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Calibration Period (calibration + period)
Selected AbstractsMethods for the analysis of trends in streamflow response due to changes in catchment conditionENVIRONMETRICS, Issue 7 2001R. A. Letcher Abstract Two algorithms for analysing changes in streamflow response due to changes in land use and farm dam development, based on the Estimated Generalized Least Squares (EGLS) and the Generalized Additive Model (GAM) methods, were compared on three catchments in the Macquarie River Basin in NSW, Australia. In order to account for the influence of climatic conditions on streamflow response, the IHACRES conceptual rainfall-runoff model was calibrated on a daily time step over two-year periods then simulated over the entire period of concurrent rainfall, streamflow and temperature data. Residuals or differences between observed and simulated flows were calculated. The EGLS method was applied to a smoothing of the residual (daily) time series. Such residuals represent the difference between the simulated streamflow response to a fixed catchment condition (in the calibration period) and that due to the actual varying conditions throughout the record period. The GAM method was applied to quarterly aggregated residuals. The methods provided similar qualitative results for trends in residual streamflow response in each catchment for models with a good fitting performance on the calibration period in terms of a number of statistics, i.e. the coefficient of efficiency R2, bias and average relative parameter error (ARPE). It was found that the fit of the IHACRES model to the calibration period is critically important in determining trend values and significance. Models with well identified parameters and less correlation between rainfall and model residuals are likely to give the best results for trend analysis. Copyright © 2001 John Wiley & Sons, Ltd. [source] Interannual to decadal changes in area burned in Canada from 1781 to 1982 and the relationship to Northern Hemisphere land temperaturesGLOBAL ECOLOGY, Issue 5 2007Martin P. Girardin ABSTRACT Aim, Temporal variability of annual area burned in Canada (AAB-Can) from ad 1781 to 1982 is inferred from tree-ring width data. Next, correlation analysis is applied between the AAB-Can estimates and Northern Hemisphere (NH) warm season land temperatures to link recent interannual to decadal changes in area burned with large-scale climate variations. The rationale in this use of tree rings is that annual radial increments produced by trees can approximate area burned through sensing climate variations that promote fire activity. Location, The statistical reconstruction of area burned is at the scale of Canada. Methods, The data base of total area burned per year in Canada is used as the predictand. A set of 53 multicentury tree-ring width chronologies distributed across Canada is used as predictors. A linear model relating the predictand to the tree-ring predictors is fitted over the period 1920,82. The regression coefficients estimated for the calibration period are applied to the tree-ring predictors for as far back as 1781 to produce a series of AAB-Can estimates. Results, The AAB-Can estimates account for 44.1% of the variance in the observed data recorded from 1920 to 1982 (92.2% after decadal smoothing) and were verified using a split sample calibration-verification scheme. The statistical reconstruction indicates that the positive trend in AAB-Can from c. 1970,82 was preceded by three decades during which area burned was at its lowest during the past 180 years. Correlation analysis with NH warm season land temperatures from the late 18th century to the present revealed a positive statistical association with these estimates. Main conclusions, As with previous studies, it is demonstrated that the upward trend in AAB-Can is unlikely to be an artefact from changing fire reporting practices and may have been driven by large-scale climate variations. [source] Identifying the Potential Loss of Monitoring Wells Using an Uncertainty AnalysisGROUND WATER, Issue 6 2005Vicky L. Freedman From the mid-1940s through the 1980s, large volumes of waste water were discharged at the Hanford Site in southeastern Washington State, causing a large-scale rise (>20 m) in the water table. When waste water discharges ceased in 1988, ground water mounds began to dissipate. This caused a large number of wells to go dry and has made it difficult to monitor contaminant plume migration. To identify monitoring wells that will need replacement, a methodology has been developed using a first-order uncertainty analysis with UCODE, a nonlinear parameter estimation code. Using a three-dimensional, finite-element ground water flow code, key parameters were identified by calibrating to historical hydraulic head data. Results from the calibration period were then used to check model predictions by comparing monitoring wells' wet/dry status with field data. This status was analyzed using a methodology that incorporated the 0.3 cumulative probability derived from the confidence and prediction intervals. For comparison, a nonphysically based trend model was also used as a predictor of wells' wet/dry status. Although the numerical model outperformed the trend model, for both models, the central value of the intervals was a better predictor of a wet well status. The prediction interval, however, was more successful at identifying dry wells. Predictions made through the year 2048 indicated that 46% of the wells in the monitoring well network are likely to go dry in areas near the river and where the ground water mound is dissipating. [source] Predicting 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] Reconstruction of the North Atlantic Oscillation, 1429,1983INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2001Mary F. Glueck Abstract The North Atlantic Oscillation (NAO) is considered to be the dominant mode of winter atmospheric variability in the Northern Hemisphere (Barnston AG, Livezey RE. 1987. Classification, seasonality and persistence of low frequency