California Irrigation Management Information System Research Articles

Development and evaluation of temperature-based deep learning models to estimate reference evapotranspiration

Efficient irrigation management of urban landscapes is critical in arid/semi-arid environments and depends on the reliable estimation of reference evapotranspiration (ETo). However, the available measured climatic data in urban areas are typically insufficient to use the standard Penman-Monteith for ETo estimation. Therefore, smart landscape irrigation controllers often use temperature-based ETo models for autonomous irrigation scheduling. This study focuses on developing deep learning temperature-based ETo models and comparing their performance with widely used empirical temperature-based models, including FAO Blaney & Criddle (BC), and Hargreaves & Samani (HS). We also developed a simple free and easy-to-access tool called DeepET for ETo estimation using the best-performing deep learning models developed in this study. Four artificial neural network (ANN) models were developed using raw weather data as inputs and the reconstructed signal obtained from the wavelet transform as inputs. In addition, long short-term memory (LSTM) recurrent neural network (NN) and one-dimensional convolution neural network (CNN) models were developed. A total of 101 active California Irrigation Management Information System (CIMIS) weather stations were selected for this study, with >725,000 data points expanding from 1985 to 2019. The performance of the models was evaluated against the standard CIMIS ETo. When evaluated at the independent sites, the temperature-based DL (Deep Learning) models showed 15–20% lower mean absolute error values than the calibrated HS model. No improvement in the performance of the ANN models was observed using reconstructed signals obtained from the wavelet transform. Our study suggests that DL models offer a promising alternative for more accurate estimations of ETo in urban areas using only temperature as input. The DeepET can be accessed from the Haghverdi Water Management Group website: http://www. ucrwater.com/software-and-tools.html.

High-resolution weather network reveals a high spatial variability in air temperature in the Central valley of California with implications for crop and pest management.

Weather is the most important driver of crop development. However, spatial variability in weather makes it hard to obtain reliable high resolution datasets across large areas. Most growers rely on data from a single station that can be up to 50km away to make decisions about irrigation, pest management and penology-associated cultural practices at the block level. In this regard, we hypothesize that kriging a large network of weather stations can improve thermal time data quality compared to using the closest station. This study aims to explore the spatial variability in California’s Central Valley and what is the relationship between the density of weather stations used and the error in the measurement of temperature related metrics and derived models. For this purpose, we used temperature records from January 1st 2020 to March 1st 2021 collected by the California Irrigation Management Information System (CIMIS) and a system of 731 weather stations placed above the canopy of trees in commercial orchards (in-orchard). We observed large discrepancies (>300 GDDTb0) in thermal time accumulation between using an interpolation of all stations available and just using the closest CIMIS station. Our data suggests these differences are not systematic bias but true differences in mesoclimate. Similar results were observed for chill accumulation in areas especially prone to not meeting pistachio chill requirements where the discrepancies between using the site-specific in-orchard weather station network and not using them were up to 10 CP. The use of this high resolution network of weather stations revealed spatial patterns in grape, almond, pistachio and pests phenology not reported before. Whereas previous studies have been focused on predictions at the county or state or regional level, our data suggests that a finer resolution can result in major improvements in the quality of data crucial for crop decision making.

An automated machine learning methodology for the improved prediction of reference evapotranspiration using minimal input parameters

