Estimating Annual Groundwater Evapotranspiration from Phreatophytes in the Great Basin Using Landsat and Flux Tower Measurements

Escalating concerns about water supplies in the Great Basin have prompted numerous water budget studies focused on groundwater recharge and discharge. For many hydrographic areas (HAs) in the Great Basin, most of the recharge is discharged by bare soil evaporation and evapotranspiration (ET) from phreatophyte vegetation. Estimating recharge from precipitation in a given HA is difficult and often has significant uncertainty, therefore it is often quantified by estimating the natural discharge. As such, remote sensing applications for spatially distributing flux tower estimates of ET and groundwater ET (ET_g) across phreatophyte areas are becoming more common. We build on previous studies and develop a transferable empirical relationship with uncertainty bounds between flux tower estimates of ET and a remotely sensed vegetation index, Enhanced Vegetation Index (EVI). Energy balance‐corrected ET measured from 40 flux tower site‐year combinations in the Great Basin was statistically correlated with EVI derived from Landsat imagery (r² = 0.97). Application of the relationship to estimate mean‐annual ET_g from four HAs in western and eastern Nevada is highlighted and results are compared with previous estimates. Uncertainty bounds about the estimated mean ET_g allow investigators to evaluate if independent groundwater discharge estimates are “believable” and will ultimately assist local, state, and federal agencies to evaluate expert witness reports of ET_g, along with providing new first‐order estimates of ET_g.     

Benchmarking Optical/Thermal Satellite Imagery for Estimating Evapotranspiration and Soil Moisture in Decision Support Tools

Generally, one expects evapotranspiration (ET) maps derived from optical/thermal Landsat and MODIS satellite imagery to improve decision support tools and lead to superior decisions regarding water resources management. However, there is lack of supportive evidence to accept or reject this expectation. We “benchmark” three existing hydrologic decision support tools with the following benchmarks: annual ET for the ET Toolbox developed by the United States Bureau of Reclamation, predicted rainfall‐runoff hydrographs for the Gridded Surface/Subsurface Hydrologic Analysis model developed by the U.S. Army Corps of Engineers, and the average annual groundwater recharge for the Distributed Parameter Watershed Model used by Daniel B. Stephens & Associates. The conclusion of this benchmark study is that the use of NASA/USGS optical/thermal satellite imagery can considerably improve hydrologic decision support tools compared to their traditional implementations. The benefits of improved decision making, resulting from more accurate results of hydrologic support systems using optical/thermal satellite imagery, should substantially exceed the costs for acquiring such imagery and implementing the remote sensing algorithms. In fact, the value of reduced error in estimating average annual groundwater recharge in the San Gabriel Mountains, California alone, in terms of value of water, may be as large as $1 billion, more than sufficient to pay for one new Landsat satellite.     

Estimating Crop Water Use via Remote Sensing Techniques vs. Conventional Methods in the South Platte River Basin,Colorado

We compared two methods of estimating crop water consumption to assess whether remote sensing techniques provide consumptive use (CU) estimates commensurate with conventional methods. Using available historical satellite and meteorological data, we applied Mapping EvapoTranspiration at high Resolution using Internalized Calibration (METRIC) to 317,455 ha in the South Platte basin, in northeastern Colorado, for the 2001 irrigation season. We then compared these derived CU estimates with values calculated by using the Colorado Water Conservation Board's South Platte Decision Support System StateCU model. Evaluating the data by irrigation ditch service area, we disaggregated the output to allow for comparison by service area size, crop type, irrigation method, water supply source, and water availability. We concluded that METRIC is a suitable alternative to StateCU in the South Platte basin and could help to identify areas with inhibited crop growth or deficit irrigation practices. In addition, METRIC could be used as a complement to StateCU to refine StateCU model parameters, allowing for more accurate estimates of crop water shortages and groundwater recharge associated with irrigation delivery and application.     

