North American Land Data Assimilation System Research Articles

Assessing the Effect of Precipitation on Asthma Emergency Department Visits in New York State From 2005 to 2014: A Case-Crossover Study.

The Earth's precipitation patterns are changing, and regional precipitation is expected to continue to increase in New York State (NYS). Heavy precipitation may negatively affect asthma prevalence through its effect on seasonally varying allergens. We employed a threshold analysis using a time-stratified semi-symmetric bi-directional case-crossover study design to assess the effect of increase in precipitation on asthma (ICD-9 code 493.xx, N=970,903) emergency department (ED) visits between 2005 and 2014 during non-winter months in NYS. Spatially contiguous gridded meteorological data from North American Land Data Assimilation System (NLDAS) were utilized. We used conditional logistic regression models and stratified the analyses by seasons. During non-winter months, we found a small, statistically significant risk of asthma ED visits for precipitation levels above 50mm, with differences by season. These results suggest that heavy precipitation may be related to an increased risk of asthma ED visits. Gridded meteorological estimates provide a means of addressing the gaps in exposure classification, and these findings provide opportunities for further research on interactions with aeroallergens and meteorological conditions in the context of climate and health.

Higher Frozen Soil Permeability Represented in a Hydrological Model Improves Spring Streamflow Prediction From River Basin to Continental Scales

AbstractDespite plentiful evidence of frozen ground effects on snowmelt infiltration from lab experiments at pedon scales, streamflow observations show a weaker or no effect in terms of timing and magnitude at larger scales. We aim to understand this conflicting phenomenon through modeling using the Noah land surface model with multi‐physics (MP; Noah‐MP) options and the Routing Application for Parallel computatIon of Discharge (RAPID) over the Mississippi River Basin. We conduct 16 experiments with combinations of two supercooled liquid water (SLW) parameterization schemes and four soil hydraulic property schemes in Noah‐MP driven by two gridded precipitation products of the North American Land Data Assimilation System (NLDAS) and the Integrated Multi‐satellitE Retrievals for GPM (IMERG) Final. We then use RAPID to route Noah‐MP modeled surface runoff and groundwater discharge to predict daily streamflow at 52 United States Geological Survey gauges from 2015 to 2019. A model with the highest permeability performs better than other schemes on daily streamflow predictions by 20%–57% throughout a water year and 29%–113% for the spring as measured by the mean Kling‐Gupta Efficiency of the 52 gauges. Different SLW schemes demonstrate negligible effects on streamflow predictions. Models forced by IMERG show a better prediction skill compared with those forced by NLDAS at most of the gauges. Both precipitation products confirm that a scheme of higher permeability yields more accurate streamflow predictions over frozen ground. Future models should represent preferential flows through macropore networks to improve the understanding of frozen soil effects on infiltration and discharge across scales.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability.

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Machine learning based estimation of field-scale daily, high resolution, multi-depth soil moisture for the Western and Midwestern United States.

High-resolution soil moisture estimates are critical for planning water management and assessing environmental quality. In-situ measurements alone are too costly to support the spatial and temporal resolutions needed for water management. Recent efforts have combined calibration data with machine learning algorithms to fill the gap where high resolution moisture estimates are lacking at the field scale. This study aimed to provide calibrated soil moisture models and methodology for generating gridded estimates of soil moisture at multiple depths, according to user-defined temporal periods, spatial resolution and extent. We applied nearly one million national library soil moisture records from over 100 sites, spanning the U.S. Midwest and West, to build Quantile Random Forest (QRF) calibration models. The QRF models were built on covariates including soil moisture estimates from North American Land Data Assimilation System (NLDAS), soil properties, climate variables, digital elevation models, and remote sensing-derived indices. We also explored an alternative approach that adopted a regionalized calibration dataset for the Western U.S. The broad-scale QRF models were independently validated according to sampling depths, land cover type, and observation period. We then explored the model performance improved with local samples used for spiking. Finally, the QRF models were applied to estimate soil moisture at the field scale where evaluation was carried out to check estimated temporal and spatial patterns. The broad-scale QRF model showed moderate performance (R2 = 0.53, RMSE = 0.078 m3/m3) when data points from all depth layers (up to 100 cm) were considered for an independent validation. Elevation, NLDAS-derived moisture, soil properties, and sampling depth were ranked as the most important covariates. The best model performance was observed for forest and pasture sites (R2 > 0.5; RMSE < 0.09 m3/m3), followed by grassland and cropland (R2 > 0.4; RMSE < 0.11 m3/m3). Model performance decreased with sampling depths and was slightly lower during the winter months. Spiking the national QRF model with local samples improved model performance by reducing the RMSE to less than 0.05 m3/m3 for grassland sites. At the field scale, model estimates illustrated more accurate temporal trends for surface than subsurface soil layers. Model estimated spatial patterns need to be further improved and validated with management data. The model accuracy for top 0-20 cm soil depth (R2 > 0.5, RMSE < 0.08 m3/m3) showed promise for adopting the methodology for soil moisture monitoring. The success of spiking the national model with local samples showed the need to collect multi-year high frequency (e.g., hourly) sensor-based field measurements to improve estimates of soil moisture for a longer time period. Future work should improve model performance for deeper depths with additional hydraulic properties and use of locally-selected calibration datasets.

