A reliable prediction of hydrologic models, among other things, requires a set of plausible parameters that correspond with physiographic properties of the basin. This study proposes a parameter estimation approach, which is based on extracting, through hydrograph diagnoses, information in the form of indices that carry intrinsic properties of a basin. This concept is demonstrated by introducing two indices that describe the shape of a streamflow hydrograph in an integrated manner. Nineteen mid-size (223–4790 km2) perennial headwater basins with a long record of streamflow data were selected to evaluate the ability of these indices to capture basin response characteristics. An examination of the utility of the proposed indices in parameter estimation is conducted for a five-parameter hydrologic model using data from the Leaf River, located in Fort Collins, Mississippi. It is shown that constraining the parameter estimation by selecting only those parameters that result in model output which maintains the indices as found in the historical data can improve the reliability of model predictions. These improvements were manifested in (a) improvement of the prediction of low and high flow, (b) improvement of the overall total biases, and (c) maintenance of the hydrograph's shape for both long-term and short-term predictions. Copyright © 2005 John Wiley & Sons, Ltd.

Intense rainstorms cause debris flows on escarpments in hyperarid environments. In contrast with more temperate environments, there have been no direct observations on rainfall intensities and durations required for initiating debris flows in hyperarid environments. Here, we report rainfall volume and intensities, acquired by gauge and radar measurements, for two successive storms along the hyperarid (\textless50 mm/yr) western escarpment of the Dead Sea basin. These rainfall data were analyzed in conjunction with detailed mapping of debris flows that occurred during these storms to determine values of rainfall intensity and duration required to generate debris flows on the Dead Sea western escarpment. The first of the two analyzed storms occurred on 2 November 1995. During this storm, two convective cells rained sequentially within a 5 It period at the lower reaches of the Nahal David and the Nahal 'Arugot that dissects the western escarpment of the Dead Sea, Israel. This storm triggered debris flows in 38 small (\textless3 km(2)) and high-gradient drainage basins along the escarpment. Total rainfall volume and spatial distribution were determined by 10 cumulative rain gauges that were also used to calibrate rainfall-intensity distributions from radar data. For this storm, region, and landscape, rainfall intensities exceeding 30 mm/h for a duration of I h were required to initiate debris flows. A second storm in the same area on 1718 October 1997 allowed the evaluation of the results determined from the 1995 storm. In this second, more regional storm, maximum rainfall intensities were 19-27 mm/h for a duration of 45 min. These values, lower than the 30 mm/h minimal threshold defined in the previous storm, are consistent with the occurrence of only three debris flows. The small number of debris flows resulted from the concentration of the highest intensities of rainfall on the desert plateau and not directly on top of the canyon walls. Most first- to third-order basins draining the Dead Sea escarpment contain evidence of zero to three late Holocene (\textless3000 yr) debris flows. From analysis of the two storms, we propose that most of these debris flows were triggered by storms similar to the 2 November 1995 event in which localized convective cells had rainfall intensities of \textgreater30 mm/h and durations of at least 1 h. The small number of debris flows that has occurred during the late Holocene indicates that such events are rare at the scale of individual drainage basins.

