Probable Maximum Precipitation Values Research Articles

Identification precipitation threshold and resulting river discharge: an IDF-based approach in the Central Himalaya, Nepal

ABSTRACT A comprehensive understanding of the intricate relationship between precipitation threshold and extreme river discharge, especially in mountainous regions, is challenging yet crucial for effective mountain risk management. This study, conducted in the Himalayan region of Nepal, employs an innovative approach using Intensity-Duration-Frequency (IDF) curves to identify potential precipitation extremes relevant to extreme river discharges. Detailed analysis of historical hydrometeorological events and their impacts in Nepal was carried out to select a specific event for thorough examination. Additionally, the research evaluates satellite-based precipitation data and utilizes frequency analysis techniques such as Gumbel and Log Pearson Type III distributions, customized to the region’s unique conditions. Furthermore, the study evaluates Probable Maximum Precipitation (PMP) and Depth-Area-Duration (DAD) curves at various sub-basin levels. The findings underscore the significant role of 3-day consecutive precipitation over 1-day events in generating extreme river discharge within the basin. Notably, the study reveals discrepancies between PMP values derived from gauge-based and satellite-based data, emphasizing the necessity for proper bias correction of the satellite data. Despite its limitations, the research highlights the potential of high-resolution satellite data for disaggregating gauge observed daily precipitation and underscores its applicability to other Himalayan basins, offering valuable insights into precipitation-related extreme events and their implications for water resource management and disaster risk reduction.

Analisis Hujan Ekstrim Probable Maximum Precipitation (PMP) menggunakan metode Hersfield dan Perhitungan Debit Banjir

Extreme rain is rain with the lowest intensity of 50 mm/24 hours or 20 mm/hour. River is an elongated surface water flow and flows from a higher place (upstream) to a lower place (downstream). The area of KIuet is 1429.93 km and the area of Kluet River is 163.51 km2 and the length of the river is 68.26 km. The problem is that Kluet Utara River experiences high rainfall, resulting in overflows that flatten agricultural land and submerge residential areas. The purpose of the study was to calculate the planned return period rainfall, analyze the PMP (probable Maximum Precipitation) value and analyze the flood discharge to evaluate the PMF (Probable Maximum Flood) value and the return flood discharge. The benefits of this research are used to see the PMF value and return period rainfall to analyze the frequency of flooding KIuet Utara Watershed (DAS). The scope of the research was to calculate rainfall and flood discharge in KIuet Utara Watershed and secondary data which includes 10 years rainfall data obtained from BMKG (Meteorology, Climatology and Geophysics Agency) Indrapuri Aceh Besar and watershed maps obtained from PT. Mediatama Indoconsult. Processing of rainfall data on return period used Log Pearson III Method and PMP used Hersfield Method while calculating the planned flood discharge using Melchior Method. The results of the research is the rainfall value for the return period for the lowest rainfall in the 2 year return period is as high as 211,836 mm and the highest rainfall for the 1000 year return period is 566,239 mm, so that the flood discharge plan for the return period for the lowest flood discharge in the 2 year return period is equal to 2061,342 m3/s and the highest flood discharge at the 1000 year return period is 5510,490 m3/s. The PMP value was obtained at 2128,432 mm so that the PMF was obtained at 20714,772 m3/s. Log Pearson III and PMP methods can be used to calculate the rainfall in KIuet Utara and the results from the rainfall are used to calculate the planned flood discharge using Melchior Method.

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.

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.

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.

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.

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 24-h (PMP 24-h) through statistical methods over Iran

Abstract Estimating the probable maximum precipitation (PMP) is necessary to calculate the probable maximum flood (PMF). It is of high importance in checking the adequacy of dam overflow capacity and other development and water transfer plans of a given area. In this research, using the for an annual 24-hour maximum rainfall data set of 45 synoptic stations throughout the country, the PMP values were calculated through the original Hershfield method and then the Hershfield-Desa method. By comparing the obtained results of 24-hour PMP estimation through the two mentioned methods, it is found that the estimation of PMP values in the original/first Hershfield method is well higher than the expected value (2.54 to 4.03). While in the modified method (Desa method), PMP values are significantly reduced and seem more reasonable (1.02 to 1.3). Meanwhile, the calculated variability and skewness coefficients also indicated more variability of PMP values in the southern stations of the country compared to rainy regions, which makes the estimation of PMP in the southern regions of the country considerably unreliable.

Estimation of Probable Maximum Flood for Dam Safety in Cameron Highlands Watershed

Dams that are not designed to withstand major storms may be destroyed and increase the flood damage opportunities downstream. For protecting the lives and land downstream of Cameron Highlands, Malaysia, the current research targeted to estimate the Probable Maximum Precipitation based on the updated rainfall and other meteorological data and predict the Probable Maximum Flood using the Hydrological Model (HEC-HMS) version 4.8. The Geographic Information System was utilized as well in order to identify the geometric and hydrologist parameters. The PMP data is obtained up to 2020 via the application of Hershfield’s Statistics and by applying the model mentioned earlier, the Probable Maximum Precipitation values can be predicted. For the calibrating the model, different sets of data were applied in which the Nash-Sutcliffe Efficiency and Percentage of Bias of 0.776 and -0.03% respectively are obtained while Nash-Sutcliffe Efficiency and Percent Bias of 0.861 and 1.47% respectively are obtained for Cameron Highland dams for model validation. The hydrological model in this study was adequately calibrated for Probable Maximum Flood simulation as it is credible to describe the hydrological process in Cameron Highlands.

