Parameter-elevation Regressions On Independent Slopes Model Research Articles

Incorporating environmental stress improves estimation of photosynthesis from NIRvP in US Great Plains pasturelands and Midwest croplands

Near-infrared reflectance of vegetation multiplied by incoming sunlight (NIRvP) is important for gross primary production (GPP) estimation. While NIRvP is a useful indicator of canopy structure and solar radiation, its association with heat or moisture stress is not fully understood. Thus, this research aimed to explore the impact of air temperature (Ta) and vapor pressure deficit (VPD) on the NIRvP-GPP relationship. Using Moderate Resolution Imaging Spectroradiometer (MODIS) observations, eddy-covariance measurements, and the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data, we found that NIRvP cannot fully explain the response of plant photosynthesis to Ta and VPD at both seasonal and daily scales. Therefore, we incorporated a polynomial function of Ta and an exponential function of VPD to correct its seasonal response to stress and calibrated the GPP residual via a linear function of Ta and VPD time-varying derivatives to account for its daily response to stress. Leave-one-site-out cross-validation suggested that the improvements relative to its original version were especially noteworthy under stress conditions while less significant when there was no water or heat stress across grasslands and croplands. When compared to six other GPP models, the enhanced NIRvP model consistently outperformed them or performed comparably with the best model in terms of bias, RSME, and coefficient of determinant against measurements in grasslands and croplands. Moreover, we found that parameterizing the fraction of photosynthetically active radiation term using NIRv notably improved the performance of the classic MOD17 and vegetation photosynthesis model, with an average RMSE reduction of 13 % across grasslands and croplands. Overall, this study highlights the need to consider environmental stressors for improved NIRvP-based GPP and shed light on future improvements of LUE models.

PRISM and Radar Estimation for Precipitation (PREP): PRISM enhancement through ANN and radar data integration in complex terrain

Site-specific estimates of precipitation can be used to assess crop productivity and identify areas vulnerable to crop damages caused by extreme weather events such as droughts and floods. Spatial interpolation of precipitation such as Parameter-elevation Regressions on Independent Slopes Model (PRISM) has been used to estimate precipitation in an area of interest. However, the reliability of spatial interpolation is often affected by the availability of precipitation measurements from weather stations in a given region especially under complex terrain conditions. Here we propose an alternative approach for site-specific estimation of precipitation using both radar reflectivity data and topographic features. At first, radar reflectivity data are used as inputs to an artificial neural network (ANN) for estimation of precipitation. These radar precipitations at each grid cell are used to represent the observations at virtual weather stations for spatial interpolation using PRISM. Furthermore, the radar precipitations are compared with the observations at actual weather stations for their bias correction. This approach is referred to as PRISM and Radar Estimation for Precipitation (PREP). A case study was conducted in Jeollabuk-do, South Korea to compare the degree of agreement between PREP and PRISM. It was found that PREP had higher degree of agreement for the daily estimates of precipitation than PRISM in the given region with a complex terrain including coast and mountains. For example, the root mean square error (RMSE) of precipitation estimates for PREP was 22.1% less than that for PRISM in 2020. PREP also had greater value of the critical success index (CSI) than PRISM especially under heavy precipitation events, e.g.,>180 mm, and no rainfall conditions. These findings indicate that the PREP would improve the reliability of site-specific estimates of precipitation, which would facilitate decision-making in agriculture and early warning of extreme weather events.

Object-oriented analysis as a foundation for building climate storylines of compounding short-term drought and crop heat stress

IntroductionCrops are vulnerable to precipitation and heat extremes during late spring through summer.MethodsWe analyzed for a north-central U.S. region short-term drought and agricultural heat stress during April-May-June-July. We used the 4-km Parameter Elevation Regression on Independent Slopes Model (PRISM) for observations, aggregated to a 25-km grid, and two 25-km Regional Climate Model version 4 (RegCM4) simulns used either GFDL- or MPI-GCM boundary conditions. We chose 1981-2000 as our contemporary time period, and 2041-2060 as our scenario time period, which used the Representative Concentration Pathway 8.5 emissions scenario. We used object-oriented analysis to identify events of interest in observations and simulations by identifying objects in a space-time domain that meet specified criteria, such as exceeding a heat-stress temperature threshold. The event diagnosis allowed analysis of compound events, occurring when temperature and drought objects overlap.ResultsIdentified objects yielded events that can undermine agricultural productivity and which are thus relevant to decision makers, making them building blocks for possible climate storylines. The observations and simulations showed similar spatial distributions of event frequencies across the analysis region. However, the simulations attained this distribution by having fewer events that tend to cover larger areas compared to observed events, suggesting that the effective resolution of the simulations was coarser than their 25-km grids. Short-term drought frequency increased and heat-stress frequency decreased in transitioning to the scenario climate. When compounding occurred heat-stress events generally preceded the short-term drought events. The overlapping, compound events tended to be more extreme compared to non-overlapping events of either type.DiscussionThe information yielded projected changes in these agriculturally motivated events. One prominent conditional behavior emerging from the work was that a heat-stress event should be a warning to watch for potential drought, as both could compound each other to more intense levels.