AbstractAn accurate and minimal meteorological parameter‐based reference evapotranspiration (ETo) computational model is highly essential to design a cost‐effective and automated solution for precise water allocation in various agricultural and hydrological applications. Automated machine learning (AutoML), a substantially progressive machine learning (ML) paradigm, expedites the data science applications in these domains by automating and alleviating the tedious tasks of developing ML pipelines, particularly in non‐linear hydrological process modelling. This paper proposes an AutoML solution for daily EToprediction in a limited input parameter scenario, for the first time in EToestimation research. Two AutoML frameworks, AutoGluon‐Tabular (AGT) and H2O AutoML were implemented using daily meteorological data of a humid tropical climatic region of Kerala, India and evaluated its performance with the radiation‐based empirical methods and traditional ML methods. The developed AutoML models imitated the input parameter combinations of radiation‐based empirical models such as Priestley‐Taylor, Makkink, Turc and Ritchie, with the Penman‐Monteith EToserving as the target for modelling process. An improvement of mean absolute error from 0.119 to 0.078 mm/day, mean squared error from 0.035 to 0.017 mm/day, root mean squared error from 0.186 to 0.129 mm/day, root mean squared logarithmic error from 0.078 to 0.026 mm/day, and coefficient of determination (R2) from 0.971 to 0.985 observed in the AutoML model, AGT relative to the empirical model, Turc describes its enhanced performance capability. For demonstrating the superiority of AutoML to traditional ML techniques, another set of AutoML models were also developed based on California Irrigation Management Information System datasets and evaluated them with existing study results in literature. The overall result analysis demonstrated the superiority of AGT model in EToprediction in all the weather stations considered in this study. The results of the study confirm that AutoML techniques can be applied to any hydrological dataset and provide an effective automated solution for hydrological process modelling.

Crop coefficients and water use of young almond orchards

An observational study was conducted in the northern Sacramento Valley in California, United States to determine crop water use and crop coefficients of three adjacent young Nonpareil/Monterey almond orchards. Methods used to quantify evapotranspiration estimates of crop water use include (1) a soil water balance and (2) a land surface energy balance using eddy covariance. Three adjacent almond orchards that were planted in 2016, 2017, and 2018 were monitored from 2018 to 2020. Actual crop coefficients were determined using actual evapotranspiration estimates from each orchard and short grass reference evapotranspiration from the Gerber South California Irrigation Management Information System station. Results showed that crop water use and crop coefficients increased until the 4th year, indicating the need to closely consider tree development and orchard age as factors in irrigation scheduling of young almond trees. The results led to the conclusion that farmers should use development or age-specific crop coefficients in developing orchards for irrigation scheduling until the 4th year when they can start using mature almond crop coefficients. The mid-season actual crop coefficients were 0.35, 0.55, 0.88, 1.04, and 0.99 for 1-, 2-, 3-, 4-, and 5-year-old almond orchards. This study has generated baseline data on crop water requirements of young almond orchards that could be useful for (1) developing irrigation scheduling tools for young almond orchards, and (2) determining water budgets for areas with new almond orchards.

Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ET-Based Smart Controller

Research-based information regarding the accuracy and reliability of smart irrigation controllers for autonomous landscape irrigation water conservation is limited in central California. A two-year irrigation research trial (2018–2019) was conducted in Parlier, California, to study the response of hybrid bermudagrass and tall fescue to varying irrigation scenarios (irrigation levels and irrigation frequency) autonomously applied using a Weathermatic ET-based smart controller. The response of turfgrass species to the irrigation treatments was visually assessed and rated. In addition, turfgrass water response functions (TWRFs) were developed to estimate the impact of irrigation scenarios on the turfgrass species based on long-term mean reference evapotranspiration (ETo) data. The Weathermatic controller overestimated ETo between 5% and 7% in 2018 and between 5% and 8% in 2019 compared with California Irrigation Management Information System values. The controller closely followed programmed watering-days restrictions across treatments in 2018 and 2019 and adjusted the watering-days based on ETo demand when no restriction was applied. The low half distribution uniformity and precipitation rate of the irrigation system were 0.78 and 28 mm h−1, respectively. The catch-cans method substantially underestimated the precipitation rate of the irrigation system and caused over-irrigation by the smart controller. No water-saving and turfgrass quality improvement was observed owing to restricting irrigation frequency (watering days). For the hybrid bermudagrass, the visual rating (VR) for 101% ETo treatment stayed above the minimum acceptable value of six during the trial. For tall fescue, the 108% ETo level with 3 d wk−1 frequency kept the VR values in the acceptable range in 2018 except for a short period in mid-trial. The TWRF provided a good fit to experimental data with r values of 0.79 and 0.75 for tall fescue and hybrid bermudagrass, respectively. The estimated VR values by TWRF suggested 70–80% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in central California during the high water demand months (i.e., May to August) based on long-term mean ETo data. The TWRF estimations suggest that 100% ETo would be sufficient to maintain the tall fescue quality for only 55 days. This might be an overestimation impacted by the relatively small tall fescue VR data in 2019 owing to minimal fertilizer applications and should be further investigated in the future.

