scholarly journals Multiobjective calibration of the MESH hydrological model on the Reynolds Creek Experimental Watershed

2010 ◽  
Vol 7 (2) ◽  
pp. 2121-2155 ◽  
Author(s):  
A. J. MacLean ◽  
B. A. Tolson ◽  
F. R. Seglenieks ◽  
E. Soulis
Keyword(s):  
Snow Water Equivalent ◽  
Model Simulation ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Mesh Model ◽  
Reynolds Creek ◽  
Spatially Distributed ◽  
Multiobjective Calibration ◽  
Computationally Intensive ◽  
Computational Budget

Abstract. The spatially distributed MESH hydrologic model (Pietroniro et al., 2007) was successfully calibrated and then validated for the prediction of snow water equivalent (SWE) and streamflow in the Reynolds Creek Experimental Watershed in Idaho, USA. The tradeoff between fitting to SWE versus streamflow data was assessed and showed that both could be simultaneously predicted with good quality by the MESH model. Not surprisingly, calibrating to only one objective (e.g. SWE) yielded poor simulation results for the other objective (e.g. streamflow). The multiobjective calibration problem in this study was efficiently solved via a simple weighted objective function approach and analyses showed that the approach yielded a balanced solution between the objectives. Our approach therefore eliminated the need to rely on a potentially more computationally intensive evolutionary multiobjective algorithm to approximate the entire tradeoff surface between objectives. Additional calibration experiments showed that for our calibration computational budget (2000 model evaluations), the autocalibration procedure would fail without being initialized to a model parameter set carefully determined for this specific case study. This study serves as a benchmark for MESH model simulation accuracy which can be compared with future versions of MESH.

scholarly journals Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change

2015 ◽  
Vol 19 (2) ◽  
pp. 857-876 ◽  
Author(s):  
S. Wi ◽  
Y. C. E. Yang ◽  
S. Steinschneider ◽  
A. Khalil ◽  
C. M. Brown
Keyword(s):  
Climate Change ◽  
High Performance ◽  
Climate Models ◽  
Optimization Problems ◽  
Model Simulation ◽  
Model Fitting ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Distributed Models ◽  
Spatially Distributed

Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

scholarly journals Calibration approaches for distributed hydrologic models using high performance computing: implication for streamflow projections under climate change

2014 ◽  
Vol 11 (9) ◽  
pp. 10273-10317 ◽  
Author(s):  
S. Wi ◽  
Y. C. E. Yang ◽  
S. Steinschneider ◽  
A. Khalil ◽  
C. M. Brown
Keyword(s):  
Climate Change ◽  
High Performance Computing ◽  
High Performance ◽  
Climate Models ◽  
Model Simulation ◽  
Model Fitting ◽  
Hydrologic Model ◽  
Simulation Accuracy ◽  
Distributed Models ◽  
Performance Computing

Abstract. This study utilizes high performance computing to test the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely-gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a step-wise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a step-wise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

scholarly journals Independent Evaluation of Frozen Precipitation from WRF and PRISM in the Olympic Mountains

2017 ◽  
Vol 18 (10) ◽  
pp. 2681-2703 ◽  
Author(s):  
William Ryan Currier ◽  
Theodore Thorson ◽  
Jessica D. Lundquist
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Hydrologic Model ◽  
Olympic Mountains ◽  
Independent Evaluation ◽  
Spatially Distributed ◽  
Ground Validation ◽  
Snow Water

Abstract Estimates of precipitation from the Weather Research and Forecasting (WRF) Model and the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) are widely used in complex terrain to obtain spatially distributed precipitation data. The authors evaluated both WRF (4/3 km) and PRISM’s (800-m annual climatology) ability to estimate frozen precipitation using the hydrologic model Structure for Unifying Multiple Modeling Alternatives (SUMMA) and a unique set of spatiotemporal snow depth and snow water equivalent (SWE) observations collected for the Olympic Mountain Experiment (OLYMPEX) ground validation campaign during water year 2016. When SUMMA was forced with WRF precipitation and used a calibrated, wet-bulb-temperature-based method for partitioning rain versus snow, its estimation of near-peak SWE was biased low by 21% on average. However, when SUMMA was allowed to partition WRF total precipitation into rain and snow based on output from WRF’s microphysical scheme (WRFMPP), simulations of snow depth and SWE were near equal to or better than simulations that used PRISM-derived precipitation with the calibrated partitioning method. Over all sites, WRFMPP and simulations that used PRISM-derived precipitation had relatively unbiased estimates of near-peak SWE, but both simulated absolute errors in near-peak SWE of 30%–60% at a few locations. Since, on average, WRFMPP had similar errors to PRISM, WRFMPP suggested a promising path forward in hydrology, as it was independent of gauge data and did not require SWE observations for calibration. Furthermore, in similar maritime environments, hydrologic modelers should pay close attention to decisions regarding rain-versus-snow partitioning, wind speed, and incoming longwave radiation.

scholarly journals NEXRAD Quantitative Precipitation Estimations for Hydrologic Simulation Using a Hybrid Hydrologic Model

