Annual Maximum Daily Precipitation Research Articles

Climate change risks to mine closure

Robust planning for mine closure is crucial for minimising negative environmental impacts and ensuring a positive post-mining legacy. Even though climate is an important factor in the design and management of most post-mine landscape features, general understanding of the multi-facetted nature of climate change risks to mine closure outcomes is limited. An approach is presented to mapping the climate change risk while elucidating the nature of its uncertainty. The approach is demonstrated using five mine closure planning and design considerations, namely: pit lake hydrology, landform stability, flooding, revegetation, and acid and metalliferous drainage. The risk to each of these considerations is quantified with a representative climate index, namely: aridity index, rainfall erosivity, annual maximum 1-day precipitation, extreme drought frequency, and drying period duration. Change in climate indices are derived relative to baseline conditions for two future 30-year periods (2040–2069, and 2070–2099) from an 11-member ensemble of dynamically downscaled simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) for two emission scenarios (Representative Concentration Pathways 4.5 and 8.5). The approach is demonstrated for mines within Queensland, Australia, with future change and uncertainty in indices mapped spatially. Results show that annual maximum 1-day precipitation and extreme drought frequency is expected to become more extreme into the future irrespective of future time period, emission scenario or geographic region; whilst the expected direction of change varies spatially for the aridity index, rainfall erosivity and drying period duration. Most indices suggest that the greatest changes will occur in the Mount Isa mining region, whereas the Weipa mining region projections are highly uncertain. The research demonstrates the multiple facets of climate change risks that need consideration in mine closure planning and design. It provides new insights into the variabilities among indices, across mining regions, over ensemble members and between time horizons that need to be factored in by mine closure stakeholders.

The Variability of Maximum Daily Precipitation and the Underlying Circulation Conditions in Kraków, Southern Poland

This article studies the intra-annual and long-term variability in the maximum daily precipitation totals and their association with atmospheric circulation in Kraków. It investigates daily precipitation maxima by year and by month. The research is based on daily precipitation totals in the years 1863–2021 and draws on the calendar of atmospheric circulation types by Niedźwiedź. It examines the frequency of precipitation maxima in individual months and their variation from one year to another. No statistically significant trend of change in precipitation over the study period has been found. All annual maximum daily precipitation totals in Kraków fall into the category of heavy precipitation (>10 mm), and almost 99% qualify as very heavy (>20 mm). In the summer months, these are about 3–4 times higher than in winter. The share of the daily precipitation maximum in the monthly total exceeds 30% in all months. The maximum daily precipitation occurring on 5 August 2021 was the highest in the period that extends from the start of instrumental measurements. The study period saw 12 cases of maximum precipitation that belong to ‘flood-inducing’ categories (over 70 mm/day). Such cases of the very heaviest precipitation occurred in cyclonic situations: Cc, Bc, Nc, NEc, Ec and SEc. Most spring and summer maxima were seen on days with a cyclonic circulation. The instances of high daily precipitation in the Kraków area led to the flooding of residential and historic buildings, as well as of municipal infrastructure.

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Changes in Extreme Rainfall in New Capital of Indonesia (IKN) Based on 20 Years of GPM-IMERG Data

The Indonesian government has decided to relocate the nation’s capital city from Jakarta on Java to the Nusantara Capital (IKN) on Kalimantan for political, socioeconomic, and infrastructure reasons, among others, including the threat of flood, earthquake, and land subsidence in Jakarta. However, IKN remains susceptible to hydro-meteorological disasters due to its equatorial location and high yearly rainfall amounts. In this article, we investigated changes in extreme rainfall in the IKN area over the last 20 years (2001- 2020) based on Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 data. Overall, extreme rain in the IKN area, especially in the Sepaku and Semboja sub-districts, has slightly declined in the last 2 decades, as shown by negative and positive Sen’s slopes of precipitation amount-based indices and the consecutive dry days (CDD), respectively. A decrease was also observed in RX1 day (annual maximum 1-day precipitation) and SDII (annual total rainfall divided by the number of wet days in the year) indices. This indicates that the IKN area is getting drier and could impact the availability of clean water in the future by increasing the intensity and duration of the drought. While the total amount of precipitation has decreased over the past 2 decades, there has been an increase in the intensity of more extreme precipitation events. This is evident from the rise in precipitation frequency-based indices (annual count of days with rainfall exceeding certain thresholds), such as R20mm and R50mm. The RX5day index (maximum 5-day precipitation) has also shown an increase. These changes increase the risk of flooding, so it is crucial to implement appropriate measures to mitigate the risk of floods in the IKN area. HIGHLIGHTS Extreme rain in the IKN area, especially in Sepaku and Semboja sub-districts, has slightly declined in the last 20 years. Increases were observed in the consecutive dry days (CDD) and some precipitation frequency-based indices. It is important to take appropriate measures to reduce the risk of floods and drought in the IKN areas. GRAPHICAL ABSTRACT

