Assessing and modelling changes in rainfall erosivity at different climate scales

An understanding of the weather drivers of soil erosion necessitates an extended instrumental meteorological series and knowledge of the processes linking climate and hydrology. The nature of such linkages remains poorly understood for the Mediterranean region. This gap is addressed through a composite analysis of long‐term climatic controls on rain erosivity in the Calore River Basin (southern Italy) for the period 1869–2006. Based on a parsimonious interpretation of rainstorm processes, a model (comparable with the Revised Universal Soil Loss Equation) was adapted to generate erosivity values on different time‐aggregation scales (yearly and seasonal). The evolution of the generated series of cumulated and extreme erosivity events was assessed by two return period (T) quantiles via a 22‐year moving window analysis (low return period, T = 2 years; high return period, T = 50 years). Erosivity extremes are shown to be characterized by increasing yearly trends (at a 100‐year rate of ～150 MJ mm ha^–1 h^–1 for T = 2 years and ～800 MJ mm ha^–1 h^–1 for T = 50 years), especially during the spring and autumn seasons. Quantile patterns on the extremes are also shown to be decoupled from trends in the cumulated values. The Buishand test was applied to detect the presence of temporal change points, and a wavelet spectrum analysis used for time‐frequency localization of climate signals. A change‐point in the evolution of climate is revealed over the 1970s in the spring series, which correlates to a distinct rain erosivity increase. The results indicate that soil erosion risk tends to rise as a consequence of an escalation of the climate erosive hazard, predominantly between April and November (associated with cultivation and tillage practices). Copyright © 2009 John Wiley & Sons, Ltd.     

Assessing changes in rainfall erosivity in Sicily during the twentieth century

Changes in rainfall erosivity are an expected consequence of climate change. Long‐term series of the single storm erosion index, EI, may be analysed to detect trends in rainfall erosivity. An indirect approach has to be applied for estimating EI, given that long series of rainfall intensities are seldom available. In this paper, a method for estimating EI from the corresponding rainfall amount, h_e, was developed for Sicily. This method was then applied at 17 Sicilian locations, representative of different climatic zones of the region, to generate a long series (i.e. from 1916 to 1999 in most cases) of EI values. Linear and step (step located at 1970) trends in annual and seasonal erosivity were detected by both classical approaches (Mann–Kendall test, Wilcoxon‐Mann‐Whitney rank‐sum test) and a new empirical approach (quantile approach, QA), based on the determination of the erosivity values corresponding to selected probability levels. A power relationship between EI and h_e with a space‐ and time‐variable scale factor and a time‐variable process parameter yielded the most accurate predictions of EI. However, a simpler model, using a time‐variable scale factor and a constant process parameter, yielded reasonably accurate EI estimates. Annual erosivity did not increase in Sicily during the twentieth century. At the most, it decreased at a few locations (three of the 17 considered locations). Significant trends were observed more frequently for winter erosivity (six locations) than for summer erosivity (two locations), suggesting that the erosive storms of winter determined the occasional occurrence of a negative trend in annual erosivity. In general, the QA compared reasonably well with more classical approaches. The QA appears promising since step trends for different return periods may be detected but efforts are needed to statistically formalize the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.     

Drop size distribution and kinetic energy load of rainstorms in Hong Kong

Data on drop size distribution and kinetic energy load of rainstorms are basic for rainfall erosivity indices. A simple and relatively inexpensive instrument was used to asses the instantaneous intensity and kinetic energy load of rainstorms in Hong Kong. Both the drop size and the instantaneous kinetic energy load of rainfall in Hong Kong are greater than in temperate and subtropical climates. The high kinetic energy results from the large size and greater number of raindrops falling per unit time. A high correlation between the kinetic energy of rainfall and the amount of rainfall allows for a convenient estimate of the energy load of storms from the amount of rainfall. Of more significance to the erosion process is the determination that about 74% of the total annual rainfall is erosive, containing about three‐quarters of the total annual energy load of the rains. The variability of rainfall parameters within a rainfall and from storm to storm is shown. The energy–intensity relationship, seasonal and annual distributions of rainfall erosivity are presented. Copyright © 2000 John Wiley & Sons, Ltd.     

