Precipitation Frequency Estimates Research Articles

Atlas of probabilistic extreme precipitation based on the early 21st century records in the United States

Study regionThe Conterminous United States Study focusExtreme precipitation property defined by the National Oceanic and Atmospheric Administration’s Precipitation-Frequency Atlas of the United States (NOAA Atlas 14) is losing its representativeness as the standard measure due to the impacts of climate change. This study aims to spatially and quantitatively assess the changes in extreme precipitation properties by comparing the NOAA Atlas 14 with a new atlas of precipitation frequency estimates (PFE) developed in this study from early 21st-century records New hydrological insights for the regionThe NOAA Atlas 14 has quantitatively diverged from the recent 20-year records due to precipitation non-stationarity across the United States. The NOAA Atlas 14 is characterized by more spatially smoothed PFEs compared to the new PFEs, suggesting that employing the NOAA Atlas 14 could lead to an underestimation of physical flood risk at a local scale. As the “new normal” in the 21st century, the majority of the United States faces three times more occurrences of extreme storms corresponding to a 1-in-100-year return period of the 20th century. The recent 20-year extreme events should be treated as the new normal. Until the NOAA Atlas 14 is updated to account for climate-driven non-stationarity and can be shown to resolve local effects better, it is recommended to employ new values between the upper and middle bounds of PFE that capture the non-stationarity of precipitation for the regions where the underestimation of PFEs is highlighted in this study

Non-Stationary Precipitation Frequency Estimates for Resilient Infrastructure Design in a Changing Climate: A Case Study in Sydney

The intensity–duration–frequency (IDF) curve is a commonly utilized tool for estimating extreme rainfall events that are used for many purposes including flood analysis. Extreme rainfall events are expected to become more intense under the changing climate, and there is a need to account for non-stationarity IDF curves to mitigate an underestimation of the risks associated with extreme rainfall events. Sydney, Australia, has recently started experiencing flooding under climate change and more intense rainfall events. This study evaluated the impact of climate change on altering the precipitation frequency estimates (PFs) used in generating IDF curves at Sydney Airport. Seven general circulation models (GCMs) were obtained, and the best models in terms of providing the extreme series were selected. The ensemble of the best models was used for comparing the projected 24 h PFs in 2031–2060 with historical values provided by Australian Rainfall and Runoff (ARR). The historical PFs consistently underestimate the projected 24 h PFs for all return periods. The projected 24 h 100 yr rainfall events are increased by 9% to 41% for the least and worst-case scenario compared to ARR historical PFs. These findings highlight the need for incorporating the impact of climate change on PFs and IDF curves in Sydney toward building a more prepared and resilient community. The findings of this study can also aid other communities in adapting the same framework for developing more robust and adaptive approaches to reducing extreme rainfall events’ repercussions under changing climates.

Assessment of the standard precipitation frequency estimates in the United States

Study regionThe Conterminous United States Study focusThe NOAA Atlases have provided the standard precipitation frequency estimates (PFEs) for over two decades in the United States, but they are losing that status due to climate change. This study evaluates the Atlases compared to new PFEs developed based on the Automated Surface Observing System and Regional Frequency Analysis (ASOS-RFA) as a benchmark and examines a radar-based precipitation product as a data source eligible for developing the post-Atlas. New hydrological insights for the regionThe stationarity assumption of precipitation was highlighted as a significant factor in increasing the uncertainties in the Atlases. The Atlases explicitly tended to underestimate PFEs compared to the ASOS-RFA. The difference between the Atlases and the ASOS-RFA increases as the return period increases and the duration decreases. The age of the Atlases correlated with the difference, suggesting that recent observations diverge from older estimates. The radar-based PFEs are well-matched with the ASOS-RFA at frequent return periods, but the uncertainties increased as the return period increased. This is because the conventional bias correction was limited in improving the annual maximum series of precipitation. Lastly, we need to pay careful attention to an increase in precipitation frequency estimates shorter than 12-hr durations.

Assessment of the benefits of climate model weights for ensemble analysis in three urban precipitation frequency studies

AbstractIn hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal‐weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal‐weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid‐box averages is provided.

