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Aim: As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways. Location: Central Valley, California, USA. Methods: We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance. Results: We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased. Main Conclusions: Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought. [ABSTRACT FROM AUTHOR]

Soil bulk density (BD) is important for measuring changes in soil chemical, physical, and biological properties; however, the measurement is tedious to collect and requires specialized equipment. Database measurements for soil surface BD do not always correspond to present field conditions as field management can alter BD in time. Saturation percentage (SP) is a routine lab measurement. The objectives of this study are to (1) understand if a relationship between BD and SP can be developed and (2) build a model that predicts BD based on a routine low‐cost lab analysis. We collected 83 soil samples from different experimental sites around California's Central Valley. At each site, BD, SP, soil organic matter (OM), and soil total organic carbon were measured. A set of models were generated and compared based on their Akaike information criterion (AIC) and adjusted R2. The best two models are presented in this paper, and their accuracy and precision in estimating BD were further compared by calculating the root mean square error (RMSE) and the R2 of the predicted versus values measured in the field. We determined that a strong relationship between BD and SP exists (R2 = 0.70) and that a cubic model that includes SP and OM resulted in the best model to predict BD in California soils. Inclusion of additional data may further strengthen this model or make it applicable for other grower regions. Core Ideas: Database bulk density values may not correspond to field conditions as management can alter bulk density.Bulk density is hard to measure and requires specialized equipment, but is needed to quantify soil carbon changes.There is a significant correlation between soil bulk density, soil saturation paste, and soil organic matter.Three models to predict bulk density based on saturation paste and organic matter were developed and evaluated. [ABSTRACT FROM AUTHOR]

Well logging data shows the change of physical properties of rocks and fluids in lithology units with depth. Well logging is one of the main parts of natural resources exploration. But in some cases, due to the lack of geophysical equipment or due to high exploration costs, it is not possible to record some geophysical logs. In this paper, electromagnetic log predicted using electrical logs for the first time. In such cases, estimating the desired log using other geophysical logs is a suitable solution. For the estimation of geophysical logs, machine learning algorithms are used in most cases. In this research, a new strategy developed for processing and preparation of geophysical logs. This strategy consists of three parts: data smoothing, correlation intensifier, and MLR (Multiple Linear Regression) or ANN (Artificial Neural Network). The purpose of the data smoothing and correlation intensifier section is to remove outliers and identify the pattern of main changes in the log data, and as a result, the accuracy in estimating the target log increases. In this article, the determination of the electromagnetic log has been done using electric logs. The well logging data have been recorded in Southern California and the Central Valley. A total of six wells have been selected, four wells for MLR and ANN training and two wells for testing. By applying data smoothing and correlation intensifier to these data, the correlation between electrical and electromagnetic data increased significantly and caused the estimation accuracy of electromagnetic log to be above 95%. The use of this strategy is not limited to the estimation of electromagnetic log and can be used in all well logging data. [ABSTRACT FROM AUTHOR]

Oilfield Produced Water (OPW) has been identified as a viable solution to mitigate water scarcity in California's Central Valley. However, consumer perception on the use of OPW outside of the oil and gas industry remains unknown and poses a hurdle for large-scale adoption. This paper, based on 134 randomly selected residents of Kern County (California's largest oil-producing county and top agricultural region), examined attitudes towards OPW reuse. In the survey, more than half (52.3%) of participants said that they have heard about oilfield-produced water and other reclaimed waters. Findings also suggest that participants mostly preferred rain and stormwater, and interestingly scored OPW higher than industrial and black waters for irrigation purposes. Around 67% of the participants supported the use of oilfield-produced water for irrigating crops. Younger people (18–29 years) expressed greater concern about using OPW compared to the older residents. Given the drought conditions in Central Valley, policymakers might need to focus on a circular economy and mandate the use of OPW in Central Valley considering that this region's economy lies at the nexus of the O&G and agriculture industries. [ABSTRACT FROM AUTHOR]