2016 ◽  
Vol 18 (1) ◽  
pp. 25-47 ◽  
Author(s):  
Younghyun Cho ◽  
Bernard A. Engel
Keyword(s):  
Model Simulation ◽  
Model Performance ◽  
Stage Iv ◽  
Rain Gauge ◽  
Hydrologic Model ◽  
Feature Model ◽  
Coefficient Of Determination ◽  
Hydrologic Simulation ◽  
Direct Runoff ◽  
Spatially Distributed

Abstract A hybrid hydrologic model (lumped conceptual and distributed feature model), Distributed-Clark, is introduced to perform hydrologic simulations using spatially distributed NEXRAD quantitative precipitation estimations (QPEs). In Distributed-Clark, spatially distributed excess rainfall estimated with the Soil Conservation Service (SCS) curve number method and a GIS-based set of separated unit hydrographs are utilized to calculate a direct runoff flow hydrograph. This simple approach using few modeling parameters reduces calibration complexity relative to physically based distributed (PBD) models by only focusing on integrated flow estimation at watershed outlets. Case studies assessed the quality of NEXRAD stage IV QPEs for hydrologic simulation compared to gauge-only analyses. NEXRAD data validation against rain gauge observations and performance evaluation with model simulation result comparisons for inputs of spatially distributed stage IV and spatially averaged gauged data for four study watersheds were conducted. Results show significant differences in NEXRAD QPEs and gauged rainfall amounts, with NEXRAD data overestimated by 7.5% and 9.1% and underestimated by 15.0% and 11.4% accompanied by spatial variability. These differences affect model performance in hydrologic applications. Rainfall–runoff flow simulations using spatially distributed NEXRAD stage IV QPEs demonstrate relatively good fit [direct runoff: Nash–Sutcliffe efficiency ENS = 0.85, coefficient of determination R2 = 0.89, and percent bias (PBIAS) = 3.92%; streamflow: ENS = 0.91, R2 = 0.93, and PBIAS = 1.87%] against observed flow as well as better fit (ENS of 3.7% and R2 of 6.0% increase in direct runoff) than spatially averaged gauged rainfall for the same model calibration approach, enabling improved estimates of flow volumes and peak rates that can be underestimated in hydrologic simulations for spatially averaged rainfall.

scholarly journals Combined assimilation of streamflow and snow water equivalent for mid-term ensemble streamflow forecasts in snow-dominated regions

2016 ◽  
Author(s):  
Jean M. Bergeron ◽  
Mélanie Trudel ◽  
Robert Leconte
Keyword(s):  
Data Assimilation ◽  
State Vector ◽  
Snow Water Equivalent ◽  
Synthetic Data ◽  
Hydrologic Model ◽  
Open Loop ◽  
The State ◽  
Wide Range ◽  
Snow Water ◽  
The Impact

Abstract. The potential of data assimilation for hydrologic predictions has been demonstrated in many research studies. Watersheds over which multiple observation types are available can potentially further benefit from data assimilation by having multiple updated states from which hydrologic predictions can be generated. However, the magnitude and time span of the impact of the assimilation of an observation varies according not only to its type, but also to the variables included in the state vector. This study examines the impact of multivariate synthetic data assimilation using the Ensemble Kalman Filter (EnKF) into the spatially distributed hydrologic model CEQUEAU for the mountainous Nechako River located in British-Columbia, Canada. Synthetic data includes daily snow cover area (SCA), daily measurements of snow water equivalent (SWE) at three different locations and daily streamflow data at the watershed outlet. Results show a large variability of the continuous rank probability skill score over a wide range of prediction horizons (days to weeks) depending on the state vector configuration and the type of observations assimilated. Overall, the variables most closely linearly linked to the observations are the ones worth considering adding to the state vector. The performance of the assimilation of basin-wide SCA, which does not have a decent proxy among potential state variables, does not surpass the open loop for any of the simulated variables. However, the assimilation of streamflow offers major improvements steadily throughout the year, but mainly over the short-term (up to 5 days) forecast horizons, while the impact of the assimilation of SWE gains more importance during the snowmelt period over the mid-term (up to 50 days) forecast horizon compared with open loop. The combined assimilation of streamflow and SWE performs better than its individual counterparts, offering improvements over all forecast horizons considered and throughout the whole year, including the critical period of snowmelt. This highlights the potential benefit of using multivariate data assimilation for streamflow predictions in snow-dominated regions.

scholarly journals Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

2021 ◽  
Vol 893 (1) ◽  
pp. 012023
Author(s):  
Puji R A Sibuea ◽  
Dewi R Agriamah ◽  
Edi Riawan ◽  
Rusmawan Suwarman ◽  
Atika Lubis
Keyword(s):  
Model Simulation ◽  
Hydrologic Model ◽  
Model Case ◽  
Probable Maximum Precipitation ◽  
Maximum Precipitation ◽  
Hydrologic Analysis ◽  
Probable Maximum Flood ◽  
The Mean ◽  
The Impact

Abstract Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.