Intense rainstorms cause debris flows on escarpments in hyperarid environments. In contrast with more temperate environments, there have been no direct observations on rainfall intensities and durations required for initiating debris flows in hyperarid environments. Here, we report rainfall volume and intensities, acquired by gauge and radar measurements, for two successive storms along the hyperarid (\textless50 mm/yr) western escarpment of the Dead Sea basin. These rainfall data were analyzed in conjunction with detailed mapping of debris flows that occurred during these storms to determine values of rainfall intensity and duration required to generate debris flows on the Dead Sea western escarpment. The first of the two analyzed storms occurred on 2 November 1995. During this storm, two convective cells rained sequentially within a 5 It period at the lower reaches of the Nahal David and the Nahal 'Arugot that dissects the western escarpment of the Dead Sea, Israel. This storm triggered debris flows in 38 small (\textless3 km(2)) and high-gradient drainage basins along the escarpment. Total rainfall volume and spatial distribution were determined by 10 cumulative rain gauges that were also used to calibrate rainfall-intensity distributions from radar data. For this storm, region, and landscape, rainfall intensities exceeding 30 mm/h for a duration of I h were required to initiate debris flows. A second storm in the same area on 1718 October 1997 allowed the evaluation of the results determined from the 1995 storm. In this second, more regional storm, maximum rainfall intensities were 19-27 mm/h for a duration of 45 min. These values, lower than the 30 mm/h minimal threshold defined in the previous storm, are consistent with the occurrence of only three debris flows. The small number of debris flows resulted from the concentration of the highest intensities of rainfall on the desert plateau and not directly on top of the canyon walls. Most first- to third-order basins draining the Dead Sea escarpment contain evidence of zero to three late Holocene (\textless3000 yr) debris flows. From analysis of the two storms, we propose that most of these debris flows were triggered by storms similar to the 2 November 1995 event in which localized convective cells had rainfall intensities of \textgreater30 mm/h and durations of at least 1 h. The small number of debris flows that has occurred during the late Holocene indicates that such events are rare at the scale of individual drainage basins.

Intense rainstorms cause debris flows on escarpments in hyperarid environments. In contrast with more temperate environments, there have been no direct observations on rainfall intensities and durations required for initiating debris flows in hyperarid environments. Here, we report rainfall volume and intensities, acquired by gauge and radar measurements, for two successive storms along the hyperarid (\textless50 mm/yr) western escarpment of the Dead Sea basin. These rainfall data were analyzed in conjunction with detailed mapping of debris flows that occurred during these storms to determine values of rainfall intensity and duration required to generate debris flows on the Dead Sea western escarpment. The first of the two analyzed storms occurred on 2 November 1995. During this storm, two convective cells rained sequentially within a 5 It period at the lower reaches of the Nahal David and the Nahal 'Arugot that dissects the western escarpment of the Dead Sea, Israel. This storm triggered debris flows in 38 small (\textless3 km(2)) and high-gradient drainage basins along the escarpment. Total rainfall volume and spatial distribution were determined by 10 cumulative rain gauges that were also used to calibrate rainfall-intensity distributions from radar data. For this storm, region, and landscape, rainfall intensities exceeding 30 mm/h for a duration of I h were required to initiate debris flows. A second storm in the same area on 1718 October 1997 allowed the evaluation of the results determined from the 1995 storm. In this second, more regional storm, maximum rainfall intensities were 19-27 mm/h for a duration of 45 min. These values, lower than the 30 mm/h minimal threshold defined in the previous storm, are consistent with the occurrence of only three debris flows. The small number of debris flows resulted from the concentration of the highest intensities of rainfall on the desert plateau and not directly on top of the canyon walls. Most first- to third-order basins draining the Dead Sea escarpment contain evidence of zero to three late Holocene (\textless3000 yr) debris flows. From analysis of the two storms, we propose that most of these debris flows were triggered by storms similar to the 2 November 1995 event in which localized convective cells had rainfall intensities of \textgreater30 mm/h and durations of at least 1 h. The small number of debris flows that has occurred during the late Holocene indicates that such events are rare at the scale of individual drainage basins.

Global precipitation is monitored from a variety of platforms including spaceborne, ground-, and ocean-based platforms. Intercomparisons of these observations are crucial to validating the measurements and providing confidence for each measurement technique. Probability distribution functions of rain rates are used to compare satellite and ground-based radar observations. A preferred adjustment technique for improving rain rate distribution estimates is identified using measurements from ground-based radar and rain gauges within the coverage area of the radar. The underwater measurement of rainfall shows similarities to radar measurements, but with intermediate spatial resolution and high temporal resolution. Reconciling these different measurement techniques provides understanding and confidence for all of the methods.