Demand for food and water are increasing while the extent of arable land and accessible fresh water are decreasing. This poses global challenges as economies continue to develop and the population grows. With agriculture as the leading consumer of water, better understanding how water is used to produce food may help support the increase of Crop Water Productivity (CWP; kg/m3), the ratio of crop output per unit of water input (or crop per drop). Previous large-scale CWP studies have been useful for broad water use modeling at coarser resolutions. However, obtaining more precise CWP, especially for specific crop types in a particular area and growing season as outlined here are important for informing farm-scale water management decision making. Therefore, this study focused on California's Central Valley utilizing high-spatial resolution satellite imagery of 30 m (0.09 hectares per pixel) to generate more precise CWP for commonly grown and water-intensive irrigated crops. First, two products were modeled and mapped. 1. Landsat based Actual Evapotranspiration (ETa; mm/d) to determine Crop Water Use (CWU; m3/m2), and 2. Crop Productivity (CP; kg/m2) to estimate crop yield per growing season. Then, CWP was calculated by dividing CP by CWU and mapped. The amount of water that can be saved by increasing CWP of each crop was further calculated. For example, in the 434 million m2 study area, a 10% increase in CWP across the 9 crops analyzed had a potential water savings of 31.5 million m3 of water. An increase in CWP is widely considered the best approach for saving maximum quantities of water. This paper proposed, developed, and implemented a workflow of combined methods utilizing cloud computing based remote sensing data. The environmental implications of this work in assessing water savings for food and water security in the 21st century are expected to be significant. [ABSTRACT FROM AUTHOR]

Simple Summary: Excessive use of groundwater resources in the Central Valley in California has led to major land sinking over the years. In this study, we rely on satellite imagery to monitor and assess the extent of this sinking. Specifically, we use images from a satellite that emits microwaves which allows us to directly obtain the deformation at a specific time. Then, we apply a machine learning algorithm to the resulting data in an attempt to effectively predict short-term future deformation. We find that the algorithm applied has a low error when compared to the actual data. This shows that machine learning algorithms could be incorporated into models that assess potential hazards associated with land sinking. Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers' integrity. [ABSTRACT FROM AUTHOR]

Reservoir operators face pressures on timing releases of water. Releasing too much water immediately can threaten future supplies and costs, but not releasing enough creates immediate economic hardship downstream. This paper examines how the economic valuation of end‐of‐year carryover storage can lead to optimal amounts of carryover storage in complex large water resource systems. Economic carryover storage value functions (COSVFs) are developed to represent the value of storage in the face of interannual inflow uncertainty and variability within water resource optimization models. The approach divides a perfect foresight optimization problem into year‐long (limited foresight) subproblems solved sequentially by a within‐year optimization engine to find optimal short‐term operations. The final storage state from the previous year provides the initial condition to each annual problem, and end‐of‐year COSVFs are the final condition. Here the COSVF parameters that maximize the interannual benefits from river basin operations are found by evolutionary search. This generalized approach can handle nonconvexity in large‐scale water resources systems. The approach is illustrated with a regional model of the California Central Valley water system including 30 reservoirs, 22 aquifers, and 51 urban and agricultural demand sites. Head‐dependent pumping costs make the optimization problem nonconvex. Optimized interannual reservoir operation improves over more cautious operation in the historical approximation, reducing the average annual scarcity volume and costs by 80% and 98%, respectively, with more realistic representation of hydrologic foresight for California's Mediterranean climate. The economic valuation of storage helps inform water storage decisions. Key Points: This paper introduces an approach to estimating the economic value of interannual reservoir storageThe approach uses an evolutionary search algorithm linked to a hydroeconomic optimization modelA regional model of the California Central Valley water resource system illustrates the approach [ABSTRACT FROM AUTHOR]

Improving nitrogen (N) fertilizer management in agricultural systems is critical to meeting environmental goals while maintaining economically viable and productive food systems. This paper applies a farm systems framework to analyze how adoption of N management practices is related to different farming operation characteristics and the extent to which fertilizer, soil and irrigation practices are related to each other. We develop a multivariate probit regression model to analyze the interdependency of these adoption behaviors from 966 farmers across three watersheds and diverse cropping systems in the Central Valley of California. Our analysis demonstrates that farmers adopt varying combinations or portfolios of practices, with the most common portfolio featuring a core set of fertilizer-focused practices. Irrigation infrastructure is an especially important farm operation characteristic for encouraging adoption of innovative practice portfolios that integrate water and fertilizer management. These findings highlight the ability for a farm systems approach to improve our understanding of farmer decision-making across diverse agricultural landscapes. [ABSTRACT FROM AUTHOR]

