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In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.

Regional flood‐frequency analysis (RFFA) methods are essential tools to assess flood hazard and plan interventions for its mitigation. They are used to estimate flood quantiles when the at‐site record of streamflow data is not available or limited. One commonly used RFFA method is the index‐flood method (IFM), which assumes that peak floods satisfy the simple scaling hypothesis. In this work we present an integrated approach to assess the spatial scaling behaviour of floods in the United Kingdom (UK) for 540 catchments, where the IFM is currently used operationally. This assessment employs product moments, probability weighted moments, and quantile analysis, and is applied to two different types of “hydrologically homogeneous” UK regions: geographical regions as defined in the Flood Studies Report (NERC, 1975) and pooling‐groups as defined in the updated Flood Estimation Handbook (FEH; Institute of Hydrology, 1999). To understand which variables play a significant role in the flood‐peak generating mechanism, the assessment approach considers scaling not only of drainage area alone but also of other hydro‐geomorphological variables. Results provided by the different methodologies consistently showed that only part (ranging from 30 to 70%) of the peak flow variability is explained by drainage area alone; this fraction increases (up to 80 ‐ 95%) when multiple regression is used. Supported by the peak flow spatial scaling assessment, we compared the proposed approach for peak flow quantile estimation with the current FEH method in ungauged catchments. The quantile regression method based on the pooling‐group outperforms the current FEH‐ungauged method, providing a 14% relative improvement in root mean square error (RMSE) over the entire country.

In the past, hydrologic modeling of surface water resources has mainly focused on simulating the hydrologic cycle at local to regional catchment modeling domains. There now exists a level of maturity among the catchment, global water security, and land surface modeling communities such that these communities are converging toward continental domain hydrologic models. This commentary, written from a catchment hydrology community perspective, provides a review of progress in each community toward this achievement, identifies common challenges the communities face, and details immediate and specific areas in which these communities can mutually benefit one another from the convergence of their research perspectives. Those include: (1) creating new incentives and infrastructure to report and share model inputs, outputs, and parameters in data services and open access, machine-independent formats for model replication or reanalysis; (2) ensuring that hydrologic models have: sufficient complexity to represent the dominant physical processes and adequate representation of anthropogenic impacts on the terrestrial water cycle, a process-based approach to model parameter estimation, and appropriate parameterizations to represent large-scale fluxes and scaling behavior; (3) maintaining a balance between model complexity and data availability as well as uncertainties; and (4) quantifying and communicating significant advancements toward these modeling goals.

In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals, however small, has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous United States having at least 14 complete water years of daily data between October 01, 1980, and September 30, 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of timing and magnitude, the timing component is converted into simulated nonexceedance probabilities and rescaled to new volumes using an independently estimated flow-duration curve (FDC). In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case, though, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations as is commonly done to reflect streamflow timing, this approach leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.

Understanding the spatial structure of daily streamflow is essential for managing freshwater resources, especially in poorly gaged regions. Spatial scaling assumptions are common in flood frequency prediction (e.g., index-flood method) and the prediction of continuous streamflow at ungaged sites (e.g. drainage-area ratio), with simple scaling by drainage area being the most common assumption. In this study, scaling analyses of daily streamflow from 173 streamgages in the southeastern United States resulted in three important findings. First, the use of only positive integer moment orders, as has been done in most previous studies, captures only the probabilistic and spatial scaling behavior of flows above an exceedance probability near the median; negative moment orders (inverse moments) are needed for lower streamflows. Second, assessing scaling by using drainage area alone is shown to result in a high degree of omitted-variable bias, masking the true spatial scaling behavior. Multiple regression is shown to mitigate this bias, controlling for regional heterogeneity of basin attributes, especially those correlated with drainage area. Previous univariate scaling analyses have neglected the scaling of low-flow events and may have produced biased estimates of the spatial scaling exponent. Third, the multiple regression results show that mean flows scale with an exponent of one, low flows scale with spatial scaling exponents greater than one, and high flows scale with exponents less than one. The relationship between scaling exponents and exceedance probabilities may be a fundamental signature of regional streamflow. This signature may improve our understanding of the physical processes generating streamflow at different exceedance probabilities.

