Fresh water resources, human societies, and ecosystems are expected to be strongly impacted by climate change, with precipitation trends being one of the most important elements that will be closely monitored. However, the natural variability of precipitation data can often mask existing trends such that the results appear as statistically insignificant. Information on the limitations of trend detection is important for risk assessment and for decision making related to adaption strategies under inherent uncertainties. This paper reports on an effort to quantify and map minimal detectable absolute trends in annual precipitation data series on a global scale. Monte Carlo simulations were conducted to generate realizations of trended precipitation data for different precipitation means and coefficients of variance, and the MannKendall method was applied for detecting the trend significance. Global Precipitation Climatology Centre (GPCC) VASClimO data was used to compute the mean and coefficient of variance of annual precipitation over land and to map minimal detectable absolute trends. It was found that relatively high magnitude trends (positive or negative) have a low chance of being detected as a result of high natural variance of the precipitation data. The largest undetectable trends were found for the tropics. Arid and semiarid regions also present high relative values in terms of percent change from the mean annual precipitation. Although the present analysis is based on several simplified assumptions, the goal was to point out an inherent problem of potentially undetectable high absolute trends that must be considered in analyzing precipitation data series and assessing risks in adaption strategies to climate change.
The FLASH project was implemented from 2006 to 2010 under the EU FP6 framework. The project focused on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms lightning data from the ZEUS network were used together with satellite derived rainfall estimates in order to understand the storm development and electrification. In addition, these case studies were simulated using mesoscale meteorological models to better understand the meteorological and synoptic conditions leading up to these intense storms. As part of this project tools for short term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long term forecasts (a few days) of the likelihood of intense convection were developed. The project also focused on educational outreach through our website http://flashproject.org supplying real time lightning observations, real time experimental nowcasts, forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented as the climate changes, long-range regional lightning networks can supply valuable data, in real time, for warning end-users and stakeholders of imminent intense rainfall and possible flash floods. ?? 2011 Elsevier Ltd.
While intense scientific efforts have focused on radar precipitation estimation in temperate climatic regimes, relatively few studies have examined dry climatic regions. This paper examines rain depth estimation for a 19 day rainfall period in Israel, where the gauge spatial distribution is particularly nonhomogeneous. This fact exacerbates the main drawback of rain gauge observations, which is undersampling. Meteorological ground‐ based radar (GR) can supplement the desired information on precipitation distribution. However, especially in a complex orographic region, radar scientists are faced with beam broadening with distance, nonhomogeneous beam filling, and partial‐beam occultation, together with changes in the vertical reflectivity profile. This paper presents an improvement of GR precipitation estimates thanks to a range adjustment based on spaceborne meteorological radar. In the past, the Tropical Rainfall Measuring Mission (TRMM) satellite radar was used for checking the GR mean field bias around the world. To our knowledge, however, it is the first time that GR‐derived cumulative rainfall amounts show a better agreement with gauges, thanks to the mean field bias and range‐ dependent compensation derived using the well‐calibrated Ku band TRMM radar as a reference. The average bias improves from +1.0 dB to −0.3 dB; more interesting and difficult to obtain is a reduction of the dispersion of the error. Using TRMM‐based range compensation, the scatter decreases from 2.21 dB to 1.93 dB. We conclude that it is well worth trying to compensate for the GR range degradation.
The climate of the eastern Mediterranean (EM), at the transition zone between the Mediterranean climate and the semi‐arid/arid climate, has been studied for a 39‐year period to determine whether climate changes have taken place. A thorough trend analysis using the nonparametric Mann‐Kendall test with Sen's slope estimator has been applied to ground station measurements, atmospheric reanalysis data, synoptic classification data and global data sets for the years 1964–2003. In addition, changes in atmospheric regional patterns between the first and last twenty years were determined by visual comparisons of their composite mean. The main findings of the analysis are: 1) changes of atmospheric conditions during summer and the transitional seasons (mainly autumn) support a warmer climate over the EM and this change is already statistically evident in surface temperatures having exhibited positive trends of 0.2–1°C/decade; 2) changes of atmospheric conditions during winter and the transitional seasons support drier conditions due to reduction in cyclogenesis and specific humidity over the EM, but this change is not yet statistically evident in surface station rain data, presumably because of the high natural precipitation variance masking such a change. The overall conclusion of this study is that the EM region is under climate change leading to warmer and drier conditions.