Application of weather research and forecasting (WRF) model in evaluating depth–duration–frequency estimates under a future climate scenario

Abstract Point precipitation frequency estimates (PPFEs) are used in the design of stormwater drainage infrastructures. The PPFEs for 1-year to 1,000-year recurrence intervals with durations ranging from 5 min to 60 days are published in the National Oceanic and Atmospheric Administration (NOAA) Atlas 14. NOAA's published PPFEs are accepted as a reliable resource for the design of urban drainage infrastructures, yet they are based on the stationary climate assumption. However, future climate change may affect the distribution, frequency, and intensity of precipitation events. To evaluate the potential impacts of climate change on the stationary climate assumption, a weather research and forecasting (WRF) model was developed based on the medium climate change emission scenario. Then, the PPFEs were determined for 2010–2015, 2030–2035, 2050–2055, and 2070–2075 at six different locations in the state of Arizona. The comparison of the published and simulated PPFEs revealed a varying trend in depth and frequency values dependent on location and climate. Additionally, depth values were diminished for <3-h events at a majority of the stations.

Application of the British Columbia MetPortal for Estimation of Probable Maximum Precipitation and Probable Maximum Flood for a Coastal Watershed

Estimation of the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) are regulatory requirements in many jurisdictions that are used in the design of dams and assessment of existing infrastructure. The recently available British Columbia MetPortal provides regionally consistent PMP and precipitation frequency estimates across the province of British Columbia (BC). This paper proposes an approach to process and apply this data for the estimation of the PMF for watersheds across British Columbia. Guidelines are presented for selection of transposition points applicable to a watershed, and algorithms are developed for processing the geospatial probable maximum storm and precipitation frequency data. The algorithms developed are generic to multiple software and programming environments, and could also be applied in other regions where spatially and temporally intact PMP estimates are available. A detailed description of data sources and development of PMF scenario inputs is provided, as well as details of important sensitivity analyses. The methodology is applied to estimate the PMF for the Cheakamus Basin north of Squamish British Columbia. The application of the MetPortal PMP and precipitation frequency estimates, when used with a consistent PMF development methodology as proposed in this paper, will help improve the consistency of PMF estimates for watersheds across the province, offering a welcome improvement for dam owners and regulators.

Addressing Climate Internal Variability on Future Intensity-Duration-Frequency Curves at Fine Scales across South Korea

Designing water infrastructure requires information about the magnitude and frequency of upcoming rainfall. A limited range of data offers just one of many realizations that occurred in the past or will occur in the future; thus, it cannot sufficiently explain climate internal variability (CIV). In this study, future relationships among rainfall intensity (RI), duration, and frequency (called the IDF curve) are established by addressing the CIV and tail characteristics with respect to frequency. Specifically, 100 ensembles of 30-year time series data were created to quantify that uncertainty. Then, the tail characteristics of future extreme rainfall events were investigated to determine whether they will remain similar to those in the present. From the RIs computed for control and future periods under two emission scenarios, following are the key results. Firstly, future RI will increase significantly for most locations, especially near the end of this century. Secondly, the spatial distributions and patterns indicate higher RI in coastal areas and lower RI for the central inland areas of South Korea, and those distributions are similar to those of the climatological mean (CM) and CIV. Thirdly, a straightforward way to reveal whether the tail characteristics of future extreme rainfall events are the same as those in the present is to inspect the slope value for the factor of change (FOC), mFOC. Fourthly, regionalizing with nearby values is very risky when investigating future changes in precipitation frequency estimates. Fifthly, the magnitude of uncertainty is large when the data length is short and gradually decreases as the data length increases for all return periods, but the uncertainty range becomes much greater as the return period becomes large. Lastly, inferring future changes in RI from the CM is feasible only for small return periods and at locations where mFOC is close to zero.

Examining the Robustness of a Spatial Bootstrap Regional Approach for Radar-Based Hourly Precipitation Frequency Analysis

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches.

Variability of precipitation areal reduction factors in the conterminous United States

Many hydrologic and hydraulic (H&H) engineering applications require spatial rainfall distribution over a watershed, but point precipitation frequency estimates, such as those provided by NOAA Atlas 14, are only applicable for relatively small areas. For larger areas, areal reduction factors (ARFs) are commonly used to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. The most common source of ARFs for the United States is Technical Paper 29 (TP-29), published in 1958, although there have been significant increases in record length and types of available data and several new methods for computing ARFs have been proposed over the last several decades. This study applied up-to-date precipitation data products and analysis methods with a watershed-based approach to investigate factors that affect ARF variabilities, and to compare ARFs across multiple US hydrologic regions. Our overall findings are in line with other recent studies showing that ARFs decrease with increasing area, increase with increasing duration, and decrease with increasing return period. In particular, we found a strong geographical variability across different US hydrologic regions, suggesting that ARF are specific to regional climate patterns and geographical characteristics and should not be applied arbitrarily to other locations. The results also reveal the importance of record length, especially for long return period ARFs. The study demonstrates the need to improve ARFs with new data and methods to support more reliable areal precipitation frequency estimates for H&H applications.