Aridland ecosystems are often susceptible to degradation resulting from disturbances like fires and grazing. By exposing the soil surface to the erosive action of winds, these disturbances contribute to the redistribution of soil nutrients associated with grassland-to-shrubland conversions and to the formation of a heterogeneous landscape. Wind erosion maintains the local heterogeneities in nutrient and vegetation distribution in arid landscapes through the removal of nutrient-rich soil from the intercanopy areas and the subsequent deposition of soil onto vegetation patches. Even though wind erosion and disturbances like fires strongly interact with each other and determine vegetation patterns in arid landscapes, very few studies have addressed these interactions. Using soil samples collected after a wildfire event at the Cimarron National Grasslands in southwestern Kansas, we demonstrate through a series of wind-tunnel experiments, laboratory measurements, and theoretical analyses how wind erosion can be enhanced by fire-induced water repellency. Results from the wind-tunnel experiments show that in semiarid grasslands fires can cause a decrease in the threshold velocity of wind erosion thereby, enhancing the post-burn erosion of (hydrophobic) soils. Further, a generalized process-based theoretical equation was derived to explain the decrease in threshold friction velocity in water-repellent soil for the case of soil particles modeled as asymmetrical cones.

By providing continuous high-resolution simulations of soil moisture fields, distributed hydrologic models could be powerful tools to advance the scientific community's understanding of the space-time variability and scaling characteristics of soil moisture fields. However, in order to use the soil moisture simulations from hydrologic models with confidence, it is important to understand whether the models are able to represent in a reliable way the processes regulating soil moisture variability. In this study, a comparison of the scaling characteristics of spatial soil moisture fields derived from a set of microwave radiometer observations from the Southern Great Plains 1997 experiment and corresponding simulations using the distributed hydrologic model GEOtop is performed through the use of generalized variograms. Microwave observations and model simulations are in agreement with respect to suggesting the existence of a scale-invariance property in the variograms of spatial soil moisture fields, and indicating that the scaling characteristics vary with changes in the spatial average soil water content. However, observations and simulations give contradictory results regarding the relationship between the scaling parameters (i.e. spatial organization) and average soil water content. The drying process increased the spatial correlation of the microwave observations at both short and long separation distances while increasing the rate of decay of correlation with distance. The effect of drying on the spatial correlation of the model simulations was more complex, depending on the storm and the simulation examined, but for the largest storm in the simulation most similar to the observations, drying increased the long-range correlation but decreased the short-range. This is an indication that model simulations, while reproducing correctly the total streamflow at the outlet of the watershed, may not accurately reproduce the runoff production mechanisms. Consideration of the scaling characteristics of spatial soil moisture fields can therefore serve as a more intensive means for validating distributed hydrologic models, compared to the traditional approach of only comparing the streamflow hydrographs.

We used a three-year (1998–2000) dataset of TRMM Precipitation Radar observations to investigate the scaling properties of spatial rainfall fields. This dataset allows consideration of spatial scales ranging from about 4.3 km to 138 km and short temporal scales corresponding to the sensor overpasses. The focus is on the marginal spatial moment scaling, which allows estimation of the scaling parameters from a single scene of data. Here we present a global perspective of the scaling properties of tropical rainfall in terms of its spatial variability, atmospheric forcing, predictability, and applicability. Our results reveal the following: 1) the scaling parameters exhibit strong variability associated with land/ocean contrast and mean precipitation at the synoptic scale; 2) there exists a one-to-one relationship between the scaling parameters and the large-scale spatial average rain rate of a universal functional form; 3) the majority of the scenes are consistent with the hypothesis of scale invariance at the moment orders of 0 and 2; 4) relatively there are more scale-invariant rain scenes over land than over ocean; and 5) for the scenes that are non-scale-invariant, deviation from scale-invariance mainly arises from the increasingly intermittent behavior of rainfall as spatial scale decreases. These results have important implications for the development and calibration of downscaling procedures designed to reproduce rainfall properties at different spatial scales and lead to a better understanding of the nature of tropical rainfall at various spatial resolutions.

