here, I use a fine-scale CONUS-wide climate and air quality co-benefit and tradeoff analysis framework (i.e., 4 km2 SMOKE-CMAQ-WRF simulations) to assess sustainable climate solutions. Analyses utilize emission scenarios that account for increased grid demand and uncertainties in grid evolution, simulate the interaction of meteorological and chemical processes, characterize changes in greenhouse gases and air pollutants, and assess economic, social, and public health consequences of sustainable transitions over two key, yet methodologically disparate, residential/commercial sectors: transportation: via the replacement of internal combustion vehicles with electric vehicles and lighting: via the replacement of low efficiency bulbs with high-efficiency LEDs.

The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two-way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF-CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF-CMAQ’s performance using data from the National Climate Data Center and EPA Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation’s meteorological and chemical performance while also resolving key details in areas of high exposure and impact, i.e., urban environments. At 1.3 km, we find that most air quality-relevant meteorological components of WRF-CMAQ perform at or above community benchmarks. WRF-CMAQ’s chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (<30%). Simulated spatial pollutant patterns show distinct urban-rural footprints, with urban NO2 and PM2.5 20-60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high-pollution areas within individual urban neighborhoods and characterize changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high-resolution simulations, particularly over urban settings.

Assessing the extent and impact of past extreme weather events within cities can help identify vulnerabilities, map potential solutions, and prevent future calamities. Modern urban environments are particularly vulnerable to hydrological extremes due to high population densities, expansive impermeable surfaces, more intense precipitation extremes, and infrastructure designed for a now obsolete climate. Intense rainfall in urban environments can lead to impacts ranging from nuisance flooding to overloading of sewage and drainage systems to neighborhood inundation. In the flood-prone city of Chicago, storm waters are contained in a network of tunnels and reservoirs until treated and released to the waterways. Management decisions for a 600 km^2 metropolitan area are made based on precipitation data collected at just 9 gauge sites. Here, we combine high-resolution radar-derived precipitation data with urban-scale hydrological models to improve our understanding of water flow, advance stormwater management practices, and potentially mitigate flood risks. Proximity of the NEXRAD system to Chicago allows us to improve the spatial resolution of rainfall estimates to 500m, which will be used to produce neighborhood-scale rainfall hindcasts. Different dual-polarimetric radar-rainfall retrieval methods, e.g., rainfall from reflectivity, attenuation, specific differential phase, and differential reflectivity will be examined to determine the most accurate representation of rainfall estimates. This suite of rainfall estimates will be used to derive catchment-level precipitation, and serve as input in a coupled hydrological-hydraulic MetroFlow model. To verify the utility of our radar precipitation data, we examine an April 2013 event that delivered a record-breaking 7 inches of rain in 2 days in some areas. We compare our highly-resolved precipitation-driven hydrological model predictions with those made using the 9 gauge stations. This research is conducted under the premise that hydrological extremes are expected to be exacerbated by climate change. Understanding drivers of urban flooding using high-resolution precipitation data and models can be used to improve resiliency-focused infrastructure design in Chicago neighborhoods.