Probable Maximum Precipitation Research Articles

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

Evaluation of Probable Maximum Precipitation (PMP) for West Virginia to Predict Current and Future PMP

Probable maximum precipitation (PMP) is essential for dam design criteria. West Virginia Department of Environmental Protection Dam Safety utilizes the 6-hr, 10-mi2 (26-km2) PMP established by Hydrometeorological Report 51 based on the analysis of extreme rainfall. These PMP are outdated because there have been unaccounted for extreme rainfall events that have occurred since 1978. In addition, a zone of uncertainty around the Appalachian Mountains exists, including West Virginia (WV). The objective of this research was to determine the PMP throughout WV by studying the area of uncertainty and consider rising dew points to predict future PMP. This study created an updated PMP map of WV, focusing on the maximization of the 1942 Smethport, PA storm. A watershed-specific analysis was conducted at all dams in the state with an approximate watershed area of 10-mi2 (26-km2). Current PMP estimations and projections for 2100 were determined. Iso-lines were determined to develop the new updated PMP map for WV that displays current and future PMP estimations. Results suggest that the greatest values of PMP occur in the western region of the state; lowest values of PMP occur in the south-eastern part of the state. The Smethport, PA storm (1942) was considered the governing storm for the entire state; however, it may not apply to  the eastern panhandle and Potomac highlands. These regions should be considered for additional analysis. The updated PMP map will be a resource for those involved in dam inspection, repair, design, and modification.

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events

Updating probable maximum precipitation for Hong Kong under intensifying extreme precipitation events

Assessing dam spillway discharge capacity in response to extreme floods in Perak river hydroelectric scheme: Simulation and proposed mitigation measures

The aged dams in Perak river Hydro-Electric Scheme (HES) have been constructed decades ago and a common concern raised during dam safety reviews is that they were not designed to safely reroute the Probable Maximum Flood (PMF) but rather the Inflow Design Flood (IDF) of 1,000 years’ design floods. Failure due to the insufficient spillway discharge capacity has been identified as one of the primary causes leading to overtopping. This applied research has thus been initiated to evaluate the spillway discharge capacity of dams in response to PMF extreme flooding. Besides, the safety of the dams has been evaluated by calculating the highest flood level achieved in response to the specified extreme floods. In order to simulate PMF hydrographs, various durations of Probable Maximum Precipitation (PMP) have been calculated using Hershfield statistical approach based on historical annual maximum rainfall statistics. Using a calibrated and validated hydrological model developed in HEC-HMS, PMF simulation was performed. It was discovered that the PMF generated at 120 hrs (5 days) resulted in the maximum flood level for all dams/reservoirs. The achieved highest PMF flood levels are still lower than the designated dam core and crest level in respective dams, indicating that the dams in Perak river HES are safe from overtopping failures during the occurrences of extreme events up to PMF level. While routing the worst-case scenario over 120 hr, the maximum spillway discharges predicted for Temengor and Bersia are beyond their design capacity, while Kenering and Chenderoh remain within design limits. Mitigation that will be implemented to minimize the possibility of risks to occur include improvement of emergency management system and dams monitoring data collection. It is anticipated that the outcomes of this applied research will be adopted for development of Risk Informed Decision Making (RIDM) frameworks for dams in Perak river HES in future.

Analyzing uncertainty in probable maximum precipitation estimation with large ensemble climate simulation data

AbstractThis study aimed to evaluate probable maximum precipitation (PMP) estimated using surface dew points (SDP) or actual precipitable water obtained from upper‐air data (UAD) in the moisture‐maximization method with the help of sufficient extreme precipitation events using large‐scale climate ensemble simulation data (d4PDF). The deviations between the PMP variables estimated by the SDP and UAD approaches were analyzed for southern and northern areas of Japan to consider the regional characteristics of the deviations. We found that the deviations were high in northern areas where the SDPs are relatively low during precipitation events. The PMPs estimated using each approach were also compared to the extreme‐scale reference precipitation proposed in this study. The SDP approach overestimated the PMPs by over 20% compared to the reference precipitation in the northern region. However, the UAD approach showed very low average errors in all southern and northern areas. This tendency of the SDP approach was significantly related to the regional climatic characteristics of the SDP, which indicated that the SDP approach may estimate an uncertain PMP value depending on each regional climatic characteristic compared to the UAD approach. Regional climatic characteristics should be considered when using the SDP approach to estimate the PMP.

