The transformation of rainfall into runoff at a basin outlet is the combined effect of many hydrological processes, which occur at a wide range of spatial and temporal scales. However, determining the scale of the combined hydrological response of the basin is still problematic and concepts for its definition are yet to be identified. In this paper high-resolution meteorological radar data are used for the determination of a characteristic temporal scale for the hydrological response of the basin - the ’response time scale’ (Ts*). Ts* is defined as the time scale at which the pattern of the time-averaged radar rainfall hietograph is most similar to the pattern of the measured outlet runoff hydrograph. The existence of such similarity at a relatively stable time scale for a specific basin indicates that it is an intrinsic property of the basin and is related to its hydrological response. The identification of the response time scale is carried out by analysis of observations only, without assuming a specific rainfall-runoff model. Ts* is examined in four small basins (10-100 km2) in Israel. The spatial scale is assumed as the entire basin. For all analyzed basins a stable response time scale is identified. Relatively short time scales are found for the urban and arid basins (15-30 min), while for the rural basins longer time scale are identified (90-180 min). The issues of relationship between the response time scale and basin properties and modeling at the response time scale have yet to be determined. ?? 2001 Elsevier Science B.V. All rights reserved.

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.

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

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.

Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m-2 and 70 g m-2) having low biases of -41 g m-2 (8%) and 52 g m-2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change.

At site flood frequency analysis (FFA) in arid/semi-arid watersheds poses unique challenges to researchers and practitioners due to the generally limited data records. This study presents a comprehensive evaluation of FFA in arid/semi-arid watersheds in relation to the unique characteristics of these regions, such as the limited number of floods occurring each year and the large variability of the flood peak discharges. Study cases in Israel and the US are examined and compared with non-arid watersheds, characterized by Mediterranean climate, and with synthetic flood records. Results show that the tail of extreme value distributions describing arid/semi-arid watersheds is found to be heavier than the one describing Mediterranean watersheds. The number of yearly floods and the variability of flood peak discharge are shown to have a crucial impact on the accuracy of the quantile estimates with smaller number of events per year and larger coefficient of variation of flood peak discharge being related to larger errors in the estimated quantiles. Partial duration series approach provides a slightly reduced bias in the estimates, but should not be blindly preferred over annual maxima series as it presents comparable estimation uncertainty. In general, the generalized extreme value and the generalized Pareto distribution are found to be non-optimal choices for the examined arid/semi-arid watersheds.

The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002-2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-hr) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24hr). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes.

Rainfall thresholds for landslides occurrence derived in real applications tend to be lower than the ones one would obtain using exact data. This letter shows how the use of coarse temporal resolution rainfall data causes a systematic overestimation of the duration of the triggering rainfall events that directly contributes to thresholds underestimation. A numeri- cal experiment is devised to quantify this systematic effect for the relevant case of power- law depth/intensity–duration thresholds. In the examined conditions, i.e., the frequentist method at 5% non-exceedance probability level, \~ 70% underestimation of the scale param- eter and \~ 60% overestimation of the shape parameter of the thresholds is to be expected using daily resolution rainfall data, but the exact quantification depends on the specific characteristics of each study case. The underestimation increases as the temporal resolu- tion becomes larger than the expected minimal duration of the triggering events. Under operational conditions, sensitivity analyses based on the methods and datasets of interest are advised.

Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2–5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( \~ 50, \~ 100, and \~ 150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

Abstract Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100-year events) using synoptic-scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south-eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.

Information on extreme precipitation is essential to managing weather-related risks and designing hydraulic structures. Research attention to frequency analyses based on remotely sensed precipitation datasets, such as those obtained from weather radars and satellites, has been rapidly increasing owing to their potential to provide information for ungauged regions worldwide. Together with the ability to measure the areal scale directly, these analyses promise to overcome the sampling limitations of traditional methods based on rain gauges. This focused review of the literature depicts the state of the art after a decade of efforts, and identifies the crucial gaps in knowledge and methodology that currently hinder the quantitative use of remotely sensed datasets in water resources system design and operation. It concludes by highlighting a set of research directions promising immediate impact with regard to the separation of the sources of uncertainty currently affecting applications based on remotely sensed datasets, the development of statistical methods that can cope with the peculiar characteristics of these datasets, and the improvement of validation methods. Important gains in knowledge are expected from the explicit inclusion of the areal dimension in the analyses and from the fine-scale investigation of extreme precipitation climatology.

Abstract Orographic impact on extreme subdaily precipitation is critical for risk management but remains insufficiently understood due to complicated atmosphere-orography interactions and large uncertainties. We investigate the problem adopting a framework able to reduce uncertainties and isolate the systematic interaction of Mediterranean cyclones with a regular orographic barrier. The average decrease with elevation reported for hourly extremes is found enhanced at subhourly durations. Tail heaviness of 10-min intensities is negligibly affected by orography, suggesting self-similarity of the distributions at the convective scale. Orography decreases the tail heaviness at longer durations, with a maximum impact around hourly scales. These observations are explained by an orographically induced redistribution of precipitation toward stratiform-like processes, and by the succession of convective cores in multihour extremes. Our results imply a breaking of scale-invariance at subhourly durations, with important implications for natural hazards management in mountainous areas.

This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (\textless30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets.

The metastatistical extreme value approach proved promising in the frequency analysis of daily precipitation from ordinary events, outperforming traditional methods based on sampled extremes. However, subdaily applications are currently restrained by two knowledge gaps: It is not known if ordinary events can be consistently examined over durations, and it is not clear to what extent their entire distributions represent extremes. We propose here a unified definition of ordinary events across durations and suggest the simplified metastatistical extreme value formulation for dealing with extremes emerging from the tail, rather than the entire distributions, of ordinary events. This unified framework provides robust estimates of extreme quantiles (\textless10% error on the 100 yr from a 26 yr long record) and allows representations in which ordinary and extreme events share the scaling exponent. Future applications could improve our knowledge of subdaily extreme precipitation and help investigate the impact of local factors and climatic forcing on their frequency.

Intensity–Duration–Frequency (IDF) curves are widely used in flood risk management because they provide an easy link between the characteristics of a rainfall event and the probability of its occurrence. Weather radars provide distributed rainfall estimates with high spatial and temporal resolutions and overcome the scarce representativeness of point-based rainfall for regions characterized by large gradients in rainfall climatology. This work explores the use of radar quantitative precipitation estimation (QPE) for the identification of IDF curves over a region with steep climatic transitions (Israel) using a unique radar data record (23yr) and combined physical and empirical adjustment of the radar data. IDF relationships were derived by fitting a generalized extreme value distribution to the annual maximum series for durations of 20min, 1h and 4h. Arid, semi-arid and Mediterranean climates were explored using 14 study cases. IDF curves derived from the study rain gauges were compared to those derived from radar and from nearby rain gauges characterized by similar climatology, taking into account the uncertainty linked with the fitting technique. Radar annual maxima and IDF curves were generally overestimated but in 70% of the cases (60% for a 100yr return period), they lay within the rain gauge IDF confidence intervals. Overestimation tended to increase with return period, and this effect was enhanced in arid climates. This was mainly associated with radar estimation uncertainty, even if other effects, such as rain gauge temporal resolution, cannot be neglected. Climatological classification remained meaningful for the analysis of rainfall extremes and radar was able to discern climatology from rainfall frequency analysis.