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Validation Period (validation + period)
Selected AbstractsPredicting river water temperatures using the equilibrium temperature concept with application on Miramichi River catchments (New Brunswick, Canada)HYDROLOGICAL PROCESSES, Issue 11 2005Daniel Caissie Abstract Water temperature influences most of the physical, chemical and biological properties of rivers. It plays an important role in the distribution of fish and the growth rates of many aquatic organisms. Therefore, a better understanding of the thermal regime of rivers is essential for the management of important fisheries resources. This study deals with the modelling of river water temperature using a new and simplified model based on the equilibrium temperature concept. The equilibrium temperature concept is an approach where the net heat flux at the water surface can be expressed by a simple equation with fewer meteorological parameters than required with traditional models. This new water temperature model was applied on two watercourses of different size and thermal characteristics, but within a similar meteorological region, i.e., the Little Southwest Miramichi River and Catamaran Brook (New Brunswick, Canada). A study of the long-term thermal characteristics of these two rivers revealed that the greatest differences in water temperatures occurred during mid-summer peak temperatures. Data from 1992 to 1994 were used for the model calibration, while data from 1995 to 1999 were used for the model validation. Results showed a slightly better agreement between observed and predicted water temperatures for Catamaran Brook during the calibration period, with a root-mean-square error (RMSE) of 1·10 °C (Nash coefficient, NTD = 0·95) compared to 1·45 °C for the Little Southwest Miramichi River (NTD = 0·94). During the validation period, RMSEs were calculated at 1·31 °C for Catamaran Brook and 1·55 °C for the Little Southwest Miramichi River. Poorer model performances were generally observed early in the season (e.g., spring) for both rivers due to the influence of snowmelt conditions, while late summer to autumn modelling performances showed better results. Copyright © 2005 John Wiley & Sons, Ltd. [source] MODIS Biophysical States and NEXRAD Precipitation in a Statistical Evaluation of Antecedent Moisture Condition and Streamflow,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2009B. P. Weissling Abstract:, The potential of remotely sensed time series of biophysical states of landscape to characterize soil moisture condition antecedent to radar estimates of precipitation is assessed in a statistical prediction model of streamflow in a 1,420 km2 watershed in south-central Texas, Moderate Resolution Imaging Spectroradiometer (MODIS) time series biophysical products offer significant opportunities to characterize and quantify hydrologic state variables such as land surface temperature (LST) and vegetation state and status. Together with Next Generation Weather Radar (NEXRAD) precipitation estimates for the period 2002 through 2005, 16 raw and deseasoned time series of LST (day and night), vegetation indices, infrared reflectances, and water stress indices were linearly regressed against observed watershed streamflow on an eight-day aggregated time period. Time offsets of 0 (synchronous with streamflow event), 8, and 16 days (leading streamflow event) were assessed for each of the 16 parameters to evaluate antecedent effects. The model results indicated a reasonable correlation (r2 = 0.67) when precipitation, daytime LST advanced 16 days, and a deseasoned moisture stress index were regressed against log-transformed streamflow. The estimation model was applied to a validation period from January 2006 through March 2007, a period of 12 months of regional drought and base-flow conditions followed by three months of above normal rainfall and a flood event. The model resulted in a Nash-Sutcliffe estimation efficiency (E) of 0.45 for flow series (in log-space) for the full 15-month period, ,0.03 for the 2006 drought condition period, and 0.87 for the 2007 wet condition period. The overall model had a relative volume error of ,32%. The contribution of parameter uncertainties to model discrepancy was evaluated. [source] CLIMATE CHHANGE SENSITIVITY ASSESSMENT ON UPPER MISSISSIPPI RIVER BASIN STREAMFLOWS USING SWAT,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 4 2006Manoj Jha ABSTRACT: The Soil and Water Assessment Tool (SWAT) model was used to assess the effects of potential future climate change on the hydrology of the Upper Mississippi River Basin (UMRB). Calibration and validation of SWAT were performed using monthly stream flows for 1968,1987 and 1988,1997, respectively. The R2 and Nash-Sutcliffe simulation efficiency values computed for the monthly comparisons were 0.74 and 0.69 for the