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Abstract. In gaining streams, groundwater seeps out into the streams. In losing streams, stream water moves into groundwater systems. The flow moving through the streambed sediments under these two types of hydrologic conditions is generally in opposite directions (upward vs. downward). The two opposite flow mechanisms affect the pore size and fine particle content of streambeds. Thus it is very likely that the opposite flow conditions affect the streambed hydraulic conductivity. However, comparisons of the hydraulic conductivity (K) of streambeds for losing and gaining streams are not well documented. In this study, we examined the K distribution patterns of sediments below the channel surface or stream banks for the Platte River and its tributaries in Nebraska, USA. Two contrasting vertical distribution patterns were observed from the test sites. In gaining reaches, hydraulic conductivity of the streambed decreased with the depth of the sediment cores. In losing reaches, hydraulic conductivity increased with the depth of the sediment cores. These contrasting patterns in the two types of streams were mostly attributed to flow directions during stream water and groundwater exchanges. In losing reaches, downward movement of water brought fine particle into the otherwise coarse sediment matrix, partially silting the pores. For gaining reaches, upward flow winnowed fine particles, increasing the pore spacing in the top parts of streambeds, leading to higher hydraulic conductivity in shallower parts of streambeds. These flux directions can impact K values to depths of greater than 5 m. At each study site, in situ permeameter tests were conducted to measure the K values of the shallow streambed layer. Statistical analyses indicated that K values from the sites of losing reaches were significantly different from the K values from the sites of gaining reaches.

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Streambed horizontal hydraulic conductivity (Kh) has a substantial role in controlling exchanges between stream water and groundwater. We propose a new approach for determining Kh of the shallow streambed sediments. Undisturbed sediment samples were collected using tubes that were horizontally driven into streambeds. The sediment columns were analysed using a permeameter test (PT) on site. This new test approach minimizes uncertainties due to vertical flow in the vicinity of test tube and stream stage fluctuations in the computation of the Kh values. Ninety‐eight PTs using the new approach were conducted at eight sites in four tributaries of the Platte River, east‐central Nebraska, USA. The Kh values were compared with the nondirectional hydraulic conductivity values (Kg) determined from 12 empirical grain‐size analysis methods. The grain‐size analysis methods used the same sediment samples as Kh tests. Only two methods, the Terzaghi and Shepherd methods, yielded Kg values close to the Kh values. Although the Sauerbrei method produced a value relatively closer to Kh than other nine grain‐size analysis methods, the values from this method were not as reliable as the methods of Terzaghi and Shepherd due to the inconsistent fluctuation of the average estimates at each of the test sites. The Zunker, Zamarin, Hazen, Beyer, and Kozeny methods overestimated Kh, while the Slichter, US Bureau of Reclamation (USBR), Harleman, and Alyamani and Sen methods underestimated Kh. Any of these specific grain‐size methods might yield good estimates of streambed Kh at some sites, but give poor estimates at other sites, indicating that the relationship between Kg and Kh is significantly site dependent in our study. Copyright © 2011 John Wiley & Sons, Ltd.

Quantifying the behavior and performance of hydrologic models is an important aspect of understanding the underlying hydrologic systems. We argue that classical error measures do not offer a complete picture for building this understanding. This study demonstrates how the information theoretic measure known as transfer entropy can be used to quantify the active transfer of information between hydrologic processes at various timescales and facilitate further understanding of the behavior of these systems. To build a better understanding of the differences in dynamics, we compare model instances of the Structure for Unifying Multiple Modeling Alternatives (SUMMA), the Variable Infiltration Capacity (VIC) model, and the Precipitation Runoff Modeling System (PRMS) across a variety of hydrologic regimes in the Columbia River Basin in the Pacific Northwest of North America. Our results show differences in the runoff of the SUMMA instance compared to the other two models in several of our study locations. In the Snake River region, SUMMA runoff was primarily snowmelt driven, while VIC and PRMS runoff was primarily influenced by precipitation and evapotranspiration. In the Olympic mountains, evapotranspiration interacted with the other water balance variables much differently in PRMS than in VIC and SUMMA. In the Willamette River, all three models had similar process networks at the daily time scale but showed differences in information transfer at the monthly timescale. Additionally, we find that all three models have similar connectivity between evapotranspiration and soil moisture. Analyzing information transfers to runoff at daily and monthly time steps shows how processes can operate on different timescales. By comparing information transfer with correlations, we show how transfer entropy provides a complementary picture of model behavior.Plain Language Summary Building a complete picture of the hydrologic system is a difficult task. When the hydrologic community builds computer models to simulate the hydrologic cycle, we often must make approximations, which introduce error and uncertainty. It is common to attempt to control the error in our predictions by choosing the level of complexity of the hydrologic model or by choosing model parameter values so that model results match observations most closely. Neither approach can identify whether we may simply be compensating for poor choices in the way that the model operates. This study evaluates a statistical method, which relies on an information theoretic measure called transfer entropy that provides insight into the internal operations of a model. We apply this method to examine the different components of water flow within three different models in four different regions across the Columbia River Basin in the Pacific Northwest of North America. We found that the method was able to disentangle the complex way in which these models operate and provide high-level insight into the similarities and differences across different models and sites. We propose that this is ...