Towards flash-flood prediction in the dry Dead Sea region utilizing radar rainfall information

Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash-flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipitation estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Validation was performed for a subsequent 5-year period for the same catchment and then for an entire 10-year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.     

Formation of lacustrine dolomite in the late Miocene marginal lakes of the East Mediterranean (Northern Israel)

The study focuses on the formation of lacustrine dolomite in late Miocene lakes, located at the East Mediterranean margins (Northern Israel). These lakes deposited the sediments of the Bira (Tortonian) and Gesher (Messinian) formations that comprise sequences of dolostone and limestone. Dolostones are bedded, consist of small‐sized (<7 μm), Ca‐rich (52 to 56 mol %) crystals with relatively low ordering degrees, and present evidence for replacement of CaCO₃ components. Limestones are comprised of a wackestone to mudstone matrix, freshwater macrofossils and intraclasts (mainly in the Bira Formation). Sodium concentrations and isotope compositions differ between limestones and dolostones: Na = ~100 to 150 ppm; ~1000 to 2000 ppm; δ¹⁸O = ?3·8 to ?1·6‰; ?2·0 to +4·3‰; δ¹³C = ?9·0 to ?3·4‰; ?7·8 to 0‰ (VPDB), respectively. These results indicate a climate‐related sedimentation during the Tortonian and early Messinian. Wet conditions and positive freshwater inflow into the carbonate lake led to calcite precipitation due to intense phytoplankton blooms (limestone formation). Dry conditions and enhanced evaporation led to precipitation of evaporitic CaCO₃ in a terminal lake, which caused an increased Mg/Ca ratio in the residual waters and penecontemporaneous dolomitization (dolostone formation). The alternating lithofacies pattern reveals eleven short‐term wet–dry climate‐cycles during the Tortonian and early Messinian. A shift in the environmental conditions under which dolomite formed is indicated by a temporal decrease in δ¹⁸O of dolostones and Na content of dolomite crystals. These variations point to decreasing evaporation degrees and/or an increased mixing with meteoric waters towards the late Messinian. A temporal decrease in δ¹³C of dolostones and limestones and appearance of microbial structures in close association with dolomite suggest that microbial activity had an important role in allowing dolomite formation during the Messinian. Microbial mediation was apparently the main process that enabled local growth of dolomite under wet conditions during the latest Messinian.     

A quasi 3-D model of an outburst pattern that explains the behavior of Comet 17P/Holmes

Upon discovery, in November of 1892, Comet 17P/Holmes was experiencing a very strong explosion, which repeated itself on an even greater scale in October of 2007. Using a numerical scheme, based on mass and energy conservation laws, a model is presented simulating the outbursting nature of Comet 17P/Holmes by regarding the comet as a stratified sphere. The model, adopting input parameters for physical and chemical characteristics, produces output for volatile, water and dust production rates and temperature distribution. Providing the model with known properties of Comet 17P/Holmes, and then filling in the blanks based on properties of other short period comets, allowed the derivation of a specific chemical composition and thermal conductivity that produced a recurring outburst pattern every ～30 years, while within these there is a near-perihelion outburst every ～10² years. The specific composition that generated the desired results, consists of an ice-dust ratio of 0.6/0.4, and mass fractions of 0.015, 0.002 and 0.0005 for CO, CO₂ and NH₃ accordingly. The effective dust conductivity accompanying this composition is K_d = 0.7 W/(m K). The results are consistent with observations on multiple levels, including the various production rates during outbursts and the time span between near perihelion outbursts.     

Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter

A new parameter estimation algorithm based on ensemble Kalman filter (EnKF) is developed. The developed algorithm combined with the proposed problem parametrization offers an efficient parameter estimation method that converges using very small ensembles. The inverse problem is formulated as a sequential data integration problem. Gaussian process regression is used to integrate the prior knowledge (static data). The search space is further parameterized using Karhunen–Loève expansion to build a set of basis functions that spans the search space. Optimal weights of the reduced basis functions are estimated by an iterative regularized EnKF algorithm. The filter is converted to an optimization algorithm by using a pseudo time-stepping technique such that the model output matches the time dependent data. The EnKF Kalman gain matrix is regularized using truncated SVD to filter out noisy correlations. Numerical results show that the proposed algorithm is a promising approach for parameter estimation of subsurface flow models.     

The linearization and adjoint of radiation transfer processes in the NMC spectral model part I: Solar radiative transfer

Summary Linearization and adjoint-model derivation for the solar radiation transfer codes in the NMC spectral model have been carried out. Verification of the validity of resulting tangent linear model and the correctness of the corresponding adjoint have been performed. Applications of derived adjoint model are considered, including parameter estimation for inputs to solar radiation codes with aid of the physics (i.e., the solar radiation codes) and a sensitivity study of the downward solar radiation flux at the earth surface with respect to water vapor amount at various heights.With 5 Figures     

Improving interpolation of daily precipitation for hydrologic modelling: spatial patterns of preferred interpolators

Detailed hydrologic models require high‐resolution spatial and temporal data. This study aims at improving the spatial interpolation of daily precipitation for hydrologic models. Different parameterizations of (1) inverse distance weighted (IDW) interpolation and (2) A local weighted regression (LWR) method in which elevation is the explanatory variable and distance, elevation difference and aspect difference are weighting factors, were tested at a hilly setting in the eastern Mediterranean, using 16 years of daily data. The preferred IDW interpolation was better than the preferred LWR scheme in 27 out of 31 validation gauges (VGs) according to a criteria aimed at minimizing the absolute bias and the mean absolute error (MAE) of estimations. The choice of the IDW exponent was found to be more important than the choice of whether or not to use elevation as explanatory data in most cases. The rank of preferred interpolators in a specific VG was found to be a stable local characteristic if a sufficient number of rainy days are averaged. A spatial pattern of the preferred IDW exponents was revealed. Large exponents (3) were more effective closer to the coast line whereas small exponents (1) were more effective closer to the mountain crest. This spatial variability is consistent with previous studies that showed smaller correlation distances of daily precipitation closer to the Mediterranean coast than at the hills, attributed mainly to relatively warm sea‐surface temperature resulting in more cellular convection coastward. These results suggest that spatially variable, physically based parameterization of the distance weighting function can improve the spatial interpolation of daily precipitation. Copyright © 2009 John Wiley & Sons, Ltd.