Microclimatic Variability of Cold-Season Minimum Temperatures in Michigan, United States: A Study with Implications for Insect Mortality

Abstract Gridded climate datasets are used by researchers and practitioners in many disciplines, including forest ecology, agriculture, and entomology. However, such datasets are generally unable to account for microclimatic variability, particularly within sites or among individual trees. One such dataset is a recent climatology of extreme minimum temperatures in the U.S. Great Lakes region, based on the Parameter–Elevation Regressions on Independent Slopes Model (PRISM) gridded temperature dataset. Development of this climatology was motivated by interest in the spatiotemporal variability of winter temperatures potentially lethal to the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive insect that causes mortality of eastern hemlock (Tsuga canadensis). In this study, cold-season daily minimum temperatures were monitored at six Michigan sites varying in latitude, elevation, Great Lakes proximity, and HWA infestation status, to address two objectives. First, we documented the spatiotemporal variability in daily minimum air temperatures recorded at multiple aspects and heights on selected hemlock trees. Second, this variability was characterized in the context of the PRISM extreme minimum temperature climatology. Tree-sensor air temperatures exhibited minimal relationships with aspect but considerable sensitivity to height. Daily minimum temperatures were higher for some tree sensors positioned ≤ 0.2 m above ground level during some time periods, with overall muted temporal variability, relative to an adjacent ambient sensor. This phenomenon was attributed to the insulating effects of snow cover, because the tree–ambient sensor temperature difference was positively correlated with snow depth. Overall, results indicate that such unresolved variability warrants consideration by gridded climate dataset users.

A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations

Abstract. High-quality, freely accessible, long-term precipitation estimates with fine spatiotemporal resolution play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China are either constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2839 gauges located across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjusted the climatology based on a monthly precipitation constraint and topographic characteristic correction, using an algorithm that combined the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes were validated using 45 992 high-density daily gauge observations from 2015 to 2019 across China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse-distance weighting method. This scheme had median values of 0.78 for the correlation coefficient, 8.8 mm d−1 for the root-mean-square deviation, and 0.69 for the Kling–Gupta efficiency for comparisons between the 45 992 high-density gauge observations and the best interpolation scheme for the 0.1∘ latitude × longitude grid cells from 2015 to 2019. This scheme had the best overall performance, as it fully considers topographic effects in the daily climatology field and it balances local data fidelity and global fitting smoothness in the interpolation of the precipitation ratio field. Therefore, this scheme was used to construct a new long-term, gauge-based gridded precipitation dataset for the Chinese mainland (called CHM_PRE, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5, 0.25, and 0.1∘ from 1961 to the present. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.21432123.v4 (Han and Miao, 2022).

A Satellite-Based Approach for Quantifying Terrestrial Water Cycle Intensity

The terrestrial water cycle intensity (WCI) is a widely used tool to quantify the impact of climate change on the distribution of global water resources. In this study, a satellite-based WCI was tested by comparing the parameter-elevation regressions on independent slopes model (PRISM) precipitation estimates with those of the Global Precipitation Measurement (GPM) satellite mission across the contiguous United States (CONUS), based on an existing Köppen–Geiger climate classification for the CONUS. Both precipitation products were not statistically different across all climate classes. Consequently, satellite-based WCI changes between two multiannual periods (2001 to 2009 and 2010 to 2019) were calculated at a 0.1-degree spatial resolution using the GPM and a validated global evapotranspiration dataset. This study showed that: (1) The water cycle is speeding up in many parts of the CONUS, particularly the West, driven by recent increases in both precipitation and evapotranspiration through much of the region. (2) The El Niño-Southern Oscillation (ENSO) may be influencing the WCI of the CONUS by driving precipitation in the west, southeast, and parts of the north, and dryness in the northeast regions. The hydrological impacts of these results cannot be generalized. However, flood and drought risks, water availability and quality issues remain key primary concerns.