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.

How does precipitation data influence the land surface data assimilation for drought monitoring?

Droughts are among the costliest natural hazards that occur annually worldwide. Their socioeconomic impacts are significant and widespread, affecting the sustainable development of human societies. This study investigates the influence of different forcing precipitation data in driving Land Surface Models (LSMs) and characterizing drought conditions. Here, we utilize our recently developed LSM data assimilation system for probabilistically monitoring drought over the Contiguous United States (CONUS). The Noah-MP LSM model is forced with two widely used precipitation data including IMERG (Integrated Multi-satellitE Retrievals for GPM) and NLDAS (North American Land Data Assimilation System). Soil moisture and evapotranspiration are known to have a strong relationship in the land-atmospheric interaction processes. Unlike other studies that attempted the individual assimilation of these variables, here we propose a multivariate data assimilation framework. Therefore, in both modeling scenarios, the data assimilation approach is used to integrate remotely sensed MODIS (Moderate Resolution Imaging Spectroradiometer) evapotranspiration and SMAP (Soil Moisture Active Passive) soil moisture observations into the Noah-MP LSM. The results of this study indicate that the source of precipitation data has a significant impact on the performance of LSM data assimilation system for drought monitoring. The findings revealed that NLDAS and IMERG precipitation can result in a significant difference in identifying drought severity depending on the region and time of the year. Furthermore, our analysis indicates that regardless of the precipitation forcing data product used in the land surface data assimilation system, our modeling framework can effectively detect the drought impacts on crop yield. Additionally, we calculated the drought probability based on the ensemble of soil moisture percentiles and found that there exist temporal and spatial discrepancies in drought probability maps generated from the NLDAS and IMERG precipitation forcings.

Hydrological drought characterization based on GNSS imaging of vertical crustal deformation across the contiguous United States

Continuous Global Navigation Satellite System (GNSS) measurements allow us to track subtle elastic crustal deformation in the response to hydrological mass variations and provide an additional tool to independently characterize hydrological extremes (e.g., droughts and floods). In this study, we develop a time-varying GNSS imaging strategy that depends on the principal component analysis of GNSS-sensed vertical crustal displacement (VCD) in 2006–2020 and the monthly images of hydrology-induced deformation are generated for drought characterization across the contiguous United States. The first 12 principal components are selected in our time-varying imaging system, which account for 85% of the data variance. Considering that surface water loads are inversely correlated with the induced elastic vertical motions, we reverse the signs of the GNSS-imaged time series in all grids in subsequent studies (referred to as negative VCD (NVCD)). The GNSS-NVCD data generally correlate well with the water estimates from the Gravity Recovery and Climate Experiment (GRACE) and North American Land Data Assimilation System (NLDAS). Using the GNSS-imaged gridded NVCD products, we produce a GNSS-based drought severity index (GNSS-DSI) based on the climatological methodology, which is implemented by standardizing the GNSS NVCD anomalies that deviate from climatological normal. In most regions, strong linear correlations are accessible for GNSS-DSI relative to GRACE-DSI and the self-calibrating Palmer Drought Severity Index (scPDSI). The new drought monitoring tool, which is based solely on GNSS-measured vertical positions, is used for hydrological drought characterization (onset, end, duration, magnitude, intensity, and recovery); it succeeds in identifying well-documented historical droughts from the US drought monitor (USDM). Our study presents a new drought characterization framework using solely GNSS-measured hydrological loading displacements from a dense GNSS network, which has great potential to strengthen operational drought monitoring and assessment.

