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Karen I. Mohr, James S. Famiglietti, and Edward J. Zipser

Abstract

This study compiled a database of precipitating cloud clusters from 85-GHz data in 10 regions of the wet Tropics for a calendar year (November 1992–October 1993). The cloud clusters were grouped into four classes of basic system types, based on size (closed 250 K contour greater or less than 2000 km2) and minimum enclosed 85-GHz brightness temperature (greater or less than 225 K) to indicate the presence or absence of large areas of active, deep convection. For each cloud cluster, instantaneous volumetric rain rates (mm km2 h−1) were calculated using an 85-GHz ice-scattering-based rain-rate retrieval algorithm. Because the ice-scattering signature is linearly related to but does not directly measure rain rate, the methodology was appropriate for estimating relative contributions rather than quantifying tropical rainfall.

For the 3-month wet season of each study region, the rainfall contributions with respect to system type, size, and intensity were calculated. Regional differences were small among the contributions with respect to system type and to precipitating area. Although mesoscale convective systems constituted 10%–20% of the regional populations, they contributed 70%–80% of the rainfall. With respect to cloud cluster area, the top 10% of cloud cluster areas contributed more than 70% of the rainfall, and the top 1% (greater than 20 000 km2) contributed about 35% of the total rainfall. Regional differences were apparent in the distributions of rainfall contribution with respect to minimum brightness temperature. The Amazon’s distribution more closely resembled the oceanic distributions than the continental distributions. The distributions of the oceanic regions peaked at 200 K, and over half of the rain in the oceanic regions was contributed by the fewer than 20% of the cloud clusters colder than 210 K. Distributions in the continental regions peaked at 175 K. A total of 70%–80% of the rain was contributed by the 20%–30% of continental cloud clusters colder than 200 K, with nonnegligible contributions from a small number of cloud clusters colder than 120 K. Sub-Saharan Africa had the largest contribution from cloud clusters colder than 120 K.

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Tajdarul H. Syed, James S. Famiglietti, and Don P. Chambers

Abstract

In this study, new estimates of monthly freshwater discharge from continents, drainage regions, and global land for the period of 2003–05 are presented. The method uses observed terrestrial water storage change estimates from the Gravity Recovery and Climate Experiment (GRACE) and reanalysis-based atmospheric moisture divergence and precipitable water tendency in a coupled land–atmosphere water mass balance. The estimates of freshwater discharge are analyzed within the context of global climate and compared with previously published estimates. Annual cycles of observed streamflow exhibit stronger correlations with the computed discharge compared to those with precipitation minus evapotranspiration (PE) in several of the world’s largest river basins. The estimate presented herein of the mean monthly discharge from South America (∼846 km3 month−1) is the highest among the continents and that flowing into the Atlantic Ocean (∼1382 km3 month−1) is the highest among the drainage regions. The volume of global freshwater discharge estimated here is 30 354 ± 1212 km3 yr−1. Monthly variations of global freshwater discharge peak between August and September and reach a minimum in February. Global freshwater discharge is also computed using a global ocean–atmosphere mass balance in order to validate the land–atmosphere water balance estimates and as a measure of global water budget closure. Results show close proximity between the two estimates of global discharge at monthly (RMSE = 329 km3 month−1) and annual time scales (358 km3 yr−1). Results and comparisons to observations indicate that the method shows important potential for global-scale monitoring of combined surface water and submarine groundwater discharge at near-real time, as well as for contributing to contemporary global water balance studies and for constraining global hydrologic model simulations.

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Jefferson S. Wong, Xuebin Zhang, Shervan Gharari, Rajesh R. Shrestha, Howard S. Wheater, and James S. Famiglietti

Abstract

Obtaining reliable water balance estimates remains a major challenge in Canada for large regions with scarce in situ measurements. Various remote sensing products can be used to complement observation-based datasets and provide an estimate of the water balance at river basin or regional scales. This study provides an assessment of the water balance using combinations of various remote sensing– and data assimilation–based products and quantifies the nonclosure errors for river basins across Canada, ranging from 90 900 to 1 679 100 km2, for the period from 2002 to 2015. A water balance equation combines the following to estimate the monthly water balance closure: multiple sources of data for each water budget component, including two precipitation products—the global product WATCH Forcing Data ERA-Interim (WFDEI), and the Canadian Precipitation Analysis (CaPA); two evapotranspiration products—MODIS, and Global Land surface Evaporation: The Amsterdam Methodology (GLEAM); one source of water storage data—GRACE from three different centers; and observed discharge data from hydrometric stations (HYDAT). The nonclosure error is attributed to the different data products using a constrained Kalman filter. Results show that the combination of CaPA, GLEAM, and the JPL mascon GRACE product tended to outperform other combinations across Canadian river basins. Overall, the error attributions of precipitation, evapotranspiration, water storage change, and runoff were 36.7%, 33.2%, 17.8%, and 12.2%, which corresponded to 8.1, 7.9, 4.2, and 1.4 mm month−1, respectively. In particular, the nonclosure error from precipitation dominated in Western Canada, whereas that from evapotranspiration contributed most in the Mackenzie River basin.