A new parameter is introduced: the lightning potential index (LPI), which is a measure of the potential for charge generation and separation that leads to lightning flashes in convective thunderstorms. The LPI is calculated within the charge separation region of clouds between 0°C and −20°C, where the noninductive mechanism involving collisions of ice and graupel particles in the presence of supercooled water is most effective. As shown in several case studies using the Weather Research and Forecasting (WRF) model with explicit microphysics, the LPI is highly correlated with observed lightning. It is suggested that the LPI may be a useful parameter for predicting lightning as well as a tool for improving weather forecasting of convective storms and heavy rainfall.
Flash floods cause some of the most severe natural disasters in Europe but Mediterranean areas are especially vulnerable. They can cause devastating damage to property, infrastructures and loss of human life. The complexity of flash flood generation processes and their dependency on different factors related to watershed properties and rainfall characteristics make flash flood prediction a difficult task. In this study, as part of the EU-FLASH project, we used an uncalibrated hydrological model to simulate flow events in a 27km2 Mediterranean watershed in Israel to analyze and better understand the various factors influencing flows. The model is based on the well-known SCS curve number method for rainfall-runoff calculations and on the kinematic wave method for flow routing. Existing data available from maps, GIS and field studies were used to define model parameters, and no further calibration was conducted to obtain a better fit between computed and observed flow data. The model rainfall input was obtained from the high temporal and spatial resolution radar data adjusted to rain gauges. Twenty flow events that occurred within the study area over a 15year period were analyzed. The model shows a generally good capability in predicting flash flood peak discharge in terms of their general level, classified as low, medium or high (all high level events were correctly predicted). It was found that the model mainly well predicts flash floods generated by intense, short-lived convective storm events while model performances for low and moderate flows generated by more widespread winter storms were quite poor. The degree of urban development was found to have a large impact on runoff amount and peak discharge, with higher sensitivity of moderate and low flow events relative to high flows. Flash flood generation was also found to be very sensitive to the temporal distribution of rain intensity within a specific storm event. ?? 2010 Elsevier B.V.
Recharge is a critical issue for water management. Recharge assessment and the factors affecting recharge are of scientific and practical importance. The purpose of this study was to develop a daily recharge assessment model (DREAM) on the basis of a water balance principle with input from conventional and generally available precipitation and evaporation data and demonstrate the application of this model to recharge estimation in the Western Mountain Aquifer (WMA) in Israel. The WMA (area 13,000 km2)isa karst aquifer that supplies 360–400 Mm3 yr−1 of freshwater, which constitutes 20% of Israel's freshwater and is highly vulnerable to climate variability and change. DREAM was linked to a groundwater flow model (FEFLOW) to simulate monthly hydraulic heads and spring flows. The models were calibrated for 1987–2002 and validated for 2003– 2007, yielding high agreement between calculated and measured values (R2 = 0.95; relative root‐mean‐square error = 4.8%; relative bias = 1.04). DREAM allows insights into the effect of intra‐annual precipitation distribution factors on recharge. Although annual precipitation amount explains ∼70% of the variability in simulated recharge, analyses with DREAM indicate that the rainy season length is an important factor controlling recharge. Years with similar annual precipitation produce different recharge values as a result of temporal distribution throughout the rainy season. An experiment with a synthetic data set exhibits similar results, explaining ∼90% of the recharge variability. DREAM represents significant improvement over previous recharge estimation techniques in this region by providing near‐real‐time recharge estimates that can be used to predict the impact of climate variability on groundwater resources at high temporal and spatial resolution.