Assessment of WRF (v 4.2.1) dynamically downscaled precipitation on subdaily and daily timescales over CONUS

Abstract. This study analyzes the quality of simulated historical precipitation across the contiguous United States (CONUS) in a 12 km Weather Research and Forecasting model version 4.2.1 (WRF v 4.2.1)-based dynamical downscaling of the fifth-generation ECMWF atmospheric reanalysis (ERA5). This work addresses the following questions. First, how well are the 3 and 24 h precipitation characteristics (diurnal and annual cycles, precipitation frequency, annual and seasonal mean and maximum precipitation, and distribution of seasonal maximum precipitation) represented in the downscaled simulation, compared to ERA5? And second, how does the performance of the simulated WRF precipitation vary across seasons, regions, and timescales? Performance is measured against the National Centers for Environmental Prediction/Environmental Modeling Center (NCEP/EMC) 4 km Stage IV and Oregon State University Parameter-Elevation Regressions on Independent Slopes Model (PRISM) data on 3 and 24 h timescales, respectively. Our analysis suggests that the 12 km WRF exhibits biases typically found in other WRF simulations, including those at convection-permitting scales. In particular, WRF simulates both the timing and magnitude of the summer diurnal precipitation peak as well as ERA5 over most of the CONUS, except for a delayed diurnal peak over the Great Plains. As compared to ERA5, both the month and the magnitude of the precipitation peak annual cycle are remarkably improved in the downscaled WRF simulation. WRF slightly overestimates 3 and 24 h precipitation maximum over the CONUS, in contrast to ERA5, which generally underestimates these quantities mainly over the eastern half of the CONUS. Notably, WRF better captures the probability density distribution (PDF) of 3 and 24 h annual and seasonal maximum precipitation. WRF exhibits seasonally dependent precipitation biases across the CONUS, while ERA5's biases are relatively consistent year round over most of the CONUS. These results suggest that dynamical downscaling to a higher resolution improves upon some precipitation metrics but is susceptible to common regional climate model biases. Consequently, if used as input data for domain-specific models, we suggest moderate bias correction be applied to the dynamically downscaled product.

Occupational heat-related illness in Washington State: A descriptive study of day of illness and prior day ambient temperatures among cases and clusters, 2006-2021.

Insufficient heat acclimatization is a risk factor for heat-related illness (HRI) morbidity, particularly during periods of sudden temperature increase. We sought to characterize heat exposure on days before, and days of, occupational HRIs. A total of 1241 Washington State workers' compensation State Fund HRI claims from 2006 to 2021 were linked with modeled parameter-elevation regressions on independent slopes model (PRISM) meteorological data. We determined location-specific maximum temperatures (Tmax,PRISM ) on the day of illness (DOI) and prior days, and whether the Tmax,PRISM was ≥10.0°F (~5.6°C) higher than the average of past 5 days ("sudden increase") for each HRI claim. Claims occurring on days with ≥10 HRI claims ("clusters") were compared with "non-cluster" claims using t tests and χ2 tests. Seventy-six percent of analyzed HRI claims occurred on days with a Tmax,PRISM ≥ 80°F. Claims occurring on "cluster" days, compared to "non-cluster" days, had both a significantly higher mean DOI Tmax,PRISM (99.3°F vs. 85.8°F [37.4°C vs. 29.9°C], t(148) = -18, p < 0.001) and a higher proportion of "sudden increase" claims (80.2% vs. 24.3%, χ2 [1] = 132.9, p < 0.001). Compared to "cluster" days, HRI claims occurring during the 2021 Pacific Northwest "heat dome" had a similar increased trajectory of mean Tmax,PRISM on the days before the DOI, but with higher mean Tmax,PRISM. CONCLUSIONS: Occupational HRI risk assessments should consider both current temperatures and changes in temperatures relative to prior days. Heat prevention programs should include provisions to address acclimatization and, when increases in temperature occur too quickly to allow for sufficient acclimatization, additional precautions.