Deciphering the Trend and Interannual Variability of Temperature and Precipitation Extremes over Greenland during 1958–2019

Abstract Greenland experienced multiple extreme weather/climate events in recent decades that led to significant melting of the ice sheet. However, how the intensity of extreme climate events over Greenland varied under recent warming has not been fully examined. Here, we collect 176 in situ observations over Greenland and demonstrate that the observed extreme temperature/precipitation events over Greenland are well captured by the RACMO2.3p2 model, in terms of climatological distribution, interannual variability, and long-term trend. Thus, we then investigate the spatiotemporal features of extreme events over Greenland during 1958–2019, using the daily model outputs at 5.5-km resolution. The simulated annual maximum temperature exhibits a significant increasing trend (∼0.13°C decade−1) during 1958–2019, whereas there is a weakening trend (−0.24°C decade−1) in annual minimum temperature over Greenland, especially after the 1990s (−1.24°C decade−1). For the interannual variability, changes in temperature extremes between warm and cold temperature years share large similarities with the distributions of long-term trends. The extreme precipitation events measured by annual maximum daily precipitation amount show a profound increasing trend (0.52 mm day−1 decade−1) over northeastern Greenland during 1958–2019, with large interannual variability in the ice-free coastal region and southern Greenland. Additionally, the changes in extreme warm and cold events are generally linked with the variation of Greenland blocking in summer and Arctic polar vortex in winter, respectively, in terms of favorable circulation background, and the extreme precipitation events are often associated with the position of the polar jet stream.

Frequency analysis of annual maximum daily precipitation in northeastern Algeria: mapping and implications under climate variability

Frequency analysis of annual maximum daily precipitation in northeastern Algeria: mapping and implications under climate variability

Exploring the uncertainty of weather generators’ extreme estimates in different practical available information scenarios

ABSTRACT Stochastic weather generators are powerful tools capable of extending the available precipitation records to the desired length. These, however, rely upon the amount of information available, which often is scarce, especially in arid and semi-arid regions. No studies can be found dealing with the uncertainty associated with these estimates related to the amount of information used in the weather generation calibration process, which is precisely the aim of the present study. A Monte Carlo simulation from a synthetic population was performed, evaluating the uncertainty of the simulated quantiles in different practical available information scenarios. The results showed that incorporating a regional study of annual maximum daily precipitation in the model parameterization clearly reduced the uncertainty of all quantile estimates. In addition, it has been proved that the uncertainty of these estimates increases with the population extremality, thus marking the importance of integrating additional information in regions with extreme precipitation patterns.

Rainfall extremes in Northern Vietnam: a comprehensive analysis of patterns and trends

This study examines the characteristics and trends of extreme rainfall in Northern Vietnam from 1961 to 2018, using daily rainfall data collected from 37 meteorological stations. The study reveals that the average annual rainfall varies significantly across stations, ranging from 1140 mm to 4758 mm. The rainy season accounts for 73% to 92% of the total annual rainfall. Most stations show a declining trend in the annual total rainfall during wet days (PRCPTOT) and the number of wet days (WDAY), while rainfall intensity (SDII) has increased in most stations, particularly during the dry season. This can be attributed to an increase in PRCPTOT and a decrease in WDAY in the dry season. The study also finds a general decreasing trend in the annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day), as well as for the number of moderate (R16mm) and heavy (R50mm) rainfall days. However, most stations in Northern Vietnam demonstrate no trend in the annual maximum number of consecutive dry days (CDD) and the annual maximum number of consecutive wet days (CWD). Furthermore, the frequency of extreme rainfall events in Northern Vietnam exceeding the 5-year and 10-year return values of 1961-2018 has decreased in recent decades at many stations. Overall, the findings of this study provide insights into the changing patterns of extreme rainfall in Northern Vietnam, with significant implications for climate change adaptation and disaster risk reduction efforts in the region.