This paper describes the types of emergency and disaster events that could potentially impact the healthcare system and the need for healthcare executives to be knowledgeable and able to respond to emergencies quickly and appropriately. It discusses the barriers to the engagement of hospital and healthcare senior executive teams in emergency preparedness activities and proposes a pragmatic approach for the engagement of healthcare executive teams. Drawing on the experience of hospitals in California's Central Valley, the article hypothesises that a full implementation of this programme would have supported hospital executive teams to provide a more effective and efficient response to the management of COVID-19. [ABSTRACT FROM AUTHOR]

Entrenched Western water rights regimes may appear to function relatively well in wet years, but extreme drought events can expose the kinds of harsh ecological and socio-economic outcomes that the hard edges of prior appropriation inherently generate. During the 2012-2016 California drought some irrigators received little or no water at all in consecutive years while others received comparatively large allocations. This paper focuses on the role that California's water rights priority system and its administration via Central Valley Project contracts have played in generating disproportionate water allocations and impacts during the drought. The analysis is structured around two key questions: (a) in what ways does strict adherence to a priority system of water allocations produce inequitable socio-ecological outcomes during severe drought? (b) how might the system be changed to foster outcomes that are more equitable and fair, and with less costly and less serious conflicts in a non-stationary climate future marked by extreme events? Using an equity perspective, I draw from the doctrine of equitable apportionment to imagine a water rights regime that is better able to create a fairer distribution of drought impacts while meaningfully elevating the importance of future generations and increasing adaptive capacity. [ABSTRACT FROM AUTHOR]

Downscaled climate change projections for California, when translated into changes in irrigation water delivery and then into profit from agriculture in the Central Valley, show an increase in conventional measures of variability such as the variance. However, these increases are modest and mask a more pronounced increase in downside risk, defined as the probability of unfavorable outcomes of water supply or profit. This paper describes the concept of downside risk and measures it as it applies to outcomes for Central Valley agriculture projected under four climate change scenarios. We compare the effect of downside risk aversion versus conventional risk aversion or risk neutrality when assessing the impact of climate change on the profitability of Central Valley agriculture. We find that, when downside risk is considered, the assessment of losses due to climate change increases substantially. [ABSTRACT FROM AUTHOR]

Surface water and groundwater management are often tightly linked, even when linkage is not intended or expected. This link is especially common in semi-arid regions, such as California. This paper summarizes a modeling study on the effects of ending long-term overdraft in California's Central Valley, the state's largest aquifer system. The study focuses on economic and operational aspects, such as surface water pumping and diversions, groundwater recharge, water scarcity, and the associated operating and water scarcity costs. This analysis uses CALVIN, a hydro-economic optimization model for California's water resource system that suggests operational changes to minimize net system costs for a given set of conditions, such as ending long-term overdraft. Based on model results, ending overdraft might induce some major statewide operational changes, including large increases to Delta exports, more intensive conjunctive-use operations with increasing artificial and in-lieu recharge, and greater water scarcity for Central Valley agriculture. The statewide costs of ending roughly 1.2 maf yr-1 of groundwater overdraft are at least $50 million per year from additional direct water shortage and additional operating costs. At its worst, the costs of ending Central Valley overdraft could be much higher, perhaps comparable to the recent economic effects of drought. Driven by recent state legislation to improve groundwater sustainability, ending groundwater overdraft has important implications statewide for water use and management, particularly in the Sacramento-San Joaquin Delta. Ending Central Valley overdraft will amplify economic pressure to increase Delta water exports rather than reduce them, tying together two of California's largest water management problems. [ABSTRACT FROM AUTHOR]

