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W. Thilini Jaksa, Venkataramana Sridhar, Justin L. Huntington, and Mandar Khanal

Abstract

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.

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Daniel J. McEvoy, John F. Mejia, and Justin L. Huntington

Abstract

Predicting sharp hydroclimatic gradients in the complex terrain of the Great Basin can prove to be challenging because of the lack of climate observations that are gradient focused. Furthermore, evaluating gridded data products (GDPs) of climate in such environments for use in local hydroclimatic assessments is also challenging and typically ignored because of the lack of observations. In this study, independent Nevada Climate-Ecohydrological Assessment Network (NevCAN) observations of temperature, relative humidity, and precipitation collected along large altitudinal gradients of the Snake and Sheep mountain ranges from water-year 2012 (October–September) are utilized to evaluate four GDPs of different spatial resolutions: Parameter–Elevation Regressions on Independent Slopes Model (PRISM) 4 km, PRISM 800 m, Daymet 1 km, and a North American Land Data Assimilation System (NLDAS)–PRISM hybrid 4-km product. Inconsistencies and biases in precipitation measurements due to station siting and gauge type proved to be problematic with respect to comparisons to GDPs. This study highlights a weakness of GDPs in complex terrain: an underestimation of inversion strength and resulting minimum temperature in foothill regions, where cold air regularly drains into neighboring valleys. Results also clearly indicate that for semiarid regions, the assumption that daily average dewpoint temperature T dew equals daily minimum temperature does not hold true and should not be used to interpolate T dew spatially. Comparison statistics of GDPs to observations varied depending on the climate variable and grid spatial resolution, highlighting the importance of conducting local evaluations for hydroclimatic assessments.

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Daniel J. McEvoy, Justin L. Huntington, John T. Abatzoglou, and Laura M. Edwards

Abstract

Nevada and eastern California are home to some of the driest and warmest climates, most mountainous regions, and fastest growing metropolitan areas of the United States. Throughout Nevada and eastern California, snow-dominated watersheds provide most of the water supply for both human and environmental demands. Increasing demands on finite water supplies have resulted in the need to better monitor drought and its associated hydrologic and agricultural impacts. Two multiscalar drought indices, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI), are evaluated over Nevada and eastern California regions of the Great Basin using standardized streamflow, lake, and reservoir water surface stages to quantify wet and dry periods. Results show that both metrics are significantly correlated to surface water availability, with SPEI showing slightly higher correlations over SPI, suggesting that the inclusion of a simple term for atmospheric demand in SPEI is useful for characterizing hydrologic drought in arid regions. These results also highlight the utility of multiscalar drought indices as a proxy for summer groundwater discharge and baseflow periods.

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Daniel J. McEvoy, Justin L. Huntington, Michael T. Hobbins, Andrew Wood, Charles Morton, Martha Anderson, and Christopher Hain

Abstract

Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

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Justin L. Huntington, Katherine C. Hegewisch, Britta Daudert, Charles G. Morton, John T. Abatzoglou, Daniel J. McEvoy, and Tyler Erickson

Abstract

The paucity of long-term observations, particularly in regions with heterogeneous climate and land cover, can hinder incorporating climate data at appropriate spatial scales for decision-making and scientific research. Numerous gridded climate, weather, and remote sensing products have been developed to address the needs of both land managers and scientists, in turn enhancing scientific knowledge and strengthening early-warning systems. However, these data remain largely inaccessible for a broader segment of users given the computational demands of big data. Climate Engine (http://ClimateEngine.org) is a web-based application that overcomes many computational barriers that users face by employing Google’s parallel cloud-computing platform, Google Earth Engine, to process, visualize, download, and share climate and remote sensing datasets in real time. The software application development and design of Climate Engine is briefly outlined to illustrate the potential for high-performance processing of big data using cloud computing. Second, several examples are presented to highlight a range of climate research and applications related to drought, fire, ecology, and agriculture that can be rapidly generated using Climate Engine. The ability to access climate and remote sensing data archives with on-demand parallel cloud computing has created vast opportunities for advanced natural resource monitoring and process understanding.

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Michael T. Hobbins, Andrew Wood, Daniel J. McEvoy, Justin L. Huntington, Charles Morton, Martha Anderson, and Christopher Hain

Abstract

Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E 0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E 0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E 0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E 0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E 0, as in sustained droughts, and 2) parallel ET and E 0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E 0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E 0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in .

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Katja Friedrich, Robert L. Grossman, Justin Huntington, Peter D. Blanken, John Lenters, Kathleen D. Holman, David Gochis, Ben Livneh, James Prairie, Erik Skeie, Nathan C. Healey, Katharine Dahm, Christopher Pearson, Taryn Finnessey, Simon J. Hook, and Ted Kowalski

Abstract

One way to adapt to and mitigate current and future water scarcity is to manage and store water more efficiently. Reservoirs act as critical buffers to ensure agricultural and municipal water deliveries, mitigate flooding, and generate hydroelectric power, yet they often lose significant amounts of water through evaporation, especially in arid and semiarid regions. Despite this fact, reservoir evaporation has been an inconsistently and inaccurately estimated component of the water cycle within the water resource infrastructure of the arid and semiarid western United States. This paper highlights the increasing importance and challenges of correctly estimating and forecasting reservoir evaporation in the current and future climate, as well as the need to bring new ideas and state-of-the-art practices for the estimation of reservoir evaporation into operational use for modern water resource managers. New ideas and practices include i) improving the estimation of reservoir evaporation using up-to-date knowledge, state-of-the-art instrumentation and numerical models, and innovative experimental designs to diagnose processes and accurately forecast evaporation; ii) improving our understanding of spatial and temporal variations in evaporative water loss from existing reservoirs and transferring this knowledge when expanding reservoirs or siting new ones; and iii) implementing an adaptive management plan that incorporates new knowledge, observations, and forecasts of reservoir evaporation to improve water resource management.

Open access