scholarly journals Precipitation runoff modeling system (PRMS) as part of an integrated hydrologic model for the Osage Nation, northeastern Oklahoma, 1915–2014

2020 ◽  
Author(s):  
Joseph A. Hevesi ◽  
Randall T. Hanson ◽  
Jason R. Masoner
Keyword(s):  
Hydrologic Model ◽  
Runoff Modeling ◽  
Integrated Hydrologic Model

scholarly journals Determining the impact of a severe dry to wet transition on watershed hydrodynamics in California, USA with an integrated hydrologic model

2020 ◽  
Vol 580 ◽  
pp. 124358 ◽  
Author(s):  
Fadji Z. Maina ◽  
Erica R. Siirila-Woodburn ◽  
Michelle Newcomer ◽  
Zexuan Xu ◽  
Carl Steefel
Keyword(s):  
Hydrologic Model ◽  
Integrated Hydrologic Model ◽  
The Impact

scholarly journals Assessing the impact of model spin-up on surface water-groundwater interactions using an integrated hydrologic model

2014 ◽  
Vol 50 (3) ◽  
pp. 2636-2656 ◽  
Author(s):  
Hoori Ajami ◽  
Matthew F. McCabe ◽  
Jason P. Evans ◽  
Simon Stisen
Keyword(s):  
Surface Water ◽  
Hydrologic Model ◽  
Integrated Hydrologic Model ◽  
The Impact

scholarly journals Assessment of the Timing of Daily Peak Streamflow during the Melt Season in a Snow-Dominated Watershed

2016 ◽  
Vol 17 (8) ◽  
pp. 2225-2244 ◽  
Author(s):  
Xing Chen ◽  
Mukesh Kumar ◽  
Rui Wang ◽  
Adam Winstral ◽  
Danny Marks
Keyword(s):  
Water Flux ◽  
Subsurface Flow ◽  
Hydrologic Model ◽  
Daily Streamflow ◽  
Penn State ◽  
Integrated Hydrologic Model ◽  
Interseasonal Variability ◽  
Peak Streamflow ◽  
Surface And Subsurface Flow ◽  
Temporal Shifts

Abstract Previous studies have shown that gauge-observed daily streamflow peak times (DPTs) during spring snowmelt can exhibit distinct temporal shifts through the season. These shifts have been attributed to three processes: 1) melt flux translation through the snowpack or percolation, 2) surface and subsurface flow of melt from the base of snowpacks to streams, and 3) translation of water flux in the streams to stream gauging stations. The goal of this study is to evaluate and quantify how these processes affect observed DPTs variations at the Reynolds Mountain East (RME) research catchment in southwest Idaho, United States. To accomplish this goal, DPTs were simulated for the RME catchment over a period of 25 water years using a modified snowmelt model, iSnobal, and a hydrology model, the Penn State Integrated Hydrologic Model (PIHM). The influence of each controlling process was then evaluated by simulating the DPT with and without the process under consideration. Both intra- and interseasonal variability in DPTs were evaluated. Results indicate that the magnitude of DPTs is dominantly influenced by subsurface flow, whereas the temporal shifts within a season are primarily controlled by percolation through snow. In addition to the three processes previously identified in the literature, processes governing the snowpack ripening time are identified as additionally influencing DPT variability. Results also indicate that the relative dominance of each control varies through the melt season and between wet and dry years. The results could be used for supporting DPTs prediction efforts and for prioritization of observables for DPT determination.

scholarly journals Examining cross-scale influences of forcing resolutions in a hillslope-resolving, integrated hydrologic model

2020 ◽  
Author(s):  
Miguel A. Aguayo ◽  
Alejandro N. Flores ◽  
James P. McNamara ◽  
Hans-Peter Marshall ◽  
Jodi Mead
Keyword(s):  
Hydrologic Modeling ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Water Storage ◽  
Hydrologic Model ◽  
Atmospheric Forcing ◽  
Hydrologic Models ◽  
Snow Water ◽  
Integrated Hydrologic Model ◽  
The Impact