Climate variability and changing cropping patterns are two main factors directly influencing catchment hydrological processes and agricultural productivity. Assessing the response of hydrological processes and agricultural production to the combined impacts is of great importance for enhancing water resources management, water productivity and agriculture sustainability. SWAT (Soil and Water Assessment Tools) models embedded with 2007 and 2018 land use were built for the San Joaquin watershed (SJWS) in California's Central Valley. Global sensitivity analysis were conducted to select parameters that have the largest influence on evapotranspiration (ET) processes. The model was calibrated and validated produced good agreement for monthly ET from remote sensing and yield of 8 major crops using county level reported yields during the periods of 2001–2009 and 2010–2018. Based on the outputs of the calibrated SJWS SWAT model, regional ET showed a significant increase, which was caused by rises in temperature and changing cropping patterns associated with increase in area under nut crop production. Seven major crop yields increased excluding alfalfa, implying that the impacts of increasing temperature and small decreases in precipitation were not dominant factors in explaining observed changes in yield. From periods 2001–2009–2010–2018, total water productivity (WP) increased slightly from 2.11 to 2.15 kg/m3, associated with more water-intensive permanent crop production systems. Total economic water productivity (EWP) increased significantly by 111% from 0.55 to 1.16 US$/m3, which also indicated the substantial economic benefit brought to the SJWS by expanding the land area under high-EWP crops such as nut crops and vegetables. Nutritional water productivity (NWP) for energy, protein and fat were reduced from 1211 kcal/m3, 183 g/m3 and 105 g/m3 to 1141 kcal/m3, 111 g/m3 and 88 g/m3 during the period 2010–2018 mostly due to high water use. Total NWP for calcium was increased from 114 to 145 mg/m3, respectively. This study provides initial insights into the response of WP, EWP and NWP to the changing cropping patterns and climate variability for major crops in the SJWS, which could be used by a wide range of stakeholders including farmers, commodity boards, irrigation districts, groundwater sustainability basins, environmental justice groups and policy makers to optimize water resources planning and management. We recommend creating a comprehensive index that incorporates WP, EWP and NWP for making sustainable water resources planning and management decisions. • Climate variability and cropping patterns change were analyzed from 1999 to 2018. • Regional evapotranspiration significantly increased over the past twenty years. • Total WP only increased slightly for the low WP of nut trees and its extended area. • Total EWP increased significantly by 111% from 0.55 to 1.16 US$/m3. • The paper also explores changes in NWP for various crops grown in the watershed. [ABSTRACT FROM AUTHOR]

Simple Summary: In California, grape cultivation for wine, juice, fresh and raisin markets is one of the most profitable agricultural sectors. Pathogens, weeds and insect pests cause millions in crop loss and result in additional costs through the use of pesticides and other control tools. Here, we report on the emergence of a new California grape pest, the carpentermoth Givira ethela (Neumoegen and Dyar, 1893). This paper also aimed at the moth's identification through molecular and morphological features and updates the distribution of G. ethela in California. Furthermore, the presence of G. ethela larval galleries appears to facilitate the mealybug pest Planococcus ficus Signoret, 1875, providing better access to vine sap and protection from natural enemies, environmental stresses, and pesticide treatments. We propose that management practices against G. ethela should include the correct identification of the pest and of its damage, but also the investigation of monitoring methods, economic thresholds, biological controls, and a better understanding of the relationship between G. ethela and P. ficus or other mealybug pest species. Grape cultivation is a billion-dollar agricultural sector in California, where invasive or novel pest species can disrupt management practices. We report herein on a new pest associated with California vineyards, the carpentermoth Givira ethela (Neumoegen and Dyar, 1893). Rather than an invasive species, G. ethela appears to be a newly recognized wood-boring pest of Vitis vinifera (L.) in regions of California's Central Valley, where its initial occurrence has been dated back to, at least, the beginning of the 2000s. The habitus of adult, genitalia and pupa is illustrated. Givira ethela distribution in California is updated including published records and new data. Carpentermoth galleries seem to facilitate the access of Planococcus ficus Signoret, 1875 to vine sap and protection from natural enemies, environmental stresses, and pesticide treatments. Notes on pest status, life history, monitoring practices, natural enemies, and management options on grapes are also discussed. Tools for the Integrated Pest Management of G. ethela should include the correct identification of the insect and its damage, a full understanding of its biology and ecology, the application of monitoring methods, and the identification of economic thresholds and injury levels. [ABSTRACT FROM AUTHOR]