This paper summarises innovative research into the assessment of long-term groundwater recharge from flood events in dryland environments of the Kuiseb (Namibia) and the Buffels (South Africa) rivers. The integrated water resource management (IWRM) policies and institutions affecting the exploitation of groundwater resources in each of these developing countries are compared. The relatively large alluvial aquifer of the Kuiseb River (similar to 240 Mm(3)) is recharged from irregular floods originating in the upper catchment. Reported abstraction of 4.6 Mm(3) per year is primarily consumed in the town of Walvis Bay, although the groundwater decay (pumping and natural losses along the period 1983-2005) was estimated in 14.8 Mm(3) per year. Recharge is variable, occurring in 11 out of 13 years in the middle Kuiseb River, but only in 11 out of 28 years in the middle-lower reaches. In contrast, the Buffels River has relatively minor alluvial aquifers (similar to 11 Mm(3)) and recharge sources derive from both lateral subsurface flow and floodwater infiltration, the latter limited to a recharge maximum of 1.3 Mm(3) during floods occurring once every four years. Current abstractions to supply the adjacent rural population and a few small-scale, irrigated commercial farms are 0.15 Mm(3) yr (-aEuro parts per thousand 1), well within the long-term sustainable yield estimated to be 0.7 Mm(3) yr (-aEuro parts per thousand 1). Since independence in 1990, Namibia`s water resource management approach has focussed on ephemeral river basin management of which the Kuiseb Basin Management Committee (KBMC) is a model. Here, some water points are managed independently by rural communities through committees while the national bulk water supplier provides for Walvis Bay Municipality from the lower aquifers. This provides a sense of local ownership through local participation between government, NGOs and CBOs (community-based organisations) in the planning and implementation of IWRM. Despite the potential for water resource development in the lower Buffels River, the scope for implementing IWRM is limited not only by the small aquifer size, but also because basin management in South Africa is considered only in the context of perennial rivers. Since 2001, water service delivery in the Buffels River catchment has become the responsibility of two newly created local municipalities. As municipal government gains experience, skills and capacity, its ability to respond to local needs related to water service delivery will be accomplished through local participation in the design and implementation of annual `integrated development plans`. These two case studies demonstrate that a variety of IWRM strategies in the drylands of developing countries are appropriate depending on scales of governance, evolving policy frameworks, scales of need and limitations inherent in the hydrological processes of groundwater resources.
Flood water infiltrates ephemeral channels, recharging local and regional aquifers, and it is the main water source in hyperarid regions. Quantitative estimations of these resources are limited by the scarcity of data from such regions. The floods of the Kuiseb River in the Namib Desert have been monitored for 46 years, providing a unique data set of flow hydrographs from one of the world's hyperarid regions. The study objectives were to: (1) subject the records to quality control; (2) model flood routing and transmission losses; and (3) study the relationships between flood characteristics, river characteristics and recharge into the aquifers. After rigorous quality-testing of the original gauge-station data, a flood-routing model based on kinematic flow with components accounting for channel-bed infiltration was constructed and applied to the data. A simplified module added to this routing model estimates aquifer recharge from the infiltrating flood water. Most of the model parameters were obtained from field surveys and GIS analyses. Two of the model parameters-Manning's roughness coefficient and the constant infiltration rate-were calibrated based on the high-quality measured flow data set, providing values of 0.025 and 8.5 mm/h, respectively. This infiltration rate is in agreement with that estimated from extensive direct TDR-based moisture measurements in the vadose zone under the Kuiseb River channel, and is low relative to those reported for other sites. The model was later verified with additional flood data and observed groundwater levels in boreholes. Sensitivity analysis showed the important role of large and medium floods in aquifer recharge. To generalize from the studied river to other streams with diverse conditions, we demonstrate that with increasing in infiltration rate, channel length or active channel width, the relative contribution of high-magnitude floods to recharge also increases, whereas medium and small floods contribute less, often not reaching the downstream parts of the arid ephemeral river at all. For example, more than three-quarters of the floods reaching the downstream Kuiseb River (with an infiltration rate of 8.5 mm/h) would not have reached similar distances in rivers with all other properties similar but with infiltration rates of 50 mm/h. The recharge volume in the downstream segment in the case of higher infiltration is mainly contributed by floods with magnitude ???93rd percentile, compared to floods in the 63rd percentile at an infiltration rate of 8.5 mm/h. ?? 2009 Elsevier B.V. All rights reserved.
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
Quantitatively estimating rainfall-runoff relations in extremely arid regions is a challenging task, mainly because of lack of in situ data. For the past 40 years, rain and floods have been monitored in the Nahal Yael catchment (0.5 km2) in southern Israel, providing a unique data set of runoff hydrographs and rainfall in a hyper-arid region. Here we present an exploratory study focusing on rainfall-runoff modeling issues for a small (0.05 km2) sub-catchment of Nahal Yael. The event-based model includes the computation of rainfall excess, hillslope and channel routing. Two model parameters of the infiltration process were found by calibration. A resampling methodology of calibration group composition is suggested to derive optimal model parameters and their uncertainty range. Log-based objective functions were found to be more robust and less sensitive than non-log functions to calibration group composition. The fit achieved between observed and computed runoff hydrographs for the calibration and validation events is considered good relative to other modeling studies in arid and semi-arid regions. The study indicates that, under the calibration scheme used, a lumped model performs better than a model representing the catchment division into three sub-catchments. In addition, the use of rain data from several gauges improves runoff prediction as compared to input from a single gauge. It was found that rainfall uncertainty dominates uncertainties in runoff prediction while parameter uncertainties have only a minor effect. ?? 2009 Elsevier B.V. All rights reserved.