Land–atmosphere interactions in midlatitude continental regions are particularly active during the warm season. It is still unclear whether and under what circumstances these interactions may involve positive or negative feedbacks between soil moisture conditions and rainfall occurrence. Assessing such feedbacks is crucially important to a better understanding of the role of land surface conditions on the regional dynamics of the water cycle. This work investigates the relationship between soil moisture and subsequent precipitation at the daily time scale in a midlatitude continental region. Sounding data from 16 locations across the midwestern United States are used to calculate two indices of atmospheric instability—namely, the convective available potential energy (CAPE) and the convective inhibition (CIN). These indices are used to classify rainfall as convective or stratiform. Correlation analyses and uniformity tests are then carried out separately for these two rainfall categories, to assess the dependence of rainfall occurrence on antecedent soil moisture conditions, using simulated soil moisture values. The analysis suggests that most of the positive correlation observed between soil moisture and subsequent precipitation is due to the autocorrelation of long stratiform events. The authors found both areas with positive and areas with negative feedback on convective precipitation. This behavior is likely due to the contrasting effects of soil moisture conditions on convective phenomena through changes in surface temperature and the supply of water vapor to the overlying air column. No significant correlation is found between daily rainfall intensity and antecedent simulated soil moisture conditions either for convective or stratiform rainfall.

In view of the importance of tropical rainfall and the ubiquitous need for its estimates in climate modeling, the authors assess the ability of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to characterize the scaling characteristics of rainfall by comparing the derived results with those obtained from the ground-based radar (GR) data. The analysis is based on 59 months of PR and GR rain rates at three TRMM ground validation (GV) sites: Houston, Texas; Melbourne, Florida; and Kwajalein Atoll, Republic of the Marshall Islands. The authors consider spatial scales ranging from about 4 to 64 km at a fixed temporal scale corresponding to the sensor “instantaneous” snapshots (∼15 min). The focus is on the scaling of the marginal moments, which allows estimation of the scaling parameters from a single scene of data. The standard rainfall products of the PR and the GR are compared in terms of distributions of the scaling parameter estimates, the connection between the scaling parameters and the large-scale spatial average rain rate, and deviations from scale invariance. The five main results are as follows: 1) the PR yields values of the rain intermittence scaling parameter within 20% of the GR estimate; 2) both the PR and GR data show a one-to-one relationship between the intermittence scaling parameter and the large-scale spatial average rain rate that can be fit with the same functional form; 3) the PR underestimates the curvature of the scaling function from 20% to 50%, implying that high rain-rate extremes would be missed in a downscaling procedure; 4) the majority of the scenes (>85%) from both the PR and GR are scale invariant at the moment orders q = 0 and 2; and 5) the scale-invariance property tends to break down more likely over ocean than over land; the rainfall regimes that are not scale invariant are dominated by light storms covering large areas. Our results further show that for a sampling size of one year of data, the TRMM temporal sampling does not significantly affect the derived scaling characteristics. The authors conclude that the TRMM PR has the ability to characterize the basic scaling properties of rainfall, though the resulting parameters are subject to some degree of uncertainty.