Open access
Karen I. Mohr, James S. Famiglietti, Aaron Boone, and Patrick J. Starks

Abstract

The Parameterization for Land–Atmosphere–Cloud Exchange (PLACE), a typical surface–vegetation–atmosphere transfer (SVAT) parameterization, was used in a case study of a 2500 km2 area in southwestern Oklahoma for 9–16 July 1997. The research objective was to assess PLACE’s simulation of the spatial variability and temporal evolution of soil moisture and heat fluxes without optimization for this case study. Understanding PLACE’s performance under these conditions may provide perspective on results from more complex coupled land–atmosphere simulations involving similar land surface schemes in data-poor environments. Model simulations were initialized with simple initial soil moisture and temperature profiles tied to soil type and forced by standard meteorological observations. The model equations and parameters were not adjusted or tuned to improve results.

For surface soil moisture, 5- and 10-cm soil temperature, and surface fluxes, the most accurate simulation (5% error for soil moisture and 2 K for 5- and 10-cm soil temperature) occurred during the 48 h following heavy rainfall on 11 and 15 July. The spatial pattern of simulated soil moisture was controlled more strongly by soil texture than was observed soil moisture, and the error was correlated with rainfall. The simplifications of the subsurface soil moisture, soil texture, and vegetation cover initialization schemes and the uncertainty in the rainfall data (>10%) could account for differences between modeled and observed surface fluxes that are on the order of 100 W m−2 and differences in soil moisture that are greater than 5%. It also is likely that the soil thermal conductivity scheme in PLACE damped PLACE’s response to atmospheric demand after 13 July, resulting in reduced evapotranspiration and warmer but slower-drying soils. Under dry conditions, the authors expect that SVATs such as PLACE that use a similar simple initialization also would demonstrate a strong soil texture control on soil moisture and surface fluxes and limited spatial variability.

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Debanjan Sinha, Tajdarul H. Syed, James S. Famiglietti, John T. Reager, and Reis C. Thomas

Abstract

Frequent recurrences of drought in India have had major societal, economical, and environmental impacts. While region-specific assessments are abundant, exhaustive appraisal over large spatial scales has been insubstantial. Here a new drought index called Water Storage Deficit Index (WSDI) is devised and analyzed for holistic representation of drought. The crux of the method is the employment of terrestrial water storage (TWS) variations from Gravity Recovery and Climate Experiment (GRACE) for quantification of drought intensity and severity. Drought events in recent times are well identified and quantified using the approach over four homogenous rainfall regions of India over the period from April 2002 to April 2015. Among the four regions, the highest peak deficit of −158.00 mm is observed in January 2015 over central India. While the drought of 2002–04 is prominent in peninsular and west-central India, the drought of 2009–10 and 2012–13 is conspicuous in almost all four regions of India. The longest deficit period of 23 months (from February 2009 to December 2010) and the highest severity value of −26.31 are observed in central and northwestern India, respectively. WSDI values show an increasing trend in west-central India (0.07 yr−1), indicating recovery from previously existing drought conditions. On the contrary, a decreasing trend in WSDI is observed in northwestern (−0.07 yr−1) and central (−0.18 yr−1) India. Results demonstrate considerable confidence in the potential of WSDI for robust characterization of drought over large spatial scales.

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Hrishikesh A. Chandanpurkar, John T. Reager, James S. Famiglietti, and Tajdarul H. Syed

Abstract

Total continental freshwater discharge into the oceans is a key feature of the global water cycle, but it is currently impossible to observe using ground-based methods alone. To characterize the uncertainty across existing modeling and satellite approaches, the authors present ensembles of historic monthly global continental discharge estimates that enforce water mass balance over land and ocean. The authors combine independent measurements of ocean–landmass change from altimetry and GRACE with multiple estimates of evaporation minus precipitation (EP) from remote sensing and reanalysis data to compute 28 time series of global discharge. Results reveal agreement in mass budget across approaches but a large spread in global EP estimates that propagates into the discharge estimates. It is found that discharges with reanalysis-based EP provide a closer comparison with current observation-based estimates. After combining GRACE- and altimetry-based mass change estimates with moisture convergences from reanalysis, the total annual mean continental discharge into the oceans is 38 550 ± 4800 km3 yr−1. Last, the authors provide continent-wise discharge estimates from GRACE and moisture convergences over land, compare them to other studies, and discuss implications for ocean modeling.

