Annual Maximum Research Articles

Dynamics of solar-induced chlorophyll fluorescence (SIF) and its response to meteorological drought in the Yellow River Basin

Solar-induced chlorophyll fluorescence (SIF) has been used since its discovery to characterize vegetation photosynthesis and is an effective tool for monitoring vegetation dynamics. Its response to meteorological drought enhances our comprehension of the ecological consequences and adaptive mechanisms of plants facing water scarcity, informing more efficient resource management and efforts in mitigating climate change. This study investigates the spatial and temporal patterns of SIF and examines how vegetation SIF in the Yellow River Basin (YRB) responds to meteorological drought. The findings reveal a gradual southeast-to-northwest decline in SIF across the Yellow River Basin, with an overall increase—from 0.1083 W m-2μm−1sr−1 in 2001 to 0.1468 W m-2μm−1sr−1 in 2019. Approximately 96% of the YRB manifests an upward SIF trend, with 75% of these areas reaching statistical significance. The Standardized Precipitation Evapotranspiration Index (SPEI) at a time scale of 4 months (The SPEI-4), based on the Liang-Kleeman information flow method, is identified as the most suitable drought index, adeptly characterizing the causal relationship influencing SIF variations. As drought intensified, the SPEI-4 index markedly deviated from the baseline, resulting in a decrease in SIF values to their lowest value; subsequently, as drought lessened, it gravitated towards the baseline, and SIF values began to gradually increase, eventually recovering to near their annual maximum. The key finding is that the variability of SIF with SPEI is relatively pronounced in the early growing season, with forests demonstrating superior resilience compared to grasslands and croplands. The responsiveness of vegetation SIF to SPEI can facilitate the establishment of effective drought early warning systems and promote the rational planning of water resources, thereby mitigating the impacts of climate change.

Nonstationary frequency analysis of extreme precipitation: Embracing trends in observations

Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes.

Comparative Analysis of Flood Estimation using Log-Pearson Type III and Gumbel Max Models in the Cauvery River, India

Flooding is one of the most destructive global disasters in scale, geographical extent, property and life loss, and population displacement. The Cauvery River is one of the flood vulnerable rivers in the Peninsular region of India. At-site flood frequency analysis is performed using flow data obtained at the Kodumudi gauged site in the Cauvery River. Log Pearson Type III and Gumbel Max distribution models are used in the present study to estimate peak floods for different return periods. The Central Water Commission provides the annual maximum discharge for the Kodumudi gauged site over 39 years (1980-2018). The goodness of fit test employing the Kolmogorov-Smirnov and Anderson- Darling tests, reveals that Log-Pearson Type III best estimates peak floods in the study area. The peak floods predicted by Log-Pearson Type III for return periods 2, 5, 10, 25, 50, 100, 200, and 500 years are approximately 929, 1886, 2998, 5303, 8002, 11929, 17633, and 29228 cumecs. Hydraulic structures can be designed in the region based on 100-year flood. The present research could help with flooding management approaches, vulnerability analyses, and hydraulic structure design in the study region.

Trend in indices of public health risk caused by air pollution in an industrial center

Introduction. In industrialized regions and cities, atmospheric air pollution greatly contributes to the hazard for public health. The study aim was to assess the state of the atmospheric air in the city of Novokuznetsk over 2017–2022 and determine the levels of risks to public health. Materials and methods. Data on gross atmospheric emissions and average annual concentrations of the substances was taken from the reports on the state of the environment of the Kemerovo region and Novokuznetsk. Maximum permissible concentrations of atmospheric pollutants were determined according to SanPiN 1.2.3685–21. Calculations of the risks to public health were carried out in accordance with Guideline P 2.1.10.1920–04. The classification of risk levels was carried out on the basis of MR 2.1.10.0156–19. 2.1.10. Results. Decrease in total atmospheric emissions from 2017 to 2022 amounted to 50.1 thousand tons (about 16%). At the same time, there was revealed an excess of the average annual maximum permissible concentrations of benzo(a)pyrene (3.5-11.2 times); formaldehyde (1.7 and 3.4 times over 2021 and 2022, respectively); suspended substances (1.6 and 2.0 times in 2021 and 2022, respectively); hydrogen fluoride (1.2 times in 2021); nitrogen dioxide (1.6 times in 2022). The hazard coefficients of benzo(a)pyrene were 3.5–11.2 (high level of non-carcinogenic risk); formaldehyde and suspended substances – 1.1–3.0 (alarming level). In 2022, the hazard coefficient for formaldehyde was 3.4 (high risk level), suspended substances – 2.0, nitrogen dioxide – 1.6 (alarming risk level). Hazard indices of pollutants were detected in the range from 7.0 to 20.1 (high level of risk). The main critical organs and systems were the immune system (hazard indices were 4.0–14.6), development during childhood (hazard indices – 3.5–11.2), respiratory organs (hazard indices – 3.0–8.6). These risk levels range from alarming to high. The highest total individual carcinogenic risk (1.6•10–4) was determined in 2021 (alarming level), the main contribution is made by formaldehyde. Limitations. In 2022, there were no data on carbon (soot) concentrations, so the level of total carcinogenic risk this year cannot be considered to be correct. Conclusion. Reducing the total volume of atmospheric emissions does not provide a significant effect on improving the environmental situation and public health, since high concentrations of pollutants remain in the city’s atmosphere. When implementing air protection measures, it is necessary to take into account risk criteria.

Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region.

Introduction to the special issue of The Natural History of Two Peoples Bay Nature Reserve, Western Australia

Context This paper introduces the special issue of Pacific Conservation Biology devoted to the natural history of Two Peoples Bay Nature Reserve on the south coast of Western Australia. Methods This paper provides the background to the special issue. Key results Two Peoples Bay Nature Reserve was gazetted in 1967 for the conservation of the native biota, including two species; the Djimaalap/noisy scrub-bird (Atrichornis clamosus) and Ngilgaitch/Gilbert’s potoroo (Potorous gilbertii), both believed extinct for over 100 years before being rediscovered on the Reserve. The Reserve is 4774.7 ha in area, with wetlands, heathlands, granite outcrops, sand dunes, beaches, cliffs, and islands. Since it was established, mean annual rainfall has decreased by 16.8%, mean annual maximum temperature has increased by 0.2°C, and mean annual minimum temperature has increased by 0.7°C. Conclusions The paper poses the question: what do the changes of drier winters, hotter summers, and more extreme weather events mean for managers of conservation areas such as Two Peoples Bay? Implications Changing climate will pose problems for the managers of Two Peoples Bay Nature Reserve in ensuring the conservation of the Djimaalap/noisy scrub-bird and Ngilgaitch/Gilbert’s potoroo.

Qualitative and quantitative changes in phenology of chlorophyll a concentrations during the transition from eutrophy to oligotrophy

AbstractThe PEG (Plankton Ecology Group) model predicts differences in phenology between eutrophic and oligotrophic lakes regarding the occurrence, timing and magnitude of annual chlorophyll maxima and minima. While these predictions have been tested between lakes, hardly any tests exist using long‐term data. We test these predictions using chlorophyll time‐series (1980–2019) from Lake Constance in which trophic status shifted from eutrophic to oligotrophic conditions. We show that oligotrophication subsequently resulted in reduction of the summer and spring blooms, and finally the loss of the clear‐water phase. In contrast to the PEG model the spring bloom was not delayed, but advanced with oligotrophication. Warming modified the seasonal patterns via advancing clear‐water timing. Oligotrophication did not only influence phenologies, but also the importance of independent variables driving phenologies. Thus, the decline of nutrients was the dominant factor in shaping the seasonal patterns of chlorophyll in Lake Constance during the last four decades.

Net primary production annual maxima in the North Atlantic projected to shift in the 21st century

Abstract. Shifts in the day of peak net primary production (NPP) were detected in different biogeochemical provinces of the North Atlantic (25–65° N). Most provinces displayed a shift toward earlier peak NPP, with the largest change points in the 21st century and in the northern parts of the domain. Furthermore, the occurrences of the first day with a mixed-layer depth (MLD) shallower than 40 m and the day of peak NPP are positively correlated over most of the domain. As was the case for the day of peak NPP, the largest change points for the day of MLD shallower than 40 m occur around or after the year 2000. Daily output from two fully coupled CMIP6 Earth system models, EC-Earth3-CC and NorESM2-LM, for the period 1750–2100 and under the SSP5-8.5 scenario, were used for the analysis. The ESM NPP data were compared with estimates derived from Carbon, Absorption and Fluorescence Euphotic-resolving (CAFE) satellite-based data. The ESMs showed significant differences from the CAFE model, though the timing of peak NPP was well captured for most provinces. The largest change points in the day of peak NPP occur earlier in EC-Earth3-CC than in NorESM2-LM. Although SSP5-8.5 is a scenario with very high warming, EC-Earth3-CC generates change points for most provinces in the early part of the 21st century, before the warming has deviated far from lower-emissions scenarios. NorESM2-LM displays the largest change points centered around the mid 21st century, with two out of eight provinces displaying the largest change point before the year 2050. The early timing of the detected shifts in some provinces in both ESMs suggests that similar shifts could already have been initiated or could start in the near future. This highlights the need for long-term monitoring campaigns in the North Atlantic.