More rain,less soil: long‐term changes in rainfall intensity with climate change

This commentary discusses the role of long‐term climate change in driving increases in soil erosion. Assuming that land use and management remain effectively constant, we discuss changes in the ability of rainfall to cause erosion (erosivity), using long daily rainfall data sets from southeast England. An upward trend in mean rainfall per rain day is detected at the century‐plus timescale. Implications for soil erosion and sediment delivery are discussed and evidence from other regions reviewed. We conclude that rates of soil erosion may well increase in a warmer, wetter world. Copyright © 2015 John Wiley & Sons, Ltd.     

Reliability of rainfall kinetic power–intensity relationships

The rainfall erosivity plays a fundamental role in water soil erosion processes and it can be expressed by its kinetic power. At first in this paper, the raindrop‐size distributions measured, in the period June 2006–March 2014, by an optical disdrometer installed at the Department of Agricultural and Forestry Sciences of University of Palermo are aggregated into rainfall intensity classes, having different ranges, and the measured kinetic power values are determined. Measured kinetic power values are initially used for testing the applicability of the kinetic power‐rainfall intensity relationships proposed by Wischmeier and Smith ( 1978 ), used in Universal Soil Loss Equation (USLE), Brown and Foster ( 1987 ) (RUSLE), and McGregor et al. ( 1995 ) (RUSLE2). Then, the reliability of a theoretical relationship for estimating the kinetic power by rainfall intensity and median volume diameter is verified. Finally, using the literature available datasets, corresponding to measurements carried out by different techniques and in different geographical sites, the analysis demonstrated that the rainfall intensity is not sufficient to determine the rainfall kinetic power. On the contrary, the theoretically deduced relationship allows to reproduce adequately the kinetic power of all available datasets, demonstrating that the knowledge of both rainfall intensity and median volume diameter allows a reliable estimate of the rainfall erosivity.     

Spatial and temporal variations in rainfall erosivity during 1960–2005 in the Yangtze River basin

Water resources and soil erosion are the most important environmental concerns in the Yangtze River basin, where soil erosion and sediment yield are closely related to rainfall erosivity. The present study explores the spatial and temporal changing patterns of the rainfall erosivity in the Yangtze River basin of China during 1960–2005 at annual, seasonal and monthly scales. The Mann–Kendall test is employed to detect the trends during 1960–2005, and the T test is applied to investigate possible changes between 1991–2005 and 1960–1990. Meanwhile the Rescaled Range Analysis is used for exploring future trend of rainfall erosivity. Moreover the continuous wavelet transform technique is using studying the periodicity of the rainfall erosivity. The results show that: (1) The Yangtze River basin is an area characterized by uneven spatial distribution of rainfall erosivity in China, with the annual average rainfall erosivity range from 131.21 to 16842 MJ mm ha^?1 h^?1. (2) Although the directions of trends in annual rainfall erosivity at most stations are upward, only 22 stations have significant trends at the 90 % confidence level, and these stations are mainly located in the Jinshajiang River basin and Boyang Lake basin. Winter and summer are the seasons showing strong upward trends. For the monthly series, significant increasing trends are mainly found during January, June and July. (3) Generally speaking, the results detected by the T test are quite consistent with those detected by the Mann–Kendall test. (4) The rainfall erosivity of Yangtze River basin during winter and summer will maintain a detected significant increasing trend in the near future, which may bring greater risks to soil erosion. (5) The annual and seasonal erosivity of Yangtze River basin all have one significant periodicity of 2–4 years.     

