A 10% increase in Global Land Evapotranspiration from 2003 to 2019
Explore the source record for details and available documents.
Madeleine Pascolini-Campbell↗
Engineering topics
Matthew Rodell
Publications and source records attributed to Matthew Rodell.
Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment
The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.
Data Assimilation↗
A High-Resolution Land Data Assimilation System Optimized for the Western United States
The Western Land Data Assimilation System (WLDAS) is a custom instance of the NASA Land Information System that combines land surface parameters, meteorological forcing data, and satellite products within a land surface model to produce daily estimates of the water and energy budget variables for the western United States. WLDAS was configured through discussions with partners, with the goal of groundwater sustainability planning for the state of California in mind. The publicly available output dataset has a 1-km grid resolution and spans 1979–present, which makes it suitable for water resources assessments. The data are also able to contextualize the recent drought events in California. Assimilation of Leaf Area Index, which is demonstrated herein to improve simulation over agricultural areas in California, specifically in terms of evapotranspiration in irrigation regions, will be included along with other data assimilation in subsequent releases of WLDAS.
land surface modeling↗
Groundwater Recharge Estimated by Land Surface Models: An Evaluation in the Conterminous U.S.
Estimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.
snowmelt↗
An Evaluation of Remotely Sensed and In-Situ Data Sufficiency for SGMA-Scale Groundwater Studies in the Central Valley, California
California’s Central Valley aquifer is a critical freshwater resource for the state, providing drinking water to 6.5 million residents and irrigation water for more than half of the nation’s produce. Increasing demands have led to a steady decline of aquifer water levels, leading to irreversible compaction and land subsidence. The Sustainable Groundwater Management Act of California (SGMA), instituted in 2014, requires high-priority regions to establish sustainability plans by 2020-2022 that mitigate groundwater depletion and land subsidence. Many regions within the Central Valley lack groundwater monitoring wells or continuous well data, rendering in-situ monitoring challenging. When combined with other data, NASA’s Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions provide valuable information about groundwater storage changes at a sub-basin scale. Interferometric Surface Aperture Radar (InSAR) measurements can map land subsidence, and Global Positioning System (GPS) can be used to estimate crustal uplift. Yet, all of these measurements have differences in resolution, coverage, discretization, and record length. Here we assess the various datasets that are potentially useful for Central Valley sub-basin groundwater monitoring and provide an analysis of gaps and other issues in regard to their suitability for SGMA-related analysis. Finally, we offer next steps and recommendations on data treatment and integration.
Kyra H Kim↗
Irrigation Water Demand Sensitivity to Climate Variability across the Contiguous United States
Climate variability is an important driver of irrigation water use in many regions. Efforts to anticipate climate change impacts on future water availability can benefit from understanding how irrigation water demand has responded to these drivers to date. Here we apply satellite derived data, meteorological reanalysis, an advanced land surface model, and available state level reports to quantify irrigation demand sensitivities to temperature and precipitation across the Contiguous United States, for the period of 2002-2017. As expected, strong negative correlations are found between precipitation and irrigation withdrawals, both simulated and reported. Temperature sensitivities, however, vary by region and season, as do the interactive effects of temperature and precipitation on irrigation. Climate-induced irrigation variability is largest in transitional climate zones. These transitional zones are generally separate from the regions where rates of irrigation withdrawals are greatest, such that climate-induced variability in irrigation demand represents a water resource consideration that is distinct from chronic over pumping.
Wanshu Nie↗
Groundwater Drought: Environmental Controls and Monitoring
Arising from a long-term deficit of precipitation, groundwater drought often lags meteorological drought by months to years, depending on climate and subsurface hydrogeological conditions. Due to the paucity of in situ groundwater data in many parts of the world, global monitoring of groundwater storage variations and drought may be best achieved using satellite observations and/or groundwater time series simulated by hydrological models. The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions have greatly benefitted the modeling and monitoring of groundwater storage changes and drought at the global scale. In this chapter, we first review environmental controls on the temporal variability of groundwater using in situ data. We then describe an approach that infuses GRACE and GRACE-FO observations into a land surface model for assessing groundwater storage changes and drought globally. We also discuss characteristics of simulated groundwater drought and the limitations of current groundwater drought monitoring approaches.
Bailing Li↗
Basin-Scale River Runoff Estimation from GRACE Gravity Satellites, Climate Models and In Situ Observations: a Case Study in the Amazon Basin
River runoff is estimated as a water budget residual using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage time series, ERA5 reanalysis data, and precipitation observations for January 2003 through December 2015 for the Obidos upstream drainage basin and for the entire Amazon basin. Estimated runoff based on the water budget agrees remarkably well with in situgauge observations at Obidos, especially at seasonal time scales, with nearly perfect phase agreementbut slightly larger seasonal amplitude. The discrepancy in the seasonal amplitude maybe attributed to underestimation of river gauge runoff during the wet season when water overflows the riverbanks. The ERA5 model appears to overestimate long-term mean evapotranspiration in the Amazon by ~2 cm/monthb ased on comparisons with precipitation and runoff observations. Using precipitation data based on satellites and gauge observations relative to gauge observations alone improved agreement between water budget runoff estimates and in situ runoff observations. Seasonal variations in ERA5 simulated runoff are about twice as large as those from in situobservations and show a large phase lag as well. Water budget based runoff for the entire Amazon (~ 7,200 km3averaged 29over the 13 yr period) is significantly larger than observed runoff (~5,700 km3) at notably larger than previous estimates for the entire Amazon. These differences may be partly related to submarine runoff from the Amazon basin that cannot be captured by surface gauges.