Abstract. Water management in semiarid regions of the western United States requires accurate and timely knowledge of runoff generated by snowmelt. This information is used to plan reservoir releases for downstream users and hydrologic models play an important role in estimating the volume of snow stored in mountain watersheds that serve as source waters for downstream reservoirs. Physically based, integrated hydrologic models are used to develop spatiotemporally dynamic estimates of hydrologic states and fluxes based on understanding of the underlying biophysics of hydrologic response. Yet this class of models are associated with many issues that give rise to significant uncertainties in key hydrologic variables of interest like snow water storage and streamflow. Underlying sources of uncertainty include difficulties in parameterizing processes associated with nonlinearities of some processes, as well as from the large variability in the characteristic spatial and temporal scale of atmospheric forcing and land-surface water and energy balance and groundwater processes. Scale issues, in particular, can introduce systematic biases in integrated atmospheric and hydrologic modeling. Reconciling these discrepancies while maintaining computational tractability remains a fundamental challenge in integrated hydrologic modeling. Here we investigate the hydrologic impact of discrepancies between distributed meteorological forcing data exhibiting a range of spatial scales consistent with a variety of numerical weather prediction models when used to force an integrated hydrologic model associated with a corresponding range of spatial resolutions characteristic of distributed hydrologic modeling. To achieve this, we design and conduct a total of twelve numerical modeling experiments that seek to quantify the impact of applied resolution of atmospheric forcings on simulated hillslope-scale hydrologic state variables. The experiments are arranged in such way to assess the impact of four different atmospheric forcing resolutions (i.e., interpolated 30 m, 1 km, 3 km and 9 km) on two hydrologic variables, snow water equivalent and soil water storage, arranged in three hydrologic spatial resolution (i.e., 30 m, 90 m and 250 m). Results show spatial patterns in snow water equivalent driven by atmospheric forcing in hillslope-scale simulations and patterns mostly driven by topographical characteristics (i.e., slope and aspect) on coarser simulations. Similar patterns are observed in soil water storage however, in addition to that, large errors are encountered primarily in riparian areas of the watershed on coarser simulations. The Weather Research Forecasting (WRF) model is used to develop the environmental forcing variables required as input to the integrated hydrologic model. WRF is an open source, community supported coupled land-atmosphere model capable of capturing spatial scales that permit convection. The integrated hydrologic modeling framework used in this work coincides with the ParFlow open-source surface-subsurface hydrology model. This work has important implications for the use of atmospheric and integrated hydrologic models in remote and ungauged areas. In particular, this work has potential ramifications for the design and development of observing system simulation experiments (OSSEs) in complex and snow-dominated landscapes. OSSEs are critical in constraining the performance characteristics of Earth-observing satellites.

scholarly journals Rainfall-runoff modeling using Doppler weather radar data for Adyar watershed, India

MAUSAM ◽  
2021 ◽  
Vol 65 (1) ◽  
pp. 49-56
Author(s):  
S.JOSEPHINE VANAJA ◽  
B.V. MUDGAL ◽  
S.B. THAMPI
Keyword(s):  
Weather Radar ◽  
Radar Data ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Rainfall Data ◽  
Doppler Weather Radar ◽  
Rainfall Runoff ◽  
Cyclonic Storm ◽  
Rain Gauges ◽  
Runoff Modeling

Precipitation is a significant input for hydrologic models; so, it needs to be quantified precisely. The measurement with rain gauges gives the rainfall at a particular location, whereas the radar obtains instantaneous snapshots of electromagnetic backscatter from rain volumes that are then converted into rainfall via algorithms. It has been proved that the radar measurement of areal rainfall can outperform rain gauge network measurements, especially in remote areas where rain gauges are sparse, and remotely sensed satellite rainfall data are too inaccurate. The research focuses on a technique to improve rainfall-runoff modeling based on radar derived rainfall data for Adyar watershed, Chennai, India. A hydrologic model called ‘Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS)’ is used for simulating rainfall-runoff processes. CARTOSAT 30 m DEM is used for watershed delineation using HEC-GeoHMS. The Adyar watershed is within 100 km radius circle from the Doppler Weather Radar station, hence it has been chosen as the study area. The cyclonic storm Jal event from 4-8 November, 2010 period is selected for the study. The data for this period are collected from the Statistical Department, and the Cyclone Detection Radar Centre, Chennai, India. The results show that the runoff is over predicted using calibrated Doppler radar data in comparison with the point rainfall from rain gauge stations.

scholarly journals Integrated hydrologic model of Pajaro Valley, Santa Cruz and Monterey Counties, California

2014 ◽  
Author(s):  
Randall T. Hanson ◽  
Wolfgang Schmid ◽  
Claudia C. Faunt ◽  
Jonathan Lear ◽  
Brian Lockwood
Keyword(s):  
Hydrologic Model ◽  
Santa Cruz ◽  
Integrated Hydrologic Model
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