Orographic Controls on Subdaily Rainfall Statistics and Flood Frequency in the Colorado Front Range, USA

AbstractGeneralizable relationships for how subdaily rainfall statistics imprint into runoff statistics are lacking. We use the Colorado Front Range, known for destructive rainfall‐triggered floods and landslides, to assess whether orographic patterns in runoff generation are a direct consequence of rainstorm climatology. Climatological analysis relies on a dense network of tipping‐bucket rain gauges and gridded precipitation frequency estimates from the National Oceanic and Atmospheric Administration to evaluate relationships among subdaily rainfall statistics, topography, and flood frequency throughout the South Platte River basin. We find that event‐scale rainfall statistics only weakly depend on elevation, suggesting that orographic gradients in runoff “extremes” are not simply a consequence of rainfall patterns. In contrast, bedrock exposure strongly varies with elevation in a way that plausibly explains enhanced runoff generation at lower elevations via reduced water storage capacity. These findings are suggestive of feedbacks between bedrock river evolution and hillslope hydrology not typically included in models of landscape evolution.

A comparison of extreme precipitation event frequency and magnitude using a high‐resolution rain gage network and NOAA Atlas 14 across Delaware

AbstractExtreme precipitation events are arguably one of the most important natural hazards in many areas of the globe, impacting nearly every societal sector. In the Northeastern United States, extreme precipitation events have been shown to be increasing with several recent events garnering national attention (i.e., Ellicott City Maryland 2018; Tropical Storm Lee 2011). The NOAA Atlas 14 product is the nation's standard for estimating the magnitude and frequency of site‐specific extreme precipitation events, containing both precipitation frequency estimates, as well as associated confidence intervals. The Atlas uses surface stations, primarily from the National Weather Service Cooperative Observer Program, and statistical methodologies to provide point‐based precipitation exceedance probability estimates for several durations and potential recurrence intervals. Unfortunately, the number and quality of Cooperative Observer sites varies greatly over space and time. This research compares observed precipitation extremes from a high‐resolution statewide mesonet to those estimated by the Atlas 14 product for a 10‐year recurrence interval at several precipitation durations. Results of the analysis indicate that Atlas 14 underestimates the number and magnitude of extreme precipitation events across the state of Delaware at longer event durations (360‐ to 1,440‐min). At shorter durations (5‐ to 240‐min) the Atlas 14 estimates are more closely aligned with the observations from the high‐resolution precipitation network. These results suggest that caution should be exercised when using Atlas 14 estimates for engineering standards and hydrologic studies, especially for longer duration events. Therefore, a more rapid update cycle for revision of the Atlas 14 product should be considered, as a changing climate regime may be responsible for the differences identified in this research.

A unified approach to evaluating precipitation frequency estimates with uncertainty quantification: Application to Florida and California watersheds

Although a variety of climate datasets are now available, including observational and model products, there remains a pressing need for the development of a unified, automated and application-focused approach for evaluating and comparing climate extremes within and across these datasets. Precipitation frequency (PF) estimates for extreme precipitation events are useful decision-relevant quantities among water managers. Our objective and quantitative framework for computing PF estimates of any duration uses known techniques in a novel combination. Our framework consists of three steps: (1) use of a regionalization-based statistical method for precipitation frequency estimation at observed sites; this involves hierarchical clustering (here we used Ward’s method) for delineating homogeneous regions, and regional frequency analysis using L-moments; (2) use of kriging for spatial mapping because of its advantages over other conventional interpolation methods; (3) use of z-scores accounts for estimation uncertainties when comparing PF estimates between datasets. The 24-h PF estimates in the Kissimmee-Southern Florida watershed (flat topography) and the Sacramento-San Joaquin watershed (complex topography) demonstrate the applicability of this approach in regions of any geographical complexity. Our results are shown to be reasonably calibrated using the probability integral transform (PIT) histogram of the best fitted distribution. Our results are compared with that of the NOAA Atlas 14 reports and show that for most of the stations the two estimates are statistically indistinguishable; and, the confidence intervals of our estimates are narrower than those of the NOAA estimates. Our approach is applicable to a variety of datasets and provides a baseline for assessing performance of climate models in historical simulations – a necessary first step towards analyzing future projections.