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 precipita- tion 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. Val- idation 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.
viously applied in the Alps of Europe. Adjustment coefficients have been derived for 28 rainfall periods using 59 independent gauges of a quality-checked training data set. The validation was based on an independent data set composed of gauges located in eleven 20 ? 20 km2 validation areas, which are representative of different climate, topography and radar distance conditions. The WR and WMR methods were found preferable with a slight better performance of the latter. Furthermore, a novel approach has been adopted in this study, whereby radar estimates are considered useable if they provide information that is better than gauge-only estimates. The latter was derived by spatial interpolation of the gauges belonging to the training data set. Note that these gauges are outside the validation areas. As for the radar-adjusted estimates, gauge-derived estimates were assessed against gauge data in the validation areas. It was found that radar-based estimates are better for the validation areas at the dry climate regime. At distances larger than 100 km, the radar underestimation becomes too large in the two northern validation areas, while in the southern one radar data are still better than gauge interpolation. It is concluded that in ungauged areas of Israel it is preferable to use WMR-adjusted (or alternatively, simply WR-adjusted) radar echoes rather than the standard bulk adjustment method and for dry ungauged areas it is preferable over the conventional gauge-interpolated values derived from point measurements, which are outside the areas themselves. The WR and WMR adjustment methods provide useful rain depth estimates for rainfall periods for the examined areas but within the limitation stated above.
Analysis of extreme hydrometeorological events is important for characterizing and better understanding the meteorological conditions that can generate severe rainstorms and the consequent catastrophic flooding. According to several studies (e.g., Alpert et al., 2004; Wittenberg et al., 2007), the occurrence of such extreme events is increasing over the eastern Mediterranean although total rain amounts are generally decreasing. The current study presents an analysis of an extreme event utilizing different methodologies: (a) synoptic maps and high resolution satellite imagery for atmospheric condition analysis; (b) rainfall analysis by rain gauges data; (c) meteorological radar rainfall calibration and analysis; (d) field measurements for estimating maximum peak discharges; and, (e) high resolution aerial photographs together with field surveying for quantifying the geomorphic impacts. The unusual storm occurred over Israel between 30 March and 2 April, 2006. Heavy rainfall produced more than 100mm in some locations in only few hours and more than 200mm in the major core area. Extreme rain intensities with recurrence intervals of more than 100 years were found for durations of 1 h and more as well as for the daily rain depth values. In the most severely affected area,Wadi Ara, extreme flash floods caused damages and casualties. Specific peak discharges were as high as 10–30m3/s/km2 for catchments of the size of 1–10 km2, values larger than any recorded floods in similar climatic regions in Israel.
Weather radar data contain detailed information about the spatial structures of rain fields previously unavail- able from conventional rain gauge networks. This information is of major importance for enhancing our understanding of precipitation and hydrometeorological systems. This study focuses on spatial features of convective rain cells in southern Israel where the climate ranges fromMediterranean to hyper-arid. Extensive data bases from two study areas covered by radar systems were analyzed. Rain cell features were extracted such as center location, area, maximal rain intensity, spatial integral of rain intensity, major radius length, minor radius length, ellipticity, and orientation. Rain cells in the two study areas were compared in terms of feature distributions and the functional relationships between cell area and cell magnitude, represented by maximal rain intensity and spatial integral of rain intensity. Analytical distribution functions were fitted to the empirical distributions and the log-normal function was found to fit well the distributions of cell area, maximal rain intensity and major and minor radius lengths. The normal distribution fits well ellipticity em- pirical distribution, and orientation distribution was well-represented by the normal or uniform distribution functions. The effect of distance fromtheMediterranean coastline on cell features was assessed. Amaximum of cell rain intensity at the coastline and maximum cell density 15 km inland from the coastline were found. In addition, a gradual change of cell orientation was observed with a northwest-southeast orientation 30 km from the coastline at the Mediterranean Sea and to almost a west-east orientation 30 km from the coastline inland
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.
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
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.