Re-inventing Bethlahmy method for estimating probable maximum precipitation

The design and risk analysis of large-scale hydraulic structures (e.g., dams) and sensitive installations (e.g., nuclear facilities) downstream of those structures rely on design flood corresponding to probable maximum precipitation (PMP). In areas where there is a sparsity of information on hydrometeorological variables, practitioners use various statistical methods to arrive at a PMP estimate, assuming it to be the possible upper bound for precipitation. However, the assumption is violated in different parts of the world. Hence, there is a need to improve the existing statistical methods and develop their potential alternatives. Against this backdrop, this paper proposes a new variant of a non-parametric method (Bethlahmy) to facilitate the estimation of PMP at locations with sparse records of extreme precipitation. It involves mapping of datapoints in annual maximum series and their ranks to a non-dimensional space (NDS) and using the information on sample size and observed maximum precipitation in the NDS to arrive at a surrogate variable representing PMP, which is eventually mapped back to PMP in the original space. The effectiveness of the proposed Bethlahmy variant over various existing statistical techniques is illustrated through Monte Carlo Simulation experiments and a case study on 37,872 stations from a global precipitation database. The existing techniques include the original Bethlahmy and Hershfield methods, conventional probabilistic approach, and relevant variant(s). Insight is provided into the relative performance of these methods, as there is a dearth of such attempts in the literature. Results indicate that the proposed Bethlahmy variant exhibits better performance than other methods/variants across samples varying in size and extreme precipitation characteristics, making it a promising statistical alternative for PMP estimation.

Evaluating the probable maximum precipitation. Case study from the Dobrogea region, Romania

" In this article, the Probable Maximum Precipitation (PMP) evaluation is done using a spatio-temporal approach – Diaconu’s method – for a set of data series collected in the Dobrudja region for 41 years. The results are compared with those obtained by applying the classical method of Hershfield and the advantages of each method are emphasized."

A Study on Dam Break Simulation considering Continuous Rainfall Events

In recent years, flood damage resulting from continuous rainfall events has been on the rise. However, existing Emergency Action Plans (EAPs) addressing reservoir or dam failure only consider Probable Maximum Precipitation (PMP), which represents a single rainfall event. This study aims to address this limitation by simulating a dam break scenario caused by continuous rainfall events. The Chungju dam watershed was selected as the study area and a mega rainfall event scenario, involving continuous 500-year frequency rainfall intensity events, was defined. Next, the flood discharge resulting from the mega rainfall event scenario was estimated using the Consecutive Storm Event Based (ConSEB) model. The Simplified Dam Break (SMPDBK) model was employed to simulate the dam break. The flood discharge estimated for this scenario amounted to 31,080.34 cms, which is about 1.2 times larger than the Probable Maximum Flood (PMF). The dam break water level simulated by the SMPDBK model was 150 m and the flood damage area was estimated to be 307.45 km2. Based on the results of the study, we highlighted the need to consider continuous rainfall events within EAPs.

Evaluation of Statistical PMP Considering RCP Climate Change Scenarios in Republic of Korea

Extreme rainfall and floods have increased in frequency and severity in recent years, due to climate change and urbanization. Consequently, interest in estimating the probable maximum precipitation (PMP) has been burgeoning. The World Meteorological Organization (WMO) recommends two types of methods for calculating the PMP: hydrometeorological and statistical methods. This study proposes a modified Hershfield’s nomograph method and assesses the changes in PMP values based on the two representative concentration pathway (RCP4.5 and RCP8.5) scenarios in South Korea. To achieve the intended objective, five techniques were employed to compute statistical PMPs (SPMPs). Moreover, the most suitable statistical method was selected by comparing the calculated SPMP with the hydrometeorological PMP (HPMP), by applying statistical criteria. Accordingly, SPMPs from the five methods were compared with the HPMPs for the historical period of 2020 and the future period of 2100 for RCP 4.5 and 8.5 scenarios, respectively. The results confirmed that the SPMPs from the modified Hershfield’s nomograph showed the smallest MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error), which are the best results compared with the HPMP with an average SPMP/HPMP ratio of 0.988 for the 2020 historical period. In addition, Hershfield’s method with varying KM exhibits the worst results for both RCP scenarios, with SPMP/HPMP ratios of 0.377 for RCP4.5 and 0.304 for RCP8.5, respectively. On the contrary, the modified Hershfield’s nomograph was the most appropriate method for estimating the future SPMPs: the average ratios were 0.878 and 0.726 for the 2100 future period under the RCP 4.5 and 8.5 scenarios, respectively, in South Korea.