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Kurt C. Solander, John T. Reager, Brian F. Thomas, Cédric H. David, and James S. Famiglietti

Abstract

The widespread influence of reservoirs on global rivers makes representations of reservoir outflow and storage essential components of large-scale hydrology and climate simulations across the land surface and atmosphere. Yet, reservoirs have yet to be commonly integrated into earth system models. This deficiency influences model processes such as evaporation and runoff, which are critical for accurate simulations of the coupled climate system. This study describes the development of a generalized reservoir model capable of reproducing realistic reservoir behavior for future integration in a global land surface model (LSM). Equations of increasing complexity relating reservoir inflow, outflow, and storage were tested for 14 California reservoirs that span a range of spatial and climate regimes. Temperature was employed in model equations to modulate seasonal changes in reservoir management behavior and to allow for the evolution of management seasonality as future climate varies. Optimized parameter values for the best-performing model were generalized based on the ratio of winter inflow to storage capacity so a future LSM user can generate reservoirs in any grid location by specifying the given storage capacity. Model performance statistics show good agreement between observed and simulated reservoir storage and outflow for both calibration (mean normalized RMSE = 0.48; mean coefficient of determination = 0.53) and validation reservoirs (mean normalized RMSE = 0.15; mean coefficient of determination = 0.67). The low complexity of model equations that include climate-adaptive operation features combined with robust model performance show promise for simulations of reservoir impacts on hydrology and climate within an LSM.

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Karen I. Mohr, R. David Baker, Wei-Kuo Tao, and James S. Famiglietti

Abstract

This study used a two-dimensional coupled land–atmosphere (cloud resolving) model to investigate the influence of land cover on the water budgets of convective lines in West Africa. Study simulations used the same initial sounding and one of three different land covers: a sparsely vegetated semidesert, a grassy savanna, and a dense evergreen broadleaf forest. All simulations began at midnight and ran for 24 h to capture a full diurnal cycle. During the morning, the forest had the highest latent heat flux, the shallowest, moistest, slowest growing boundary layer, and more convective available potential energy than the savanna and semidesert. Although the savanna and forest environments produced virtually the same total rainfall mass (semidesert 18%), the spatial and temporal patterns of the rainfall were significantly different and can be attributed to the boundary layer evolution. The forest produced numerous convective cells with very high rain rates mainly during the early afternoon. During the morning, the savanna built up less but still significant amounts of convective available potential energy and enough convective inhibition so that the strongest convection in the savanna did not occur until late afternoon. This timing resulted in the largest, most intense convective line of the three land covers.

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Charlotte M. Emery, Cédric H. David, Konstantinos M. Andreadis, Michael J. Turmon, John T. Reager, Jonathan M. Hobbs, Ming Pan, James S. Famiglietti, Edward Beighley, and Matthew Rodell

Abstract

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 from −11.32 to 0.55 at observed gauges and from −12.24 to −1.10 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.

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Catalina M. Oaida, John T. Reager, Konstantinos M. Andreadis, Cédric H. David, Steve R. Levoe, Thomas H. Painter, Kat J. Bormann, Amy R. Trangsrud, Manuela Girotto, and James S. Famiglietti

Abstract

Numerical simulations of snow water equivalent (SWE) in mountain systems can be biased, and few SWE observations have existed over large domains. New approaches for measuring SWE, like NASA’s ultra-high-resolution Airborne Snow Observatory (ASO), offer an opportunity to improve model estimates by providing a high-quality validation target. In this study, a computationally efficient snow data assimilation (DA) approach over the western United States at 1.75-km spatial resolution for water years (WYs) 2001–17 is presented. A local ensemble transform Kalman filter implemented as a batch smoother is used with the VIC hydrology model to assimilate the remotely sensed daily MODIS fractional snow-covered area (SCA). Validation of the high-resolution SWE estimates is done against ASO SWE data in the Tuolumne basin (California), Uncompahgre basin (Colorado), and Olympic Peninsula (Washington). Results indicate good performance in dry years and during melt, with DA reducing Tuolumne basin-average SWE percent differences from −68%, −92%, and −84% in open loop to 0.6%, 25%, and 3% after DA for WYs 2013–15, respectively, for ASO dates and spatial extent. DA also improved SWE percent difference over the Uncompahgre basin (−84% open loop, −65% DA) and Olympic Peninsula (26% open loop, −0.2% DA). However, in anomalously wet years DA underestimates SWE, likely due to an inadequate snow depletion curve parameterization. Despite potential shortcomings due to VIC model setup (e.g., water balance mode) or parameterization (snow depletion curve), the DA framework implemented in this study shows promise in overcoming some of these limitations and improving estimated SWE, in particular during drier years or at higher elevations, when most in situ observations cannot capture high-elevation snowpack due to lack of stations there.

Open access