Reliability-based calibration of companion load combination factors by considering concurrent wind and ice loading for structural design

Transmission towers and overhead transmission lines are designed and constructed by considering the combined ice load and wind-on-ice load if the ice accretion hazard is not negligible. The structural design codes provide clauses with a range of values to evaluate such a combined load. However, it is unclear which of the values suggested in the codes one should use for specified regions, and the reliability-based calibration of such a combination is unavailable. To fill this gap, in the present study, we carried out the reliability-based calibration of the companion load combination factors by using statistics of the ice accretion thickness and concurrent wind speed available from more than 250 meteorological stations in Canada. For the calibration, a nonlinear combination problem needs to be considered since the wind-on-ice load depends on the accreted ice thickness, making this calibration task differ from those commonly reported in the literature, which is focused on the linear load combination problem. A parametric investigation was also carried out to assess the effect of using different return periods and the correlation between ice accretion and concurrent wind speed on the companion load combination factors. The calibration results were used to recommend the load combination format, the companion load combination factors, and the ratio of the square equivalent concurrent wind speed to the return period value of the annual maximum wind speed, which is commonly implemented in design codes.

Nonstationarity in Extreme Precipitation Return Values along the U.S. Gulf and Southeastern Coasts

Abstract This study estimates extreme rainfall trends across the Gulf Coast and southeastern coast of the United States while applying methods for extending the temporal record and aggregating across spatial trend variations. Nonstationary generalized extreme value (GEV) models are applied to historical annual daily maximum precipitation data (1890–2019) while using CMIP5 global mean surface temperature (GMST) as the covariate. County composites and multicounty regions are used for local data record extension and pooling. Unlike most previous studies, return periods as long as 100 years are analyzed. The local trend estimates themselves are found to be too noisy to be reliable as estimates of climate-driven trends. However, application of a Gaussian process model to the spatial distribution of observed trends yields overall trend detection at the 95% significance level. The overall historical increase due to nonstationarity across the study region, with associated 95% confidence intervals, is 9% (3%, 15%) for the 2-yr return period and 16% (4%, 26%) for the 100-yr return period. A trend is also detectable in the Gulf Coast subregion, but not in the smaller southeast subregion. Recent weather events and nonstationarity have caused the official return value estimates for parts of North and South Carolina to be much lower than the return values estimated here. Significance Statement Protection of people and infrastructure from flooding relies on accurate estimates of potential extreme rainfall intensity. Some official estimates of extreme rainfall near the Gulf Coast and southeastern coast of the United States are over 20 years old. We show that, across this region, there is a clear trend in daily rainfall so extreme that it only has a 1% chance of happening in any given year (the so-called 100-yr rainfall). This trend means that many existing estimates of extreme rainfall are too low, both now and in the future, so flooding risks based on those estimates would be underestimated as well.

An environment-driven basin scale tropical cyclone model

This paper presents an environment-driven tropical cyclone (TC) model for the Western North Pacific basin, which comprises a revised Poisson regression genesis model, a tailored beta-advection track model, and a fast intensity model. The TC model reproduces the temporal and spatial distributions of genesis events, the motion pattern of tracks, as well as the intensity evolutions along tracks. Risk analyses for Hong Kong and along the southeast coastline of mainland China demonstrate that this model can simulate extreme TC events with high fidelity. And the Gaussian mixture model outperforms the Frank Copula in approximating the joint distributions of the annual maximum wind speeds and the corresponding wind directions. This model is driven by a set of environmental variables including relative vorticity, relative humidity, sea surface temperature, vertical wind shear, potential intensity, sub mixed layer depth stratification, mixture layer depth and so on. This enables the model to not only reproduce historical records, but also make predictions for future TC behaviors under climate change with combination of global climate models. Besides, the computational efficiency of the TC model is comparable to traditional purely statistical models. The proposed model can also be coupled with other natural hazard models to conduct multi-hazard analysis.