Calibration and Evaluation of Empirical Methods to Estimate Reference Crop Evapotranspiration in West Texas

Evapotranspiration is an essential component of the hydrologic cycle, and its accurate quantification is crucial for managing crop water requirements and the operation of irrigation systems. Evapotranspiration data is key to hydrological and water management research investigations, including studying the impact of various climatic factors on crop water requirements. It has been estimated as the product of the reference crop evapotranspiration and crop coefficient. Daily reference crop evapotranspiration (ETo) can be determined by several methods and equations. The Food and Agriculture Organization Penman-Monteith equation requires complete weather data, whereas empirical equations such as Hargreaves and Samani, Valiantzas, Priestley-Taylor, Makkink, and Stephens-Stewart require limited weather data. This work evaluated different empirical equations for West Texas using the standard FAO Penman-Monteith method and calibrated their parameters to improve ETo estimation. Detailed meteorological data from West Texas Mesonet and high resolution (800 m) Parameter-elevation Regressions on Independent Slopes Model (PRISM) datasets from 2007 to 2016 were used. Daily ETo calculated using the standard FAO Penman-Monteith equation was compared to ETo estimated based on different empirical methods. The results show that all original empirical equations underestimated ETo. Calibration improved the performance of tested equations; however, there seems to be underestimation of ETo in the 8–16 mm range. Overall, the monthly Hargreaves and Samani equation with either original or calibrated values of its parameters outperformed all tested models. This equation seems to be a reasonable estimator, especially under limited weather data conditions.

Performance evaluation of IMERG and TMPA daily precipitation products over CONUS (2000–2019)

Ground validation of satellite-based precipitation products is necessary to evaluate the performance of satellite observation at local, regional, and global scales. The evaluation of the rainfall data accuracy will reveal both its strengths and weaknesses, thereby determining its suitability for hydrometeorological applications. To reach this goal, this study comprehensively evaluated and characterized the systematic bias in the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals for GPM (IMERG) precipitation product, to be compared with its predecessor Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), based on the wide-spread ground-based observation data, PRISM (Parameter-Elevation Regression on Independent Slopes Model) over the Conterminous United States. The evaluation was conducted for daily datasets from June 2000 to the end of December 2019, in which IMERG and TMPA overlap using statistical error metrics both in space and time. While some statistical measures do not show significant improvement in daily IMERG compared to TMPA, systematic bias is substantially diminished. We detected systematic bias related to the vertical relief for both IMERG and TMPA but with a substantial decrease in the IMERG. The systematic bias in both satellite products distinctively differs in the summer and winter seasons, but, both products showed high performance in summer. According to our findings, the spatiotemporal performance of the IMERG precipitation product varies in different seasons, regions, time scales, and topographic conditions but with superiority over the TMPA daily product.

Ambient temperature and risk of urinary tract infection in California: A time-stratified case-crossover study using electronic health records.

Background:In the United States (US), urinary tract infections (UTI) lead to more than 10 million office visits each year. Temperature and season are potentially important risk factors for UTI, particularly in the context of climate change.Methods:We examined the relationship between ambient temperature and outpatient UTI diagnoses among patients followed from 2015 to 2017 in two California healthcare systems: Kaiser Permanente Southern California (KPSC) and Sutter Health in Northern California. We identified UTI diagnoses in adult patients using diagnostic codes and laboratory records from electronic health records. We abstracted patient age, sex, season of diagnosis, and linked community-level Index of Concentration at the Extremes (ICE-I, a measure of wealth and poverty concentration) based on residential address. Daily county-level average ambient temperature was assembled from the Parameter-elevation Regressions on Independent Slopes Model (PRISM). We implemented distributed lag nonlinear models (DLNM) to assess the association between UTI and lagged daily temperatures. Main analyses were confined to women. In secondary analyses, we stratified by season, healthcare system, and community-level ICE-I.Results:We observed 787,186 UTI cases (89% among women). We observed a threshold association between ambient temperature and UTI among women: an increase in daily temperature from the 5th percentile (6.0 °C) to the mean (16.2 °C) was associated with a 3.2% (95% CI: 2.4, 3.9%) increase in same-day UTI diagnosis rate, whereas an increase from the mean to 95th percentile was associated with no change in UTI risk (0.0%, 95% CI: −0.7, 0.6%). In secondary analyses, we observed the clearest monotonic increase in the rate of UTI diagnosis with higher temperatures in the fall. Associations did not differ meaningfully by healthcare system or community-level ICE-I. Results were robust to alternate model specifications.Discussion:Increasing temperature was related to higher rate of outpatient UTI, particularly in the shoulder seasons (spring, autumn).

