Water Resources Research, Volume 56, Issue 5

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American Geophysical Union (AGU)
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Many Commonly Used Rainfall‐Runoff Models Lack Long, Slow Dynamics: Implications for Runoff Projections
Keirnan Fowler | Wouter Knoben | Murray C. Peel | Tim Peterson | Dongryeol Ryu | Margarita Saft | Ki‐Weon Seo | Andrew W. Western

Evidence suggests that catchment state variables such as groundwater can exhibit multiyear trends. This means that their state may reflect not only recent climatic conditions but also climatic conditions in past years or even decades. Here we demonstrate that five commonly used conceptual “bucket” rainfall‐runoff models are unable to replicate multiyear trends exhibited by natural systems during the “Millennium Drought” in south‐east Australia. This causes an inability to extrapolate to different climatic conditions, leading to poor performance in split sample tests. Simulations are examined from five models applied in 38 catchments, then compared with groundwater data from 19 bores and Gravity Recovery and Climate Experiment data for two geographic regions. Whereas the groundwater and Gravity Recovery and Climate Experiment data decrease from high to low values gradually over the duration of the 13‐year drought, the model storages go from high to low values in a typical seasonal cycle. This is particularly the case in the drier, flatter catchments. Once the drought begins, there is little room for decline in the simulated storage, because the model “buckets” are already “emptying” on a seasonal basis. Since the effects of sustained dry conditions cannot accumulate within these models, we argue that they should not be used for runoff projections in a drying climate. Further research is required to (a) improve conceptual rainfall‐runoff models, (b) better understand circumstances in which multiyear trends in state variables occur, and (c) investigate links between these multiyear trends and changes in rainfall‐runoff relationships in the context of a changing climate.

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In Situ Estimates of Freezing/Melting Point Depression in Agricultural Soils Using Permittivity and Temperature Measurements
Renato Pardo Lara | Aaron Berg | Jon Warland | Erica Tetlock

We present a method to characterize soil moisture freeze‐thaw events and freezing/melting point depression using permittivity and temperature measurements, readily available from in situ sources. In cold regions soil freeze‐thaw processes play a critical role in the surface energy and water balance, with implications ranging from agricultural yields to natural disasters. Although monitoring of the soil moisture phase state is of critical importance, there is an inability to interpret soil moisture instrumentation in frozen conditions. To address this gap, we investigated the freeze‐thaw response of a widely used soil moisture probe, the HydraProbe, in the laboratory. Soil freezing curves (SFCs) and soil thawing curves (STCs) were identified using the relationship between soil permittivity and temperature. The permittivity SFC/STC was fit using a logistic growth model to estimate the freezing/melting point depression (Tf/m) and its spread (s). Laboratory results showed that the fitting routine requires permittivity changes greater than 3.8 to provide robust estimates and suggested that a temperature bias is inherent in horizontally placed HydraProbes. We tested the method using field measurements collected over the last 7 years from the Environment and Climate Change Canada and the University of Guelph's Kenaston Soil Moisture Network in Saskatchewan, Canada. By dividing the time series into freeze‐thaw events and then into individual transitions, the permittivity SFC/STC was identified. The freezing and melting point depression for the network was estimated as Tf/m = − 0.35 ± 0.2,with Tf = − 0.41 ± 0.22 °C and Tm = − 0.29 ± 0.16 °C, respectively.