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Bailing Li, Matthew Rodell, Christa Peters-Lidard, Jessica Erlingis, Sujay Kumar, and David Mocko

Abstract

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 United States 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 United States 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.

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Youlong Xia, David Mocko, Maoyi Huang, Bailing Li, Matthew Rodell, Kenneth E. Mitchell, Xitian Cai, and Michael B. Ek

Abstract

To prepare for the next-generation North American Land Data Assimilation System (NLDAS), three advanced land surface models [LSMs; i.e., Community Land Model, version 4.0 (CLM4.0); Noah LSM with multiphysics options (Noah-MP); and Catchment LSM-Fortuna 2.5 (CLSM-F2.5)] were run for the 1979–2014 period within the NLDAS-based framework. Unlike the LSMs currently executing in the operational NLDAS, these three advanced LSMs each include a groundwater component. In this study, the model simulations of monthly terrestrial water storage anomaly (TWSA) and its individual water storage components are evaluated against satellite-based and in situ observations, as well as against reference reanalysis products, at basinwide and statewide scales. The quality of these TWSA simulations will contribute to determining the suitability of these models for the next phase of the NLDAS. Overall, it is found that all three models are able to reasonably capture the monthly and interannual variability and magnitudes of TWSA. However, the relative contributions of the individual water storage components to TWSA are very dependent on the model and basin. A major contributor to the TWSA is the anomaly of total column soil moisture content for CLM4.0 and Noah-MP, while the groundwater storage anomaly is the major contributor for CLSM-F2.5. Other water storage components such as the anomaly of snow water equivalent also play a role in all three models. For each individual water storage component, the models are able to capture broad features such as monthly and interannual variability. However, there are large intermodel differences and quantitative uncertainties, which are motivating follow-on investigations in the NLDAS Science Testbed developed by the NASA and NCEP NLDAS teams.

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Sujay V. Kumar, Michael Jasinski, David M. Mocko, Matthew Rodell, Jordan Borak, Bailing Li, Hiroko Kato Beaudoing, and Christa D. Peters-Lidard

Abstract

This article describes one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover, and irrigation intensity environmental data records (EDRs) from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), Advanced Scatterometer (ASCAT), Moderate-Resolution Imaging Spectroradiometer (MODIS), Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), Soil Moisture Ocean Salinity (SMOS) mission, and Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of the NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing it to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and evapotranspiration. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g., SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g., SMMR, SSM/I, AMSR-E). The evaluation also indicates the high skill of NCA-LDAS when compared with other LSM products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of the western United States during 1979–2015, particularly in the southwestern United States, consistent with the trends from the U.S. Drought Monitor, albeit for a shorter 2000–15 time period.

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Sujay V. Kumar, Benjamin F. Zaitchik, Christa D. Peters-Lidard, Matthew Rodell, Rolf Reichle, Bailing Li, Michael Jasinski, David Mocko, Augusto Getirana, Gabrielle De Lannoy, Michael H. Cosh, Christopher R. Hain, Martha Anderson, Kristi R. Arsenault, Youlong Xia, and Michael Ek

Abstract

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

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Michael F. Jasinski, Jordan S. Borak, Sujay V. Kumar, David M. Mocko, Christa D. Peters-Lidard, Matthew Rodell, Hualan Rui, Hiroko K. Beaudoing, Bruce E. Vollmer, Kristi R. Arsenault, Bailing Li, John D. Bolten, and Natthachet Tangdamrongsub

Abstract

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° × 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−1 in the upper Great Plains and Northeast to −1 to −9 mm yr−1 in the West and South, net radiation flux trends range from +0.05 to +0.20 W m−2 yr−1 in the East to −0.05 to −0.20 W m−2 yr−1 in the West, and U.S.-wide temperature trends average about +0.03 K yr−1. 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−1 while trend detection increased 11%. Trends in latent heat flux were hardly affected, with RMSE decreasing only from 0.17 to 0.16 W m−2 yr−1, while trend detection increased 2%. NCA-LDAS runoff trends degraded significantly from 2.6 to 16.1 mm yr−1 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.

Open access
M. Ades, R. Adler, Rob Allan, R. P. Allan, J. Anderson, Anthony Argüez, C. Arosio, J. A. Augustine, C. Azorin-Molina, J. Barichivich, J. Barnes, H. E. Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier. Bock, Michael G. Bosilovich, Olivier. Boucher, S. A. Buehler, Laura. Carrea, Hanne H. Christiansen, F. Chouza, John R. Christy, E.-S. Chung, Melanie Coldewey-Egbers, Gil P. Compo, Owen R. Cooper, Curt Covey, A. Crotwell, Sean M. Davis, Elvira de Eyto, Richard A. M de Jeu, B.V. VanderSat, Curtis L. DeGasperi, Doug Degenstein, Larry Di Girolamo, Martin T. Dokulil, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, G. Duveiller, James W. Elkins, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Richard A. Frey, Stacey M. Frith, Lucien Froidevaux, J. Garforth, S. K. Gupta, Leopold Haimberger, Brad D. Hall, Ian Harris, Andrew K Heidinger, D. L. Hemming, Shu-peng (Ben) Ho, Daan Hubert, Dale F. Hurst, I. Hüser, Antje Inness, K. Isaksen, Viju John, Philip D. Jones, J. W. Kaiser, S. Kelly, S. Khaykin, R. Kidd, Hyungiun Kim, Z. Kipling, B. M. Kraemer, D. P. Kratz, R. S. La Fuente, Xin Lan, Kathleen O. Lantz, T. Leblanc, Bailing Li, Norman G Loeb, Craig S. Long, Diego Loyola, Wlodzimierz Marszelewski, B. Martens, Linda May, Michael Mayer, M. F. McCabe, Tim R. McVicar, Carl A. Mears, W. Paul Menzel, Christopher J. Merchant, Ben R. Miller, Diego G. Miralles, Stephen A. Montzka, Colin Morice, Jens Mühle, R. Myneni, Julien P. Nicolas, Jeannette Noetzli, Tim J. Osborn, T. Park, A. Pasik, Andrew M. Paterson, Mauri S. Pelto, S. Perkins-Kirkpatrick, G. Pétron, C. Phillips, Bernard Pinty, S. Po-Chedley, L. Polvani, W. Preimesberger, M. Pulkkanen, W. J. Randel, Samuel Rémy, L. Ricciardulli, A. D. Richardson, L. Rieger, David A. Robinson, Matthew Rodell, Karen H. Rosenlof, Chris Roth, A. Rozanov, James A. Rusak, O. Rusanovskaya, T. Rutishäuser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, S. Geoffey Schladow, R. W Schlegel, Eawag Schmid, Martin, H. B. Selkirk, S. Sharma, Lei Shi, S. V. Shimaraeva, E. A. Silow, Adrian J. Simmons, C. A. Smith, Sharon L Smith, B. J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse Jr., Wolfgang Steinbrecht, Dimitri A. Streletskiy, G. Taha, Hagen Telg, S. J. Thackeray, M. A. Timofeyev, Kleareti Tourpali, Mari R. Tye, Ronald J. van der A, Robin, VanderSat B.V. van der Schalie, Gerard van der SchrierW. Paul, Guido R. van der Werf, Piet Verburg, Jean-Paul Vernier, Holger Vömel, Russell S. Vose, Ray Wang, Shohei G. Watanabe, Mark Weber, Gesa A. Weyhenmeyer, David Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xungang Yin, Lin Zhao, Guanguo Zhao, Xinjia Zhou, Jerry R. Ziemke, and Markus Ziese
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