Impacts of annual precipitation extremes on soil and nutrient losses in vineyards of NE Spain

The objective of this research was to characterise annual precipitation extremes in a Mediterranean vineyard region. The number of exceptional events (P > 95th percentile) and annual extreme events (P > 99th percentile), as well as their strength, erosive character and return period were analysed for 2000–2004. The erosive character was evaluated according to the R‐factor (kinetic energy × maximum intensity in 30‐min periods). Soil and nutrient losses caused by these events were evaluated by combining field sampling and a hydrological model to estimate total runoff in a vineyard plot. The results show a clear increase in the number of very wet days and extreme events (P > 95th percentile), which represented up to 88% of annual rainfall. The severity of the extreme events (TS = precipitation event P > 99th percentile) reached values higher than 50 mm almost every year. These values were far exceeded in 2000, when one extraordinary event recorded 50% of the annual rainfall, with TS of 189 mm, about 80% of total rainfall being lost as runoff. Annual erosivity was driven not only by extreme events, but also by short events of less depth but high intensity. During some of the years analysed, rainfall erosivity was two or three times the average in the area. Most soil and nutrient losses occurred in a small number of events: one or two events every year were responsible for more than 75% of the annual soil and nutrient losses on average. Antecedent soil moisture conditions, runoff rates, and events with a return period higher than two years were responsible for the higher erosion rates. Apart from an exceptional event recorded in 2000, which produced more than 200 Mg ha^?1 soil losses, annual soil losses up to 25 Mg ha^?1 were recorded, which are much higher than the soil loss tolerance. Copyright © 2008 John Wiley & Sons, Ltd.     

Enhancing RUSLE to include runoff‐driven phenomena

RUSLE2 (Revised Universal Soil Loss Equation) is the most recent in the family of Universal Soil Loss Equation (USLE)/RUSLE/RUSLE2 models proven to provide robust estimates of average annual sheet and rill erosion from a wide range of land use, soil, and climatic conditions. RUSLE2's capabilities have been expanded over earlier versions using methods of estimating time‐varying runoff and process‐based sediment transport routines so that it can estimate sediment transport/deposition/delivery on complex hillslopes. In this report we propose and evaluate a method of predicting a series of representative runoff events whose sizes, durations, and timings are estimated from information already in the RUSLE2 database. The methods were derived from analysis of 30‐year simulations using a widely accepted climate generator and runoff model and were validated against additional independent simulations not used in developing the index events, as well as against long‐term measured monthly rainfall/runoff sets. Comparison of measured and RUSLE2‐predicted monthly runoff suggested that the procedures outlined may underestimate plot‐scale runoff during periods of the year with greater than average rainfall intensity, and a modification to improve predictions was developed. In order to illustrate the potential of coupling RUSLE2 with a process‐based channel erosion model, the resulting set of representative storms was used as an input to the channel routines used in Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) to calculate ephemeral gully erosion. The method was applied to a hypothetical 5‐ha field cropped to cotton in Marshall County, MS, bisected by a potential ephemeral gully having channel slopes ranging from 0·5 to 5% and with hillslopes on both sides of the channel with 5% steepness and 22·1 m length. Results showed the representative storm sequence produced reasonable results in CREAMS indicating that ephemeral gully erosion may be of the same order of magnitude as sheet and rill erosion. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial patterns of suspended sediment yields in a humid tropical watershed in Costa Rica