The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

Abstract. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (tens of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) – Daily Surface Weather and Climatological Summaries (Daymet), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km-resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using the temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), and sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications for model calibration and watershed management decisions.

Spatiotemporal assessment of potential drivers of salt marsh dieback in the North Inlet-Winyah Bay estuary, South Carolina (1990–2019)

Previous studies in the Gulf of Mexico and Atlantic states have suggested that a suite of possibly abiotic and biotic attributes is responsible for salt marsh dieback, e.g., drought, soil waterlogging, soil chemistry, top-down consumers control, etc. However, there are no conclusive answers in current literature explaining what led to marsh dieback in past decades, especially from the spatiotemporal perspective. Exploring all Landsat-retrieved marsh dieback events in 1990–2019, this research investigates the spatiotemporal relationships between the dieback series and the associated environmental variables in an intertidal marsh in South Carolina (SC). Based on our previous study, a series of marsh dieback events in the past 30 years were identified and dieback pixels in the estuary were extracted. Among these were the most severe marsh dieback events (1991, 1999, 2000, 2002, 2004, and 2013). Daily Evaporative Demand Drought Index (EDDI), daily precipitation data from Parameter Elevation Regressions on Independent Slopes Model (PRISM), and station-based water quality observations (dissolved oxygen, specific conductivity, salinity, turbidity, pH, and temperature) in the estuary were retrieved. Integrated with the proof-by-exhaustion method, statistical analysis showed marsh dieback were highly related to moisture imbalance in a period of 90 days before the dieback events. Respectively, pH for Clambank and Debidue Creek, salinity and turbidity for Thousand Acre were found to be the key water quality variables influencing marsh dieback besides drought. This study cogitates the environmental influence on coastal marsh dieback from a spatiotemporal perspective using a long-term satellite time series analysis. The findings could provide insights into marsh ecological resilience and facilitate coastal ecosystem management.

Assessing the characteristics of recent drought events in South Korea using WRF-Hydro

Drought is a complex and slow-moving disaster that is difficult to monitor and define. This study, therefore, aims to demonstrate the characteristics of recent droughts occurring from 2008 to 2015 over South Korea using a process-based land and hydrologic model, Weather Research and Forecasting-Hydro modeling system (WRF-Hydro). To drive the standalone WRF-Hydro, gridded meteorological data (5 km) were generated using station-based observations and the Parameter-elevation Regressions on Independent Slopes Model (PRISM). The model was calibrated and evaluated using inflow observations at four locations with dams; for 2008–2010 (calibration) and 2011–2015 (evaluation), it demonstrated average R2 values of 0.80 and 0.75, respectively. While Standardized Precipitation Index is used for calculating meteorological drought using precipitation from PRISM, Standardized Soil Moisture Index and Standardized Streamflow Index, at different timescales, are used to calculate agricultural and hydrological droughts, respectively, with WRF-Hydro simulations. The correlation coefficients between SPI and both SSFI and SSMI were calculated to detect their response times. The hydrological and agricultural droughts showed response times 0.5–1 month later than meteorological drought. In 2008–2015, agricultural and hydrological drought events occurred 1.6 times per year on average in South Korea, whereas meteorological droughts occurred 4.3 times per year on average. Agricultural and hydrological droughts lagged behind meteorological droughts by up to 53, 65, and 83 days, when using 1-, 3-, and 6-month SPI, respectively. Moreover, hydrological droughts were less severe than meteorological droughts due to the propagation of drought by attenuation. This study demonstrates that WRF-Hydro can be used to quantitatively determine the different types of drought events and their propagation, which could help policy makers manage drought risks.

Low-Level Groundwater Atrazine in High Atrazine Usage Nebraska Counties: Likely Effects of Excessive Groundwater Abstraction.

