Weather Generator Research Articles

Impact of climate change on the hydrology and water salinity in the Anzali Wetland, northern Iran

ABSTRACTThe impact of climate change on hydrology and water salinity of a valuable coastal wetland (Anzali) in northern Iran is assessed using daily precipitation and temperature data from 19 models of Coupled Model Inter-comparison Project Phase 5. The daily data are transiently downscaled using the Long Ashton Research Station Weather Generator to three climatic stations. The temperature is projected to increase by +1.6, +1.9 and +2.7°C and precipitation to decrease by 10.4%, 12.8% and 12.2% under representative concentration pathway (RCP) scenarios RCP2.6, RCP4.5 and RCP8.5, respectively. The wetland hydrology and water salinity are assessed using the water balance approach and mixing equation, respectively. The upstream river flow modelled by the Soil and Water Assessment Tool is projected to reduce by up to 18%, leading to reductions in wetland volume (154 × 106 m3), area (57.47 km2) and depth (2.77 m) by 34%, 21.1% and 20.2%, respectively, under climate change, while the mean annual total dissolved solids (1675 mg/L) would increase by 49%. The reduced volume and raised salinity may affect the wetland ecology.

Built form, urban climate and building energy modelling: case-studies in Rome and Antofagasta

The energy performance of urban buildings is affected by multiple climate phenomena such as heat island intensity, wind flow, solar obstructions and infrared radiation exchange in urban canyons, but a modelling procedure to account for all of them in building performance simulation is still missing. This paper contributes to fill this gap by describing a chain strategy to model urban boundary conditions suitable for annual simulations using dynamic thermal simulation tools. The methodology brings together existing physical and empirical climate models and it is applied to 10 case studies in Rome (Italy) and Antofagasta (Chile). The results show that urban climate varies significantly across a city depending on the density of urban texture and its impact on the annual energy demand depends on the region's climate. The urban shadows are crucial in cooling-dominated climates (Antofagasta) while the urban heat island intensity is more important in temperate climates (Rome).Abbreviations: ACH: Air change per hour; BPS: Building Performance Simulation; BS: British Standard; CNV: Controlled natural ventilation; H/W: height-to-width ratio of urban canyons; L/W: length-to-width ratio of urban canyons; UHI: Urban Heat Island; UWG: Urban Weather Generator model

POSSIBILITIES OF THE LONG-RANGE INFLOW FORECAST TO THE BUREYSKAYA RESERVOIR

Development of the long-range inflow forecast system to the Bureyskaya reservoir, carried out in 2008-2010 at the request of the RusHydro Company, is presented. Estimations were performed based on terms of statistical probabilities with about month-quarter lead-time. Considering features of extreme flow formation processes and wetness of territory, the focus was warm period (June-September), which is known for its heavy rainfall events. During the investigations there were examined the dynamic-stochastic approach, based on the deterministic hydrological model with using of meteorological characteristics ensemble by weather generator as input, and inflow forecast to the reservoir in terms of probability curve based on multidimensional stochastic modeling. The project included two main stages: development of precipitation and temperature forecast method with month-six months lead-time; development of inflow probabilistic forecast method with month-quarter lead-time based on expected values of precipitation and temperature. Methods pretesting demonstrated reasonable inflow forecast reliability (about 90%), mean absolute deviation of expected inflow volume from observed is 23-28% relative to mean value. Driven by the developed framework, it was forecasted precipitation sums and reservoir inflow at the individual seasons. By comparing of measured and forecasted values, it was made a conclusion, that accuracy of the forecast scheme is satisfactory. The mean values of the precipitation forecast reliability is 75-80%. Forecasted reservoir inflow in 2009 and 2010 is assessed as close to a measured one.

Prospects of decreasing winter chill for deciduous fruit production in Chile throughout the 21st century