An Erratum has been published for this article in Hydrological Processes 16(5) 2002, 1130–1131. Humid tropical regions are often characterized by extreme variability of fluvial processes. The Rio Terraba drains the largest river basin, covering 4767 km², in Costa Rica. Mean annual rainfall is 3139±419_sd mm and mean annual discharge is 2168±492_sd mm (1971–88). Loss of forest cover, high rainfall erosivity and geomorphologic instability all have led to considerable degradation of soil and water resources at local to basin scales. Parametric and non‐parametric statistical methods were used to estimate sediment yields. In the Terraba basin, sediment yields per unit area increase from the headwaters to the basin mouth, and the trend is generally robust towards choice of methods (parametric and LOESS) used. This is in contrast to a general view that deposition typically exceeds sediment delivery with increase in basin size. The specific sediment yield increases from 112±11·4_sd t km^?2 year^?1 (at 317·9 km² on a major headwater tributary) to 404±141·7_sd t km^?2 year^?1 (at 4766·7 km²) at the basin mouth (1971–92). The analyses of relationships between sediment yields and basin parameters for the Terraba sub‐basins and for a total of 29 basins all over Costa Rica indicate a strong land use effect related to intensive agriculture besides hydro‐climatology. The best explanation for the observed pattern in the Terraba basin is a combined spatial pattern of land use and rainfall erosivity. These were integrated in a soil erosion index that is related to the observed patterns of sediment yield. Estimated sediment delivery ratios increase with basin area. Intensive agriculture in lower‐lying alluvial fans exposed to highly erosive rainfall contributes a large part of the sediment load. The higher elevation regions, although steep in slope, largely remain under forest, pasture, or tree‐crops. High rainfall erosivity (>7400 MJ mm ha^?1 h^?1 year ^?1) is associated with land uses that provide inadequate soil protection. It is also associated with steep, unstable slopes near the basin mouth. Improvements in land use and soil management in the lower‐lying regions exposed to highly erosive rainfall are recommended, and are especially important to basins in which sediment delivery ratio increases downstream with increasing basin area. Copyright © 2001 John Wiley & Sons, Ltd.     

Assessment of GCM simulations of annual and seasonal rainfall and daily rainfall distribution across south‐east Australia

Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large‐scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south‐east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI‐ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO‐MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS‐AOM, MIROC‐M, NCAR‐PCM1, IAP and NCAR‐CCSM. However, the reduction in the combined score as we move from the best‐ to the worst‐performing GCMs is gradual, and there is no evident cut‐off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd.     

Simulating precipitation in the Northeast United States using a climate-informed K-nearest neighbour algorithm

Decadal prediction using climate models faces long-standing challenges. While global climate models may reproduce long-term shifts in climate due to external forcing, in the near term, they often fail to accurately simulate interannual climate variability, as well as seasonal variability, wet and dry spells, and persistence, which are essential for water resources management. We developed a new climate-informed K-nearest neighbour (K-NN)-based stochastic modelling approach to capture the long-term trend and variability while replicating intra-annual statistics. The climate-informed K-NN stochastic model utilizes historical data along with climate state information to provide improved simulations of weather for near-term regional projections. Daily precipitation and temperature simulations are based on analogue weather days that belong to years similar to the current year's climate state. The climate-informed K-NN stochastic model is tested using 53 weather stations in the Northeast United States with an evident monotonic trend in annual precipitation. The model is also compared to the original K-NN weather generator and ISIMIP-2b GFDL general circulation model bias-corrected output in a cross-validation mode. Results indicate that the climate-informed K-NN model provides improved simulations for dry and wet regimes, and better uncertainty bounds for annual average precipitation. The model also replicates the within-year rainfall statistics. For the 1961–1970 dry regime, the model captures annual average precipitation and the intra-annual coefficient of variation. For the 2005–2014 wet regime, the model replicates the monotonic trend and daily persistence in precipitation. These improved modelled precipitation time series can be used for accurately simulating near-term streamflow, which in turn can be used for short-term water resources planning and management.     

Assessment of the impact of climate change on the flow regime of the Han River basin using indicators of hydrologic alteration

Most natural disasters are caused by water‐/climate‐related hazards, such as floods, droughts, typhoons, and landslides. In the last few years, great attention has been paid to climate change, and especially the impact of climate change on water resources and the natural disasters that have been an important issue in many countries. As climate change increases the frequency and intensity of extreme rainfall, the number of water‐related disasters is expected to rise. In this regard, this study intends to analyse the changes in extreme weather events and the associated flow regime in both the past and the future. Given trend analysis, spatially coherent and statistically significant changes in the extreme events of temperature and rainfall were identified. A weather generator based on the non‐stationary Markov chain model was applied to produce a daily climate change scenario for the Han River basin for a period of 2001–2090. The weather generator mainly utilizes the climate change SRES A2 scenario driven by input from the regional climate model. Following this, the SLURP model, which is a semi‐distributed hydrological model, was applied to produce a long‐term daily runoff ensemble series. Finally, the indicator of hydrologic alteration was applied to carry out a quantitative analysis and assessment of the impact of climate change on runoff, the river flow regime, and the aquatic ecosystem. It was found that the runoff is expected to decrease in May and July, while no significant changes occur in June. In comparison with historical evidence, the runoff is expected to increase from August to April. A remarkable increase, which is about 40%, in runoff was identified in September. The amount of the minimum discharge over various durations tended to increase when compared to the present hydrological condition. A detailed comparison for discharge and its associated characteristics was discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Spatial distribution and temporal trends of rainfall and erosivity in the Eastern Africa region