Recent studies observed a correlation between estrogen-related cancers and groundwater atrazine in eastern Nebraska counties. However, the mechanisms of human exposure to atrazine are unclear because low groundwater atrazine concentration was observed in counties with high cancer incidence despite having the highest atrazine usage. We studied groundwater atrazine fate in high atrazine usage Nebraska counties. Data were collected from Quality Assessed Agrichemical Contaminant Nebraska Groundwater, Parameter–Elevation Regressions on Independent Slopes Model (PRISM), and water use databases. Descriptive statistics and cluster analysis were performed. Domestic wells (59%) were the predominant well type. Groundwater atrazine was affected by well depth. Clusters consisting of wells with low atrazine were characterized by excessive groundwater abstraction, reduced precipitation, high population, discharge areas, and metropolitan counties. Hence, low groundwater atrazine may be due to excessive groundwater abstraction accompanied by atrazine. Human exposure to atrazine in abstracted groundwater may be higher than the estimated amount in groundwater.

Extreme minimum temperatures in the Great Lakes region of the United States: A climatology with implications for insect mortality

AbstractWinter‐season extreme minimum temperatures may play a major role in limiting population growth and spread of the hemlock woolly adelgid (HWA) (Adelges tsugae Annand) (Hemiptera: Adelgidae), an invasive sap‐feeding insect that has caused extensive mortality of hemlock trees (Tsuga spp.) in many eastern United States (US) forests. This atypical insect feeds throughout the winter but populations can sustain high mortality when winter‐season extreme minimum temperatures drop below −20 to −30°C. Detection of HWA in Michigan during 2015 motivated interest in HWA winter survival in the US Great Lakes region. Here, we used the parameter‐elevation regressions on independent slopes model (PRISM) gridded daily minimum temperature dataset to construct a 1981–2018 climatology of extreme minimum temperatures in the Great Lakes region, the first such effort for this region. Metrics examined include absolute and mean annual extreme minimum temperatures (defined as the lowest daily minimum temperature during the study period and the study‐period mean lowest daily minimum temperature during each calendar year, respectively), and the frequency of daily minimum temperatures below −20 and −30°C. Minimum temperature patterns we identified support the following two hypotheses: first, proximity to water, surface elevation and latitude are the principal controls for extreme minimum temperatures in the Great Lakes region; second, the modifying influence of the relatively warm Lake Michigan serves to protect locations within about 10–25 km of the lakeshore from severe and potentially lethal temperatures for HWA. Analysis of projected minimum temperatures at the end of the 21st century (2080–2099) reveals a range of HWA distribution expansion scenarios. Although the original motivation for this study arose from interest in potential HWA mortality, a climatological study of extreme minimum temperatures has potentially broad relevance to, for example, human health and safety and forest ecology.

Exposure to heat during pregnancy and preterm birth in North Carolina: Main effect and disparities by residential greenness, urbanicity, and socioeconomic status

BackgroundAlthough previous literature suggested that several factors may be associated with higher risk of adverse health outcomes related to heat, research is limited for birth outcomes. ObjectivesWe investigated associations between exposure to heat/heat waves during the last week of gestation and preterm birth (PTB) in North Carolina (NC) and evaluated effect modification by residential greenness, urbanicity, and socioeconomic status (SES). MethodsWe obtained individual-level NC birth certificate data for May–September 2003–2014. We estimated daily mean temperature at each maternal residential address using Parameter-elevation Regressions on Independent Slopes Model (PRISM) data. We created 3 definitions of heat waves (daily temperature ≥95th, 97th, 99th percentile for NC warm season temperature, for ≥2 consecutive days). Normalized Difference Vegetation Index (NDVI) was used to assess residential greenness. Community-level modifiers (e.g., income, urbanicity) were considered. We applied Cox proportional hazard models to estimate the association between exposure to heat/heat waves and PTB, controlling for covariates. Stratified analyses were conducted to evaluate whether the association between heat and PTB varied by several individual and community characteristics. ResultsOf the 546,441 births, 8% were preterm. Heat exposure during the last week before delivery was significantly associated with risk of PTB. The hazard ratio for a 1 °C increase in temperature during the last week before delivery was 1.01 (95% CI: 1.00, 1.02). Higher heat-PTB risk was associated with some characteristics (e.g., areas that were urbanized, low SES, or in the Coastal Plain). We also found significant PTB-heat risk in areas with low greenness for urbanized area. For heat waves, we did not find significantly positive associations with PTB. DiscussionFindings provide evidence that exposure to heat during pregnancy increases risk of PTB and suggest disparities in these risks. Our results have implications for future studies of disparity in heat and birth outcomes associations.