Calibration and Uncertainty Analysis Using the Sparrow Model for Dissolved‐Solids Transport in the Upper Colorado River Basin

Salinity in the Upper Colorado River Basin (UCRB) is due to both natural sources and processes, and anthropogenic activities. Given economic damage due to salinity of $295 million in 2010, understanding salinity sources and production together with transport are of great importance. SPAtially Referenced Regressions On Watershed (SPARROW) is a nonlinear regression water quality model that simulates sources and transport of contaminants such as dissolved‐solids. However, SPARROW simulations of dissolved‐solids in the UCRB only represent conditions through 1998 due to limited data availability. More importantly, prior simulations focused on a single year calibration and its transferability to other years, and the validity of this approach is questionable, given the changing hydrologic and climatic conditions. This study presents different calibration approaches to assess the best approach for reducing model uncertainty. This study conducted simulations from 1999 to 2011, and the results showed good model accuracy. However, the number of monitoring stations decreased significantly in recent years resulting in higher model uncertainty. The uncertainty analysis was conducted using SPARROW results and bootstrapping. The results suggest that the watershed rankings based on salinity yields changed due to the uncertainty analysis and therefore, uncertainty consideration should be an important part of the management strategy.     

A Calibrated,High‐Resolution GOES Satellite Solar Insolation Product for a Climatology of Florida Evapotranspiration1

Paech, Simon J., John R. Mecikalski, David M. Sumner, Chandra S. Pathak, Quinlong Wu, Shafiqul Islam, and Taiye Sangoyomi, 2009. A Calibrated, High‐Resolution GOES Satellite Solar Insolation Product for a Climatology of Florida Evapotranspiration. Journal of the American Water Resources Association (JAWRA) 45(6):1328‐1342. Abstract: Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10‐year period (1995‐2004). These insolation estimates were developed into well‐calibrated half‐hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2‐week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground‐based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three‐step process: (1) comparison with ground‐based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station‐averaged model error of 2.2 MJ m^?2/day (13%). Calibration reduced errors to 1.7 MJ m^?2/day (10%), and also removed temporal‐related, seasonal‐related, and satellite sensor‐related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2‐km resolution maps of estimated daily reference and potential evapotranspiration for water management‐related activities.     

Calculating Field‐Scale Evapotranspiration with Closed‐Chamber and Remote Sensing Methods

Currently, there is no agreed upon method for estimating evapotranspiration (ET) across large regions such as the state of New Mexico. Remote sensing methods have potential for providing a solution, but require validation. A comparison between field‐scale ET measurements using a portable chamber ET measurement device and modeled ET using the remote sensing Regional Evapotranspiration Estimation Model (REEM) was performed where the model had not been previously evaluated. Data were collected during the growing season of 2015 in three irrigated agricultural valleys of northern New Mexico in agricultural and nonagricultural settings. No statistically significant difference was observed in agricultural datasets between means of measured (M = 3.7 mm/day, SE = 0.31 mm/day) and modeled (M = 4.0 mm/day, SE = 0.01 mm/day) daily ET; t(17) = ?1.50, p = 0.15, α = 0.05. As there was no statistical difference observed between agricultural datasets, results support the use of REEM in irrigated agricultural areas of northern New Mexico. A statistically significant difference was observed in nonagricultural datasets between means of measured (M = 1.7 mm/day, SE = 0.22 mm/day) and modeled (M = 0.0 mm/day, SE = 0.00 mm/day) daily ET; t(9) = 1.79, p = 5.7 × 10^?6, α = 0.05. With additional calibrations and air temperature sensors placed outside of agricultural areas, REEM may be suitable for use in nonagricultural areas of northern New Mexico.     

Automated Calibration of the METRIC‐Landsat Evapotranspiration Process

A remaining challenge to applying satellite‐based energy‐balance algorithms for operational estimation of evapotranspiration (ET) is the calibration of the energy‐balance model. Customized calibration for each image date is generally required to overcome biases associated with radiometric accuracy of the image, uncertainties in aerodynamic features of the landscape, background thermal conditions, and model assumptions. The CIMEC process (calibration using inverse modeling at extreme conditions) is an endpoint calibration procedure where near extreme conditions in the image are identified where the ET can be estimated and assigned. In the Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC?) energy‐balance model, two endpoints represent the dry and wet ends of the ET spectrum. Generally, user‐intervention is required to select locations in the image to produce best accuracy. To bring the METRIC and similar processes into the domain of less experienced operators, a consistent, reproducible, and dependable statistics‐based procedure is introduced where relationships between vegetation amount and surface temperature are used to identify a subpopulation of locations (pixels) in an image that may best represent the calibration endpoints. This article describes the background and logic for the statistical approach, how the statistics were developed, area of interest requirements and assumptions, adjustment for dry conditions in desert climates, and implementation in a common image processing environment (ERDAS Imagine).     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin,Texas