A Nonstationary Stochastic Rainfall Generator Conditioned on Global Climate Models for Design Flood Analyses in the Mississippi and Other Large River Basins

AbstractExisting stochastic rainfall generators (SRGs) are typically limited to relatively small domains due to spatial stationarity assumptions, hindering their usefulness for flood studies in large basins. This study proposes StormLab, an SRG that simulates precipitation events at 6‐hr and 0.03° resolution in the Mississippi River Basin (MRB). The model focuses on winter and spring storms caused by water vapor transport from the Gulf of Mexico—the key flood‐generating storm type in the basin. The model generates anisotropic spatiotemporal noise fields that replicate local precipitation structures from observed data. The noise is transformed into precipitation through parametric distributions conditioned on large‐scale atmospheric fields from a climate model, reflecting spatial and temporal nonstationarity. StormLab can produce multiple realizations that reflect the uncertainty in fine‐scale precipitation arising from a specific large‐scale atmospheric environment. Model parameters were fitted monthly from December–May, based on storms identified from 1979 to 2021 ERA5 reanalysis data and Analysis of Record for Calibration (AORC) precipitation. StormLab then generated 1,000 synthetic years of precipitation events based on 10 CESM2 ensemble simulations. Empirical return levels of simulated annual maxima agree well with AORC data and show an overall increase in 1‐ to 500‐year events in the future period (2022–2050). To our knowledge, this is the first SRG simulating nonstationary, anisotropic high‐resolution precipitation over continental‐scale river basins, demonstrating the value of conditioning such stochastic models on large‐scale atmospheric variables. StormLab provides a wide range of extreme precipitation scenarios for design floods in the MRB and can be further extended to other large river basins.

Quantifying effects of blood pressure control on neuroimaging utilization in a large multi-institutional healthcare population.

Essential hypertension is a common chronic condition that can exacerbate or complicate various neurological diseases that may necessitate neuroimaging. Given growing medical imaging costs and the need to understand relationships between population blood pressure control and neuroimaging utilization, we seek to quantify the relationship between maximum blood pressure recorded in a given year and same-year utilization of neuroimaging CT or MR in a large healthcare population. A retrospective population-based cohort study was performed by extracting aggregate data from a multi-institutional dataset of patient encounters from 2016, 2018, and 2020 using an informatics platform (Cosmos) consisting of de-duplicated data from over 140 academic and non-academic health systems, comprising over 137 million unique patients. A population-based sample of all patients with recorded blood pressures of at least 50 mmHg DBP or 90 mmHg SBP were included. Cohorts were identified based on maximum annual SBP and DBP meeting or exceeding pre-defined thresholds. For each cohort, we assessed neuroimaging CT and MR utilization, defined as the percentage of patients undergoing ≥1 neuroimaging exam of interest in the same calendar year. The multi-institutional population consisted of >38 million patients for the most recent calendar year analyzed, with overall utilization of 3.8-5.1% for CT and 1.5-2.0% for MR across the study period. Neuroimaging utilization increased substantially with increasing annual maximum BP. Even a modest BP increase to 140 mmHg systolic or 90 mmHg diastolic is associated with 3-4-fold increases in MR and 5-7-fold increases in CT same-year imaging compared to BP values below 120 mmHg / 80 mmHg. Higher annual maximum recorded blood pressure is associated with higher same-year neuroimaging CT and MR utilization rates. These observations are relevant to public health efforts on hypertension management to mitigate costs associated with growing imaging utilization.

Effects of lethal management on gray wolf pack persistence and reproduction in Wisconsin, USA.