A Comparative Analysis of the Historical Accuracy of the Point Precipitation Frequency Estimates of Four Data Sets and Their Projections for the Northeastern United States

Many studies have projected that as the climate changes, the magnitudes of extreme precipitation events in the Northeastern United States are likely to continue increasing, regardless of the emission scenario. To examine this issue, we analyzed observed and modeled daily precipitation frequency (PF) estimates in the Northeastern US on the rain gauge station scale based on both annual maximum series (AMS) and partial duration series (PDS) methods. We employed four Coupled Model Intercomparison Project Phase 5 (CMIP5) downscaled data sets, including a probabilistic statistically downscaled data set developed specifically for this study. The ability of these four data sets to reproduce the observed features of historical point PF estimates was compared, and the two with the best historical accuracy, including the newly developed probabilistic data set, were selected to produce projected PF estimates under two CMIP5-based emission scenarios, namely Representative Concentration Pathway 4.5 (RCP4.5) and Representative Concentration Pathway 8.5 (RCP8.5). These projections indeed demonstrate a likely increase in PF estimates in the Northeastern US with noted differences in magnitudes and spatial distributions between the two data sets and between the two scenarios. We also quantified how the exceedance probabilities of the historical PF estimate values are likely to increase under each scenario using the two best performing data sets. Notably, an event with a current exceedance probability of 0.01 (a 100-year event) may have an exceedance probability for the second half of the 21st century of ≈0.04 (a 27-year event) under the RCP4.5 scenario and ≈0.05 (a 19-year event) under RCP8.5. Knowledge about the projected changes to the magnitude and frequency of heavy precipitation in this region will be relevant for the socio-economic and environmental evaluation of future infrastructure projects and will allow for better management and planning decisions.

Precipitation frequency analyses based on radar estimates: An evaluation over the contiguous United States

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

Temporal and spatial evaluation of stormwater engineering standards reveals risks and priorities across the United States

Stormwater infrastructure in the United States is designed using governmental precipitation frequency documents and informed by State Departments of Transportation (DOT) guidelines that balance risks and costs. However, both governmental precipitation documents and State DOT guidelines are updated infrequently, which enhances risks in areas where precipitation patterns have changed over time. This study reviewed State DOT design manuals from the 48 contiguous US states and the District of Columbia and found wide variation in design return period standards recommended for similar roadways and infrastructure types. Precipitation differences between successive US precipitation documents for 43 states over the period of 1961–2000 were found to be statistically significant in more than 90% of the study area. These differences indicate that stormwater infrastructure installed prior to the latest update of precipitation frequency documents could be under-designed for present and future climate conditions. Comparing State DOT design storm values for each roadway and infrastructure type, an index for each climate region was developed to assess the relative stringency of each state’s requirements. Using these index values, the observed change in precipitation frequency estimates, and each state’s design manual publication date, this research identified the states that need to prioritize revision of their stormwater standards to maintain the originally intended design performance over time. Eight out of 43 states were found to have the highest priority for immediately revising their stormwater standards. In addition, these states should assess whether existing infrastructure requires additional adaptive capacity to manage observed precipitation increases. The priority increased for all states under both the RCP 4.5 and RCP 8.5 emissions scenarios for 2050. While local assessments comparing infrastructure costs of increasing the stringency of standards versus the expected costs of future damages under climate change remain necessary, a no-regret action is revising stormwater standards to incorporate observed precipitation increases.

Intercomparison of selected fixed-area areal reduction factor methods

Summary The areal reduction factor (ARF) is a concept used in many hydrologic designs to transform a point precipitation frequency estimate of a given duration and frequency to a corresponding areal estimate. Various methods have been proposed in the literature to calculate ARFs. Proposed ARFs could vary significantly, and it is unclear if discrepancies are primarily due to differences in methodologies, the dissimilar datasets used to calculate ARFs, or if they originate from regional uniqueness. Our goal in this study is to analyze differences among ARFs derived from different types of fixed-area ARF methods, which are suitable for use with precipitation frequency estimates. For this intercomparison, all the ARFs were computed using the same, high-quality rainfall-radar merged dataset for a common geographic region. The selected ARFs methods represent four commonly used approaches: empirical methods, methods that are based on the spatial correlation structure of rainfall, methods that rely on the scaling properties of rainfall in space and time, and methods that utilize extreme value theory. The state of Oklahoma was selected as the study area, as it has a good quality radar data and a dense network of rain gauges. Results indicate significant uncertainties in the ARF estimates, regardless of the method used. Even when calculated from the same dataset and for the same geographic area, the ARF estimates from the selected methods differ. The differences are more pronounced for the shorter durations and larger areas. Results also indicate some ARF dependence on the average recurrence intervals.