ECOSTRESS and CIMIS: A Comparison of Potential and Reference Evapotranspiration in Riverside County, California

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides remotely-sensed estimates of evapotranspiration at 70 m spatial resolution every 1–5 days, sampling across the diurnal cycle. This study, in partnership with an operational water management organization, the Eastern Municipal Water District (EMWD) in Southern California, was conducted to evaluate estimates of evapotranspiration under ideal conditions where water is not limited. EMWD regularly uses a ground-based network of reference evapotranspiration (ETo) from the California Irrigation Management Information System (CIMIS); yet, there are gaps in spatial coverage and questions of spatial representativeness and consistency. Space-based potential evapotranspiration (PET) estimates, such as those from ECOSTRESS, provide consistent spatial coverage. We compared ECOSTRESS ETo (estimated from PET) to CIMIS ETo at five CIMIS sites in Riverside County, California from July 2018–June 2020. We found strong correlations between CIMIS ETo and ECOSTRESS ETo across all five sites (R2 = 0.89, root mean square error (RMSE) = 0.11 mm hr−1). Both CIMIS and ECOSTRESS ETo captured similar seasonal patterns through the study period as well as diurnal variability. There were site-specific differences in the relationship between ECOSTRESS AND CIMIS, in part due to spatial heterogeneity around the station site. Consequently, careful examination of landscapes surrounding CIMIS sites must be considered in future comparisons. These results indicate that ECOSTRESS successfully retrieves PET that is comparable to ground-based reference ET, highlighting the potential for providing observation-driven guidance for irrigation management across spatial scales.

The Influence of Seasonal Meteorology on Vehicle Exhaust PM2.5 in the State of California: A Hybrid Approach Based on Artificial Neural Network and Spatial Analysis

This study aims to develop a hybrid approach based on backpropagation artificial neural network (ANN) and spatial analysis techniques to predict particulate matter of size 2.5 µm (PM2.5) from vehicle exhaust emissions in the State of California using aerosol optical depth (AOD) and several meteorological indicators (relative humidity, temperature, precipitation, and wind speed). The PM2.5 data were generated using the Motor Vehicle Emission Simulator (MOVES). The measured meteorological variables and AOD were obtained from the California Irrigation Management Information System (CIMIS) and NASA’s Moderate Resolution Spectroradiometer (MODIS), respectively. The data were resampled to a seasonal format and downscaled over grids of 10 by 10 to 150 by 150. Coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RMSE) were used to assess the quality of the ANN prediction model. The model peaked at winter seasons with R2 = 0.984, RMSE = 0.027, and MAPE = 25.311, whereas it had the lowest performance in summer with R2 = 0.920, RMSE = 0.057, and MAPE = 65.214. These results indicate that the ANN model can reasonably predict the PM2.5 mass and can be used to forecast future trends.

Developing irrigation water conservation strategies for hybrid bermudagrass using an evapotranspiration-based smart irrigation controller in inland southern California

