Evaluation of the Complementary Relationship Using Noah Land Surface Model and North American Regional Reanalysis (NARR) Data to Estimate Evapotranspiration in Semiarid Ecosystems

Estimating evapotranspiration using the complementary relationship can serve as a proxy to more sophisticated physically based approaches and can be used to better understand water and energy budget feedbacks. The authors investigated the existence of complementarity between actual evapotranspiration (ET) and potential ET (ETp) over natural vegetation in semiarid desert ecosystems of southern Idaho using only the forcing data and simulated fluxes obtained from Noah land surface model (LSM) and North American Regional Reanalysis (NARR) data. To mitigate the paucity of long-term meteorological data, the Noah LSM-simulated fluxes and the NARR forcing data were used in the advection–aridity (AA) model to derive the complementary relationship (CR) for the sagebrush and cheatgrass ecosystems. When soil moisture was a limiting factor for ET, the CR was stable and asymmetric, with b values of 2.43 and 1.43 for sagebrush and cheatgrass, respectively. Higher b values contributed to decreased ET and increased ETp, and as a result ET from the sagebrush community was less compared to that of cheatgrass. Validation of the derived CR showed that correlations between daily ET from the Noah LSM and CR-based ET were 0.76 and 0.80 for sagebrush and cheatgrass, respectively, while the root-mean-square errors were 0.53 and 0.61 mm day--1

Development of a Complete Landsat Evapotranspiration and Energy Balance Archive to Support Agricultural Consumptive Water Use Reporting and Prediction in the Central Valley, CA

Mapping evapotranspiration (ET) from agricultural areas in Californias Central Valley is critical for understanding historical consumptive use of surface and groundwater. In addition, long histories of ET maps provide valuable training information for predictive studies of surface and groundwater demands. During times of drought, groundwater is commonly pumped to supplement reduced surface water supplies in the Central Valley. Due to the lack of extensive groundwater pumping records, mapping consumptive use using satellite imagery is an efficient and robust way for estimating agricultural consumptive use and assessing drought impacts. To this end, we have developed and implemented an algorithm for automated calibration of the METRIC remotely sensed surface energy balance model on NASAs Earth Exchange (NEX) to estimate ET at the field scale. Using automated calibration techniques on the NEX has allowed for the creation of spatially explicit historical ET estimates for the Landsat archive dating from 1984 to the near present. Further, our use of spatial NLDAS and CIMIS weather data, and spatial soil water balance simulations within the NEX METRIC workflow, has helped overcome challenges of time integration between satellite image dates. This historical and near present time archive of agricultural water consumption for the Central Valley will be an extremely useful dataset for water use and drought impact reporting, and predictive analyses of groundwater demands

Applying the FAO-56 Dual \u3ci\u3eK\u3csub\u3ec\u3c/sub\u3e\u3c/i\u3e Method for Irrigation Water Requirements over Large Areas of the Western U.S.

The FAO-56 dual crop coefficient procedure was used to determine evapotranspiration (ET) and net irrigation water requirements for all agricultural areas of the states of Idaho and Nevada and in a western U.S. study on effects of climate change on future irrigation water requirements. The products of the applications are for use by state governments for water rights management, irrigation system planning and design, wastewater application system design and review, hydrologic water balances, and groundwater modeling. The products have been used by the U.S. federal government for assessing impacts of current and future climate change on irrigation water demands. The procedure was applied to data from more than 200 weather station locations across the state of Idaho, 200 weather station locations across the state of Nevada, and eight major river basins in the western U.S. for available periods of weather records. Estimates were made over daily, monthly, and annual time intervals. Methods from FAO-56 were employed for calculating reference ET and crop coefficients (Kc), with ET calculations performed for all times of the calendar year including winter. Expressing Kc as a function of thermal-time units allowed application across a wide range of local climates and elevations. The ET estimates covered a wide range of agricultural crops grown in the western U.S. plus a number of native plant systems, including wetlands, rangeland, and riparian trees. Evaporation was estimated for three types of open-water surfaces ranging from deep reservoirs to small farm ponds

The Third wave in globalization theory

This essay examines a proposition made in the literature that there are three waves in globalization theory—the globalist, skeptical, and postskeptical or transformational waves—and argues that this division requires a new look. The essay is a critique of the third of these waves and its relationship with the second wave. Contributors to the third wave not only defend the idea of globalization from criticism by the skeptics but also try to construct a more complex and qualified theory of globalization than provided by first-wave accounts. The argument made here is that third-wave authors come to conclusions that try to defend globalization yet include qualifications that in practice reaffirm skeptical claims. This feature of the literature has been overlooked in debates and the aim of this essay is to revisit the literature and identify as well as discuss this problem. Such a presentation has political implications. Third wavers propose globalist cosmopolitan democracy when the substance of their arguments does more in practice to bolster the skeptical view of politics based on inequality and conflict, nation-states and regional blocs, and alliances of common interest or ideology rather than cosmopolitan global structures