In Mediterranean climates, many deciduous fruit trees are unable to meet their seasonal chill requirements, a situation that may be exacerbated by global warming. Modeling approaches can be used to assess the impacts of climate change on tree crops for the past and forecast possible impacts for the future. We apply modeling approaches to assess climate change impacts on Chilean fruit production in nine major agricultural sites. Using a weather generator, we obtained 100 years of plausible weather data for scenarios produced by 15 climate models for two Representative Concentration Pathways (RCPs). Winter chill, safe winter chill (both quantified in Chill Portions - CP), and the percentage of years with spring frost were estimated for two reference years, 2050 and 2085. Projections show a considerable chill reduction in northern regions in all future scenarios. Relatively stable conditions are projected for southern regions, whereas most of the central sites are expected to lose between 10 and 15 CP by 2050. For 2085, expected chill reduction is much more severe. The percentage of years with spring frost is projected to decrease in all locations, varying according to RCP scenario and year. Results highlight that in northern sites, some species may fail to meet their chill requirements in the future. In other regions, strategies to mitigate the effects of chill shortfalls may become critical to ensure adequate yields. Results may be useful for geneticists, breeders, physiologists, dormancy researchers, and growers making production decisions, e.g., on selection of appropriate species and varieties.

Forecasting the Future Temperature Using a Downscaling Method by LARS-WG Stochastic Weather Generator at the Local Site of Phitsanulok Province, Thailand

The study evaluates the effect of climate change on temperature, which is one of the most important variables in water resources management and irrigation scheduling. Climate prediction is necessary in the agricultural and hydrological analysis. This study proposed an approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Inter-comparison Project Phase 5 (CMIP5) under EC-Earth and MPI-ESM-MR. The first step is model calibration, where the observed dataset is analyzed statistically. In the second stage, the synthetic data and observed data are checked for Kolmogorov-Smirnov and the means and standard deviations. In order to evaluate the response of temperature under future warmer climate trends, the approach was assessed using data series. These parameters consisted of the minimum and maximum temperature at the Phitsanulok Meteorological Station (WMO Index 48378) and RCP4.5 climate change scenario for the base period as well as for 2021-2040 (the near future), 2041-2060 (the medium future) and 2061-2080 (the far future). The results of the numerical applications indicated that the linkage between the observed data spatially downscaled from LARS-WG simulations with the historical one of the locations during the baseline period had a very good accuracy. It was also found that the future climate change of temperature contributed to higher change. The mean of minimum temperature in the baseline year was 23.13°C while the mean of minimum temperature in the projection period for 2021-2040, 2041-2060 and 2061-2080 is expected to be 24.09 (+4.18%), 24.49 (+5.94%) and 24.82 (+7.36%)°C, and 24.12 (+4.32%), 24.82 (+7.36%) and 25.08 (+8.48%)°C for the EC-Earth and MPI-ESM-MR, respectively. While, the mean of maximum temperature in the baseline year was 33.41°C, the maximum temperatures are projected to increase at 34.47 (+3.19%), 34.88 (+4.43%) and 35.21 (+5.40%)°C, and 34.53 (+3.36%), 35.19 (+5.34%) and 35.30 (+5.67%)°C, respectively. Furthermore, the future local surface temperatures from the MPI-ESM-MR project tended to be higher than EC-Earth. In conclusion, the study results indicate that in coming three time periods, the minimum and maximum temperature increase is expected in Phitsanulok province, Thailand.

Climate change impacts on summer flood frequencies in two mountainous catchments in China and Switzerland

Abstract Flood events are difficult to characterize if available observation records are shorter than the recurrence intervals, and the non-stationarity of the climate adds additional uncertainty. In this study, we use a hydrological model coupled with a stochastic weather generator to simulate the summer flood regime in two mountainous catchments located in China and Switzerland. The models are set up with hourly data from only 10–20 years of observations but are successfully validated against 30–40-year long records of flood frequencies and magnitudes. To assess the climate change impacts on flood frequencies, we re-calibrate the weather generator with the climate statistics for 2021–2050 obtained from ensembles of bias-corrected regional climate models. Across all assessed return periods (10–100 years) and two emission scenarios, nearly all model chains indicate an intensification of flood extremes. According to the ensemble averages, the potential flood magnitudes increase by more than 30% in both catchments. The unambiguousness of the results is remarkable and can be explained by three factors rarely combined in previous studies: reduced statistical uncertainty due to a stochastic modelling approach, hourly time steps and the focus on headwater catchments where local topography and convective storms are causing runoff extremes within a confined area.