A three-year (2017–2019) irrigation research trial was conducted to evaluate the response of hybrid bermudagrass to a wide range of irrigation scenarios and assess the efficacy of Weathermatic Evapotranspiration-based (ET-based) smart controller for autonomous landscape irrigation management during dry seasons in inland southern California. The irrigation levels applied throughout the experiment ranged between 39% and 103% reference ET (ETo) and the irrigation frequency restrictions imposed were 3, 5 and 7 d/wk. Normalized difference vegetation index (NDVI) data were continuously collected to evaluate the response of hybrid bermudagrass [‘Tifgreen’ Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy] to irrigation treatments. Plots were also visually assessed and scaled from 1 (dead plot) to 9 (ideal turfgrass) following the National Turfgrass Evaluation Program (NTEP) standards. Turfgrass water response function (TWRF) was introduced as a statistical regression model to estimate hybrid bermudagrass quality response (NDVI values) to irrigation levels over time. In the years 2018 (p < 0.01) and 2019 (p < 0.001), the irrigation levels showed a significant effect on NDVI values. The irrigation frequency restrictions showed no significant impact on NDVI in any of the years. We observed a high correlation (r = 0.84) between visual rating (VR) and NDVI data. The TWRF shows a high accuracy (RMSE = 0.047, no units), and estimated NDVI values were highly correlated (r = 0.89) with measured NDVI values. A comparison between the California irrigation management information system (CIMIS) reference evapotranspiration (ETo) versus temperature-based ETo estimations by the controller revealed the smart controller on average over-irrigated by 12%, 2% and 3% throughout the experimental periods in 2017, 2018 and 2019, respectively. A long term (34 years) analysis using CIMIS ETo data and TWRF model revealed 75% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in the inland southern California semiarid climate for months with high irrigation demand (i.e., May to November). The results also showed that hybrid bermudagrass could withstand more severe deficit irrigation treatments for shorter periods depending on ETo demand.

Effect of temperature on fecundity, development, and emergence of the invasive ambrosia beetle Euwallacea kuroshio (Coleoptera: Scolytinae)

The Kuroshio shot hole borer (KSHB) Euwallacea kuroshio is an invasive ambrosia beetle pest in Southern California, where it causes Fusarium dieback in a number of different host tree species. Since its discovery in California in about 2014, the KSHB has established or been identified across a wide geographic range along the California coast, spanning north from the Mexican border to San Luis Obispo. KSHB were reared at temperatures ranging from 16 to 32 °C to determine the effect of temperature on development and emergence rates, brood size, and colonization success. The highest total emergence and number of successful colonies occurred when KSHB was reared at 28 °C. Mean days until first offspring emergence decreased as temperature increased up to 30 °C, after which emergence ceased. The thermal constant was estimated to be 318 degree‐days. Using this approximation in conjunction with weather data from the California Irrigation Management Information System, we predict the annual number of generations of KSHB in several locations across its invasive range in California. Results can be used to predict the timing and number of generations in other invaded areas, as well as estimate the range of this pest.

Echo state network-based spatio-temporal model for solar irradiance estimation

In this study, echo state network-based spatio-temporal (STESN) model is proposed to predict the solar irradiance of a given target station, depending on the historical data of its surrounding sites. Hourly solar irradiance datasets for the whole year 2017, obtained from California Irrigation Management Information System (CIMIS) at eight different stations are used. Comparison studies show that SPESN could achieve more accurate prediction, compared with the conventional persistence (PSS) benchmark model.

Evaluating landscape rose performance on reduced irrigation

Water conservation has become a critical issue in urban landscapes in summer-dry climate regions where irrigation must be applied to keep plants healthy. Part of the strategy for reducing landscape water use is incorporating plants with low water needs into the design. To implement this, landscape professionals need information on which available plants can perform acceptably in the landscape on low water, but research-based plant water-use information is often unavailable. Since 2005, University of California researchers have performed trials to evaluate in-ground landscape plant performance on four levels of reduced irrigation, including 10 Rosa hybrida cultivars between 2009 and 2016: ‘Aushouse’, ‘Gruss an Aachen’, ‘KORbin’, ‘KORelamba’, ‘KORfloci01’, ‘KORsixkono’, ‘KORsteimm’, ‘Meidrifora’, ‘Meigalpio’, and ‘Meijocos’. The irrigation treatments were based on levels of reference evapotranspiration (ET(0)) at 20, 40, 60, and 80% of ET(0) in a water budget model using data from a nearby weather station in the California Irrigation Management Information System. After one year of establishment irrigation at 100% of ET(0), irrigation treatments were applied during the second year to six plants of each cultivar on each treatment. Data taken monthly were growth measurements and quality ratings of foliage, flowering, pest tolerance, disease resistance, and overall appearance. Statistical analyses using ANOVA and Tukey’s HSD showed no significant differences in growth between treatments at p≤0.05. For several cultivars, some or all quality parameters were higher on one or more levels of irrigation than others. While some cultivars performed best on the 60% ET(0) treatment, most performed acceptably at 40 and 20% of ET(0) as well. These data have been used to make recommendations for grouping plants by water need in the landscape and in facilitating optimization of landscape water applications

