The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses

Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table drivenby three publicly available, gridded meteorological forcing (GMF) – Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30 % of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributedvariables such as SWE. By comparing models forced by different temporal resolutions of NLDAS (hourly to daily), GMF in sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.

Stochastic watershed models using a logarithmic transformation of ratio residuals

<p>There is an increasing need to develop stochastic watershed models using post-processing methods to generate stochastic streamflow ensembles from deterministic watershed models (DWMs).  Stochastic streamflow ensembles are needed for a wide variety of water resource planning applications relating to both short-term forecasting and long-range simulation. Current methods often involve post-processing of ordinary, differenced residuals defined as the difference between the simulations (S) and observations (O). However, ordinary, differenced residuals from daily and sub-daily DWMs exhibit a high degree of non-normality, heteroscedasticity, and stochastic persistence leading to the need for extremely complex post-processing methods. Using deterministic simulations of daily streamflow at over 1,400 sites across the United States, we document that logarithmically transformed ratio residuals – defined as the natural log of the quotient of S divided by O –  are approximately homoscedastic, are approximately normally distributed, and can be well-represented as an autoregressive process. These characteristics make them preferable to ordinary, differenced residuals for post-processing DWMs. Though issues with seasonal fluctuation and long-term persistence are not fully resolved, this simple transformation addresses much of the stochastic complexity of the residuals from a deterministic watershed model and produces streamflow ensemble simulations that more accurately replicate essential elements of the statistical distributions of streamflow (including design events, higher-order moments and extreme values). The use of this transformation and autoregressive models demonstrates that more accurate stochastic modeling of natural resources phenomena can be achieved with relatively elegant solutions to support natural resource management in the past, present and future.</p>

Estimating Watershed Subsurface Permeability From Stream Discharge Data Using Deep Neural Networks

Subsurface permeability is a key parameter in watershed models that controls the contribution from the subsurface flow to stream flows. Since the permeability is difficult and expensive to measure directly at the spatial extent and resolution required by fully distributed watershed models, estimation through inverse modeling has had a long history in subsurface hydrology. The wide availability of stream surface flow data, compared to groundwater monitoring data, provides a new data source to infer soil and geologic properties using integrated surface and subsurface hydrologic models. As most of the existing methods have shown difficulty in dealing with highly nonlinear inverse problems, we explore the use of deep neural networks for inversion owing to their successes in mapping complex, highly nonlinear relationships. We train various deep neural network (DNN) models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models.

Watershed models for assessment of hydrological behavior of the catchments: a comparative study

Hydrological parameters like overland flow, soil loss and nutrient losses can be studied by using different watershed models. However, all these models vary significantly in their analysis of parameters, input and output flexibility, scale accountability, processing ability, computational efficiency and capability of modeling the changes in catchments. This paper reviews different watershed models used for analyzing overland flow, soil loss and sediment yield with their shortcomings and strengths. These watershed models are described briefly along with their capabilities and shortcomings with their examples of applications, results and comparisons. An outcome of these discussions is presented in tabular format as a screening tool to allow the researchers and decision makers to choose the appropriate watershed model for the specific purpose.