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Abstract Large‐scale models often use a single grid to represent an entire catchment assuming homogeneity; the impacts of such an assumption on simulating evapotranspiration (ET) and streamflow remain poorly understood. Here, we compare hydrological dynamics at Shale Hills (PA, USA) using a complex model (spatially explicit, >500 grids) and a simple model (spatially implicit, two grids using “effective” parameters). We asked two questions:What hydrological dynamics can a simple model reproduce at the catchment scale? What processes does it miss by ignoring spatial details?Results show the simple model can reproduce annual runoff ratios and ET, daily discharge peaks (e.g., storms, floods) but not discharge minima (e.g., droughts) under dry conditions. Neither can it reproduce different streamflow from the two sides of the catchment with distinct land surface characteristics. The similar annual runoff ratios between the two models indicate spatial details are not as important as climate in reproducing annual scale ET and discharge partitioning. Most of the calibrated parameters in the simple model are within the ranges in the complex model, except that effective porosity has to be reduced to 40% of the average porosity from the complex model. The form of the storage‐discharge relationship is similar. The effective porosity in the simple model however represents the dynamic and mobile water storage in the effective drainage area of the complex model that connects to the stream and contributes to high streamflow; it does not represent the passive, immobile water storage in the often disconnected uphill areas. This indicates that an additional uphill functioning unit is needed in the simple model to simulate the full spectrum of high‐low streamflow dynamics in natural catchments.
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Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine
Abstract Flow recession analysis, relating dischargeQand its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dtversusQ, typically form a wide point cloud due to noise and hysteresis in the storage‐discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage‐discharge relationship can be estimated based on the attractor.
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- Award ID(s):
- 2120113
- PAR ID:
- 10415870
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Water Resources Research
- Volume:
- 59
- Issue:
- 5
- ISSN:
- 0043-1397
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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