Ensemble stationary-based support vector regression for drought prediction under changing climate

Non-stationarity due to climate change and/or variability can reduce the capabilities of drought prediction models. The objective of this study is to improve drought prediction by eliminating non-stationarity from temperature time series, a key factor in development and propagation of droughts in a changing climate. In order to relax the assumption of stationarity, an ensemble stationary-based support vector regression (ESSVR) method was developed and compared with the traditional support vector regression (SVR). Three types of drought indices in three time scales (monthly, seasonal, and semiannual), including multivariate, bivariate standardized drought indices, and univariate standardized drought indices were used as the target variables. In an application to the Red River of the North Basin (RRB), the North American Land Data Assimilation System (NLDAS) data from 1979 to 2016 were used for the training and testing of the prediction model. The Pearson correlation, root mean square error (RMSE), and Taylor diagram were used to evaluate the performances of the ESSVR. Remarkably, the distribution of identified change points varies by climate divisions. The results of the SVR and ESSVR in the RRB were compared, demonstrating the better performances of the ESSVR for most of the drought indices, particularly those with higher sensitivity to temperature. It was found that the extreme (high and low) values of hyperparameters mostly assigned by SVR cause a higher risk of overfitting for SVR. In contrast, ESSVR improves the drought prediction by removing the non-stationarity, thus providing more accurate drought predictions, especially for a warming climate.

Ensemble Skill Gains Obtained From the Multi‐Physics Versus Multi‐Model Approaches for Continental‐Scale Hydrological Simulations

AbstractMulti‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense sampling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.

Blending Noah, SMOS and In-Situ Soil Moisture Using Multiple Weighting and Sampling Schemes

AbstractSoil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates the importance of in-situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in-situ soil moisture data to generate an accurate and spatially-continuous soil moisture product at 4-km resolution. Four different datasets are used: Antecedent Precipitation Index (API), KAPI, which incorporates in-situ soil moisture observations with the API using regression kriging, SMOS L3 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least square weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density and data formats (absolute, anomalies and percentiles) are also investigated. The results reveal KAPI outperforms API. This indicates that incorporating in-situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p&gt;0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide sub-optimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies or percentiles).

Seasonal hydrological loading in the Great Lakes region detected by GNSS: a comparison with hydrological models

SUMMARY Crustal deformation caused by hydrological processes has long been detected using space geodetic techniques, yet questions remain about the relative contributions of surface water and groundwater to the geodetic signals in different regions. Here, we investigate forward models of elastic loading deformation caused by a variety of water-storage changes within the Great Lakes region, including fluctuations in lake-water volume, soil moisture and snow load. We use lake-level data from the Great Lakes Environmental Research Laboratory, soil-moisture content from the North American Land Data Assimilation System (NLDAS), snow load from the Snow Data Assimilation System (SNODAS) and background hydrological load at the global scale from Gravity Recovery and Climate Experiment (GRACE). We compare the modelled surface deformation with estimates of hydrological loading deformation inferred from Global Navigation Satellite System (GNSS) measurements. We find that seasonal deformation measured by GNSS is dominated by regional-scale hydrological loading based on strong correlations with the modelled loading displacements. The mean correlation coefficient for the study network is 0.56. The correlation coefficients vary spatially within the study region and exceed 0.9 at some stations near to the Great Lakes. We assess the relative contribution of each individual hydrological component to the total integrated hydrological load. We find that soil moisture consistently explains the largest percentage (27–69 per cent) of the total vertical loading deformation for 87 per cent of GNSS stations in the Great Lakes region. Snow loading and soil moisture contribute relatively equally in the northern reaches of the study area (e.g. Canadian shield, northern Superior basin). Lake loading accounts for about 10–25 per cent of the total loading signal in the immediate vicinity of the lakes. We also investigate the sensitivities of the surface loading displacements to three different Earth models, including two with lateral variations in structure. The structural variations considered here have limited impact (&amp;lt;0.2 mm) on the predicted hydrological loading displacements and could be neglected at the current level of observational precision.