Persistent and nonpersistent regional extreme total, daytime, and night‐time precipitation events over southwest China (1961–2019)

AbstractExtreme persistent precipitation is becoming increasingly common globally, and persistent extreme regional precipitation events (PERPEs) pose a particularly serious threat to humans and environmental systems. This study investigated total precipitation (TP), daytime precipitation (DP), and night‐time precipitation (NP) in southwest China (SWC) during 1961–2019. The occurrence of TP, DP, and NP was analysed under the conditions of persistence over 1–5 days, temporal persistence over >2 days, and temporal nonpersistence over ≤2 days, and spatiotemporal overlap for at least 3 days (i.e., a PERPE) using the objective identification technique for regional extreme events. The results indicated the following. (a) Annual maximum daily precipitation ≥200 (100) mm in terms of TP (NP) was recorded mainly in the Sichuan Basin (SCB) and eastern parts of the Yunnan–Guizhou Plateau (YGP), and it occurred mostly in summer. For DP, annual maximum daily precipitation of ≥100 mm was recorded throughout SWC, and it occurred mainly in winter in the Henduan Mountains, summer in the SCB, and spring in western parts of the YGP. (b) The magnitudes of NP and DP differed for events persisting for 1–5 days. The average amount of precipitation with temporal persistence increased during 1961–2019 for TP, DP, and NP. However, the frequency of occurrence of events with temporal persistence increased more in comparison with that of events with temporal nonpersistence. Overall, the frequency of occurrence of events with temporal persistence or temporal nonpersistence in the western SCB and eastern YGP increased more for TP and NP than for DP. (c) In the study period, 22, 44, and 4 PERPEs were detected for TP, NP, and DP, respectively, occurring mainly in the SCB, indicating that TP was dominated by night‐time PERPEs. These findings improve understanding of PERPEs changes in SWC and provide scientific support for implementation of measures intended to prevent and mitigate extreme flood events and waterlogging.

Severe Precipitation Phenomena in Crimea in Relation to Atmospheric Circulation

The increase in the frequency and intensity of hazardous hydrometeorological phenomena is one of the most dangerous consequences of climate instability. In this study, we summarize the data on severe weather phenomena using the data from 23 meteorological stations in Crimea from 1976 to 2020. Particular attention was paid to the precipitation phenomena descriptions. For the last 45 years, a significant positive trend of interannual variability of the annual occurrence of severe weather phenomena was estimated to be +2.7 cases per decade. The trend for severe precipitation phenomena was estimated to be +1.3 cases per decade. The probable maximum annual daily precipitation as a quantitative indicator of hazardous events was estimated for each station by using both the stationary and the non-stationary GEV models. For at least half of the meteorological stations, a non-stationary GEV model was more appropriate for the estimation of the precipitation extremes. An analysis of the main synoptic processes that drive severe weather phenomena of precipitation was carried out. The greatest contribution to the formation of severe precipitation was made by Mediterranean–Black Sea cyclones. At the same time, half of all of the cases of extreme precipitation were caused by cyclones generated over the Black Sea only, in all seasons apart from winter. In the mid-troposphere, four types of meridional circulation were identified depending on the location of troughs and ridges, with respect to the Black Sea region. More than 42% of severe precipitation phenomena were accompanied by an isolated high-altitude cyclone in the mid-troposphere over the Black Sea region. The main recommendation that can be drawn from this study is that long-term climatic non-stationarity should be taken into account whenever the risk assessment or hazard analysis is to be carried out. The results can also favor the designing of drainage and sewerage systems in urban areas. The findings of atmospheric patterns can be used for the improvement of extreme precipitation forecasts.