This paper describes a new distributed hydrological model, called GEOtop. The model accommodates very complex topography and, besides the water balance, unlike most other hydrological models, integrates all the terms in the surface energy balance equation. GEOtop uses a discretization of the landscape based on digital elevation data. These digital elevation data are preprocessed to allow modeling of the effect of topography on the radiation incident on the surface, both shortwave (including shadowing) and longwave (accounting for the sky view factor). For saturated and unsaturated subsurface flow, GEOtop makes use of a numerical solution of the 3D Richards’ equation in order to properly model, besides the lateral flow, the vertical structure of water content and the suction dynamics. These characteristics are deemed necessary for consistently modeling hillslope processes, initiation of landslides, snowmelt processes, and ecohydrological phenomena as well as discharges during floods and interstorm periods. An accurate treatment of radiation inputs is implemented in order to be able to return surface temperature. The motivation behind the model is to combine the strengths and overcome the weaknesses of flood forecasting and land surface models. The former often include detailed spatial description and lateral fluxes but usually lack appropriate knowledge of the vertical ones. The latter are focused on vertical structure and usually lack spatial structure and prediction of lateral fluxes. Outlines of the processes simulated and the methods used to simulate them are given. A series of applications of the model to the Little Washita basin of Oklahoma using data from the Southern Great Plains 1997 Hydrology Experiment (SGP97) is presented. These show the model’s ability to reproduce the pointwise energy and water balance, showing that just an elementary calibration of a few parameters is needed for an acceptable reproduction of discharge at the outlet, for the prediction of the spatial distribution of soil moisture content, and for the simulation of a full year’s streamflow without additional calibration.

The GEOtop model makes it possible to analyze the short- and long-term effects of geomorphic variation on the partitioning of the lateral surface and subsurface water and surface energy fluxes. The topography of the Little Washita basin (Oklahoma) and of the Serraia basin (Trentino, Italy) have been used as base topographies from which virtual topographies with altered slopes and elevations have been created with corresponding modifications of the soil thickness and the extension of the channel network, according to applicable geomorphological theories, in order to quantify the contribution of these topographic features to the spatial and temporal variability of energy and water fluxes. Simulation results show that both a more extended channel network and more accentuated slopes cause an increase in the discharge balanced by a diminution of the evapotranspiration. The diminution of the latent heat flux is balanced by the increase in the sensible heat flux. Net radiation shows a minor sensitivity to topography. Evaporative fraction, on the contrary, is shown to be strongly dependent on geomorphic characteristics. The results confirm the importance of including an adequate treatment of topography in large-scale land surface models.

Wind erosion is a dominant geomorphological process in arid and semi-arid regions with major impacts on regional climate and desertification. The erosion process occurs when the wind speed exceeds a certain threshold value, which depends on a number of factors including surface soil moisture. The understanding and modelling of aeolian erosion requires a better understanding of the soil erodibility associated with different moisture conditions. In arid regions during the dry season, the atmospheric humidity plays an important role in determining the surface moisture content and the threshold shear velocity. By a series of wind tunnel tests and theoretical analyses, this dependence of threshold velocity on near surface air humidity is shown for three soils of different textures: sand, sandy loam, and clay loam. The results show that the threshold shear velocity decreases with increasing values of relative humidity for values of relative humidity between about 40% and 65%, while above and below this range the threshold shear velocity increases with air humidity. A theoretical framework is developed to explain these dependencies assuming an equilibrium between the surface soil moisture and the humidity of the overlying atmosphere. The conditions under which soil-atmosphere equilibrium occurs were tested experimentally in the laboratory for different soils in order to determine the effect of grain surface area and texture on the time required to reach equilibrium starting from different initial conditions.

Remote sensing is an important source of snow-cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow-cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow-cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow-cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow-cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM-calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow-cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt-dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.

Snowmelt runoff dominates streamflow in the Upper Rio Grande (URG) basin of New Mexico and Colorado. Annual variations in streamflow timing and volume at most stations in the region are strongly influenced by the El Nino-southern oscillation (ENSO) through its modulation of the seasonal cycles of temperature and precipitation, and hence on snow accumulation and melting. After removing long-term trends over the study period (water years 1952-99), the dependence of monthly temperature, precipitation, snow water equivalent (SWE) at snowcourse stations, and streamflow throughout the URG on ENSO was investigated using composite analyses of the detrended residuals and through dependence of the residuals on the Climate Prediction Center southern oscillation index during the preceding summer and fall. The climate of La Nina years was found to differ significantly from either El Nino or neutral years. Moreover, significant climatological ENSO-related effects are confined to certain months, predominantly at the beginning and end of the winter season. In particular, March of La Nina years is significantly warmer and drier than during either El Nino or neutral years, and November of El Nino years is significantly colder and wetter. Differences in temperature and precipitation lead to significant differences in SWE and streamflow in the URG between the three ENSO phases.

