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1. Stochastic modeling of interannual variation of hydrologic variables: INTERANNUAL HYDROLOGIC VARIABILITY

   https://doi.org/10.1002/2017GL074139  

   Dralle, David; Karst, Nathaniel; Müller, Marc; Vico, Giulia; Thompson, Sally E. (July 2017, Geophysical Research Letters)
2. Hydrologic controls on junction angle of river networks: HYDROLOGIC CONTROLS ON JUNCTION ANGLE

   https://doi.org/10.1002/2016WR020267  

   Hooshyar, Milad; Singh, Arvind; Wang, Dingbao (May 2017, Water Resources Research)
3. Hydrologic controls on hyporheic exchange in a headwater mountain stream: HYDROLOGIC CONTROLS ON HYPORHEIC FLOW

   https://doi.org/10.1002/2017WR020576  

   Schmadel, Noah M.; Ward, Adam S.; Wondzell, Steven M. (July 2017, Water Resources Research)
4. Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

   https://doi.org/10.5194/hess-24-2253-2020  

   Feng, Dongmei; Beighley, Edward (January 2020, Hydrology and Earth System Sciences)

   Abstract. Assessing impacts of climate change on hydrologic systemsis critical for developing adaptation and mitigation strategies for waterresource management, risk control, and ecosystem conservation practices. Suchassessments are commonly accomplished using outputs from a hydrologic modelforced with future precipitation and temperature projections. The algorithmsused for the hydrologic model components (e.g., runoff generation) canintroduce significant uncertainties into the simulated hydrologic variables.Here, a modeling framework was developed that integrates multiple runoffgeneration algorithms with a routing model and associated parameteroptimizations. This framework is able to identify uncertainties from bothhydrologic model components and climate forcings as well as associatedparameterization. Three fundamentally different runoff generationapproaches, runoff coefficient method (RCM, conceptual), variableinfiltration capacity (VIC, physically based, infiltration excess), andsimple-TOPMODEL (STP, physically based, saturation excess), were coupledwith the Hillslope River Routing model to simulate surface/subsurface runoffand streamflow. A case study conducted in Santa Barbara County, California,reveals increased surface runoff in February and March but decreasedrunoff in other months, a delayed (3 d, median) and shortened (6 d,median) wet season, and increased daily discharge especially for theextremes (e.g., 100-year flood discharge, Q100). The Bayesian modelaveraging analysis indicates that the probability of such an increase can be up to85 %. For projected changes in runoff and discharge, general circulationmodels (GCMs) and emission scenarios are two major uncertainty sources,accounting for about half of the total uncertainty. For the changes inseasonality, GCMs and hydrologic models are two major uncertaintycontributors (∼35 %). In contrast, the contribution ofhydrologic model parameters to the total uncertainty of changes in thesehydrologic variables is relatively small (<6 %), limiting theimpacts of hydrologic model parameter equifinality in climate change impactanalysis. This study provides useful information for practices associatedwith water resources, risk control, and ecosystem conservation and forstudies related to hydrologic model evaluation and climate change impactanalysis for the study region as well as other Mediterranean regions. 

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5. Interpolating Hydrologic Data Using Laplace Formulation

   https://doi.org/10.3390/rs15153844  

   Xu, Tianle; Merwade, Venkatesh; Wang, Zhiquan (August 2023, Remote Sensing)

   Spatial interpolation techniques play an important role in hydrology, as many point observations need to be interpolated to create continuous surfaces. Despite the availability of several tools and methods for interpolating data, not all of them work consistently for hydrologic applications. One of the techniques, the Laplace Equation, which is used in hydrology for creating flownets, has rarely been used for data interpolation. The objective of this study is to examine the efficiency of Laplace formulation (LF) in interpolating data used in hydrologic applications (hydrologic data) and compare it with other widely used methods such as inverse distance weighting (IDW), natural neighbor, and ordinary kriging. The performance of LF interpolation with other methods is evaluated using quantitative measures, including root mean squared error (RMSE) and coefficient of determination (R2) for accuracy, visual assessment for surface quality, and computational cost for operational efficiency and speed. Data related to surface elevation, river bathymetry, precipitation, temperature, and soil moisture are used for different areas in the United States. RMSE and R2 results show that LF is comparable to other methods for accuracy. LF is easy to use as it requires fewer input parameters compared to inverse distance weighting (IDW) and Kriging. Computationally, LF is faster than other methods in terms of speed when the datasets are not large. Overall, LF offers a robust alternative to existing methods for interpolating various hydrologic data. Further work is required to improve its computational efficiency. 

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