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[1] This paper presents the formulation and calibration of the temperature portion of a two-zone temperature and solute (TZTS) model which separates transient storage into surface (STS) and subsurface transient storage (HTS) zones. The inclusion of temperature required the TZTS model formulation to differ somewhat from past transient storage models in order to accommodate terms associated with heat transfer. These include surface heat fluxes in the main channel (MC) and STS, heat and mass exchange between the STS and MC, heat and mass exchange between the HTS and MC, and heat exchange due to bed and deeper ground conduction. To estimate the additional parameters associated with a two-zone model, a data collection effort was conducted to provide temperature time series within each zone. Both single-objective and multiobjective calibration algorithms were then linked to the TZTS model to assist in parameter estimation. Single-objective calibrations based on MC temperatures at two different locations along the study reach provided reasonable predictions in the MC and STS. The HTS temperatures, however, were typically poorly estimated. The two-objective calibration using MC temperatures simultaneously at two locations illustrated that the TZTS model accurately predicts temperatures observed in MC, STS, and HTS zones, including those not used in the calibration. These results suggest that multiple data sets representing different characteristics of the system should be used when calibrating complex in-stream models.

Floods are the most common and widespread climate-related hazard on Earth. Flood forecasting can reduce the death toll associated with floods. Satellites offer effective and economical means for calculating areal rainfall estimates in sparsely gauged regions. However, satellite-based rainfall estimates have had limited use in flood forecasting and hydrologic stream flow modeling because the rainfall estimates were considered to be unreliable. In this study we present the calibration and validation results from a spatially distributed hydrologic model driven by daily satellite-based estimates of rainfall for sub-basins of the Nile and Mekong Rivers. The results demonstrate the usefulness of remotely sensed precipitation data for hydrologic modeling when the hydrologic model is calibrated with such data. However, the remotely sensed rainfall estimates cannot be used confidently with hydrologic models that are calibrated with rain gauge measured rainfall, unless the model is recalibrated.

[1] This paper presents the multiobjective calibration results for temperature and solute from a two-zone temperature and solute (TZTS) model which separates transient storage into surface (STS) and subsurface (HTS) transient storage components. This model contains terms associated with surface heat fluxes in the main channel (MC) and STS, heat and mass exchange between the STS and MC, heat and mass exchange between the HTS and MC, and heat exchange due to bed and deeper ground conduction. To estimate the additional parameters associated with a multiple-zone model, a data collection effort was conducted to provide temperature time series and solute tracer curves representing the movement of heat and/or solute through each zone. A multiobjective calibration algorithm was linked to the TZTS model to assist in parameter estimation and to provide information about parameter uncertainty and tradeoffs associated with matching different combinations of observations (e.g., solute and/or temperature data gathered in various zones). Results generated from three different combinations of calibration data illustrated that the two-zone model accurately reproduces temperatures and tracer concentrations observed in different zones when considered independently. However, there were many parameter sets that resulted in objectively indistinguishable results. When tracer and temperature observations were considered simultaneously in model calibration, the simplistic representation of the surface and subsurface zones did not adequately reproduce both observation types in each zone. If the uncertainty in model parameters and the data are taken into account, however, the results of the study suggest that it is plausible to use temperature and tracer information simultaneously to better inform transient storage modeling approaches.

Abstract. We develop a hydroclimatological approach to the modeling of regional shallow landslide initiation that integrates spatial and temporal dimensions of parameter uncertainty to estimate an annual probability of landslide initiation based on Monte Carlo simulations. The physically based model couples the infinite-slope stability model with a steady-state subsurface flow representation and operates in a digital elevation model. Spatially distributed gridded data for soil properties and vegetation classification are used for parameter estimation of probability distributions that characterize model input uncertainty. Hydrologic forcing to the model is through annual maximum daily recharge to subsurface flow obtained from a macroscale hydrologic model. We demonstrate the model in a steep mountainous region in northern Washington, USA, over 2700 km 2 . The influence of soil depth on the probability of landslide initiation is investigated through comparisons among model output produced using three different soil depth scenarios reflecting the uncertainty of soil depth and its potential long-term variability. We found elevation-dependent patterns in probability of landslide initiation that showed the stabilizing effects of forests at low elevations, an increased landslide probability with forest decline at midelevations (1400 to 2400 m), and soil limitation and steep topographic controls at high alpine elevations and in post-glacial landscapes. These dominant controls manifest themselves in a bimodal distribution of spatial annual landslide probability. Model testing with limited observations revealed similarly moderate model confidence for the three hazard maps, suggesting suitable use as relative hazard products. The model is available as a component in Landlab, an open-source, Python-based landscape earth systems modeling environment, and is designed to be easily reproduced utilizing HydroShare cyberinfrastructure.