Dam Break Flood Mapping and Analysis Using Open Source GIS Tool and Data

The impact of climate change may influence on extreme weather conditions and may lead to high intensity precipitation, which will impact on huge structures such as dam. Although risk mitigation and protection of dams from advanced technical expertise have been reviewed regularly, a maximum non-risk guarantee is not achievable, and incidents may occur due to water that usually flows over a spillway and high-speed discharge or unexpected peak discharge. Hence, the immediate impact of dam failure is on the population at risk near the downstream area. This study evaluates the impact of dam break scenario under probable maximum precipitation (PMF) condition at Puah hydropower dam. The aims of this study are to determine the potential risk area at downstream and to assess the flooding impact on population and public facilities due to dam break event. Integration of an open source GIS tool, Google Satellite Image, Open Street Map, and flood hazard map derived from hydrodynamic model has been used to efficiently analyse the impact of dam break flood by using spatial analysis method, and to generate possible evacuation route network. Analysis has estimated that 187,796 people at risk, 3,177 km of road, 2-unit hospital and 76 amenities (mosque and school) will be impacted. This study portrays the possibility of using open source data and tool as an alternative for emergency preparedness application. The method used in this study could be adopted to other area of interest and disaster event, helping the local authorities for disaster emergency preparedness and early warning systems focusing on rescue work if disaster occurs.

Assessing the effects of increased impervious surface on the aquifer recharge through river flow network, case study of Jackson, Tennessee, USA

Understanding pathways connecting urbanization to the recharge process across the land surface and river environment is of great significance in achieving low-impact development. Accordingly, the contribution of an urbanized region with a low and high development rate, along with the expected overflow into the river network resulting from increased impervious surfaces, was assessed in the recharge rate at Jackson, Tennessee. To this end, first, the losses were calculated using the standard and modified SCS-CN methods for the maximum probable flood condition. Then, TUFLOW was applied to simulate the two-dimensional flood for a historic 24-h probable maximum precipitation event with a 100-year return period. The results of TUFLOW were later calibrated using the results of standard and modified SCS-CN methods. A calibrated MODFLOW was employed to assess the effects of urbanization and, consequently, the plausible extended river network on the recharge rate. Results revealed that the West Wood contribution in groundwater recharge was 19 % less than the Musa Street, while it supplies approximately 2.7 % more flow than Musa Street. The performance evaluation results of TUFLOW showed 0.4916 and 0.689 as Nash–Sutcliffe, respectively, for the standard and modified SCS-CN methods. Although the flow velocity and depth were respectively increased by 3.3 % and 8.3 % under modified SCS-CN compared to the standard one, the soil water storage capacity remained constant at equal to 0.16 mm. Results revealed that the maximum soil water storage capacity was fulfilled soon through the modified SCS-CN than the standard method leading to higher flood volume and discharge. To this end, the discharge resulting from modified SCS-CN was approximately 1.5 times higher than that in the standard method under the same precipitation condition. Our findings suggest that designing any construction, mainly dams downstream, based on the modified SCS-CN estimations will provide more safety, particularly in crowded regions. Also, overflowing the excess surface runoff into the river network resulted from the increased impervious surface amplifying the flow volume, depth, and velocity across the river networks, finally leaving the area without increasing the aquifer's recharge rate. The results provide insights into possible sustainable development options and flood management in the built-up area.