Historical trend of probable maximum precipitation in Utah and associated weather types

AbstractThis study assesses the impact global climate change has had on the theoretical upper limit of precipitation for Utah's watersheds by examining historical trends in probable maximum precipitation (PMP), a metric widely used for dam construction and management. Trends in the factors of PMP driven by both frontal and monsoonal storm scenarios were studied using two networks of surface weather stations and modern gridded datasets spanning 1981–2018. It was found that the historical trend in both 3‐hr and 24‐hr PMP estimations exhibited a statistically significant increasing trend, despite that the magnitude of the trend was sensitive to which dataset was examined and the data's spatial resolution. Over the 24‐hr time scale, an interesting deviation in PMP trends was found where frontal storm events demonstrated increasing PMP trends compared to decreasing PMP trends for monsoonal storm events. However, both storm types demonstrated increasing trends over shorter 3‐hr durations, suggesting a universal increase in convective storm intensity. PMP estimates also demonstrated a dependency on the type and resolution of meteorological data used, where higher‐resolution data generally produced greater PMP values and weaker trends. A discussion on the potential causes of these dependencies is provided. This study implies the need to take the historical PMP trend into account for future PMP estimates, especially when coarse‐resolution data are used for conventional PMP studies.

Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.

Estimating probable maximum precipitation for Bangladesh

In this paper, one-day probable maximum precipitation (PMP) is estimated for 29 meteorological stations of Bangladesh. A widely recognised statistical method, viz. Hershfield method, is used for PMP estimation. Afterward, the spatial prediction map of PMP is generated based on geostatistical analysis. It is found that Hershfield's frequency factor (Km) for estimating PMP varies from 2.03 to 8.03 while most are around 3 to 4. Results indicate that higher values of PMP are found in the eastern part of the country whereas lower at the western part. However, maximum PMP (811 mm) is found in Bhola which is located in the coastal region. The second highest is also found in the coastal region. Results from the geostatistical analysis shows that empirical Bayesian kriging (EBK) performs slightly better in the spatial prediction map generation.

Changing Lateral Boundary Conditions for Probable Maximum Precipitation Studies: A Physically Consistent Approach

AbstractThis article presents a conceptual study toward establishing a new method for altering lateral boundary conditions in numerical model based estimates for probable maximum precipitation (PMP). We altered an extreme event in a physically and dynamically consistent way in a regional convective-scale weather prediction model (AROME-MetCoOp) by applying fields from a global ensemble climate model approach based on EC-EARTH. Ten ensemble members are downscaled with the regional model, which results in 10 different realizations of an extreme precipitation event for the west coast of Norway. We show how the position and orientation of the moisture flow is different between the individual ensemble members, which leads to relatively large changes in precipitation values for a selected catchment. For example, the modification of the moisture transport on scales of several hundred kilometers impacts the extreme precipitation amount by about 75% among the model members. Compared with historical rainfall records, precipitation changes of 62% and 71% are found for two selected catchments. Although the present study is restricted to one particular extreme event that is modified 10 times with the ensemble approach, there is a considerable spread of the moisture transport compared to the spread of the moisture transport of extreme precipitation events of the past 40 years. We conclude that the described approach is a step toward a new method to derive PMP values for a given catchment; however, a larger amount of events and larger ensembles would have to be considered to estimate PMP values.

Analysis of uncertainty and non-stationarity in probable maximum precipitation in Brazos River basin

Probable Maximum Precipitation (PMP) is used for designing major hydraulic structures, such as dams and reservoirs, nuclear power plants, and flood protection works. However, the estimated PMP values are associated with uncertainties that have received significant attention in recent years, partly because hydrologic extremes are projected to become more frequent, severe, and uncertain with non-stationarity and natural climate variability. This study compared four methods of estimating PMP, ranging from hydrometeorological to statistical, including up-to-date grid based and site-specific in the Brazos River basin (BRB). BRB is the largest river basin in Texas and it contains a range of climates from subtropical arid to subtropical humid. The objective of this study was to quantify the uncertainty associated with PMP estimation in terms of change in the PMP value and emphasize the necessity of including the effect of non-stationarity on PMP. The uncertainty analysis incorporates the effect of three sources of error: (1) PMP estimation method, (2) topography, and (3) non-stationarity. The contribution of each of the uncertainty sources to the 24-hour PMP estimation was quantified and found to be as 53.5% (selection of method), 31.4% (effect of non-stationarity), and 15.1% (effect of topography). The uncertainty of PMP estimation was more sensitive to the existing observation statistics and the selection of method than to the differences between climate zones. Results showed an overall significant increase in 24-hour record precipitation (+19.5 mm) and PMP (+22.3 mm) in BRB between two historical periods 1940–1976 and 1977–2013. Thus, it is concluded that extreme precipitation in BRB showed non-stationarity which affected PMP shift during the historical period.

