River basins as groundwater exporters and importers: Implications for water cycle and climate modeling

Abstract

[1] The groundwater reservoir and its interaction with surface water facilitate lateral transport of continental water and energy. Current climate models do not account for long-distance groundwater flow between model cells but route the atmospheric surplus (precipitation (P) minus evapotranspiration (ET)) directly to stream discharge within a model grid cell. We ask how much water exits a river basin without ever passing through its surface outlet? What are the climatologic and geologic factors influencing this flux? To answer these questions, a separation of groundwater flow from river flow is necessary. We use the ratio of stream discharge (Qr) to basin recharge (R = P À ET) for this purpose; where Qr:R < 1, a basin is considered a groundwater exporter; and where Qr:R > 1, a basin is considered a groundwater importer. Here Qr is obtained from 39 years of U.S. Geological Survey Hydro-Climatic Data Network observed stream discharge from 1555 basins across the continental United States, and R (P À ET) is derived from 50 years of hydrologic simulation by the Variable Infiltration Capacity model. It was found that the Qr:R ratio deviates significantly from 1 across the continent. Detailed investigations of individual basins suggest that the deviations are primarily a function of geology, while climate and basin scale influence the magnitude of these deviations. Further, a marked incongruity between the surface and groundwater flow directions is apparent, suggesting that surface drainage is only partially indicative of subsurface flow regimes. The apparent significance of this long-distance groundwater flow component reinforces the need for inclusion of the groundwater reservoir in current water cycle and climate modeling efforts.

Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint

“…The difficulty of using the surface observation to improve root zone soil moisture has been reported by Walker et al (2001) and Houser et al (1998) who showed that soil moisture estimates in the lower soil zone deteriorated with the assimilation of surface observations. As pointed out by Walker et al (2001) that data assimilation could only achieve what model physics is capable of delivering, the failure of the conventional EnKF in updating the lower layers, as shown in this study, is a result of inappropriate model physics.…”

“…In addition to preserving mass, Eq. (6) avoids updating the lower layers with the conventional EnKF which has been shown to yield undesired increments due to inappropriate model physics (Houser et al, 1998;Walker et al, 2001). Preserving water mass does not necessarily lead to improved soil moisture estimates in the lower layers, but Eq.…”

“…While the bias reduction at the surface was achieved in these studies, improvements in the deeper soil layers did not always occur. Houser et al (1998) and Walker et al (2001) showed that assimilation of surface observations actually adversely impacted the soil moisture state in the lower soil layers. The representation of the hydrological condition in the lower soil zone is often a weak point in land surface models due to lack of knowledge and observations.…”

“…If retrieved appropriately, they can be used to improve the spatial mean of modeled soil moisture fields, at finer spatial resolutions than the footprint of AMSR-E, as well as the temporal mean through continuous assimilations in time. While interest in assimilating satellite retrieved soil moisture estimates began more than a decade ago (Houser et al, 1998;Margulis et al, 2002;Walker et al, 2001), recent studies have focused on using the anomaly information extracted from the sensor data by removing the mean of the observations priori to assimilation to improve model's anomaly detection (Bolten et al, 2008;Crow and Zhan, 2007;Draper et al, 2009;Reichle et al, 2007). While assimilation of anomalies does not directly address if models are unbiased which is required for optimal estimators (Kalnay, 2003), it preserves the water budget of forecasts.…”

“…Recognizing this potential, studies have been conducted to assimilate actual values of satellite data without using any bias correction techniques (Houser et al, 1998;Margulis et al, 2002;Ni-Meister et al, 2006;Walker et al, 2001). While the bias reduction at the surface was achieved in these studies, improvements in the deeper soil layers did not always occur.…”