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1. Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

   https://doi.org/10.1016/j.jhydrol.2023.129304  

   Tyson, Conor ; Longyang, Qianqiu ; Neilson, Bethany T. ; Zeng, Ruijie ; Xu, Tianfang ( April 2023 , Journal of Hydrology)

   Marco Borga ; Francesco Avanzi (Ed.)
2. High resolution water body mapping for SWAT evaporative modelling in the Upper Oconee watershed of Georgia, USA

   https://doi.org/10.1002/hyp.11398  

   Ignatius, Amber R. ; Jones, John W. ( January 2018 , Hydrological Processes)
3. Simulating high-resolution soil moisture patterns in the Shale Hills watershed using a land surface hydrologic model: Simulating High-Resolution Soil Moisture Patterns

   https://doi.org/10.1002/hyp.10593  

   Shi, Yuning ; Baldwin, Douglas C. ; Davis, Kenneth J. ; Yu, Xuan ; Duffy, Christopher J. ; Lin, Henry ( October 2015 , Hydrological Processes)
4. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

   https://doi.org/10.1029/2021WR030993  

   Xu, Tianfang ; Longyang, Qianqiu ; Tyson, Conor ; Zeng, Ruijie ; Neilson, Bethany T. ( March 2022 , Water Resources Research)

   Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.

    

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5. Impact of Climate and Land Use Change on Streamflow and Sediment Yield in a Snow‐Dominated Semiarid Mountainous Watershed

   https://doi.org/10.1111/1752-1688.12803  

   Khatri, K. B. ; Strong, C. ; von Stackelberg, N. ; Buchert, M. ; Kochanski, A. K. ( November 2019 , JAWRA Journal of the American Water Resources Association)

   This study investigates the impact of climate and land use change on the magnitude and timing of streamflow and sediment yield in a snow‐dominated mountainous watershed in Salt Lake County, Utah using a scenario approach and the Hydrological Simulation Program — FORTRAN model for the 2040s (year 2035–2044) and 2090s (year 2085–2094). The climate scenarios were statistically and dynamically downscaled from global climate models. Land use and land cover (LULC) changes were estimated in two ways — from a regional planning scenario and from a deterministic model. Results indicate the mean daily streamflow in the Jordan River watershed will increase by an amount ranging from 11.2% to 14.5% in the 2040s and from 6.8% to 15.3% in the 2090s. The respective increases in sediment load in the 2040s and 2090s is projected to be 6.7% and 39.7% in the canyons and about 7.4% to 14.2% in the Jordan valley. The historical 50th percentile timing of streamflow and sediment load is projected to be shifted earlier by three to four weeks by mid‐century and four to eight weeks by late‐century. The projected streamflow and sediment load results establish a nonlinear relationship with each other and are highly sensitive to projected climate change. The predicted changes in streamflow and sediment yield will have implications for water supply, flood control and stormwater management.

    

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