UC ANR research and education influences landscape water conservation and public policy

UC has been heavily involved in research and extension efforts impacting landscape water conservation legislation for over 30 years. In 1981, UC implemented the California Irrigation Management Information System, a network of weather stations that provides data for local estimates of plant water needs. Those estimates led to UC being able to advise the California Legislature on policies for maximum applied water allowances for residential and large landscaping projects. The allowances have been reduced significantly with UC guidance, and UC has helped landscapers to meet the increasingly restrictive requirements. Best practices that reduce water losses have been developed in collaboration with equipment manufacturers and landscaping specialists, and explained to end users. In addition, UC has developed the WUCOLS database, which classifies over 3,500 plants by their water needs. UC's involvement in landscape water conservation continues on many fronts, developing science and contributing to policy.

Sources of Variation in the Adult Flight of Walnut Husk Fly (Diptera: Tephritidae): A Phenology Model for California Walnut Orchards.

A phenology model of the walnut husk fly, Rhagoletis completa Cresson, was developed to more accurately predict the timing of the flight period and optimize management decisions. A data set of 153 orchard years in which adults were trapped throughout the season was used for the development and validation of this model. Data from California Irrigation Management Information System (CIMIS) weather stations were used to match orchard-year datasets with historical climatic data on degree-day (DD) accumulation, winter chill, and winter rainfall. A cumulative Weibull distribution was used to model the relationship between cumulative trap catch and DD accumulation for R. completa in California. The model was used to predict thermal requirements for the start (5% cumulative trap catch) and mid-point (50% cumulative trap catch) of the flight period, which were 1,670 and 2,179 DDs, respectively. The prediction for 50% cumulative trap catch of R. completa in California was much higher than the thermal requirement estimated in Oregon previously (1,751 DDs). Linear mixed effects models were used to evaluate other environmental and orchard-specific factors which could explain the large variation between predicted and observed thermal requirements for both the start and mid-point of the flight period. Latitude, walnut cultivar leaf-out time, orchard age and year, as a continuous variable, all contributed significantly to explain deviations from the predictions of the DD model for individual orchard years. Such factors can be used both to adjust predicted thermal requirements for these two specific and informative stages of the flight period, and to provide a basis for ecological and evolutionary hypotheses.

Evapotranspiration Trends (1979–2015) in the Central Valley of California, USA: Contrasting Tendencies During 1981–2007

AbstractTrends in monthly evapotranspiration (ET) rates across three watersheds covering the Central Valley in California were calculated by the latest calibration‐free version of the complementary relationship of evaporation for 1979–2015. While a recent study concluded that ET rates of the irrigated fields in the Central Valley were declining in 1981–2007, here an ET trend of about 2.6 ± 12 mm per decade was found over the same period in spite of a drop in precipitation (−22 ± 30 mm per decade) and ET rates (−9.5 ± 10 mm per decade) for the rest of the watersheds, none of them statistically significant. After 2007, the precipitation decline accelerated causing a sharp drop in both irrigated and nonirrigated ET rates across the watersheds. Observations from the California Irrigation Management Information System support the present findings: Under increasing air temperatures, both dew point temperature and relative humidity values increased (at a statistically significant rate) during 1983–2007, while they reversed afterwards, in agreement with the estimated sharp irrigation ET trend decline for the remainder of the study period. Actual (in this case over irrigated fields) and reference ET rates complement each other, that is, they express opposite tendencies, as was demonstrated with California Irrigation Management Information System data, yielding a statistically significant plot‐scale irrigation ET rate increase of 31 to 41 (±17) millimeters per decade for 1983–2007 in accordance with a similar drop in reference ET rates of −28 to −50 (±16) millimeters per decade, depending on whether published monthly or daily values (aggregated to monthly ones after leaving out spurious measurements) were employed.