Challenges in Observation-Based Mapping of Daily Precipitation across the Conterminous United States

AbstractThere is a great need for gridded daily precipitation datasets to support a wide variety of disciplines in science and industry. Production of such datasets faces many challenges, from station data ingest to gridded dataset distribution. The quality of the dataset is directly related to its information content, and each step in the production process provides an opportunity to maximize that content. The first opportunity is maximizing station density from a variety of sources and assuring high quality through intensive screening, including manual review. To accommodate varying data latency times, the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) Climate Group releases eight versions of a day’s precipitation grid, from 24 h after day’s end to 6 months of elapsed time. The second opportunity is to distribute the station data to a grid using methods that add information and minimize the smoothing effect of interpolation. We use two competing methods, one that utilizes the information in long-term precipitation climatologies, and the other using weather radar return patterns. Last, maintaining consistency among different time scales (monthly vs daily) affords the opportunity to exploit information available at each scale. Maintaining temporal consistency over longer time scales is at cross purposes with maximizing information content. We therefore produce two datasets, one that maximizes data sources and a second that includes only networks with long-term stations and no radar (a short-term data source). Further work is under way to improve station metadata, refine interpolation methods by producing climatologies targeted to specific storm conditions, and employ higher-resolution radar products.

The impact of rainfall distribution methods on streamflow throughout multiple elevations in the Rocky Mountains using the APEX model-Price River watershed, Utah.

The hydrology of mountainous watersheds in the western United States is significantly influenced by snow year-round. It is widely known that topography affects precipitation; however, the knowledge of how watershed rainfall designation methods affect streamflow is not well understood for high-relief areas. The objectives of this study were to assess the predictive capability of the Agricultural Policy/Environmental eXtender (APEX) model to simulate streamflow in a snowmelt-dominated watershed with high spatial rainfall variability through (a) allocating weather stations to sub-basins based on a conventional Thiessen polygon method (CM) or a rainfall-elevation-based input (RE) and using an areal average Parameter-Elevation Regression on Independent Slopes Model (PRISM) rainfall designation and (b) improving the snowmelt processes in the Price River watershed, Utah. The updated APEX model with snowmelt parameters significantly improved spring flood simulation. The RE was the most robust method in snowmelt and seasonal streamflow simulations compared with the CM and PRISM rainfall designations. Adapting the APEX model to simulate snow-dominant complex terrains will provide crucial water quantity and quality predictions for reliable environmental and watershed management assessment.

Evaluation of Subseasonal-to-Seasonal (S2S) precipitation forecast from the North American Multi-Model ensemble phase II (NMME-2) over the contiguous U.S.

The second phase of the North America Multi-Model Ensemble (NMME-2) provides globally available Subseasonal-to-Seasonal (S2S) precipitation forecasts with a daily resolution. The S2S precipitation forecasts are getting increasing attention for their potentials in providing hydrometeorological forcing information for water resources planning at an extended range. However, the forecast skills of many existing S2S forecast products will significantly decrease when the lead time increases, hindering their applicability for watershed-scale hydrologic modeling. Therefore, forecast validation and large-scale evaluation are of great importance for water resources planning and hydrological applications. In this study, we comprehensively evaluate the S2S precipitation forecasts from the NMME-2 dataset over the contiguous United States (CONUS) and during the study period from 1982 to 2011. Three aspects of precipitation forecast capabilities are compared and analyzed: bias, skill scores, and the ability to predict extreme precipitation events. The Parameter-elevation Regressions on Independent Slopes Model (PRISM) is used as ground truth reference. Differs from other regional forecast validation study, we further examined and analyzed the dependences of NMME-2 precipitation forecast skills according to different seasonality, geographical locations, and lead times. Results show that the forecast biases are not sensitive to lead times but are seasonally dependent of all NMME-2 models. Overestimations are found in the Western U.S. in cooler seasons while underestimations are observed in the central regions of the U.S. in warmer seasons. The forecast skill of all individual NMME-2 models generally decreases as increases of lead times. The simple model averaging (SMA) of five NMME-2 models demonstrates a higher forecast skill than any individual NMME-2 models. Spatially, the highest forecast skill scores are observed at coastal areas in the Western U.S. with an one-week lead time. As compared to the historical resampled forecasts, NMME-2 also shows better performance in predicting extreme precipitation events above 99% percentiles and below 1% percentiles with higher probability of detections and lower false alarm ratios. The obtained results suggest the great potentials of NMME-2 precipitation forecasts in assisting ensemble hydrologic forecasts at the S2S scale over the CONUS.