Validation of Hershfield probable maximum precipitation estimation using homogeneous region in Malaysia

Probable maximum precipitation (PMP) is used to design major hydraulic structures, such as dams and spillways, flood protection works, and nuclear power plants. Long observation data are required for PMP estimates using the Hershfield statistical approach. In Malaysia, the PMP calculated using state boundary is commonly used, but the estimated PMP values are close to historical maximums. Homogeneous regions are regions containing stations with similar rainfall characteristics. Therefore, homogeneous region boundaries could be used for PMP estimations. In this paper, 1-day rainfall data of more than 25 years for 161 stations in Peninsular Malaysia; were analysed to estimate the PMP for 1-day duration based on an appropriate frequency factor by considering homogeneous regions. Using PMP estimates, a generalised graph was prepared to show the spatial distribution of 1-day PMP. The 1-day PMP estimates in Peninsular Malaysia by considering state boundary, varied from 146 to 1364.7 mm and by considering the homogeneous regions, the 1-day PMP estimates varied from 208.5 to 2094 mm. Hence, the PMP considering homogeneous regions is considered more accurate and safe to be used in designs.

Improving Confidence in Model-Based Probable Maximum Precipitation: How Important is Model Uncertainty in Storm Reconstruction and Maximization?

Abstract We analyze uncertainty in model-based estimates of probable maximum precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from Weather Research and Forecasting (WRF) Model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions [so-called relative humidity maximization (RHM)]. By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently used techniques [documented in NOAA’s Hydrometeorological Reports (HMRs)], which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including the following: 1) initial condition error, 2) choice of physics parameterizations, and 3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-h precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from ±7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river–driven flood events we investigated.

Determination of Clark unit hydrograph parameters for estimating probable maximum flood

Abstract The probable maximum flood (PMF) is the flood caused by the probable maximum precipitation (PMP). A unit hydrograph (UH) is generally used to derive the PMF for the given PMP, but a method is needed to modify the UH parameters to reflect the PMP condition. This study presents a new method using the estimated channel velocity to modify the Clark UH parameters under the ordinary condition into those under the PMP condition. This study considers major dam basins in Korea and evaluates the application results in comparison to several previous studies. As application results of the proposed method, the Clark UH parameters under the PMP condition are found to be within the range 39–53% of those under the ordinary condition, with their mean of about 44%. The UH derived by applying this mean ratio shows that its peak time and the peak flow are just 44 and 227% of the UH under the ordinary condition, respectively. This change from the ordinary condition to the PMP condition is more extreme in Korea than that in Australia and the United Kingdom. This extreme change seems to be due to the climate in Korea, located in the Asian Monsoon region.

Probable maximum precipitation analysis of high rainfall regions in India

Uttarakhand, Maharashtra, Assam & Meghalaya are the potential areas to study the precipitation, perform trend analysis and calculate the probable maximum precipitation. Probable Maximum Precipitation (PMP) has virtually zero risk of being exceeded for a given catchment area over a certain duration of time. The present work applies Hershfield Method to determine the PMP for rainfall data for the high rainfall regions of Uttarakhand, Maharashtra, Assam & Meghalaya (Assam and Meghalaya are considered as one region). Gumbel method, which is an extreme value distribution function is used to obtain the extreme value (EV) of precipitation. Further, the obtained PMP and EV values are compared with high rainfall months of La Niña years. Observation of the results showed that the obtained values from Hershfield Method are greater than the La Niña years precipitation values that have been observed till 2017, thus validating the obtained results. The outcomes from Probable Maximum Precipitation (PMP) works give insights into the extreme rainfall that can ever happen in a given catchment. The result obtained serves as indispensable input data for designing structures like dams, reservoirs, spillways, flood protection walls, sluice gates, weirs, etc. The design life of hydraulic structures is worked out using Probable Maximum Precipitation (PMF). The adopted technique and results of this study can be directly used as a design parameter for design as well as for water resources management.

Hydrological Analysis of Batu Dam, Malaysia in the Urban Area: Flood and Failure Analysis Preparing for Climate Change

Extensive hydrological analysis is carried out to estimate floods for the Batu Dam, a hydropower dam located in the urban area upstream of Kuala Lumpur, Malaysia. The study demonstrates the operational state and reliability of the dam structure based on hydrologic assessment of the dam. The surrounding area is affected by heavy rainfall and climate change every year, which increases the probability of flooding and threatens a dense population downstream of the dam. This study evaluates the adequacy of dam spillways by considering the latest Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) values of the concerned dams. In this study, the PMP estimations are applied using comparison of both statistical method by Hershfield and National Hydraulic Research Institute of Malaysia (NAHRIM) Envelope Curve as input for PMF establishments. Since the PMF is derived from the PMP values, the highest design flood standard can be applied to any dam, ensuring inflow into the reservoirs and limiting the risk of dam structural failure. Hydrologic modeling using HEC-HMS provides PMF values for the Batu dam. Based on the results, Batu Dam is found to have 200.6 m3/s spillway discharge capacities. Under PMF conditions, the Batu dam will not face overtopping since the peak outflow of the reservoir level is still below the crest level of the dam.