In regions experiencing aquifer depletion, planning for groundwater sustainability requires both accurate accounting of current groundwater budgets and an assessment of future conditions, with changes in recharge and pumping. Hydrologic variability, climate change effects on water flows, changing water infrastructure operations, and inherent uncertainties in modeling, challenge the plans to achieve groundwater sustainability. This paper examines the importance, magnitude, and policy implications of uncertainties in groundwater overdraft estimation for water management in California. We review water balance estimates from two regional-scale groundwater models—C2VSim and CVHM—for sub-regions within California's Central Valley, and examine the variability and uncertainty in historical and future estimates of groundwater overdraft. Assuming reductions in agricultural water use for sub-regions with overdraft, we estimate the probabilities of ending groundwater overdraft for different periods. We also obtain the economic costs associated with these reductions in agricultural production. Results from both groundwater models show significant inter-annual variability in flows affecting groundwater storage, and our model comparison highlights the uncertainty in water budget estimates for Central Valley sub-regions given the differences between models. The analysis of the probabilities of achieving sustainability at the sub-regional scale show that the average overdraft rate is important and that greater variance in annual groundwater storage increases uncertainties in ending overdraft, especially for shorter periods. Greater reductions in annual net water increases the reliability of achieving groundwater sustainability, but rising rapidly agricultural economic losses. Setting management thresholds below groundwater levels can ease meeting sustainability criteria, but also can introduce a false pathway to sustainability. Finally, we discuss policy implications for the design of local groundwater sustainability plans and state assessment and regulation of local plans. • Many regions in California's Central Valley use groundwater unsustainably. • Uncertainty in models and future hydrology challenge groundwater sustainability. • Comparison of models shows significant uncertainty in estimates for water budgets. • Less water use increases sustainable paths but also agricultural economic losses. • Consideration of uncertainty and costs is essential for planning for sustainability. [ABSTRACT FROM AUTHOR]

• A novel network to perform crop classification for multitemporal multisensor images. • Excellent model performance but fewer parameters and smaller computational complexity. • It is feasible to distinguish the subtle phenological differences between crops. The multitemporal observation capability of remote sensing satellites is of increasing importance in crop monitoring. Nowadays we can obtain dense multitemporal data more easily because more and more Earth observation satellites are put into use. Multiband images derived from different satellite sensors usually have different spatial resolutions, and how to utilize different spatial resolution information of multisensors in multitemporal sequence is of great significance. In this paper, we propose a crop classification network based on interleaved group convolution for multitemporal multisensor data. Firstly, we selected the Central Valley of California as research area with rich crop categories. And multitemporal sequence of length 65 in this region was obtained from satellites Sentinel-2A/B and Landsat-8. Then, to coordinate the sensors with different spatial-spectral acquisition parameters, we convolve different bands of sensors to perform multiresolution fusion, and then further align the spectral dimensions. The interleaved group convolution, which has comparable model performance and fewer parameters and lower computational complexity than regular convolution, is introduced to reduce network parameters and reduce the computational complexity of multitemporal classification. Experimental results on our proposed multitemporal multisensor dataset have proved that our method has better performance in crop classification compared with traditional methods. And compared with regular convolution, our method can achieve roughly equivalent classification accuracy. [ABSTRACT FROM AUTHOR]

Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and agricultural production. The main objective of this paper is to characterize the spatiotemporal dynamics of crop water use in the Central Valley of California using Landsat-based annual actual evapotranspiration (ETa) from 2008 to 2018 derived from the Operational Simplified Surface Energy Balance (SSEBop) model. Crop water use for 10 crops is characterized at multiple scales. The Mann–Kendall trend analysis revealed a significant increase in area cultivated with almonds and their water use, with an annual rate of change of 16,327 ha in area and 13,488 ha-m in water use. Conversely, alfalfa showed a significant decline with 12,429 ha in area and 13,901 ha-m in water use per year during the same period. A pixel-based Mann–Kendall trend analysis showed the changing crop type and water use at the level of individual fields for all of Kern County in the Central Valley. This study demonstrates the useful application of historical Landsat ET to produce relevant water management information. Similar studies can be conducted at regional and global scales to understand and quantify the relationships between land cover change and its impact on water use. [ABSTRACT FROM AUTHOR]