Soil erosion by water is one of the main environmental concerns in the drought‐prone Eastern Africa region. Understanding factors such as rainfall and erosivity is therefore of utmost importance for soil erosion risk assessment and soil and water conservation planning. In this study, we evaluated the spatial distribution and temporal trends of rainfall and erosivity for the Eastern Africa region during the period 1981–2016. The precipitation concentration index, seasonality index, and modified Fournier index have been analysed using 5 × 5‐km resolution multisource rainfall product (Climate Hazards Group InfraRed Precipitation with Stations). The mean annual rainfall of the region was 810 mm ranging from less than 300 mm in the lowland areas to over 1,200 mm in the highlands being influenced by orography of the Eastern Africa region. The precipitation concentration index and seasonality index revealed a spatial pattern of rainfall seasonality dependent on latitude, with a more pronounced seasonality as we go far from the equator. The modified Fournier index showed high spatial variability with about 55% of the region subject to high to very high rainfall erosivity. The mean annual R‐factor in the study region was calculated at 3,246 ± 1,895 MJ mm ha^?1 h^?1 yr^?1, implying a potentially high water erosion risk in the region. Moreover, both increasing and decreasing trends of annual rainfall and erosivity were observed but spatial variability of these trends was high. This study offers useful information for better soil erosion prediction as well as can support policy development to achieve sustainable regional environmental planning and management of soil and water resources.     

Applying the QREI30 index within the USLE modelling environment

The USLE/RUSLE model was designed to predict long‐term (~20 years) average annual soil loss by accounting for the effects of climate, soil, topography and crops. The USLE/RUSLE model operates mathematically in two steps. The first step involves the prediction of soil loss from the ‘unit’ plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope. Appropriate values of the factors accounting for slope length, gradient, crops and crop management and soil conservation practice are then used to adjust that soil loss to predict soil loss from areas that have conditions that are different from the unit plot. Replacing EI₃₀, the USLE/RUSLE event erosivity index, by the product of the runoff ratio (Q_R) and EI₃₀, can enhance the capacity of the model to predict short‐term soil loss from the unit plot if appropriate data on runoff is available. Replacing the EI₃₀ index by another index has consequences on other factors in the model. The USLE/RUSLE soil erodibility factor cannot be used when the erosivity factor is based on Q_REI₃₀. Also, the USLE/RUSLE factors for slope length, slope gradient crops and crop management, and soil conservation practice cannot be used when runoff from other than the unit plot is used to calculate Q_R. Here, equations are provided to convert the USLE/RUSLE factors to values suitable for use when the erosivity factor is based on the Q_REI₃₀ index under these circumstances. At some geographic locations, non linear relationships exist between soil loss from bare fallow areas and the Q_REI₃₀ index. The effect of this on the slope length factor associated with the Q_REI₃₀ index is demonstrated using data from runoff and soil loss plots located at the Sparacia site, Sicily. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of climate change on water availability in the Macquarie‐Castlereagh River Basin in Australia