Modification of the Cardiovascular Effects of Temperature by Neighborhood Socio-Economic Deprivation

Background: Ambient daily temperature, both elevated and low, has been associated with daily cardiovascular mortality; studies have consistently observed larger associations for neighborhoods of higher socio-economic deprivation (NSED). However, no study has assessed whether NSED modifies the timing of this effect. We investigated how NSED modifies the association between daily temperature and risk of myocardial infarction (MI) in New York City (NYC).Methods: We used 2000-2015 MI hospitalization and residence data from the Statewide Planning and Research Cooperative System (SPARCS) dataset of the New York Department of Health. Temperature and relative humidity came from the North American Land Data Assimilation System (NLDAS) at 11x14 km2 resolution. We aggregated data to 42 neighborhoods defined by NYC Department of Health. We estimated NSED with a Neighborhood Deprivation Index (NDI), from principal components analysis of US Census variables. We employed a time series design with a distributed lag nonlinear model for 14 days lag of temperature. We controlled for seasonality, long-term trends, relative humidity, NDI, and random effects for neighborhood. We then stratified by NDI quartile.Results: We observed 329,457 MI hospitalizations over the study period. Same-day high temperature was nonlinearly associated with MI; an increase from mean temperature (12.3oC) to the 95th percentile (28oC) was associated with a 7.0% increase (95%CI: 3.7, 10.4%) in MI hospitalization rate. The second quartile of NDI showed the largest association (8.6%; 95%CI: 5.2, 12.0%); the least deprived quartile had the smallest association (6.0%; 95%CI: 2.6, 9.4%). For all subpopulations, only same-day temperature was statistically significant.Conclusion: Our findings suggest that same-day temperature is associated with daily MI rates, across levels of NDI, and this was strongest in the second-most-deprived neighborhoods. The timing of the association was consistent across quartiles of NDI. Further research is needed to elucidate factors that provide resilience to heat exposure.

Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system.

Methods for Estimating Wet Bulb Globe Temperature From Remote and Low-Cost Data: A Comparative Study in Central Alabama.

Heat stress is a significant health concern that can lead to illness, injury, and mortality. The wet bulb globe temperature (WBGT) index is one method for monitoring environmental heat risk. Generally, WBGT is estimated using a heat stress monitor that includes sensors capable of measuring ambient, wet bulb, and black globe temperature, and these measurements are combined to calculate WBGT. However, this method can be expensive, time consuming, and requires careful attention to ensure accurate and repeatable data. Therefore, researchers have attempted to use standard meteorological measurements, using single data sources as an input (e.g., weather stations) to calculate WBGT. Building on these efforts, we apply data from a variety of sources to calculate WBGT, understand the accuracy of our estimated equation, and compare the performance of different sources of input data. To do this, WBGT measurements were collected from Kestrel 5400 Heat Stress Trackers installed in three locations in Alabama. Data were also drawn from local weather stations, North American Land Data Assimilation System (NLDAS), and low cost iButton hygrometers. We applied previously published equations for estimating natural wet bulb temperature, globe temperature, and WBGT to these diverse data sources. Correlation results showed that WBGT estimates derived from all proxy data sources—weather station, weather station/iButton, NLDAS, NLDAS/iButton—were statistically indistinguishable from each other, or from the Kestrel measurements, at two of the three sites. However, at the same two sites, the addition of iButtons significantly reduced root mean square error and bias compared to other methods.

Spring discharge and thermal regime of a groundwater dependent ecosystem in an arid karst environment

In semi-arid regions, groundwater-dependent ecosystems rely on stable hydro-thermal regimes where refugia have supported aquatic biota for millennia. In karst systems, springs provide consistent flows and stenothermal conditions that buffer extremes. Our objective was to assess the impacts of spring discharge on instream temperatures, using the pristine Devils River in Texas as a case study, where climate change and groundwater development threaten to reduce spring flows and aquatic habitats of protected species (Devils River minnow and Texas hornshell mussel). Instream temperatures and discharge were monitored for three years above and below the Finegan Spring complex. These time series were extended back 30 years using temperature data from North American Land Data Assimilation System (NLDAS) land surface models. Monitoring data revealed that springs contributed ~40% of total river discharge. Spring temperatures were consistently 22.6 ± 0.3 °C providing thermal refugia to 200 m of river—cooling the streamflow in summer and warming it in winter, with a noted stratification overturning each winter. High correlations between NLDAS air and soil temperatures and instream temperatures allowed the water temperature record to be extended over 30 years. While air and soil temperatures increased 0.35 °C and 0.30 °C per decade, spring inputs from karst aquifer buffer downstream temperature increases to 0.12 °C per decade. Furthermore, spring discharge reduced the duration of extreme thermal habitat thresholds by 50–70%. A similar approach could be applied to other groundwater dependent ecosystems with sparse temperature data. The results underscore the importance of spring discharge in maintaining heterogeneous aquatic habitats in karst terrain.