Integrated framework for assessing climate change impact on extreme rainfall and the urban drainage system

Abstract Urban areas are becoming increasingly vulnerable to extreme storms and flash floods, which could be more damaging under climate change. This study presented an integrated framework for assessing climate change impact on extreme rainfall and urban drainage systems by incorporating a number of statistical and modelling techniques. Starting from synthetic future climate data generated by the stochastic weather generator, the simple scaling method and the Huff rainfall design were adopted for rainfall disaggregation and rainfall design. After having obtained 3-min level designed rainfall information, the urban hydrological model (i.e., Storm Water Management Model) was used to carry out the runoff analysis. A case study in a tropical city was used to demonstrate the proposed framework. Particularly, the impact of selecting different general circulation models and Huff distributions on future 1-h extreme rainfall and the performance of the urban drainage system were investigated. It was revealed that the proposed framework is flexible and easy to implement in generating temporally high-resolution rainfall data under climate model projections and offers a parsimonious way of assessing urban flood risks considering the uncertainty arising from climate change model projections, downscaling and rainfall design.

Space-time simulation of precipitation based on weather pattern sub-sampling and meta-Gaussian model

Simulation methods for design flood estimations in dam safety studies require fine scale precipitation data to provide quality input for hydrological models, especially for extrapolation to extreme events. This leads to use statistical models such as stochastic weather generators. The aim here is to develop a stochastic model adaptable on mountainous catchments in France and accounting for spatial and temporal dependencies in daily precipitation fields. To achieve this goal, the framework of spatial random processes is adopted here.The novelty of the model developed in this study resides in the combination of an autoregressive meta-Gaussian process accounting for the spatio-temporal dependencies and weather pattern sub-sampling discriminating the different rainfall intensity classes. The model is tested from rain gauges in the Ardèche catchment located in South of France. The model estimation is performed in four steps, dealing respectively with: (i) the at-site marginal distribution, (ii) the mapping of the marginal distribution parameters at the target resolution, (iii) the at-site temporal correlation and (iv) the spatial covariance function.The model simulations are evaluated in terms of marginal distribution, inter-site dependence and areal rainfall properties and compared to the observations at calibration stations and also on a set of independent validation stations. Regarding all these aspects, the model shows good abilities to reproduce the observed statistics and presents really small discrepancies compared to the stations data. The sub-sampling is particularly efficient to reproduce the seasonal variations and the marginal mapping procedure induces very small differences in terms of daily rain amounts and daily occurrence probabilities.

Coupled annual and daily multivariate and multisite stochastic weather generator to preserve low- and high-frequency variability to assess climate vulnerability

Multivariate and multisite stochastic weather generators have been proposed to produce an ensemble of climate time series but are often limited to preserving low-frequency variability of climate variables as well as producing extreme rainfall events. This study presents a new, two-stage, multivariate, multisite weather generator by coupling annual and daily weather generators. A daily weather generator is first developed using heavy tailed distribution, spatial tail dependence, and multivariate autoregressive models. For the second stage, annual climate variables over the region are modeled using a wavelet decomposition approach coupled with a multivariate autoregressive model. The generated annual time series are used to reconstruct daily simulations to embed multiple low-frequency oscillations in a daily time series. The proposed weather generator is applied to the Geum River Basin in South Korea, and its performance is compared to that of a nested simplified model. Results show that the proposed model performs well with respect to reproducing marginal distributional attributes, multisite dependencies, and climate variability at the daily and annual scales. Lastly, the weather generator adopts a quantile mapping procedure to incorporate long-term distributional changes into generated climate sequences for use in climate change assessments. Results show that inter-annual variability is also well preserved while climate sequences are adjusted by various alterations.

Assessment of potential impacts of climate change on water resources in Ngerengere catchment, Tanzania

Global climate change has substantial impacts on local and regional hydrological regime. However, the general understanding of how hydrological regime is affected by climate change at catchment scale is limited. In this study, the potential impacts of climate change on water resources in Ngerengere River catchment in Tanzania were assessed. The HBV model was used to undertake water resources assessment for the Ngerengere catchment and subsequently impacts of climate change on the same. Future climate for 2050s (2040–2069) and 2080s (2070–2099) were downscaled using a statistical weather generator (LARS-WG) from outputs of the Global Circulation Model (HadCM3) under SRES A2 scenario.The HBV model was calibrated and validated using observed streamflow data at Mgude gauge station for the baseline period (1971–2000). The Nash-Sutcliffe efficiency (NSE) of 0.7 and 0.6 was attained during calibration and validation respectively. Downscaling results revealed that LARS-WG performed well in generating future rainfall and temperature data series. The results indicated an increase in minimum and maximum temperature of around 0.2–2.6 °C in the 2050s and around 2.7–4.4 °C in the 2080s. Future rainfall is predicted to decrease by 12–37% in April, May, June and July, while rainfall in the remaining months are predicted to increase by 3–58%. The impact assessment on streamflow of the Ngererengere catchment revealed a decline in the mean annual streamflow by 2.1% in 2050s, whereas an increase of up to 58% in mean annual streamflow is predicted in the 2080s.