An important problem in hydrologic science is understanding how river flow is influenced by rainfall properties and drainage basin characteristics. In this paper we consider one approach, the use of mass exponents, in examining the relation of river flow to rainfall and the channel network, which provides the primary conduit for transport of water to the outlet in a large basin. Mass exponents, which characterize the power-law behavior of moments as a function of scale, are ideally suited for defining scaling behavior of processes that exhibit a high degree of variability or intermittency. The main result in this paper is an expression relating the mass exponent of flow resulting from an instantaneous burst of rainfall to the mass exponents of spatial rainfall and that of the network width function. Spatial rainfall is modeled as a random multiplicative cascade and the channel network as a recursive replacement tree; these fractal models reproduce certain types of self-similar behavior seen in actual rainfall and networks. It is shown that under these modeling assumptions the scaling behavior of flow mirrors that of rainfall if rainfall is highly variable in space, and on the other hand flow mirrors the structure of the network if rainfall is not so highly variable.

The hypothesis of statistical self-similarity, or scaling invariance, in the spatial variability of rainfall, channel network structures and floods has been supported by recent advances in data analyses. This hypothesis is used here to calculate the statistical scaling exponents of peak river flows using a random cascade model of spatial rainfall intensities and the Peano basin as an idealized model of a river basin. The ‘maximum contributing set’ approximately determines the magnitudes of peak flows in a self-similar manner in different subbasins of the Peano basin. For an instantaneously applied random, spatially uniform rainfall, the Hausdorff dimension of the maximum contributing set appear, as the statistical simple scaling exponent of peak flows. This result is generalized to an instantaneously applied cascade rainfall, and it is shown to give rise to statistical multiscaling in peak flows. The multiscaling exponent of peak flows is computed and interpreted as a Hausdorff dimension of a fractal set supporting rainfall intensity on the maximum contributing set of the Peano basin. Potential implications of this interpretation are illustrated using the regional food frequency analysis of the Appalachian flood data in the United States and a rainfall data set from the tropical Atlantic ocean. It is argued that the hypothesis of self-similarity identifies a powerful theoretical framework which can unify a statistical theory of regional flood frequency with important empirical features of topographic, rainfall and flood data sets and distributed rainfall-landform-runoff relationships.

Following a brief review of relevant theoretical and empirical spatial results, a theory of space-time rainfall, applicable to fields advecting without deformation of the coordinates, is presented and tested. In this theory, spatial rainfall fields are constructed from discrete multiplicative cascades of independent and identically distributed (iid) random variables called generators. An extension to space-time assumes that these generators are iid stochastic processes indexed by time. This construction preserves the spatial structure of the cascades, while enabling it to evolve in response to a nonstationary large-scale forcing, which is specified externally. The construction causes the time and space dimensions to have fundamentally different stochastic structures. The time dimension of the process has an evolutionary behavior that distinguishes between past and future, while the spatial dimensions have an isotropic stochastic structure. This anisotropy between time and space leads to the prediction of the breakdown of G. I. Taylor's hypothesis of fluid turbulence after a short time, as is observed empirically. General, nonparametric, predictions of the theory regarding the spatial scaling properties of two-point temporal cross moments are developed and applied to a tracked rainfall field in a case study. These include the prediction of the empirically observed increase of correlation times as resolution decreases and the scaling of temporal cross moments, a new finding suggested by this theory.

The fractal scaling of river networks has been described in the context of both thin and fat fractals. Whereas the thin-fractal characterization is presented as a fractal dimension, D, derived from the length properties of the river channels, a fat-fractal characterization is given as a scaling exponent, β, derived from the behavior of river-channel area. Several authors have related D to the bifurcation ratio, Rb, and length ratio, Rl, of idealized Hortonian-network trees. Here, for these types of trees, we asymptotically relate β to Rb, Rl, and to a diameter exponent, Δ, which governs the downstream channel-widening process. Using this result, we present a linkage of D to β and discuss the implications of the relative values of Rb, Rl, and Δ on this linkage and on network channel behavior. Finally, we illustrate bias in the estimation of β from preasymptotic trees.