Changes in extreme daily precipitation over Africa: Insights from a non-asymptotic statistical approach

Extreme precipitation heavily affects society and economy in Africa because it triggers natural hazards and contributes large amounts of freshwater. Understanding past changes in extreme precipitation could help us improve our projections of extremes, thus reducing the vulnerability of the region to climate change. Here, we combine high-resolution satellite data (1981–2019) with a novel non-asymptotic statistical approach, which explicitly separates intensity and occurrence of the process. We investigate past changes in extreme daily precipitation amounts relevant to engineering and risk management. Significant (α=0.05) positive and negative trends in annual maximum daily precipitation are reported in ∼20 % of Africa both at the local scales (0.05°) and mesoscales (1°). Our statistical model is able to explain ∼90% of their variance, and performs well (72% explained variance) even when annual maxima are explicitly censored from the parameter estimation. This suggests possible applications in situations in which the observed extremes are not quantitatively trusted. We present results at the continental scale, as well as for six areas characterized by different climatic characteristics and forcing mechanisms underlying the ongoing changes. In general, we can attribute most of the observed trends to changes in the tail heaviness of the intensity distribution (25% of explained variance, 38% at the mesoscale), while changes in the average number of wet days only explain 4% (12%) of the variance. Low-probability extremes always exhibit faster trend rates than annual maxima (∼44% faster, in median, for the case of 100-year events), implying that changes in infrastructure design values are likely underestimated by approaches based on trend analyses of annual maxima: flexible change-permitting models are needed. No systematic difference between local and mesoscales is reported, with locally-varying impacts on the areal reduction factors used to transform return levels across scales.

Precipitation Trends Analysis Using Gridded Dynamic Sampling Zones: Case Study Yangtze Delta Megalopolis

As a result of the fast growth of remote sensing and data assimilation technology, many global land use land cover (LULC) and climate reanalysis data sets have been used to advance our understanding of climate and environmental change. This paper investigates the precipitation variations of the Yangtze Delta Megalopolis by using precipitation reanalysis data under conditions of dynamic urban sprawl. Compared with current precipitation characteristic analyses, which are often based on a limited number of ground rainfall stations, the approach followed in this study comprises a grid-based statistical method using large sets of samples with a uniform distribution and a same representative grid area. This novel approach of dynamic sampling is applied in this study to overcome the temporal and spatial inconsistency of stationary sampling. This approach allows to examine the impact of urbanization on regional precipitation characteristics. The Yangtze Delta Megalopolis (YDM) region, one of the most developed regions in China, was selected as a case study to evaluate the impact of urbanization on subsequent precipitation features. The results reveal that the annual total precipitation (TP) and the maximum daily precipitation (MDP) in both urban and non-urban areas of the YDM region generally have increased during the past 30 years. Hence, the region has become increasingly humid. Extrema of annual MDP and TP show obvious spatial characteristics, in which most maxima are located in the southern part of YDM while minima are more concentrated in the northern part. This newly developed approach has potentials for application in studies where underlying surface features exhibit rapid alterations. The findings of this case study provide relevant information for planning and design of regional water resources management, flood risk management, and planning of the urban drainage system of the YDM region.

Hazard Characterization of the Annual Maximum Daily Precipitation in the Southwestern Iberian Peninsula (1851–2021)

High-intensity rainfall can raise fluvial channel levels, increasing the risk of flooding. Maximum precipitation depths are used to estimate return periods and, thus, calculate the risk of this type of event. To improve these estimates in Southwest Europe, we studied the behavior of extreme rainfall using the historical records of San Fernando (Cádiz, southwest Spain), obtaining the maximum daily annual rainfall (period 1851–2021). Local risk levels for intense precipitation were established based on the mean values and standard deviation of daily precipitation. In this series, 38% of the years had some type of risk (>53.7 mm), of which 13% of these years had high risk (>73.2 mm) or disaster risk (>92.7 mm). In these risk thresholds, the maximum daily precipitation is mostly concentrated in the autumn months. The SQRT-ETMax model used fits well with the instrumental historical records for return periods of up to 25 years, although it may present appreciable deviations for longer return periods. Using a 170-year secular series, a more precise understanding of extreme periods and precipitation variability was obtained.