Cascading Dynamics of the Hydrologic Cycle in California Explored through Observations and Model Simulations

As drought occurs in a region it can have cascading effects through the water cycle. In this study, we explore the temporal co-evolution of various components of the hydrologic cycle in California from 2002 to 2018. We combine information from the Gravity Recovery and Climate Experiment (GRACE) satellites, the North American Land Data Assimilation System (NLDAS) suite of models, and the California Department of Water Resources (DWR) reservoir levels to analyze dynamics of Total Water Storage (TWS), soil moisture, snow pack, large reservoir storage, and ultimately, groundwater. For TWS, a trend of −2 cm/yr is observed during the entire time period of our analysis; however, this rate increases to about −5 cm/yr during drought periods (2006−2010 and 2012−2016). Results indicate that the majority of the loss in TWS is caused by groundwater depletion. Using proper error accounting, we are able to identify the start, the peak, and the ending of the drought periods for each individual water state variable in the study domain. We show that snow and soil moisture are impacted earlier and recover faster than surface water and groundwater. The annual and year-to-year dynamics shown in our results portray a clear cascading effect of the hydrologic cycle on the scale of 8−16 months.

Restricted water allocations: Landscape-scale energy balance simulations and adjustments in agricultural water applications

Research that incorporates information from satellites into conventional biophysical models has great importance and interest. Comprehensive crop water algorithms can help track crop stress, schedule irrigation, and acquire water right information for effective water management and increased productivity in semi-arid and arid environments. Overall objective was to utilize the automated biophysical surface energy balance model BAITSSS (Backward‐Averaged Iterative Two‐Source Surface temperature and energy balance Solution) to understand critical agricultural water management issues. BAITSSS served as an advanced digital landscape crop water tracker and irrigation scheduler to simulate hourly landscape evapotranspiration (ET) at 30 m spatial resolution. North American Land Data Assimilation System (NLDAS) weather data and Landsat-based vegetation indices were inputs of BAITSSS to simulate surface energy balance components along with irrigation (Irr). Two agricultural-dominated groundwater regions of northwest Kansas, USA within a section of the Ogallala aquifer were studied during a five-year period (2013–2017). We compared model-simulated irrigation to reported within water right management units (WRMU). The sum of reported irrigation and precipitation (P), representing in-season water supply, was also compared to model simulated ET as an indicator of well-watered ET. The model was able to simulate reasonably ET values, and irrigation quantities, and to differentiate various spatial distribution patterns of crops within WRMU. However, unknown water management, within WRMU, constrained explicit inference of actual ET and irrigation amounts. The model appears suitable for quantifying the upper bound of in-season water supply (irrigation plus P) expected for well-watered crops in the U.S. Central High Plains. A WRMU exhibiting significantly different in-season water supply than the simulated ET may present opportunities to modify irrigation rates or to gain inference about deficit irrigation.

