Watersheds aggregate precipitation signals of many intensities and from many locations into a single observable streamflow at an outlet point. This dependency between precipitation and streamflow varies seasonally and can shift over time due to changes in land cover, climate, human water uses, or changes in properties of precipitation events themselves. We apply information theory-based measures to capture temporal linkages, or information transfers, from daily precipitation occurrence at different locations in a basin to streamflow at an outlet. We detect critical magnitudes of precipitation and lag times associated with the strongest precipitation-streamflow relationships, and further partition information transfers to determine relative contributions from the knowledge of wet versus dry past states. Based on an analysis of daily U.S. Geological Survey (USGS) streamflow and Climate Prediction Center (CPC) gridded gauge-based precipitation data sets in the Colorado Headwaters Basin, this dependency is strongest in fall, the longest dominant lag times occur in spring, and the strengths of dependencies have increased in spring and summer over the past 65 years. These features relate to both seasonal and spatial characteristics of precipitation and the landscape. A partitioning of information components shows that in this basin, the particular knowledge of a lagged, or past, wet state tends to be more informative to flow than a lagged dry state, even though dry days are more frequent. This study introduces several signatures of precipitation-streamflow relationships that can also more broadly characterize strengths, thresholds, and timescales associated with various interconnected processes.

Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.

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