Trends in Rainfall Exceedances in the Observed Record in Selected Areas of the United States1

Bonnin, Geoffrey M., Kazungu Maitaria, and Michael Yekta, 2011. Trends in Rainfall Exceedances in the Observed Record in Selected Areas of the United States. Journal of the American Water Resources Association (JAWRA) 47(6): 1173–1182. DOI: 10.1111/j.1752-1688.2011.00603.x Abstract: Semantic differences have led to a gap in the understanding of the impacts of climate change on precipitation frequency estimates. There is popular perception that heavy rainfalls have become more frequent, and that this trend will increase with global warming. Most of the literature examines this question from the point of view of climatology using definitions of “heavy,”“very heavy,” or “extreme” rainfall, which are different from those commonly used by civil engineers. This article identifies the differences in meaning used by the climate and civil engineering communities and examines trends in the observed record in the frequency of exceedances (not trends in magnitudes). Using concepts recognized as the basis for design of the Nation’s civil infrastructure, we look at trends in the number of exceedances of thresholds for a variety of precipitation frequencies and event durations used by civil engineers. We found that the estimated trends in exceedances at one-day and multiday durations were statistically significant and increasing for the Ohio River Basin and surrounding states but the reverse was true for the Semiarid Southwest (i.e., not significant and decreasing trends). In addition, we found the magnitude of the trends was small for all but the more frequent events and also small with respect to the uncertainty associated with the precipitation frequency estimates themselves.

Why Rare Storms Occur so Frequently and What to Do About It

Why Rare Storms Occur so Frequently and What to Do About ItMany people wonder why our cities have experienced so many X-year (fill in the blank for X) recurrence interval storms recently. The public has a hard time believing us when we report the third 10-year recurrence interval storm in seven years. We don't understand it ourselves. Do the statistics experts who develop precipitation frequency estimates really know what they are talking about? This...Author(s)William S. GonwaSourceProceedings of the Water Environment FederationSubjectSession 42: Stormwater Management Challenges and Solutions in the Urban WatershedDocument typeConference PaperPublisherWater Environment FederationPrint publication date Jan, 2011ISSN1938-6478SICI1938-6478(20110101)2011:14L.2573;1-DOI10.2175/193864711802721398Volume / Issue2011 / 14Content sourceWEFTECFirst / last page(s)2573 - 2584Copyright2011Word count149Subject keywordsPrecipitation frequencyRainfall frequencyRecurrence-interval100-year storm

Discussion of “Updated Precipitation Frequency Estimates for Kansas City: Comparison with TP-40 and HYDRO-35” by C. Bryan Young and Bruce M. McEnroe

694 / JOURNAL OF HYDROLOGIC ENGINEERING © ASCE / NOVEMBER/D There are three variables in rainfall characterization—depth or intensity, duration, and frequency or return period. Given a duration and return period, the intensity is established by using wellfitted probability distributions whose parameters are established by data analysis. In the procedure developed by Hershfield 1961 , and later used by Huff and Angel 1992 , the 24-h rainfall depth corresponding to a given frequency is estimated. The rainfall depths corresponding to other durations are fixed proportions of the 24-h rainfall depth. This assumption deserves some investigation. Hourly data from 20 stations in Indiana within about 100 km in a region where the rainfall is homogeneous were used in the investigation presented herein. The average data length is about 30 years. We fitted generalized extreme value GEV distributions to the data. Examples of the distributions fitted are shown in Fig. 1. KT is the frequency factor of GEV Rao and Hamed 2000 . The GEV distribution fitted the observed data well according to both the 2 and Kolmogorov–Smirnov goodness-of-fit tests. The rainfall depths for different durations and frequencies were extracted from the fitted distributions. The ratios of rainfall

Updated Precipitation Frequency Estimates for Kansas City: Comparison with TP-40 and HYDRO-35

This paper summarizes methods and results for a recent precipitation frequency study of the Kansas City metropolitan area. The study was motivated by prevalent concern that frequency estimates used for hydrologic design and analysis are based on studies published decades ago. Data currently used for design were extracted from National Weather Service (NWS) rainfall atlases TP-40 and HYDRO-35, published in 1961 and 1977, respectively. Results of this study are compared to those two atlases and a third regional study published by Huff and Angel in 1992. In general, the rainfall depths predicted by this study are higher than TP-40 and HYDRO-35 for return periods longer than 10years, yet for short durations and low return periods, the converse is true. For the most part, rainfall depths from this study are within a few percent of NWS estimates.