The impact of future climate on runoff generation and the implications of these changes for management of water resources in a river basin are investigated by running these changes through catchment and river system models. Two conceptual daily rainfall‐runoff models are used to simulate runoff across the Macquarie‐Castlereagh region for historical (1895–2006) and future (～2030) climate based on outputs from 15 of the 23 IPCC AR4 GCMs for the A1B global warming scenario. The estimates of future runoff are used as inputs to the river system model. The mean annual historical rainfall averaged across the Macquarie‐Castlereagh region is 544 mm and the simulated runoff is 34 and 30 mm for SIMHYD and Sacramento rainfall‐runoff models, respectively. The mean annual future rainfall and runoff across the region are projected to decrease. The modelling results show a median estimate of a 5% reduction for SIMHYD (50% confidence interval ? 11 to + 7%) and a 7% reduction for Sacramento (50% confidence interval ? 15 to + 8%) in mean annual runoff under a ～2030 climate for the region. The results from the river system modelling indicate that under the ～2030 climate scenario, the median of general security and supplementary diversions are projected to decrease by 4% (50% confidence interval ? 10 to + 5%) and 2% (50% confidence interval ? 5 to + 3%) respectively for the SIMHYD inflows and 8% (50% confidence interval ? 17 to + 6%) and 5% (50% confidence interval ? 11 to + 3%) for the Sacramento inflows. The future annual and seasonal storage volumes for the Burrendong Dam and inflows at all major locations across the region are projected to be lower than the historical records. Copyright © 2011 John Wiley & Sons, Ltd.     

A new procedure to estimate the RUSLE EI30 index, based on monthly rainfall data and applied to the Algarve region, Portugal

One of the best indicators of the potential erosion risks is the rainfall–runoff erosivity factor (R) of the revised universal soil loss equation (RUSLE). Frequently, however, there is not enough data available to compute the R value, and other parameters, such as the modified Fournier index (F_mod), are used instead. But RUSLE is less effective if only the alternative procedures exist. One of the major discrepancies between R and the alternative parameters is time resolution: individual storms are used to calculate R while monthly averages over the year are used to calculate F_mod.

In this study, a multiple linear regression (r²=0.89) involving monthly EI₃₀, monthly rainfall for days with ≥10.0 mm and monthly number of days with rainfall ≥10.0 mm, for the Algarve region, is presented. Twenty-seven years of monthly rainfall erosivity values were computed for the 32 standard daily-read raingauge stations of the Algarve region.     

Assessment of future groundwater recharge in semi‐arid regions under climate change scenarios (Serral‐Salinas aquifer,SE Spain). Could increased rainfall variability increase the recharge rate?

The projected impact of climate change on groundwater recharge is a challenge in hydrogeological research because substantial doubts still remain, particularly in arid and semi‐arid zones. We present a methodology to generate future groundwater recharge scenarios using available information about regional climate change projections developed in European Projects. It involves an analysis of regional climate model (RCM) simulations and a proposal for ensemble models to assess the impacts of climate change. Future rainfall and temperature series are generated by modifying the mean and standard deviation of the historical series in accordance with estimates of their change provoked by climate change. Future recharge series will be obtained by simulating these new series within a continuous balance model of the aquifer. The proposed method is applied to the Serral‐Salinas aquifer, located in a semi‐arid zone of south‐east Spain. The results show important differences depending on the RCM used. Differences are also observed between the series generated by imposing only the changes in means or also in standard deviations. An increase in rainfall variability, as expected under future scenarios, could increase recharge rates for a given mean rainfall because the number of extreme events increases. For some RCMs, the simulations predict total recharge increases over the historical values, even though climate change would produce a reduction in the mean rainfall and an increased mean temperature. A method based on a multi‐objective analysis is proposed to provide ensemble predictions that give more value to the information obtained from the best calibrated models. The ensemble of predictions estimates a reduction in mean annual recharge of 14% for scenario A2 and 58% for scenario A1B. Lower values of future recharge are obtained if only the change in the mean is imposed. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessing possible changes in flood frequency due to climate change in mid‐sized watersheds in New York State,USA