Probabilistic Approach to Determine the Spatial Distribution of the Antecedent Moisture Conditions for Different Return Periods in the Atlántico Region, Colombia

Previous soil moisture conditions play an important role in the design of hydraulic structures because they are directly related to the runoff threshold associated with a return period. These represent one of the main determinants of the runoff response of a drainage basin. One of the main difficulties facing hydrologists in Colombia lies in the time spent gathering and analyzing information related to the selection of antecedent moisture conditions. In this study, complete records from 19 rainfall stations located in the Atlántico region, Colombia, were used to analyze the cumulative precipitation during the 5 days prior to the annual maximum daily precipitation associated with different return periods using the Gev, Gumbel, Pearson Type III and Log Pearson Type III probability distributions. Different interpolation methods (IDW, kriging and spline) were applied to evaluate the spatial distribution of the antecedent moisture conditions. The main contribution of this research is establishing, using a probabilistic approach, the behavior of antecedent moisture conditions in a particular region, which can be used by engineers and designers to plan water infrastructure. This probabilistic approach was applied to a case study of the Atlántico region, Colombia, where the spatial distribution of antecedent moisture conditions was calculated for several return periods. The results indicate that the better results were obtained with the IDW interpolation method, and the Pearson Type III and Gumbel distributions also showed the best fits based on the Akaike criterion.

Evaluation of Quantile Delta Mapping as a bias‐correction method in maximum rainfall dataset from downscaled models in São Paulo state (Brazil)

AbstractAn essential step for improving climate change models' performance is to evaluate their ability to represent the current climate conditions, especially extreme events. On such background, this study aims at evaluating the performance of the Quantile Delta Mapping (QDM) as a bias correction method for annual maximum daily precipitation series (bmax) generated from downscaled climate change models under tropical–subtropical conditions of Brazil. We selected the QDM due to its ability to correct bias in extreme quantile of wet days. Climate projections obtained from 20 NASA Earth Exchange Daily Downscaled Projections models (NEX‐GDDP) from 1950 to 2005 were subjected to validation processes based on the QDM method. Two climate change scenarios (RCP 4.5 and RCP 8.5 W m−2) have also been considered. Several goodness‐of‐fit measures, such as root‐mean‐square‐error (RMSE), SD, percentual bias (pbias), mean absolute error (MAE), Pearson correlation test, modified Willmott test (dm), have been calculated from the outcomes of the models and their corresponding observed data (obtained from rain gauges). These goodness‐of‐fit measures were calculated before and after applying the QDM method. The QDM was able to correct virtually all biases. More specifically, the QDM successfully adjusted the empirical cumulative distribution of climate change projections, removing the systematic error of raw data. The QDM also presented a suitable performance when applied to future projections (2020–2095). This statement holds for all NEX‐GDDP models, except for the ACCESS1‐0 model in RCP 8.5. In such a scenario, this latter model presented unrealistic rainfall values. Finally, with the improvement resulting from applying the bias correction method QDM, there was an increase in the number of climate projections suitable for end‐users in the study region.

Global climate shift in 1970s causes a significant worldwide increase in precipitation extremes

The shift in climate regimes around 1970s caused an overall enhancement of precipitation extremes across the globe with a specific spatial distribution pattern. We used gridded observational-reanalysis precipitation dataset and two important extreme precipitation measures, namely Annual Maximum Daily Precipitation (AMDP) and Probable Maximum Precipitation (PMP). AMDP is reported to increase for almost two-third of the global land area. The variability of AMDP is found to increase more than its mean that eventually results in increased PMP almost worldwide, less near equator and maximum around mid-latitudes. Continent-wise, such increase in AMDP and PMP is true for all continents except some parts of Africa. The zone-wise analysis (dividing the globe into nine precipitation zones) reveals that zones of ‘moderate precipitation’ and ‘moderate seasonality’ exhibit the maximum increases in PMP. Recent increased in pole-ward heat and moisture transport as a result of Arctic Amplification may be associated with such spatial redistribution of precipitation extremes in the northern hemisphere.