The Role of Hourly Temperature in Risk of Myocardial Infarction

TPS 664: Climate change: temperature effects 2, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background: Temperature, both elevated and low, has been associated with daily cardiovascular mortality. Most epidemiological studies have measured temperature at a daily (mean, max, min) scale, and much remains unknown about the impacts of temperature extremes at the hourly scale. To comprehensively address this question, we investigated the association between hourly temperature exposure and cardiovascular health. Methods: We used myocardial infarction (MI) hospitalization and residential address data from the Statewide Planning and Research Cooperative System (SPARCS) dataset of the New York Department of Health for 2000-2015 over New York State. We assumed a 2 hour delay between symptom onset and hospitalization. We linked events to hourly temperature and relative humidity from the North American Land Data Assimilation System (NLDAS) at a 11x14 km2 resolution. We employed a time-stratified case-crossover design—matched by month, day of the week, and hour —coupled with distributed lag models to examine which temperature exposure window(s) were most strongly associated with MI risk. We assessed exposure during the 48 hours prior to event. Hourly temperature and relative humidity were modeled as nonlinear terms to estimate the potentially nonlinear exposure-response relationship at each lag, using natural splines with 4 degrees of freedom. We used natural splines with 6 degrees of freedom to model the lag constraints. Results: We observed 845,192 MI hospitalizations over the study period. Higher temperatures were nonlinearly associated with MI risk; we found no association for lower temperatures, but an increasing response above ~13 oC. This association was only statistically significant in the 7 hours preceding the event, and null for hours 8 – 48 prior to event. Conclusion: Our findings suggest that temperature exposure has a very short-term association with MI risk. Studies that focus on daily temperatures, thus, likely provide attenuated effect estimates.

Predicted Northward Expansion of the Geographic Range of the Tick Vector Amblyomma americanum in North America under Future Climate Conditions.

Background:The geographic range of the tick Amblyomma americanum, a vector of diseases of public health significance such as ehrlichiosis, has expanded from the southeast of the United States northward during the 20th century. Recently, populations of this tick have been reported to be present close to the Canadian border in Michigan and New York states, but established populations are not known in Canada. Previous research suggests that changing temperature patterns with climate change may influence tick life cycles and permit northward range expansion of ticks in the northern hemisphere.Objectives:We aimed to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range and to investigate the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades.Methods:A simulation model of the tick A. americanum was used, via simulations using climate data from meteorological stations in the United States and Canada, to estimate minimal temperature conditions for survival of A. americanum populations at the northern edge of the tick’s range.Results:The predicted geographic scope of temperature suitability [ annual cumulative degree days (DD) ] included most of the central and eastern U.S. states east of longitude 110°W, which is consistent with current surveillance data for the presence of the tick in this region, as well as parts of southern Quebec and Ontario in Canada. Regional climate model output raises the possibility of northward range expansion into all provinces of Canada from Alberta to Newfoundland and Labrador during the coming decades, with the greatest northward range expansion (up to by the year 2100) occurring under the greenhouse gas (GHG) emissions of Representative Concentration Pathway (RCP) 8.5. Predicted northward range expansion was reduced by approximately half under the reduced GHG emissions of RCP4.5.Discussion:Our results raise the possibility of range expansion of A. americanum into northern U.S. states and southern Canada in the coming decades, and conclude that surveillance for this tick, and the diseases it transmits, would be prudent. https://doi.org/10.1289/EHP5668

Investigating the Relationship Between Satellite-Based Freeze/Thaw Products and Land Surface Temperature

This paper investigates surface temperature variables as they relate to passive microwave-derived surface freeze/thaw (FT) state and assesses the accuracy of such FT products relative to surface temperature. Utilizing retrievals from the soil moisture active/passive (SMAP), advanced microwave scanning radiometer, and special sensor microwave imager instruments, surface FT records have previously been derived globally. Moderate Resolution Imaging Spectroradiometer skin temperature, North American Land Data Assimilation System (NLDAS) skin, 0–10-cm soil layer, and 2-m air temperatures are compared to the various FT state products (FTSPs) by defining the threshold for FT state transitions at 0 °C. This paper utilizes the 2015–2016 overlap period in FT records within the NLDAS domain. Spatial variability of classification accuracy (CA) is then investigated over the study area. A proportional differencing method also enables the identification of biases between FTSPs and surface temperature variables. Additionally, by analyzing probability distribution functions of FTSPs associated with temperature values, we assess the distribution of temperature variables as they relate to FT classifications. Classification agreement is shown to vary with sensor configuration, seasonality, and retrieval time. Air temperature is found to have the highest CAs across FTSPs (81%–91%), while NLDAS soil exhibits a close relationship to FTSPs, especially in regard to SMAP products. Finally, ascending ( p.m. ) retrievals are shown to be increasingly linked to the selected temperature parameters as compared to descending ( a.m. ) observations. This paper contributes to an improved understanding of current FTSPs and will benefit efforts to enhance future FT products.