Water Requirements of Landscape Plants Studies Conducted by the University of California Researchers

University of California (UC) researchers have been involved in research and extension pertaining to measuring evapotranspiration (ET) rates and determining the minimum irrigation requirements of landscape plants for more than 30 years. Early work included the design and implementation of the California Irrigation Management Information System (CIMIS) weather station network and determining crop coefficients for warm and cool season turfgrasses based on historical ET and CIMIS data. Other researchers determined the minimum irrigation requirements for several species of established landscape trees, shrubs, and groundcovers in diverse climate zones throughout the state. In addition, the Water Use Classification of Landscape Species (WUCOLS) system was developed by UC personnel in the early 1990s which, to date, has classified more than 3500 landscape species into very low, low, moderate, and high water-use categories based on observation and personal experience by industry experts and UC personnel. Future work in the area of landscape water use and conservation will include updating WUCOLS as more data from replicated trials become available. New research at UC Riverside aims to improve irrigation efficiency (IE) through precision irrigation using smart controllers, remote sensing, and geospatial analysis under controlled conditions. Irrigation training and certification for public and private landscape managers must remain a priority because, even with advanced smart controller technologies, water savings will not occur with poorly designed and functioning irrigation systems.

Temperature and Term Low Birth Weight in California.

Few investigations have explored temperature and birth outcomes. In a retrospective cohort study, we examined apparent temperature, a combination of temperature and relative humidity, and term low birth weight (LBW) among 43,629 full-term LBW infants and 2,032,601 normal-weight infants in California (1999-2013). The California Department of Public Health provided birth certificate data, while meteorological data came from the California Irrigation Management Information System, US Environmental Protection Agency, and National Centers for Environmental Information. After considering several temperature metrics, we observed the best model fit for term LBW over the full gestation (per 10-degrees-Fahrenheit (°F) increase in apparent temperature, 13.0% change, 95% confidence interval: 4.1, 22.7) above 55°F, and the greatest association was for third-trimester exposure above 60°F (15.8%, 95% confidence interval: 5.0, 27.6). Apparent temperature during the first month of pregnancy exhibited no significant risk, while the first trimester had a significantly negative association, and second trimester, last month, and last 2 weeks had slightly increased risks. Mothers who were black or older, delivered male infants, or gave birth during the warm season had infants at the highest risks. This study provides further evidence for adverse birth outcomes from heat exposure for vulnerable subgroups of pregnant women.

Effects of rain and soil moisture on background neutron measurements with the SuperMISTI neutron array

Background neutron measurements were recorded for approximately two months at Camp Blanding, FL, with a large array of moderated gas proportional detectors and EJ-309 liquid scintillator detectors. The variations in the local neutron background rate were on the order of 10% and were observed to be primarily due to the changing level of moisture in the local soil due to precipitation and evaporation. Simple models were constructed based only on very basic climatological information and were able to reproduce the major variations in our measured neutron counts with time. These simple models compare favorably to the more-complex modified Penman equation developed for the California Irrigation Management Information System. An accurate model to describe local neutron background variations based on easily measured climatological data would be invaluable to provide corrections for stationary neutron monitors.

Observing entrainment mixing, photochemical ozone production, and regional methane emissions by aircraft using a simple mixed-layer framework