Explaining the Spatial Pattern of U.S. Extreme Daily Precipitation Change

AbstractObserved United States trends in the annual maximum 1-day precipitation (RX1day) over the last century consist of 15%–25% increases over the eastern United States (East) and 10% decreases over the far western United States (West). This heterogeneous trend pattern departs from comparatively uniform observed increases in precipitable water over the contiguous United States. Here we use an event attribution framework involving parallel sets of global atmospheric model experiments with and without climate change drivers to explain this spatially diverse pattern of extreme daily precipitation trends. We find that RX1day events in our model ensembles respond to observed historical climate change forcing differently across the United States with 5%–10% intensity increases over the East but no appreciable change over the West. This spatially diverse forced signal is broadly similar among three models used, and is positively correlated with the observed trend pattern. Our analysis of model and observations indicates the lack of appreciable RX1day signals over the West is likely due to dynamical effects of climate change forcing—via a wintertime atmospheric circulation anomaly that suppresses vertical motion over the West—largely cancelling thermodynamic effects of increased water vapor availability. The large magnitude of eastern U.S. RX1day increases is unlikely a symptom of a regional heightened sensitivity to climate change forcing. Instead, our ensemble simulations reveal considerable variability in RX1day trend magnitudes arising from internal atmospheric processes alone, and we argue that the remarkable observed increases over the East has most likely resulted from a superposition of strong internal variability with a moderate climate change signal. Implications for future changes in U.S. extreme daily precipitation are discussed.

Stationarity in the variability of arid precipitation: A case study of arid Central Asia

Precipitation is particularly scarce in arid Central Asia (CA), and is expected to be severely impacted by future warming, and the assessment of the stationarity of precipitation variability is important for managing surface water resources in this region. In this study, we investigated the statistics of stationarity in the totals and extremes of precipitation in CA based on the longest observational records (1881–2006), tree-ring reconstructed records (1756–2012 and 1760–2015), and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, applying the autocorrelation function and testing criteria established based on the statistical definitions of stationarity. We analyzed the longest daily precipitation record (Tashkent station, 1881–2006) and found that the autocorrelation coefficient of the precipitation totals (PRCPTOT) and annual maximum 1-day precipitation amount (Rx1day) were statistically insignificant for all lags, implying stationary behavior. Regionally, nearly all the Global Historical Climatology Network-Daily Database (GHCN-D) observatory sites (1925–2005) indicated likely stationary behavior. The reconstructed records were also indistinguishable from a random process. For the CMIP5 models, the simulated and projected PRCPTOT closely approximated a purely random process; however, the projected Rx1day maintained non-stationary means in most of the models under the representative concentration pathway (RCPs), implying that extreme events would increase in the future. The mean precipitation changes (ΔP) can be expressed as an exponential function depending on the length of the successive mean periods (μ) and variance (σ2). The ΔP of the next decade is projected to be within ±14.8% of the previous decades mean annual PRCPTOT over CA. The higher the RCPs, the higher the ΔP over CA. The results show that the detection and prediction of precipitation change will be challenging in arid CA.

Probability distribution of heavy rainfall and determination of IDF in the city of Caruaru – PE

In the design of hydraulic engineering works, the estimation of project precipitation is fundamental. Rain forecasting depends on several factors, which makes estimating it simpler with stochastic processes. In this sense, the distributions of Gumbel (GUM), Log-Normal two-parameter (LN2P), Generalized Extreme Value (GEV), Fréchet with two and three parameters (FRE2P and FRE3P), Weibull with two and three parameters (W2P and W3P), Gamma (GAM2P), and Pareto with two and three parameters (PAR2P and PAR3P) were evaluated to the annual maximum daily precipitation (AMDP) adjustment in the city of Caruaru (Pernambuco´s Agreste). A series of AMDP was used, based on data obtained from the National Water Agency (Agência Nacional de Águas - ANA). Anderson Darling (AD), Kolmogorov-Smirnov (KS) and Pearson Chi-square (χ2) adherence tests, and the determination coefficient (R²) were used to assess the adherence quality of the distributions. The Likelihood Method presented a better fit quality than the Moment Method. The GEV distribution obtained the best results for the AD test in both methods to estimate the parameters. Among the adherence tests used, the AD test was considered the most restrictive. To verify the quality parameters’ fitness to the IDF relations, the Willmott performance coefficient was used. For all distributions employed in this study, Willmott performance coefficients presented values above 0.99, giving a perfect fit of IDF relations with determination coefficients close to 1.0.