Stochastic Modeling of Extreme Precipitation: a Regional Approach

A generalized step-by-step statistical method for calculating the maximal precipitation sums of low probability is proposed. The method is applied to the case of daily precipitation for the Amur River basin. Refined statistical characteristics of maximum daily precipitation for the warm period are obtained. A map of daily precipitation of 1% exceedance probability is compiled over the territory of the Amur River. The obtained quantiles of the daily maximum precipitation are compared with probable maximum precipitation values obtained using WMO methodology.

Probable maximum precipitation in tropical zone (Thailand) as estimated by generalized method and statistical method

AbstractThe objective of this research was to estimate probable maximum precipitation (PMP) for the duration of 1 to 3 days in Thailand. Rainfall data were collected from 217 meteorological stations. Two methods for estimating PMP were applied: the generalized method which analysed actual storms data using meteorological process, and the Hershfield's statistical method which applied the frequency analysis of the annual maximum rainfall. Both methods were commonly used and were recommended by the World Meteorological Organization (WMO) for estimating PMP. PMP estimation results were presented as isohyetal maps. For the generalized method, most PMP values were found higher in the east and decreased towards the west. However, there was no clear trend with the statistical method in the PMP's spatial distribution. An analysis of the 3‐day PMP averages showed that PMP estimation using the generalized method is 10% higher than that of the statistical method. The study results were then applied to estimate PMP of six large dams in Thailand and compared with the ones used in the design of the spillways. It was found that PMP estimation based on the generalized method was closer to the value used in the spillway design of those dams than PMP estimation based on the statistical method.

Probable Maximum Precipitation Comparison using Hershfield’s Statistical Method and Hydro-Meteorological Method for Sungai Perak Hydroelectric Scheme

One of the potential risks attributed to the occurrence of dam overtopping and dam wall failure due to the inadequacy of the spillway capacities is the loss of life and property damages in the downstream area. The current practices in most countries in minimizing these risks are by analyzing the extreme precipitation that leads to extreme flood. Extreme precipitation is best known as Probable Maximum Precipitation (PMP) and this estimation is useful in determining Probable Maximum Flood (PMF) in reviewing the spillway adequacy of dam structures. This paper presented PMP estimations using two approaches; physical method (Hydro-meteorological Method) and statistical approach (Hershfield’s Method) at the Sungai Perak Hydroelectric Scheme that consists of four cascading dams namely Temengor dam, Bersia dam, Kenering dam and Chenderoh dam. The highest PMP estimates from these two methods will be chosen as the rainfall input to establish PMF hydrographs. Estimations using Hydro-meteorological generalized map produces 40-50% higher estimates compared to Hersfield’s method with the PMP values of 550mm (1hours), 600mm (3hours), 800mm (6hours), 820mm (12hours), 1300mm (24hour) and 1600mm (72 hours). Accepting the Hydro-meteorological Method to determine PMF values for this hydroelectric scheme may be the best course since the estimations of the extreme precipitations using this method are the highest.

A methodological framework to assess PMP and PMF in snow-dominated watersheds under changing climate conditions – A case study of three watersheds in Québec (Canada)

Climate change will affect precipitation and flood regimes. It is anticipated that the Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) will be modified in a changing climate. This paper aims to quantify and analyze climate change influences on PMP and PMF in three watersheds with different climatic conditions across the province of Québec, Canada. Output data from the Canadian Regional Climate Model (CRCM) was used to estimate PMP and Probable Maximum Snow Accumulation (PMSA) in future climate projections, which was then used to force the SWAT hydrological model to estimate PMF. PMP and PMF values were estimated for two time horizons each spanning 30 years: 1961–1990 (recent past) and 2041–2070 (future). PMP and PMF were separately analyzed for two seasons: summer-fall and spring. Results show that PMF in the watershed located in southern Québec would remain unchanged in the future horizon, but the trend for the watersheds located in the northeastern and northern areas of the province is an increase of up to 11%.

Estimation of probable maximum precipitation for tropical catchment

Probable Maximum Precipitation (PMP) is the maximum precipitation depth for specific region or station within a certain time. The main purpose of PMP estimation is calculate the Probable Maximum Flood (PMF). The PMF is considered necessary for design and manage the hydraulic structures. PMP can be estimate using two methods, either using a physical method or by using statistical method. In this study, statistical approach was used to estimate the PMP for Temengor catchment in Perak state, Malaysia. Extreme value type-1 distribution (EV1) is adopted to estimate the extreme rainfall and Hershfeid method was used to estimate PMP value. Also, intensity duration curve (IDC) was derived for 1, 2 and 3 days storm duration with return period 5, 10, 50, 100, 500 years. The results showed that the values of PMP for 1000 return period are 222.361mm, 311.847mm and 348.307mm for 1, 2 and 3 days respectively.