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Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km 2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km 2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales. [ABSTRACT FROM AUTHOR]

Accurately quantifying air–sea heat and gas exchange is crucial for comprehending thermoregulation processes and modeling ocean dynamics; these models incorporate bulk formulae for air–sea exchange derived in unstable atmospheric conditions. Therefore, their applicability in stable atmospheric conditions, such as desert-enclosed basins in the Gulf of Eilat/Aqaba (coral refugium), Red Sea, and Persian Gulf, is unclear. We present 2-year eddy covariance results from the Gulf of Eilat, a natural laboratory for studying air–sea interactions in stable atmospheric conditions, which are directly related to ocean dynamics. The measured mean evaporation, 3.22 myr-1 , approximately double that previously estimated by bulk formulae, exceeds the heat flux provided by radiation. Notably, in arid environments, the wind speed seasonal trend drives maximum evaporation in summer, with a minimum winter rate. The higher evaporation rate appears when elevated wind, particularly in the afternoon, coincides with an increase in vapor pressure difference. The inability of the bulk formulae approach to capture the seasonal (opposite from our measurements) and annual trend of evaporation is linked to errors in quantifying the atmospheric boundary layer stability parameter. Most of the year, there is a net cooling effect of surface water (- 79 Wm-2), primarily through evaporation. The substantial heat deficit is compensated by the advection of heat via northbound currents from the Red Sea, which we indirectly quantify from energy balance considerations. Cold and dry synoptic-scale winds induce extreme heat loss through air–sea fluxes and are correlated with the destabilization of the water column during winter and initiation of vertical water-column mixing. [ABSTRACT FROM AUTHOR]

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside. © 2005 Elsevier Ltd. All rights reserved.

Radar-based estimates of rainfall rates and accumulations are one of the principal tools used by the National Weather Service (NWS) to identify areas of extreme precipitation that could lead to flooding. Radar-based rainfall estimates have been compared to gauge observations for 13 convective storm events over a densely instrumented, experimental watershed to derive an accurate reflectivity-rainfall rate (i.e., Z-R) relationship for these events. The resultant Z-R relationship, which is much different than the NWS operational Z-R, has been examined for a separate, independent event that occurred over a different location. For all events studied, the NWS operational Z-R significantly overestimates rainfall compared to gauge measurements. The gauge data from the experimental network, the NWS operational rain estimates, and the improved estimates resulting from this study have been input into a hydrologic model to "predict" watershed runoff for an intense event. Rainfall data from the gauges and from the derived Z-R relation produce predictions in relatively good agreement with observed streamflows. The NWS Z-R estimates lead to predicted peak discharge rates that are more than twice as large as the observed discharges. These results were consistent over a relatively wide range of subwatershed areas (4-148 km2). The experimentally derived Z-R relationship may provide more accurate radar estimates for convective storms over the southwest United States than does the operational convective Z-R used by the NWS. These initial results suggest that the generic NWS Z-R relation, used nationally for convective storms, might be substantially improved for regional application. © 2005 American Meteorological Society.

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.

A spatial rainfall model was applied to radar data of air mass thunderstorms to yield a rainstorm representation as a set of convective rain cells. The modeled rainfall was used as input into hydrological model, instead of the standard radar-grid data. This approach allows a comprehensive linkage between runoff responses and rainfall structures. Copyright © 2005 Royal Meteorological Society.

Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time, therefore, to better understand and manage the related risks, knowledge of their probability at different spatial-temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial-temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid and arid areas across a few tens of kilometres, and its susceptibility to flash-flood. At-site intensity-duration-area-frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25 km2–500 km2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations, but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial-temporal scales. [ABSTRACT FROM AUTHOR]