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r2 = 0.90), and high agreement when estimates are aggregated to the state level (r2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km2) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California

Operational Remote Sensing of ET and Challenges

Satellite imagery now provides a dependable basis for computational models that determine evapotranspiration (ET) by surface energy balance (EB). These models are now routinely applied as part of water and water resources management operations of state and federal agencies. They are also an integral component of research programs in land and climat

Global production and free access to Landsat-scale Evapotranspiration with EEFlux and eeMETRIC

EEFlux (Earth Engine Evapotranspiration Flux) is a version of the METRIC (mapping evapotranspiration at high resolution with internal calibration) application that operates on the Google Earth Engine (EE). EEFlux has a web-based interface and provides free public access to transform Landsat images into 30 m spatial evapotranspiration (ET) data for terrestrial land areas around the globe. EE holds the entire Landsat archive to power EEFlux along with NLDAS/CFSV2 gridded weather data for estimating reference ET. EEFlux is a part of the upcoming OpenET platform (https://openetdata.org/ ) that has leveraged nonprofit funding to provide ET information to all of the lower 48 states for free, as a means to foster water exchange between agriculture, cities and environment (Melton et al., 2020). The METRIC version in OpenET is named eeMETRIC, and includes cloud detection and time integration of ET snapshots into monthly ET estimates. EEFlux and eeMETRIC employ METRIC’s “mountain” algorithms for estimating aerodynamics and solar radiation in complex terrain. Calibration is automated and ET images are computed for download in seconds using EE’s large computational capacity

Uncertainty Transfer in Modeling Layers: From GCM to downscaling to hydrologic surface-groundwater modeling

In this presentation we show how model uncertainty is transferred from GCMs to hydrologic model results for different downscaling strategies. We use a USGS Groundwater and Surface-water FLOW (GSFLOW) model applied to three small catchments in the northeastern Lake Tahoe basin. A framework is developed for assessing the benefits and difficulties associated with using historical and future climate projections from CMIP3 and CMIP5 datasets for hydrologic investigations. Here we downscale 10 km gridded GCM climate data to a 60m grid using daily values from climate stations and PRISM average monthly climate. Hydrologic model results show that an ensemble/probabilistic station- based downscaling approach provides reasonable downscaled climate data that can be used to evaluate sub-regional scale impacts in hydrologic processes

EEFlux: A Landsat-based Evapotranspiration mapping tool on the Google Earth Engine

“EEFlux” is an acronym for ‘Earth Engine Evapotranspiration Flux.’ EEFlux is based on the operational surface energy balance model “METRIC” (Mapping ET at high Resolution with Internalized Calibration), and is a Landsat-imagebased process. Landsat imagery supports the production of ET maps at resolutions of 30 m, which is the scale of many human-impacted and human-interest activities including agricultural fields, forest clearcuts and vegetation systems along streams. ET over extended time periods provides valuable information regarding impacts of water consumption on Earth resources and on humans. EEFlux uses North American Land Data Assimilation System hourly gridded weather data collection for energy balance calibration and time integration of ET. Reference ET is calculated using the ASCE (2005) Penman-Monteith and GridMET weather data sets. The Statsgo soil data base of the USDA provides soil type information. EEFlux will be freely available to the public and includes a web-based operating console. This work has been supported by Google, Inc. and is possible due to the free Landsat image access afforded by the USGS

Establishing Relationships between Drought Indices and Wildfire Danger Outputs: A Test Case for the California-Nevada Drought Early Warning System

Relationships between drought indices and fire danger outputs are examined to (1) incorporate fire risk information into the National Integrated Drought Information System California&ndash;Nevada Drought Early Warning System and (2) provide a baseline analysis for application of drought indices into a fire risk management framework. We analyzed four drought indices that incorporate precipitation and evaporative demand (E0) and three fire indices that reflect fuel moisture and potential fire intensity. Seasonally averaged fire danger outputs were most strongly correlated to multi-scalar drought indices that use E0 (the Evaporative Demand Drought Index (EDDI) and the Standardized Precipitation Evapotranspiration Index (SPEI)) at approximately annual time scales that reflect buildup of antecedent drought conditions. Results indicate that EDDI and SPEI can inform seasonal fire potential outlooks at the beginning of summer. An E0 decomposition case study of conditions prior to the Tubbs Fire in Northern California indicate high E0 (97th percentile) driven predominantly by low humidity signaled increased fire potential several days before the start of the fire. Initial use of EDDI by fire management groups during summer and fall 2018 highlights several value-added applications, including seasonal fire potential outlooks, funding fire severity level requests, and assessing set-up conditions prior to large, explosive fire cases.</p