Global precipitation is monitored from a variety of platforms including spaceborne, ground-, and ocean-based platforms. Intercomparisons of these observations are crucial to validating the measurements and providing confidence for each measurement technique. Probability distribution functions of rain rates are used to compare satellite and ground-based radar observations. A preferred adjustment technique for improving rain rate distribution estimates is identified using measurements from ground-based radar and rain gauges within the coverage area of the radar. The underwater measurement of rainfall shows similarities to radar measurements, but with intermediate spatial resolution and high temporal resolution. Reconciling these different measurement techniques provides understanding and confidence for all of the methods.

Global precipitation is monitored from a variety of platforms including spaceborne, ground-, and ocean-based platforms. Intercomparisons of these observations are crucial to validating the measurements and providing confidence for each measurement technique. Probability distribution functions of rain rates are used to compare satellite and ground-based radar observations. A preferred adjustment technique for improving rain rate distribution estimates is identified using measurements from ground-based radar and rain gauges within the coverage area of the radar. The underwater measurement of rainfall shows similarities to radar measurements, but with intermediate spatial resolution and high temporal resolution. Reconciling these different measurement techniques provides understanding and confidence for all of the methods.

Global precipitation is monitored from a variety of platforms including spaceborne, ground-, and ocean-based platforms. Intercomparisons of these observations are crucial to validating the measurements and providing confidence for each measurement technique. Probability distribution functions of rain rates are used to compare satellite and ground-based radar observations. A preferred adjustment technique for improving rain rate distribution estimates is identified using measurements from ground-based radar and rain gauges within the coverage area of the radar. The underwater measurement of rainfall shows similarities to radar measurements, but with intermediate spatial resolution and high temporal resolution. Reconciling these different measurement techniques provides understanding and confidence for all of the methods.

Global precipitation is monitored from a variety of platforms including spaceborne, ground-, and ocean-based platforms. Intercomparisons of these observations are crucial to validating the measurements and providing confidence for each measurement technique. Probability distribution functions of rain rates are used to compare satellite and ground-based radar observations. A preferred adjustment technique for improving rain rate distribution estimates is identified using measurements from ground-based radar and rain gauges within the coverage area of the radar. The underwater measurement of rainfall shows similarities to radar measurements, but with intermediate spatial resolution and high temporal resolution. Reconciling these different measurement techniques provides understanding and confidence for all of the methods.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

A new characteristic timescale of a catchment is presented, the response timescale (RTS). It is a range of averaging time intervals which, when applied to catchment rainfall, yield smoothed time series that best approximate that of the resultant runoff. In determining the RTS, nothing is assumed about the nature of the rainfall-runoff transformation. In addition, this new measure is shown to be robust against measurement errors. An objective, automatic, observations-based algorithm is described that introduces the concept of peaks density for the estimation of RTS. Estimation is exemplified for single and multiple rainfall-runoff events through application to small catchments in Panama and Israel. In all cases, relatively stable values of response timescale are obtained. It is concluded that at least for the case studies, the response timescale is an intrinsic characteristic of the catchment and it is generally expected to be different from the catchment lag time and time of concentration. INDEX

A new characteristic timescale of a catchment is presented, the response timescale (RTS). It is a range of averaging time intervals which, when applied to catchment rainfall, yield smoothed time series that best approximate that of the resultant runoff. In determining the RTS, nothing is assumed about the nature of the rainfall-runoff transformation. In addition, this new measure is shown to be robust against measurement errors. An objective, automatic, observations-based algorithm is described that introduces the concept of peaks density for the estimation of RTS. Estimation is exemplified for single and multiple rainfall-runoff events through application to small catchments in Panama and Israel. In all cases, relatively stable values of response timescale are obtained. It is concluded that at least for the case studies, the response timescale is an intrinsic characteristic of the catchment and it is generally expected to be different from the catchment lag time and time of concentration. INDEX