Jianli Chen↗
Groundwater and Terrestrial Water Storage
Groundwater, soil moisture, surface water, snow, and ice are the components of terrestrial water storage (TWS). On multi-annual timescales groundwater typically controls TWS variability, except in permanently frozen regions (Li et al. 2015). Even on an annual basis TWS changes are a reasonable proxy for groundwater storage changes, the latter being insufficiently observed in most of the world. From 2002 to 2017 the Gravity Recovery and Climate Experiment (GRACE; Tapley et al. 2004) and since 2018 the GRACE Follow On (GRACE-FO) satellite missions have enabled estimation of TWS anomalies (departures from the long-term mean) based on precise observations of variations in Earth’s gravity field. To bridge the 11-month gap between GRACE and GRACE-FO we make use of output from a land surface model that assimilates data from both missions (Li et al. 2019)
Matthew Rodell↗
Underlying Fundamentals of Kalman Filtering for River Network Modeling
The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from211.32 to 0.55 at observed gauges and from212.24 to21.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.
Streamflow↗
GRACE Improves Seasonal Groundwater Forecast Initialization Over the United States
We evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the United States, focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the2003–16 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth to water table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts toward an improved U.S. drought monitoring and forecast system.
Augusto Getirana↗
NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment
Terrestrial hydrologic trends over the conterminous United States are estimated for 1980-47 2015 using the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah Version 3.3 land surface model at 0.125 degreesx0.125 degrees forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth and irrigation products. Mean annual trends are reported using the nonparametric Mann-Kendall test at p<0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends, and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the Upper Great Plains and Northeast to -1 to -9 in the West and South; net radiation flux trends range from +0.05 to +0.20 W/m(exp2)/yr in the East to -0.05 to -0.20 in the West; U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from West to East. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%; Trends in latent heat flux were hardly affected, RMSE decreasing only 0.17 to 0.16 W/m(exp 2)/yr, while trend detection increased 2%; NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gaged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.
Snow depth↗
NCA-LDAS: Overview and Analysis of Hydrologic Trends for the National Climate Assessment
Terrestrial hydrologic trends over the conterminous United States are estimated for 1980–2015 using the National Climate Assessment Land Data Assimilation System (NCA-LDAS) reanalysis. NCA-LDAS employs the uncoupled Noah version 3.3 land surface model at 0.125° 3 0.125° forced with NLDAS-2 meteorology, rescaled Climate Prediction Center precipitation, and assimilated satellite-based soil moisture, snow depth, and irrigation products. Mean annual trends are reported using the nonparametric Mann–Kendall test at p < 0.1 significance. Results illustrate the interrelationship between regional gradients in forcing trends and trends in other land energy and water stores and fluxes. Mean precipitation trends range from +3 to +9 mm/yr in the upper Great Plains and Northeast to -1 to -9 mm/yr in the West and South, net radiation flux trends range from +0.05 to +0.20 W/sq. m yr in the East to -0.05 to -0.20 W/sq. m yr in the West, and U.S.-wide temperature trends average about +0.03K/yr. Trends in soil moisture, snow cover, latent and sensible heat fluxes, and runoff are consistent with forcings, contributing to increasing evaporative fraction trends from west to east. Evaluation of NCA-LDAS trends compared to independent data indicates mixed results. The RMSE of U.S.-wide trends in number of snow cover days improved from 3.13 to 2.89 days/yr while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W/sq. m yr, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm/yr while trend detection was unaffected. Analysis also indicated that NCA-LDAS exhibits relatively more skill in low precipitation station density areas, suggesting there are limits to the effectiveness of satellite data assimilation in densely gauged regions. Overall, NCA-LDAS demonstrates capability for quantifying physically consistent, U.S. hydrologic climate trends over the satellite era.
Michael F. Jasinski↗
Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models
This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5-7 decades with modeled rates ranging from 0.01 to 2.18 mm year-1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.
Groundwater storage↗
Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models
This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate experiment (GRACe) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5–7 decades with modeled rates ranging from 0.01 to 2.18 mm year−1. this large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.
Bailing Li↗
Chapter Two: Terrestrial Water Storage
Explore the source record for details and available documents.
water storage droughts↗
Earth Observations and Integrative Models in Support of Food and Water Security
Global food production depends upon many factors that Earth observing satellites routinely measure about water, energy, weather, and ecosystems. Increasingly sophisticated, publicly-available satellite data products can improve efficiencies in resource management and provide earlier indication of environmental disruption. Satellite remote sensing provides a consistent, long-term record that can be used effectively to detect large-scale features over time, such as a developing drought. Accuracy and capabilities have increased along with the range of Earth observations and derived products that can support food security decisions with actionable information. This paper highlights major capabilities facilitated by satellite observations and physical models that have been developed and validated using remotely-sensed observations. Although we primarily focus on variables relevant to agriculture, we also include a brief description of the growing use of Earth observations in support of aquaculture and fisheries.
Water Resources↗
Comparing Potential Recharge Estimates From Three Land Surface Models Across the Western US
Groundwater is a major source of water in the western US. However, there are limited recharge estimates available in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01-15% for Mosaic, 3.2-42% for Noah, and 6.7-31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge rates in data limited regions.
groundwater↗