Application of WetSpa model for assessing land use impacts on floods in the Margecany–Hornad watershed, Slovakia

2006 ◽  
Vol 53 (10) ◽  
pp. 37-45 ◽  
Author(s):  
A. Bahremand ◽  
F. De Smedt ◽  
J. Corluy ◽  
Y.B. Liu ◽  
J. Poórová ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Hydrologic Model ◽  
Land Use Impacts ◽  
M Cell ◽  
Time To Peak ◽  
Wetspa Model ◽  
Spatially Distributed ◽  
The Impact ◽  
Good Agreement

The spatially distributed hydrologic model WetSpa combines elevation, soil and land use data within GIS, to predict flood hydrographs and spatial distribution of hydrologic characteristics in a watershed. The model is applied to the Margecany–Hornad river basin (1,131 km2) in Slovakia. Daily hydrometeorological data from 1991–2000, including precipitation data from nine stations, temperature data from four stations and evaporation data measured at one station are used as input to the model. Three base maps, i.e. DEM, land use and soil type are prepared in GIS form, using 100 × 100 m cell size. Results of the simulations show good agreement between calculated and measured hydrographs. The model predicts the daily/hourly hydrographs with 75–80% accuracy according to the Nash–Sutcliff criteria. For assessing the impact of land use changes on floods, the calibrated model is applied for a reforestation scenario, which considers a 50% increase of forest areas. The model results show that the reforestation scenario decreases the peak discharge by 12%. Investigation of peak discharges from the whole simulation period, shows that the scenario results are reduced by 18% on average, while for small discharges the reduction is even about 34%. The time to peak of the simulated hydrograph of the reforestation scenario is 20 hours longer than for the present land use.

scholarly journals Spatially distributed tracer-aided runoff modelling and dynamics of storage and water ages in a permafrost-influenced catchment

2019 ◽  
Vol 23 (6) ◽  
pp. 2507-2523 ◽  
Author(s):  
Thea I. Piovano ◽  
Doerthe Tetzlaff ◽  
Sean K. Carey ◽  
Nadine J. Shatilla ◽  
Aaron Smith ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Temporal Dynamics ◽  
Snow Accumulation ◽  
Yukon Territory ◽  
Distributed Model ◽  
Runoff Generation ◽  
Frozen Ground ◽  
Isotope Data ◽  
Spatially Distributed ◽  
Thaw Layer

Abstract. Permafrost strongly controls hydrological processes in cold regions. Our understanding of how changes in seasonal and perennial frozen ground disposition and linked storage dynamics affect runoff generation processes remains limited. Storage dynamics and water redistribution are influenced by the seasonal variability and spatial heterogeneity of frozen ground, snow accumulation and melt. Stable isotopes are potentially useful for quantifying the dynamics of water sources, flow paths and ages, yet few studies have employed isotope data in permafrost-influenced catchments. Here, we applied the conceptual model STARR (the Spatially distributed Tracer-Aided Rainfall–Runoff model), which facilitates fully distributed simulations of hydrological storage dynamics and runoff processes, isotopic composition and water ages. We adapted this model for a subarctic catchment in Yukon Territory, Canada, with a time-variable implementation of field capacity to include the influence of thaw dynamics. A multi-criteria calibration based on stream flow, snow water equivalent and isotopes was applied to 3 years of data. The integration of isotope data in the spatially distributed model provided the basis for quantifying spatio-temporal dynamics of water storage and ages, emphasizing the importance of thaw layer dynamics in mixing and damping the melt signal. By using the model conceptualization of spatially and temporally variable storage, this study demonstrates the ability of tracer-aided modelling to capture thaw layer dynamics that cause mixing and damping of the isotopic melt signal.

Seismic Hazard and Loss Analysis for Spatially Distributed Infrastructure in Christchurch, New Zealand

2016 ◽  
Vol 32 (2) ◽  
pp. 697-712 ◽  
Author(s):  
Hasan Manzour ◽  
Rachel A. Davidson ◽  
Nick Horspool ◽  
Linda K. Nozick
Keyword(s):  
New Zealand ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Regional Scale ◽  
Average Error ◽  
Loss Analysis ◽  
Spatially Distributed ◽  
Small Set ◽  
Computationally Intensive ◽  
Distributed Infrastructure

The new Extended Optimization-Based Probabilistic Scenario method produces a small set of probabilistic ground motion maps to represent the seismic hazard for analysis of spatially distributed infrastructure. We applied the method to Christchurch, New Zealand, including a sensitivity analysis of key user-specified parameters. A set of just 124 ground motion maps were able to match the hazard curves based on a million-year Monte Carlo simulation with no error at the four selected return periods, mean spatial correlation errors of 0.03, and average error in the residential loss exceedance curves of 2.1%. This enormous computational savings in the hazard has substantial implications for regional-scale, policy decisions affecting lifelines or building inventories since it can allow many more downstream analyses and/or doing them using more sophisticated, computationally intensive methods. The method is robust, offering many equally good solutions and it can be solved using free open source optimization solvers.

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