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.

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.

Recent climate changes have observable impacts on hydrologic processes and will further affect hydrologic systems in the future. The temperature and precipitation modeled with eight global circulation models (GCMs) (selected from 22 GCMs published in the Fourth Assessment of the Intergovernmental Panel on Climate Change) under three typical emission scenarios entitled A1B, A2, and B1 were evaluated in this study for future projections in the Yangtze River Basin, China. The artificial neural network model was used to assess the evolutional trend of hydrologic processes (e.g., streamflow) and the possibility of extreme floods in the Yangtze River Basin by using data generated by selected GCMs under future climate changes. The results indicate that the future annual streamflow tends to decrease in the Yangtze River Basin. The future average annual flow is reduced by 500 m3/s compared with that of the historic record (1951–2005) observed at Yichang Hydrologic Station of the Middle Yangtze River, and ...