Estimation of Probable Maximum Precipitation at Talar Basin of Mazandaran Province using Synoptic Method

Estimation of Probable Maximum Precipitation at Talar Basin of Mazandaran Province using Synoptic Method

The Impact of Climate Change on Operational Probable Maximum Precipitation Estimates

AbstractThe safety of high‐risk water infrastructure, such as dams and nuclear power plants, is often assessed by reference to their ability to accommodate floods derived from the Probable Maximum Precipitation (PMP). However, a key shortcoming of traditional PMP estimates is the assumption of a stationary climate, with evidence indicating that key meteorological conditions related to the magnitudes of extreme storms, such as atmospheric moisture, are changing in a warming climate. Due to the pragmatic nature of PMP methods derived for design purposes, inferring potential changes in PMP estimates based solely on trends or projections of atmospheric variables can ignore PMP method complexities and constraints. Here we explore how different traditional PMP methods will respond to a potential increase in atmospheric moisture. We find that increases in persisting dewpoint will lead to increases in PMP estimates, and the nature of this impact depends on whether the moisture maximization step is based on local or transposed regional information. An historical trend analysis reveals annual maximum persisting dewpoint temperatures have increased continuously over Australia over the past 60 years, with further increases predicted over the coming decades for all Shared Socioeconomic Pathways (SSPs). PMP estimates across Australia are predicated to increase by an average value of 13% by 2100 based on the conservative SSP1‐2.6, compared to 33% for SSP5‐8.5. We conclude PMP methods will require regular updating to account for changing persisting dewpoints and likely progressive increases in PMP, and the ensuing flood estimates.

In the shadow of the dam – Hydrology of the Little Conemaugh river and its South Fork, with insights about past and future flooding

The Little Conemaugh River watershed and its South Fork sub-basin figure prominently in historical flooding of Johnstown, Pennsylvania and nearby communities with catastrophic flooding in 1889, 1936, and 1977 (reviewed herein). Historical stream gage data and data from a new gage on the South Fork (established via a novel, portable cableway system) are used with Nexrad rainfall data to assess watershed response and provide novel analysis of flood hydrology in the Little Conemaugh basin and the sub-basin. Using unit hydrograph estimates for longer duration storms (>8 h) and different baseflow conditions, we probe possible effects of several design storms, including those stemming from a hurricane remnant scenario (Agnes in 1972) and 50-, 100-, and 500-year 12-hour precipitation depths. The unit hydrographs provided peak discharge (Qpeak) estimates for 1977 (the only flood event with available hourly rainfall data) that are in good agreement with empirical peak discharges.Significant channel improvements completed in 1943 were designed to carry the largest known natural flow on record at that time (1936 Qpeak). Preliminary results from design storm scenarios indicate the need for a careful evaluation of extreme discharges and their return periods (including snowmelt-related contributions), as future flood levels in Johnstown may occur more frequently than originally thought. The 1977 flood, which triggered 7 dam failures and eclipsed 1936 Qpeak, resulted from less than 40% the estimated probable maximum precipitation (PMP) for a 12-hour storm. Peak discharges of similar magnitude would have ensued in 1972 had remnants of Hurricane Agnes tracked slightly westward. Flooding and infrastructure problems could be compounded for storms of 24-hour or longer durations, similar to record flooding seen in central Pennsylvania and New York in 1972. Flood recurrence, emergency procedures, and dam safety (particularly, spillway capacity in the Little Conemaugh basin and surrounding region) should likely be reassessed and protective early-warning measures (ineffective in 1977) implemented for the people of Johnstown.

Probable Maximum Precipitation Evaluation for a West Virginia Watershed, United States

Probable Maximum Precipitation Evaluation for a West Virginia Watershed, United States