Parametric gridded weather generator for use in present and future climates: focus on spatial temperature characteristics

This study presents results of the pilot experiments made with new parametric multi-site multi-variable stochastic daily weather generator (WG) SPAGETTA. The experiments are performed for eight European regions and we focus on spatial characteristics of temperature. The WG is calibrated using the gridded weather data E-OBS. In evaluating the generator, the spatial and temporal temperature autocorrelations derived from the synthetic series were found to perfectly fit the values derived from the calibration data. Next, the WG is validated in terms of the frequency of “spatial hot days” and the annual maximum length of “spatial hot spells”. The results indicate a very good correspondence between characteristics derived from synthetic and calibration data. As part of the validation tests, the performance of the WG is compared with a regional climate model (RCM), which shows a similar performance as the generator. In a final experiment, the use of the WG for the future climate is demonstrated, the WG parameters (including the temperature autocorrelations) calibrated with the observed data are modified according to the RCM-based changes in these parameters. While analyzing synthetic series produced with the modified generator, we discuss partial impacts due to changes in individual WG parameters on the spatial hot days and spells. We show that the impacts are mainly (but not only) due to changes in temperature averages. The projected changes in temperature autocorrelations have also some impacts, larger for the spatial hot spells than for the spatial hot days. Climate change impacts on spatial hot days/spells based on the WG are compared with impacts based on the RCM, and we conclude that the differences are mainly due to simplifying assumptions adopted in our pilot experiment.

An Economic-Based Evaluation of Maize Production under Deficit and Supplemental Irrigation for Smallholder Farmers in Northern Togo, West Africa

While the world population is expected to reach 9 billion in 2050, in West Africa, it will more than double. This situation will lead to a high demand for cereals in the region. At the same time, farmers are experiencing yield losses due to erratic rainfall. To come up with a sound and effective solution, the available but limited water should be used to achieve high yields through irrigation. Therefore, full and deficit irrigation management strategies were evaluated. The expected profit that can be obtained by a smallholder farmer under a conventional irrigation system in the short-term of investment was also assessed considering rope and bucket, treadle pump, and motorized pump water-lifting methods. The study focused on maize in northern Togo. The framework used in this study consisted of (i) a weather generator for simulating long-term climate time series; (ii) the AquaCrop model, which was used to simulate crop yield response to water; and (iii) a problem-specific algorithm for optimal irrigation scheduling with limited water supply. Results showed high variability in rainfall during the wet season leading to significant variability in the expected yield under rainfed conditions. This variability was substantially reduced when supplemental irrigation was applied. This holds for the irrigation management strategies evaluated in the dry season. Farmers’expected net incomes were US$ 133.35 and 78.11 per hectare for treadle pump and rope and bucket methods, respectively, under 10% exceedance probability. The motorized pump method is not appropriate for smallholder farmers in the short run.

Linking Climate, Basin Morphology and Vegetation Characteristics to Fu’s Parameter in Data Poor Conditions

The prediction of long term water balance components is not a trivial issue, even when empirical Budyko’s type approaches are used, because parameter estimation is often hampered by missing or poor hydrological data. In order to overcome this issue, we provided regression equations that link climate, morphological, and vegetation parameters to Fu’s parameter. Climate is here defined as a specific seasonal pattern of potential evapotranspiration and rain: five climatic scenarios have been considered to mimic different conditions worldwide. A weather generator has been used to create stochastic time series for the related climatic scenario, which in turn has been used as an input to a conceptual hydrological model to obtain long-term water balance components with low computational effort, while preserving fundamental process descriptions. The morphology and vegetation’s role in determining water partitioning process has been epitomized in four parameters of the conceptual model. Numerical simulations explored a large set of basins in the five climates. Results show that climate superimposes partitioning rules for a given basin; morphological and vegetation watershed properties, as conceptualized by model parameters, determine the Fu’s parameter within a given climate. A sensitive analysis confirmed that vegetation has the most influencing role in determining water partitioning rules, followed by soil permeability. Finally, linear regressions relating basin characteristics to Fu’s parameter have been obtained in the five climates and tested in a basin for each case, obtaining encouraging results. The small amount of data required and the very low computational effort of the method make this approach ideal for practitioners and hydrologists involved in annual runoff assessment.