The frequency of flooding is often presumed to increase with climate change because of projected increases in rainfall intensities. In this paper, using 50‐plus years of historical discharge and meteorological data from three watersheds in different physiographic regions of New York State, USA, we find that annual maximum stream discharges are associated with 20% or less of the annual maximum rainfall events. Instead of rainfall events, approximately 20% of annual maximum stream discharges are associated with annual maximum snowmelt events while 60% of annual maximum discharges are associated with moderate rainfall amounts and very wet soil conditions. To explore the potential for changes in future flood risk, we employed a compound frequency distribution that assumes annual maximum discharges can be modelled by combining the cumulative distribution functions of discharges resulting from annual maximum rainfall, annual maximum snowmelt, and occurrences of moderate rain on wet soils. Basing on a compound frequency distribution comprised of univariate general extreme value (GEV) and gamma distributions, we found that a hypothetical 20% increase in the magnitude of rainfall‐related stream discharge results in little change in 96th percentile annual maximum discharge. For the 99th percentile discharge, two waterbodies in our study had a 10% or less increase in annual maximum discharge when annual maximum rainfall‐related discharges increased 20% while the third waterbody had a 16% increase in annual maximum discharges. Additionally, in some cases, annual maximum discharges could be offset by a reduction in the discharge resulting from annual maximum snowmelt events. While only intended as a heuristic tool to explore the interaction among different flood‐causing mechanisms, use of a compound flood frequency distribution suggests a case can be made that not all waterbodies in humid, cold regions will see extensive changes in flooding due to increased rainfall intensities. Copyright © 2011 John Wiley & Sons, Ltd.     

Spatial variability of rainfall on a sub‐kilometre scale

The variability of rainfall in space and time is an essential driver of many processes in nature but little is known about its extent on the sub‐kilometre scale, despite many agricultural and environmental experiments on this scale. A network of 13 tipping‐bucket rain gauges was operated on a 1·4 km² test site in southern Germany for four years to quantify spatial trends in rainfall depth, intensity, erosivity, and predicted runoff. The random measuring error ranged from 10% to 0·1% in case of 1 mm and 100 mm rainfall, respectively. The wind effects could be well described by the mean slope of the horizon at the stations. Except for one station, which was excluded from further analysis, the relative differences due to wind were in maximum ±5%. Gradients in rainfall depth representing the 1‐km² scale derived by linear regressions were much larger and ranged from 1·0 to 15·7 mm km^?1 with a mean of 4·2 mm km^?1 (median 3·3 mm km^?1). They mainly developed during short bursts of rain and thus gradients were even larger for rain intensities and caused a variation in rain erosivity of up to 255% for an individual event. The trends did not have a single primary direction and thus level out on the long term, but for short‐time periods or for single events the assumption of spatially uniform rainfall is invalid on the sub‐kilometre scale. The strength of the spatial trend increased with rain intensity. This has important implications for any hydrological or geomorphologic process sensitive to maximum rain intensities, especially when focusing on large, rare events. These sub‐kilometre scale differences are hence highly relevant for environmental processes acting on short‐time scales like flooding or erosion. They should be considered during establishing, validating and application of any event‐based runoff or erosion model. Copyright © 2009 John Wiley & Sons, Ltd.     

Incorporating uncertainty analysis into a regional IDF formula

A rainfall intensity–duration–frequency (IDF) relationship was generated by pooling annual maximum rainfall series from 14 recording rain gauges in southern Taiwan. Dimensionless frequency curves, plotted by the growth curve method, can be well fitted by regression equations for a duration ranging from 10 mins to 24 hours. As the parameters in regression equations have a good statistical relationship with average annual rainfall, a generalized regional IDF formula was then formulated. The formula, based on average annual rainfall as an index, can be easily applied to non-recording rain gauges. This paper further applies the mean value first-order second moment (MFOSM) method to estimate the uncertainty of the proposed regional IDF formula. From a stochastic viewpoint, the generalized regional IDF formula can accurately simulate the IDF relationship developed using frequency analysis (EV1) at individual stations. The method can provide both rainfall intensity and variance isohyetal maps for various rainfall durations and return periods over the study area. © 1998 John Wiley & Sons, Ltd.