Teaching is an important aspect in people's lives and cultures. We explore it from a cognitive-developmental perspective. Teaching may be a natural cognition that, despite its complexity, is learned at an early age without any apparent instruction. We propose that theory of mind may be an important cognitive prerequisite for teaching. We briefly describe a study that tested relations between children's developing theory of mind and actual teaching. Children at the ages of 3-and 5-years were presented new theory of mind tasks about teaching and then were observed teaching peers a game they had just learned. We found corresponding age-related differences in children's understanding of teaching as manifested in their performance on the teaching-theory of mind tasks and in their teaching strategies. It is suggested that theory and research on theory of mind might need to be expanded to include on-line, interactive situations such as teaching.
<p>Water resources studies often rely on simulated streamflow from hydrologic models. Model-based streamflow estimates are often not directly usable in water resources studies because all models, no matter how well-calibrated, contain systematic errors. Water resources studies rely on simulated streamflow as inputs to compute reservoir releases and diversions and do not function well if those inputs are significantly biased in time and/or space. Post-processing is therefore used to reduce these systematic errors in model outputs. This post-processing step to remove model errors is typically referred to as bias-correction, and often impacts the entire distribution of flows rather than just the mean.</p><p>Existing post-processing techniques typically have three short-comings. First, simulated streamflow at unique locations are often bias-corrected independently, disregarding the connection between locations that is imposed by the river network. This destroys the spatial consistency of the streamflow across a river network. Second, bias-correction methods often rely on simple, time-invariant mappings between observed and simulated streamflow, without regard for the different hydrological processes that drive streamflow. For example, a hydrological model may have different systematic errors in representing snowmelt than in representing soil drainage, necessitating different corrections. Third, the application of a bias-correction method is often restricted to locations where observed and simulated streamflow exist, even though these locations represent only a small subset of streamflow input locations to a water resources model.</p><p>We present a post-processing method for streamflow that addresses all three of these shortcomings of existing streamflow bias-correction methods. The method accounts for the spatial relations imposed by the river network, allows for the incorporation of process-information, and applies the bias-correction for all reaches in a stream network. We develop a mapping from the modeled output at the gages with flow observations, which we use as the basis for training a machine learning (ML) model to perform the site-specific bias-correction. We then apply the ML model to local streamflow contributions for each river segment, including river segments without flow observations. Finally, we combine the local bias-corrections across the stream network, to create accumulated bias-corrected streamflow time series that are spatially-consistent across the stream network. We demonstrate our method for daily streamflow in a river basin in the western United States.</p>
Water resources planning often uses streamflow predictions made by hydrologic models. These simulated predictions have systematic errors which limit their usefulness as input to water management models. To account for these errors, streamflow predictions are bias-corrected through statistical methods which adjust model predictions based on comparisons to reference datasets (such as observed streamflow). Existing bias-correction methods have several shortcomings when used to correct spatially-distributed streamflow predictions. First, existing bias-correction methods destroy the spatio-temporal consistency of the streamflow predictions, when these methods are applied independently at multiple sites across a river network. Second, bias-correction techniques are usually built on time-invariant mappings between reference and simulated streamflow without accounting for the processes which underpin the systematic errors. We describe improved bias-correction techniques which account for the river network topology and allow for corrections that account for other processes. Further, we present a workflow that allows the user to select whether to apply these techniques separately or in conjunction. We evaluate four different bias-correction methods implemented with our workflow in the Yakima River Basin in the Northwestern United States. We find that all four methods reduce systematic bias in the simulated streamflow. The spatially-consistent bias-correction methods produce spatially-distributed streamflow as well as bias-corrected incremental streamflow, which is suitable for input to water management models. We demonstrate how the spatially-consistent method avoids creating flows that are inconsistent between upstream and downstream locations, while performing similar to existing methods. We also find that conditioning on daily minimum temperature, which we use as a proxy for snowmelt processes, improves the timing of the corrected streamflow.
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