Extracting and Generating PV Soiling Profiles for Analysis, Forecasting, and Cleaning Optimization

The identification and prediction of the daily soiling profiles of a photovoltaic site is essential to plan the optimal cleaning schedule. In this article, we analyze and propose various methods to extract and generate photovoltaic soiling profiles, in order to improve the analysis and the forecast of the losses. New soiling rate extraction methods are proposed to reflect the seasonal variability of the soiling rates and, for this reason, are found to identify the most convenient cleaning day with the highest accuracy for the investigated sites. Also, we present an approach that could be used to predict future soiling losses through the implementation of stochastic weather generation algorithms whose ability to identify in advance the best cleaning schedule is also successfully tested. The methods presented in this article can optimize the operation and maintenance schedule and could make it possible, in the future, to predict soiling losses through analysis based only on environmental parameters, such as rainfall and particulate matter, without the need of long-term soiling data.

Simulating the Impact of Climate Change on the Hydrological Regimes of a Sparsely Gauged Mountainous Basin, Northern Pakistan

Projected climate changes for the 21st century may cause great uncertainties on the hydrology of a river basin. This study explored the impacts of climate change on the water balance and hydrological regime of the Jhelum River Basin using the Soil and Water Assessment Tool (SWAT). Two downscaling methods (SDSM, Statistical Downscaling Model and LARS-WG, Long Ashton Research Station Weather Generator), three Global Circulation Models (GCMs), and two representative concentration pathways (RCP4.5 and RCP8.5) for three future periods (2030s, 2050s, and 2090s) were used to assess the climate change impacts on flow regimes. The results exhibited that both downscaling methods suggested an increase in annual streamflow over the river basin. There is generally an increasing trend of winter and autumn discharge, whereas it is complicated for summer and spring to conclude if the trend is increasing or decreasing depending on the downscaling methods. Therefore, the uncertainty associated with the downscaling of climate simulation needs to consider, for the best estimate, the impact of climate change, with its uncertainty, on a particular basin. The study also resulted that water yield and evapotranspiration in the eastern part of the basin (sub-basins at high elevation) would be most affected by climate change. The outcomes of this study would be useful for providing guidance in water management and planning for the river basin under climate change.

Validation of The Weather Generator CLIGEN with Season Precipitation Data in Tokat Province

In models that predict hydrologic, ecological and product-yield relationship, the climate interface file is widely used. Today, CLIGEN is the most widely used climate model. While this model is extensively used in many countries around the world, it is not used commonly in our country. In this study, daily data belonging to Tokat climate conditions were simulated with CLIGEN. Observed and simulated precipitation was classified as the wet/dry period. The performance of the CLIGEN climate model was evaluated in both wet and dry periods according to the seasons. The relationship between the obtained precipitation data was statistically determined by Kolmogorov-Smirnov and t-test. Tokat province is located in the arid and semi-arid climate zone. The performance of the model in predicting precipitation in all seasons during the dry period was found to be quite successful.

Current and future test reference years at a 5 km resolution

Frequently, the computer modelling of the natural and human-made environment requires localised weather files. Traditionally, the weather files are based on the observed weather at a small number of locations (14 for the UK). Unfortunately, both the climate and the weather are known to be highly variable across the landscape, so the small number of locations has the potential to cause large errors. With respect to buildings, this results in incorrect estimates of the annual energy use (sometimes by a factor of 2), or of overheating risk. Here we use a validated weather generator running on a 5 × 5 km grid to create probabilistic test reference years (pTRYs) for the UK at 11,326 locations. We then investigate the spatial variability of these pTRYs and of annual energy estimates and temperatures in buildings generated by them, both now and in 2080. Further pTRYs targeted at understanding the impact of minimum and maximum temperatures are proposed and produced at the same locations. Finally, we place these pTRYs, which represent the first set of reference weather files at this spatial resolution in the world and that include the urban heat island effect, into a publicly accessible database so researchers and industry can access them.Practical applications: Insufficiently localised weather data for building simulations have limited the accuracy of previous estimations of energy use and overheating risk in buildings. This work produces localised probabilistic test reference years (pTRYs) across the whole UK for now and future climates. In addition, a new pTRY method has been proposed in order to overcome an unexpected shortcoming of traditional pTRYs in representing typical maximum and minimum temperatures. These current and future weather data will be of interest to various disciplines including those interested in low carbon design, renewable energy and climate resilience.