Under the theory of independent and identically distributed random cascades, the probability distribution of the cascade generator determines the spatial and the ensemble properties of spatial rainfall. Three sets of radar-derived rainfall data in space and time are analyzed to estimate the probability distribution of the generator. A detailed comparison between instantaneous scans of spatial rainfall and simulated cascades using the scaling properties of the marginal moments is carried out. This comparison highlights important similarities and differences between the data and the random cascade theory. Differences are quantified and measured for the three datasets. Evidence is presented to show that the scaling properties of the rainfall can be captured to the first order by a random cascade with a single parameter. The dependence of this parameter on forcing by the large-scale meteorological conditions, as measured by the large-scale spatial average rain rate, is investigated for these three da...

[1] This study presents the results of a detailed river flow fluctuation analysis on daily records from 14 stations in the Flint River Basin in Georgia in the southeastern United States with special focus on the effect of watershed area on long memory of river flow fluctuations. The areas of the watersheds draining to the stations range from 23 to 19,606 km2. The climatic and seasonal trends are removed using the detrended fluctuation analysis technique. Results show that (1) river flow fluctuations have two distinct scaling regimes, and the scaling break is delayed for large watershed areas; (2) large watersheds have more persistent river flow fluctuations and stronger long memory (i.e., for lag times beyond the scale break) than small watersheds do; (3) the long memory of river flow fluctuations does not come from the long memory of precipitation; (4) a linear reservoir unit hydrograph transfer function approach does not capture correctly the basin processes that convert short-memory precipitation to long-memory streamflow; and (5) the degree of multifractality of river flow fluctuations decreases with increasing watershed area. The results clearly indicate that watershed area is an important factor in the long-memory studies of streamflow such as streamflow prediction.

[1] Shrub encroachment, a widespread phenomenon in arid landscapes, creates “islands of fertility” in degraded systems as wind erosion removes nutrient-rich soil from intercanopy areas and deposits it in nearby shrub-vegetated patches. These islands of fertility generally are considered to be irreversible. Recently, fire has been observed to alter this pattern of resource heterogeneity through the redistribution of nutrients from the fertile islands of burnt shrubs to the surrounding bare soil areas. Despite the recognized relevance of both fires and wind erosion to the structure and function of arid ecosystems, the interactions between these two processes remains poorly understood. This study tests the hypothesis that fire-induced soil hydrophobicity developing in the soils beneath burned shrubs enhances soil erodibility by weakening the interparticle wet-bonding forces. To test this hypothesis, the effects of grass and shrub fires on changes in soil erodibility and on the intensity of fire-induced soil water repellency are compared at both the field and patch scales in heterogeneous arid landscapes. Higher water repellency was observed in conjunction with a stronger decrease in wind erosion threshold velocity around the shrubs than in grass-dominated patches affected by fire, while neither water repellency nor changes in threshold velocity was noticed in the bare soil interspaces. Thus, fires are found to induce soil hydrophobicity and to consequently enhance soil erodibility in shrub-vegetated islands of fertility. These processes create temporally dynamic islands of fertility and contribute to a decrease in resource heterogeneity in aridland ecosystems following fire.