Quality of precipitation products from the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) was evaluated over the Lower Colorado River Basin of Texas. Observations of several rainfall events of a wide range of magnitudes during May 2015 by a very dense network of 241 rain gauges over the basin were used as a reference. The impact of temporal and spatial downscaling of different satellite products (near/post‐real‐time) on their accuracy was studied. Generally, all IMERG products perform better when the temporal and spatial resolutions are downscaled. The Final product shows relatively better performance compared to the near‐real‐time products in terms of basic performance measures; however, regarding rainfall detection, all products show nearly similar performance. When considering rainfall detection, IMERG adequately captures the precipitation events; however, in terms of spatial patterns and accuracy, more improvements are needed. IMERG products analysis results may help developers gain insight into the regional performance of the product, improve the product algorithms, and provide information to end users on the products’ suitability for potential hydrometeorological applications. Overall, the IMERG products, even the uncalibrated product at its finest resolution, showed reasonable performance indicating their great potential for applications such as water resources management, prevention of natural disasters, and flood forecasting.     

Assessing Calibration Uncertainty and Automation for Estimating Evapotranspiration from Agricultural Areas Using METRIC

Agricultural irrigation accounts for a large fraction of the total water use in the western United States. The Mapping Evapotranspiration at high Resolution with Internalized Calibration (METRIC) remote sensing energy balance model is being used to estimate historical agricultural water use in western Nevada to evaluate basin‐wide water budgets. Each METRIC evapotranspiration (ET) estimate must be calibrated by a trained user, which requires some iterative time investment and results in variation in ET estimates between users. An automated calibration algorithm for the METRIC model was designed to generate ET estimates comparable to those from trained users by mimicking the manual calibration process. Automated calibration allows for rapid generation of METRIC ET estimates with minimal manual intervention, as well as uncertainty and sensitivity analysis of the model. The variation in ET estimates generated by the automated calibration algorithm was found to be similar to the variation in manual ET estimates. Results indicate that uncertainty was highest for fields with low ET levels and lowest for fields with high ET levels, with a seasonal mean uncertainty of approximately 5% for all fields. In addition, in a blind comparison, automated daily and seasonal ET estimates compared well with flux tower measurement ET data at multiple sites. Automated methods can generate first‐order ET estimates that are similar to time intensive manual efforts with less time investment.     

A High‐Resolution National‐Scale Hydrologic Forecast System from a Global Ensemble Land Surface Model

Warning systems with the ability to predict floods several days in advance have the potential to benefit tens of millions of people. Accordingly, large‐scale streamflow prediction systems such as the Advanced Hydrologic Prediction Service or the Global Flood Awareness System are limited to coarse resolutions. This article presents a method for routing global runoff ensemble forecasts and global historical runoff generated by the European Centre for Medium‐Range Weather Forecasts model using the Routing Application for Parallel computatIon of Discharge to produce high spatial resolution 15‐day stream forecasts, approximate recurrence intervals, and warning points at locations where streamflow is predicted to exceed the recurrence interval thresholds. The processing method involves distributing the computations using computer clusters to facilitate processing of large watersheds with high‐density stream networks. In addition, the Streamflow Prediction Tool web application was developed for visualizing analyzed results at both the regional level and at the reach level of high‐density stream networks. The application formed part of the base hydrologic forecasting service available to the National Flood Interoperability Experiment and can potentially transform the nation's forecast ability by incorporating ensemble predictions at the nearly 2.7 million reaches of the National Hydrography Plus Version 2 Dataset into the national forecasting system.     

Analyzing the Variability of Remote Sensing and Hydrologic Model Evapotranspiration Products in a Watershed in Michigan

The goal of this paper was to statistically explore the spatiotemporal performance of remotely sensed actual evapotranspiration (ETa) datasets and a remotely sensed ensemble in a region that lacks observed data. The remotely sensed datasets were further compared with ETa results from a physically based hydrologic model (Soil and Water Assessment Tool) to examine the differences and determine the level of agreement between the ETa datasets and the model outputs. ETa datasets were compared on temporal (i.e., monthly and seasonal basis) and spatial (i.e., landuse) scales at both watershed and subbasin levels. The results showed a lack of consistent similarities and differences among the datasets when evaluating the monthly ETa variations; however, the seasonal aggregated data presented more consistent similarities and differences during the spring and summer compared to the fall and winter. Meanwhile, spatial analysis of the datasets showed the MOD16A2 500 m ETa product was the most versatile of the tested datasets, being able to differentiate between landuses during all seasons. Furthermore, the use of an averaging ensemble was able to improve overall ETa performance in the study area. This study showed that the remotely sensed ETa products are not similar throughout the year, but the appropriate application periods for different ETa products were identified. Finally, spatial variabilities of the ETa products are more in tune with landuse and climate characteristics.     