calibration period and 0.82 and 0.81 for the validation period. The effects of nine 30-year (1968 to 1997) sensitivity runs and six climate change scenarios were then analyzed, relative to a scenario baseline. A doubling of atmospheric CO2 to 660 ppmv (while holding other climate variables constant) resulted in a 36 percent increase in average annual streamflow while average annual flow changes of ,49, ,26, 28, and 58 percent were predicted for precipitation change scenarios of ,20, ,10, 10, and 20 percent, respectively. Mean annual streamflow changes of 51,10, 2, ,6, 38, and 27 percent were predicted by SWAT in response to climate change projections generated from the CISRO-RegCM2, CCC, CCSR, CISRO-Mk2, GFDL, and HadCMS general circulation model scenarios. High seasonal variability was also predicted within individual climate change scenarios and large variability was indicated between scenarios within specific months. Overall, the climate change scenarios reveal a large degree of uncertainty in current climate change forecasts for the region. The results also indicate that the simulated UMRB hydrology is very sensitive to current forecasted future climate changes. [source] COMPARISON OF PROCESS-BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2006Puneet Srivastava ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash-Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ,0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds. [source] Modelling snowpack surface temperature in the Canadian Prairies using simplified heat flow modelsHYDROLOGICAL PROCESSES, Issue 18 2005Purushottam Raj Singh Abstract Three practical schemes for computing the snow surface temperature Ts, i.e. the force,restore method (FRM), the surface conductance method (SCM), and the Kondo and Yamazaki method (KYM), were assessed with respect to Ts retrieved from cloud-free, NOAA-AVHRR satellite data for three land-cover types of the Paddle River basin of central Alberta. In terms of R2, the mean Ts, the t -test and F -test, the FRM generally simulated more accurate Ts than the SCM and KYM. The bias in simulated Ts is usually within several degrees Celsius of the NOAA-AVHRR Ts for both the calibration and validation periods, but larger errors are encountered occasionally, especially when Ts is substantially above 0 °C. Results show that the simulated Ts of the FRM is more consistent than that of the SCM, which in turn was more consistent than that of the KYM. This is partly because the FRM considers two aspects of heat conduction into snow, a stationary-mean diurnal (sinusoidal) temperature variation at the surface coupled to a near steady-state ground heat flux, whereas the SCM assumes a near steady-state, simple heat conduction, and other simplifying assumptions, and the KYM does not balance the snowpack heat fluxes by assuming the snowpack having a vertical temperature profile that is linear. Copyright © 2005 John Wiley & Sons, Ltd. [source] HYDROLOGIC SIMULATION OF THE LITTLE WASHITA RIVER EXPERIMENTAL WATERSHED USING SWAT,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2003Michael W. Van Liew ABSTRACT: Precipitation and streamflow data from three nested subwatersheds within the Little Washita River Experimental Watershed (LWREW) in southwestern Oklahoma were used to evaluate the capabilities of the Soil and Water Assessment Tool (SWAT) to predict streamflow under varying climatic conditions. Eight years of precipitation and streamflow data were used to calibrate parameters in the model, and 15 years of data were used for model validation. SWAT was calibrated on the smallest and largest sub-watersheds for a wetter than average period of record. The model was then validated on a third subwatershed for a range in climatic conditions that included dry, average, and wet periods. Calibration of the model involved a multistep approach. A preliminary calibration was conducted to estimate model parameters so that measured versus simulated yearly and monthly runoff were in agreement for the respective calibration periods. Model parameters were then fine tuned based on a visual inspection of daily hydrographs and flow frequency curves. Calibration on a daily basis resulted in higher baseflows and lower peak runoff rates than were obtained in the preliminary calibration. Test results show that once the model was calibrated for wet climatic conditions, it did a good job in predicting streamflow responses over wet, average, and dry climatic conditions selected for model validation. Monthly coefficients of efficiencies were 0.65, 0.86, and 0.45 for the dry, average, and wet validation periods, respectively. Results of this investigation indicate that once calibrated, SWAT is capable of providing adequate simulations for hydrologic investigations related to the impact of climate variations on water resources of the LWREW. [source] | ||||||||||