The ability to use radar -based rainfall estimates either alone or as a supplement to rain -gage data i n real -time or historical rainfall -runoff simulations or other hydrologic applications would be valuable because of the relative sparseness of available gaged rainfall data. To investigate this ability, a comparison study between rainfall estimates from th e National Weather Service (NWS) Next Generation Weather Radar (NEXRAD) and the recorded measurements collected from a network of 27 tipping -bucket rain gages located in or near DuPage County in northeastern Illinois over the period from July 1997 through September 2005 was carried out at the daily time scale. The NEXRAD data used in this comparison consist of Stage III (1997 -2001) and Multisensor Precipitation Estimate (MPE) (2002 -2005) gridded hourly products. These NEXRAD products were corrected using ra in -gage data, but data from the DuPage County gage network were not used for this purpose. Periods of missing or frozen precipitation were excluded from the comparisons. Results show that early in the study period, NEXRAD data first under -estimated and the n over -estimated the gage measurements on a spatial average basis, while since 2001, the long -term spatial averages were similar. For most of the study period, the differences between gage and NEXRAD estimated daily totals decreased with distance from the nearest radar site. An analysis of the distribution of daily rainfall values shows that over most of the period , the radar data has more small values and fewer large values than the gage data, which is consistent with the radar sampling a larger area. Over all, these findings indicate that, at least in this region, while total NEXRAD precipitation estimates have become more comparable to the rainfall recorded by the gage network over the period of the study, spatially -variable biases may remain, and extreme values may have a different distribution. As a result, caution should be employed when using NEXRAD Stage III and MPE data for hydrologic modeling.

Estimates of uncertainty of discharge at time scales from 5 minutes to 1 year were obtained for two index-velocity gages on the Chicago Sanitary and Ship Canal (CSSC), Ill., instrumented with acoustic velocity meters (AVMs). The velocity measurements obtained from the AVMs are corrected to a mean channel velocity by use of an index-velocity rating (IVR). The IVR is a regression-derived relation between the AVM velocity estimates and those obtained using acoustic Doppler current profilers (ADCPs). The uncertainty estimation method is based on the firstorder variance method, but the AVM velocity error is estimated from an empirical perspective, using the statistics of the IVR regression. It is not clear whether to include the standard error of the IVR regression ( ) in the discharge uncertainty. At the 5-minute time scale when is included, has the dominant contribution to the discharge uncertainty, and the discharge uncertainty (expressed as the standard deviation of the discharge estimate) is about 5 m 2 e σ 2 e σ 2 e σ

[1] Wind erosion is a widespread process in drylands, and contributes to loss of soil fertility, alteration of atmospheric radiation, and air pollution. Erosion occurs when wind speed exceeds a certain threshold, which depends on a number of factors, including surface soil moisture. It is shown that in air-dry soils surface moisture and threshold wind speed depend significantly on air humidity. Thus, in arid regions variations in surface soil moisture can be significantly affected by changes in atmospheric humidity, with an important effect on wind erosion potential. Wind tunnel tests were conducted to investigate this dependence of threshold velocity on air humidity in air-dry soils. It was found that at these moisture levels, the threshold velocity decreases with an increase in air humidity. This result is explained by the effect of hygroscopic forces and by their dependence on soil matric potential in dry soils. INDEX TERMS: 1815 Hydrology: Erosion and sedimentation; 1866 Hydrology: Soil moisture; 1809 Hydrology: Desertification. Citation: Ravi, S., P. D’Odorico, T. M. Over, and T. M. Zobeck (2004), On the effect of air humidity on soil susceptibility to wind erosion: The case of air-dry soils, Geophys. Res. Lett., 31, L09501, doi:10.1029/2004GL019485.

Microerosion processes due to the impact of raindrops on the soil (rainsplash) represent an important mechanism of detachment and removal of soil parcels. The annual and interannual patterns of rainsplash erosion are controlled by the variability of rainfall, vegetation cover, and land-use practice. This paper presents a study of the interannual variability of rainfall erosivity (which is an indicator of the erosive power of rainfall) due to winter precipitation in the southwestern United States and its connection to the El Nino–Southern Oscillation (ENSO). A remarkable degree of dependence was found between the values of winter erosivity and the Southern Oscillation index (SOI) in the months proceeding and during each winter. In general, it was observed that the erosive power of rainfall is stronger during El Nino years and weaker during the La Nina phase in the U.S. southwest, as is the rainfall itself. It was also observed that the erosivity and SOI are nonlinearly related. This dependence of ...