Operational Evapotranspiration Mapping Using Remote Sensing and Weather Datasets: A New Parameterization for the SSEB Approach

The increasing availability of multi‐scale remotely sensed data and global weather datasets is allowing the estimation of evapotranspiration (ET) at multiple scales. We present a simple but robust method that uses remotely sensed thermal data and model‐assimilated weather fields to produce ET for the contiguous United States (CONUS) at monthly and seasonal time scales. The method is based on the Simplified Surface Energy Balance (SSEB) model, which is now parameterized for operational applications, renamed as SSEBop. The innovative aspect of the SSEBop is that it uses predefined boundary conditions that are unique to each pixel for the “hot” and “cold” reference conditions. The SSEBop model was used for computing ET for 12 years (2000‐2011) using the MODIS and Global Data Assimilation System (GDAS) data streams. SSEBop ET results compared reasonably well with monthly eddy covariance ET data explaining 64% of the observed variability across diverse ecosystems in the CONUS during 2005. Twelve annual ET anomalies (2000‐2011) depicted the spatial extent and severity of the commonly known drought years in the CONUS. More research is required to improve the representation of the predefined boundary conditions in complex terrain at small spatial scales. SSEBop model was found to be a promising approach to conduct water use studies in the CONUS, with a similar opportunity in other parts of the world. The approach can also be applied with other thermal sensors such as Landsat.     

Continental‐Scale River Flow Modeling of the Mississippi River Basin Using High‐Resolution NHDPlus Dataset

As a key component of the National Flood Interoperability Experiment (NFIE), this article presents the continental scale river flow modeling of the Mississippi River Basin (MRB), using high‐resolution river data from NHDPlus. The Routing Application for Parallel computatIon of Discharge (RAPID) was applied to the MRB with more than 1.2 million river reaches for a 10‐year study (2005‐2014). Runoff data from the Variable Infiltration Capacity (VIC) model was used as input to RAPID. This article investigates the effect of topography on RAPID performance, the differences between the VIC‐RAPID streamflow simulations in the HUC‐2 regions of the MRB, and the impact of major dams on the streamflow simulations. The model performance improved when initial parameter values, especially the Muskingum K parameter, were estimated by taking topography into account. The statistical summary indicates the RAPID model performs better in the Ohio and Tennessee Regions and the Upper and Lower Mississippi River Regions in comparison to the western part of the MRB, due to the better performance of the VIC model. The model accuracy also increases when lakes and reservoirs are considered in the modeling framework. In general, results show the VIC‐RAPID streamflow simulation is satisfactory at the continental scale of the MRB.     

Estimation of Evapotranspiration Across the Conterminous United States Using a Regression With Climate and Land‐Cover Data1

Sanford, Ward E. and David L. Selnick, 2012. Estimation of Evapotranspiration Across the Conterminous United States Using a Regression with Climate and Land‐Cover Data. Journal of the American Water Resources Association (JAWRA) 1‐14. DOI: 10.1111/jawr.12010 Abstract: Evapotranspiration (ET) is an important quantity for water resource managers to know because it often represents the largest sink for precipitation (P) arriving at the land surface. In order to estimate actual ET across the conterminous United States (U.S.) in this study, a water‐balance method was combined with a climate and land‐cover regression equation. Precipitation and streamflow records were compiled for 838 watersheds for 1971‐2000 across the U.S. to obtain long‐term estimates of actual ET. A regression equation was developed that related the ratio ET/P to climate and land‐cover variables within those watersheds. Precipitation and temperatures were used from the PRISM climate dataset, and land‐cover data were used from the USGS National Land Cover Dataset. Results indicate that ET can be predicted relatively well at a watershed or county scale with readily available climate variables alone, and that land‐cover data can also improve those predictions. Using the climate and land‐cover data at an 800‐m scale and then averaging to the county scale, maps were produced showing estimates of ET and ET/P for the entire conterminous U.S. Using the regression equation, such maps could also be made for more detailed state coverages, or for other areas of the world where climate and land‐cover data are plentiful.     

Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data1

Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges‐Brahmaputra‐Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313‐1327. Abstract: Large‐scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (?) 8% to (+) 20% in the Ganges basin, by (?) 15 to (+) 12% in the Brahmaputra basin, and by (?) 15 to (+) 19% in the Meghna basin. Our large‐scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs.     

Climatic Fluctuations and Forecasting of Streamflow in the Lower Colorado River Basin1

Abstract:  Water‐resource managers need to forecast streamflow in the Lower Colorado River Basin to plan for water‐resource projects and to operate reservoirs for water supply. Statistical forecasts of streamflow based on historical records of streamflow can be useful, but statistical assumptions, such as stationarity of flows, need to be evaluated. This study evaluated the relation between climatic fluctuations and stationarity and developed regression equations to forecast streamflow by using climatic fluctuations as explanatory variables. Climatic fluctuations were represented by the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and Southern Oscillation Index (SOI). Historical streamflow within the 25‐ to 30‐year positive or negative phases of AMO or PDO was generally stationary. Monotonic trends in annual mean flows were tested at the 21 sites evaluated in this study; 76% of the sites had no significant trends within phases of AMO and 86% of the sites had no significant trends within phases of PDO. As climatic phases shifted in signs, however, many sites had nonstationary flows; 67% of the sites had significant changes in annual mean flow as AMO shifted in signs. The regression equations developed in this study to forecast streamflow incorporate these shifts in climate and streamflow, thus that source of nonstationarity is accounted for. The R² value of regression equations that forecast individual years of annual flow for the central part of the study area ranged from 0.28 to 0.49 and averaged 0.39. AMO was the most significant variable, and a combination of indices from both the Atlantic and Pacific Oceans explained much more variation in flows than only the Pacific Ocean indices. The average R² value for equations with PDO and SOI was 0.15.     

Evaluation of CFSR climate data for hydrologic prediction in data‐scarce watersheds: an application in the Blue Nile River Basin

Data scarcity has been a huge problem in modeling the water resources of the Upper Blue Nile basin, Ethiopia. Satellite data and different statistical methods have been used to improve the quality of conventional meteorological data. This study assesses the applicability of the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) climate data in modeling the hydrology of the region. The Soil and Water Assessment Tool was set up to compare the performance of CFSR weather with that of conventional weather in simulating observed streamflow at four river gauging stations in the Lake Tana basin — the upper part of the Upper Blue Nile basin. The conventional weather simulation performed satisfactorily (e.g., NSE ≥ 0.5) for three gauging stations, while the CFSR weather simulation performed satisfactorily for two. The simulations with CFSR and conventional weather yielded minor differences in the water balance components in all but one watershed, where the CFSR weather simulation gave much higher average annual rainfall, resulting in higher water balance components. Both weather simulations gave similar annual crop yields in the four administrative zones. Overall the simulation with the conventional weather performed better than the CFSR weather. However, in data‐scarce regions such as remote parts of the Upper Blue Nile basin, CFSR weather could be a valuable option for hydrological predictions where conventional gauges are not available.     

The Water‐Year Water Balance of the Colorado River Basin

Model‐estimated monthly water balance components (i.e., potential evapotranspiration, actual evapotranspiration, and runoff (R)) for 146 United States (U.S.) Geological Survey 8‐digit hydrologic units located in the Colorado River Basin (CRB) are used to examine the temporal and spatial variability of the CRB water balance for water years 1901 through 2014 (a water year is the period from October 1 of one year through September 30 of the following year). Results indicate that the CRB can be divided into six subregions with similar temporal variability in monthly R. The water balance analyses indicated that approximately 75% of total water‐year R is generated by just one CRB subregion and that most of the R in the basin is derived from surplus (S) water generated during the months of October through April. Furthermore, the analyses show that temporal variability in S is largely controlled by the occurrence of negative atmospheric pressure anomalies over the northwestern conterminous U.S. (CONUS) and positive atmospheric pressure anomalies over the southeastern CONUS. This combination of atmospheric pressure anomalies results in an anomalous flow of moist air from the North Pacific Ocean into the CRB, particularly the Upper CRB. Additionally, the occurrence of extreme dry and wet periods in the CRB appears to be related to variability of the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.