Abstract. In situ flight data from two distinct campaigns during winter and summer seasons in the San Joaquin Valley (SJV) of California are used to calculate boundary-layer entrainment rates, ozone photochemical production rates, and regional methane emissions. Flights near Fresno, California, in January and February 2013 were conducted in concert with the NASA DISCOVER-AQ project. The second campaign (ArvinO3), consisting of 11 days of flights spanning June through September 2013 and 2014, focused on the southern end of the SJV between Bakersfield and the small town of Arvin, California – a region notorious for frequent violations of ozone air quality standards. Entrainment velocities, the parameterized rates at which free tropospheric air is incorporated into the atmospheric boundary layer (ABL), are estimated from a detailed budget of the inversion base height. During the winter campaign near Fresno, we find an average midday entrainment velocity of 1.5 cm s−1, and a maximum of 2.4 cm s−1. The entrainment velocities derived during the summer months near Bakersfield averaged 3 cm s−1 (ranging from 0.9 to 6.5 cm s−1), consistent with stronger surface heating in the summer months. Using published data on boundary-layer heights we find that entrainment rates across the Central Valley of California have a bimodal annual distribution peaking in spring and fall when the lower tropospheric stability (LTS) is changing most rapidly.Applying the entrainment velocities to a simple mixed-layer model of three other scalars (O3, CH4, and H2O), we solve for ozone photochemical production rates and find wintertime ozone production (2.8 ± 0.7 ppb h−1) to be about one-third as large as in the summer months (8.2 ± 3.1 ppb h−1). Moreover, the summertime ozone production rates observed above Bakersfield–Arvin exhibit an inverse relationship to a proxy for the volatile organic compound (VOC) : NOx ratio (aircraft [CH4] divided by surface [NO2]), consistent with a NOx-limited photochemical environment. A similar budget closure approach is used to derive the regional emissions of methane, yielding 100 (±100) Gg yr−1 for the winter near Fresno and 170 (±125) Gg yr−1 in the summer around Bakersfield. These estimates are 3.6 and 2.4 times larger, respectively, than current state inventories suggest. Finally, by performing a boundary-layer budget for water vapor, surface evapotranspiration rates appear to be consistently ∼ 55 % of the reference values reported by the California Irrigation Management Information System (CIMIS) for nearby weather stations.

A method for estimating transpiration of irrigated urban trees in California

Transpiration of urban forests in southern California is highly uncertain and challenging to quantify because of variability of tree characteristics and stomatal responses among species and locations. However, as California undergoes the most severe drought on record, it is imperative to develop approaches to estimating transpiration of irrigated urban trees (ETrees). We examined the landscape coefficient method recommended by the California Irrigation Management Information System (CIMIS) and widely used to estimate irrigation needs of urban landscapes. The CIMIS method uses reference evapotranspiration (ET0) calculated from the Penman-Monteith equation and a set of species-specific factors to adjust ET0 for particular landscapes. We found a mismatch between CIMIS predictions and actual patterns of urban tree transpiration that we attributed to underrepresentation of tree physiological mechanisms in ET0. As an alternative, we propose an empirical model of ETrees based on in situ measurements on 108 urban trees (14 species) in the Los Angeles region: ETrees=Eref(0.23lnD+0.002I0+0.55). Here D is the vapor pressure deficit of the air, I0 is incoming solar radiation and Eref is species-specific parameter representing ETrees at D=1kPa that may be estimated using mean sapwood area of a tree stand. This model may be used to estimate ETrees for practical applications and to improve representation of irrigated urban forests in hydrologic models.

Association Between High Ambient Temperature and Risk of Stillbirth in California.

Recent studies have linked elevated apparent temperatures with adverse birth outcomes, such as preterm delivery, but other birth outcomes have not been well studied. We examined 8,510 fetal deaths (≥20 weeks' gestation) to estimate their association with mean apparent temperature, a combination of temperature and humidity, during the warm season in California (May-October) from 1999 to 2009. Mothers whose residential zip codes were within 10 km of a meteorological monitor were included. Meteorological data were provided by the California Irrigation Management Information System, the US Environmental Protection Agency, and the National Climatic Data Center, while the California Department of Public Health provided stillbirth data. Using a time-stratified case-crossover study design, we found a 10.4% change (95% confidence interval: 4.4, 16.8) in risk of stillbirth for every 10°F (5.6°C) increase in apparent temperature (cumulative average of lags 2-6 days). Risk varied by maternal race/ethnicity and was greater for younger mothers, less educated mothers, and male fetuses. The highest risks were observed during gestational weeks 20-25 and 31-33. No associations were found during the cold season (November-April), and the observed associations were independent of air pollutants. This study adds to the growing body of literature identifying pregnant women and their fetuses as subgroups vulnerable to heat exposure.