Assessment of the effectiveness of a multi-site stochastic weather generator on hydrological modelling in the Red Deer River watershed, Canada

ABSTRACT To improve the convergence of multiple-site weather generators (SWGs) based on the brute force algorithm (MBFA), a genetic algorithm (GA) is proposed to search the overall optimal correlation matrix. Precipitation series from weather generators are used as input to the hydrological model, the soil and water assessment tool (SWAT), to generate runoff over the Red Deer watershed, Canada for further runoff analysis. The results indicate that the SWAT model using SWG-generated data accurately represents the mean monthly streamflow for most of the months. The multi-site generators were capable of better representing the monthly streamflow variability, which was notably underestimated by the single-site version. In terms of extreme flows, the proposed method reproduced the observed extreme flow with smaller bias than MBFA, while the single-site generator significantly underestimated the annual maximum flows due to its poor capability in addressing partial precipitation correlations.

Estimating the Responses of Hydrological and Sedimental Processes to Future Climate Change in Watersheds with Different Landscapes in the Yellow River Basin, China.

This study concerned the sediment issue of the Yellow River basin. The responses of hydrological and sedimental processes to future climate change in two upland watersheds with different dominant landscapes were estimated. Four Representative Concentration Pathway (RCP) scenarios with different radiative forcing levels were considered. The outputs of eleven Global Climate Models (GCMs) were used to represent the future climate status of the 2050s and 2070s, and an ensemble means was achieved to avoid uncertainty. The Long Ashton Research Station Weather Generator (LARS-WG) was employed to downscale the outputs of GCMs for future site-scale daily weather data estimations. The Generalized Watershed Loading Functions (GWLF) model was employed to model the streamflow and sediment yields under various scenarios and periods. The results showed that there would be generally hotter and wetter weather conditions in the future. Increased erosion and sediment yields could be found in the study area, with lesser increments in sediment in woodland than in cultivated field. The peak of sediment would appear in the 2050s, and integrated measures for sediment control should be implemented to reduce erosion and block delivery. The multi-model approach proposed in this study had reliable performance and could be applied in other similar areas with modest data conditions.

A Review of the Science and Logic Associated with Approach Used in the Universal Soil Loss Equation Family of Models

Soil erosion caused by rain is a major factor in degrading agricultural land, and agricultural practices that conserve soil should be used to maintain the long-term sustainability of agricultural land. The Universal Soil Loss Equation (USLE) was developed in the 1960s and 1970s to predict the long-term average annual soil loss from sheet and rill erosion on field-sized areas as an aid to making management decisions to conserve soil. The USLE uses six factors to take account of the effects of climate, soil, topography, crops, and crop management, and specific actions designed to conserve soil. Although initially developed as an empirical model based on data from more than 10,000 plot years of data collected in plot experiments in the USA, the selection of the independent factors used in the model was made taking account of scientific understanding of the drivers involved in rainfall erosion. In addition, assumptions and approximations were needed to make an operational model that met the needs of the decision makers at that time. Those needs have changed over time, leading to the development of the Revised USLE (RUSLE) and a second version of that, the Revised USLE, Version 2 (RUSLE2). While the original USLE model was not designed to predict short-term variations in erosion well, these developments have involved more use of conceptualization in order to deal with the time-variant impacts of the drivers involved in rainfall erosion. The USLE family of models is based on the concept that the “unit” plot, a bare fallow area 22.1 m long on a 9% slope gradient with cultivation up and down the slope, provides a physical situation where the effect of climate and soil on rainfall erosion can be determined without the need to consider the impact of the four other factors. The science and logic associated with this approach is reviewed. The manner by which the soil erodibility factor is determined from plot data ensures that the long-term average annual soil loss for the unit plot is predicted well, even when the assumption that event soil loss is directly related to the product of event rainfall energy, and the maximum 30-min intensity is not wholly appropriate. RUSLE2 has a capacity to use CLIGEN, the weather generator used in WEPP, and so can predict soil losses based on individual storms in a similar way to WEPP. Including a direct consideration of runoff in determining event erosivity enhances the ability to predict event soil losses when runoff is known or predicted well, but similar to more process-based models, this ability is offset by the difficulty in predicting runoff well.