atmospheric circulation patterns. Monthly Weather Review115: 1083,1126), especially in the North Atlantic region. A better understanding of its recent variability in the context of pre-instrumental period variations is critical for prediction purposes. A 555-year (1429,1983) multi-proxy reconstruction of the cool season NAO, calibrated against the Lisbon,Iceland (LISJHI) NAO, is presented. Predictor variables include tree-ring chronologies from Morocco and Finland, GISP2 ,18O annual series, and a GISP2 snow accumulation record. Although the reconstructed values are generally lower than the instrumental values during the calibration period (1863,1983), the final reconstruction does capture the low frequency of the instrumental NAO. The reconstruction compares favourably with existing shorter NAO reconstructions and with the instrumental NAO. The variability in the reconstructed NAO is also discussed within the context of lengthy regional climate records. Results suggest that the occurrence and length of the recent persistently high phase of the NAO are not unusual over the 555-year period of time, but that the magnitude of some of the instrumental values may, in fact, be unique. Copyright © 2001 Royal Meteorological Society [source] Forecasting new product trial in a controlled test market environmentJOURNAL OF FORECASTING, Issue 5 2003Peter S. Fader Abstract A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ,components' on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data.,Copyright © 2003 John Wiley & Sons, Ltd. [source] Water Resources Modeling of the Ganges-Brahmaputra-Meghna River Basins Using Satellite Remote Sensing Data,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 6 2009Bushra Nishat Nishat, Bushra and S.M. Mahbubur Rahman, 2009. Water Resources Modeling of the Ganges-Brahmaputra-Meghna River Basins Using Satellite Remote Sensing Data. Journal of the American Water Resources Association (JAWRA) 45(6):1313-1327. Abstract:, Large-scale water resources modeling can provide useful insights on future water availability scenarios for downstream nations in anticipation of proposed upstream water resources projects in large international river basins (IRBs). However, model set up can be challenging due to the large amounts of data requirement on both static states (soils, vegetation, topography, drainage network, etc.) and dynamic variables (rainfall, streamflow, soil moisture, evapotranspiration, etc.) over the basin from multiple nations and data collection agencies. Under such circumstances, satellite remote sensing provides a more pragmatic and convenient alternative because of the vantage of space and easy availability from a single data platform. In this paper, we demonstrate a modeling effort to set up a water resources management model, MIKE BASIN, over the Ganges, Brahmaputra, and Meghna (GBM) river basins. The model is set up with the objective of providing Bangladesh, the lowermost riparian nation in the GBM basins, a framework for assessing proposed water diversion scenarios in the upstream transboundary regions of India and deriving quantitative impacts on water availability. Using an array of satellite remote sensing data on topography, vegetation, and rainfall from the transboundary regions, we demonstrate that it is possible to calibrate MIKE BASIN to a satisfactory level and predict streamflow in the Ganges and Brahmaputra rivers at the entry points of Bangladesh at relevant scales of water resources management. Simulated runoff for the Ganges and Brahmaputra rivers follow the trends in the rated discharge for the calibration period. However, monthly flow volume differs from the actual rated flow by (,) 8% to (+) 20% in the Ganges basin, by (,) 15 to (+) 12% in the Brahmaputra basin, and by (,) 15 to (+) 19% in the Meghna basin. Our large-scale modeling initiative is generic enough for other downstream nations in IRBs to adopt for their own modeling needs. [source] Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 1 2009Kenneth J. Tobin Abstract:, Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar , NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical-based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash-Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three-day period) with NS values of (0.60-0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ,16%; NS = 0.38-0.94; RMSE = 0.07-0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite-estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground-based radar networks (i.e., much of the third world). [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] Potential impact of a new blood glucose monitoring device: the GlucoWatch® BiographerPRACTICAL DIABETES INTERNATIONAL (INCORPORATING CARDIABETES), Issue 4 2002NN Chan Abstract Home blood glucose monitoring may be laborious, time-consuming, inconvenient and painful. Failure to test may preclude optimisation of glycaemic control. We aimed to evaluate the potential usefulness of a new noninvasive automatic glucose monitor, the GlucoWatch® Biographer. Eight patients with type 1 diabetes and two with type 2 diabetes (4M:6F) aged between 23 and 65 years participated in this study. All participants were given 1 hour of instruction prior to provision of the GlucoWatch®. They were given contact numbers and reviewed weekly. Several disadvantages were encountered by the participants, which included the daily 3 hour calibration period (n = 10), skin irritations (n = 6) and skipped measurements (n = 2) due to unsatisfactory probe contact due to skin temperature or sweats. Several patients, however, found it invaluable to have their daily profile monitored to allow insulin dosage adjustment and detection of hypoglycaemia. The GlucoWatch® Biographer is an invaluable tool that allows noninvasive detection of glucose trends, which contributes to glycaemic control. However, it is not suitable for every patient. Self-motivation and ability to learn how to use the device are the key factors. Copyright © 2002 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] | ||||||||||