Direct human-caused mortality accounts for about half of all large mammal mortality in North America. For social species like gray wolves (Canis lupus), the death of pack members can disrupt pack structure and cause pack dissolution, and mortality of breeding adults or wolves during reproduction and pup-rearing can decrease pup recruitment. We estimated minimum and maximum probability of wolf pack persistence in Wisconsin, USA, during biological years (15 April-14 April) 2011-2019 and evaluated the influence of pack size and legal harvest mortality on pack persistence during 2012-2014. Harvests comprised 75-161 mortalities within 194 monitored packs during 2012-2014, with 56-74% of packs having no wolves harvested each year. As an index of reproduction during 2013-2019, we also estimated the proportion of packs where pups responded to howl surveys. We evaluated the influence of pack size, legal harvest, and agency removal on reproduction during 2013-2015. Annual maximum pack persistence probability was uniformly high (0.95-1.00), and annual minimum pack persistence probability ranged from 0.86-0.98 with a possible decline during years of harvest. Reproduction was similar in years following harvest and agency removal (2013-2015, pup response = 0.27-0.40), and years without harvest or agency removal the year prior (2016-2019, pup response = 0.28-0.66). Pack size had a positive effect on pack persistence and reproduction. Total number of wolf mortalities and number of adult male and females removed did not influence pack persistence or reproduction. We suggest that low per-pack mortality, timing of harvest and agency removal, and harvest characteristics during 2012-2014 supported stable pack persistence and reproduction.

APPLICATION OF GENERALIZED EXTREME VALUE DISTRIBUTION TO ANNUAL MAXIMUM RAINFALL

Generalized Extreme Value Distributions was used to model annual maximum rainfall data in Akure, Ondo State, Nigeria from 1981-2019. The parameters of the distribution are estimated using the maximum likelihood estimation method. The model fit indicated that the shape parameter is negative this suggests that Fréchet distribution is the appropriate model for describing annual maximum rainfall in Ondo-state Nigeria. The estimated return levels for different return periods revealed an increase in the value over the years.

Snow depth in high-resolution regional climate model simulations over southern Germany – suitable for extremes and impact-related research?

Abstract. Snow dynamics play a critical role in the climate system, as they affect the water cycle, ecosystems, and society. In climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany in an elevation range of 150 to 1000 m over the time period 1987–2018. Two simulations stem from high-resolution regional climate models – the Weather Research & Forecasting (WRF) model at 1.5 km resolution and the COnsortium for Small scale MOdelling model in CLimate Mode (COSMO-CLM; abbreviated to CCLM hereafter) at 3 km resolution. Additionally, the hydrometeorological snow model Alpine MUltiscale Numerical Distributed Simulation ENgine (AMUNDSEN) is run at point scale at the locations of the climate stations, based on the atmospheric output of CCLM. To complement the comparison, the ERA5-Land dataset (9 km), a state-of-the-art reanalysis land-surface product, is also compared. All four simulations are driven by the atmospheric boundary conditions of ERA5. Due to an overestimation of the snow albedo, the WRF simulation features a cold bias of 1.2 °C, leading to the slight overestimation of the snow depth in low-lying areas, whereas the snow depth is underestimated at snow-rich stations. The number of snow days (days with a snow depth above 1 cm) is reproduced well. The WRF simulation can recreate extreme snow depths, i.e. annual maxima of the snow depth, their timings, and inter-station differences, and thereby shows the best performance of all models. The CCLM reproduces the climatic conditions with very low bias and error metrics. However, all snow-related assessments show a strong systematic underestimation, which we relate to deficiencies in the snow module of the land-surface model. When driving AMUNDSEN with the atmospheric output of the CCLM, the results show a slight tendency to overestimate snow depth and number of snow days, especially in the northern parts of the study area. Snow depth extremes are reproduced well. For ERA5-Land (ERA5L), the coarser spatial resolution leads to larger differences between the model elevation and the station elevation, which contributes to a significant correlation of climatic biases with the elevation bias. In addition, the mean snow depth and number of snow days are strongly overestimated, with conditions that are too snowy in the late winter. Extreme snow depth conditions are reproduced well in the low-lying areas, whereas strong deviations occur with more complex topography. In sum, due to the high spatial resolution of convection-permitting climate models, they show the potential to reproduce the winter climate (temperature and precipitation) in southern Germany. However, different sources of uncertainties, i.e. the spatial resolution, the snow albedo parametrisation, and other parametrisations within the snow model, prevent their further use in a straightforward manner for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, such as in the context of climate change research.

Detection of a recent large Hyuga-nada long-term slow slip event and estimation of its spatiotemporal slip distributions

Using horizontal and vertical GNSS time series data from the GSI in sotheastern Kyushu from January 1, 2017 to June 30, 2022, we detected a recent long-term slow slip event (L-SSE) that occurred in the Hyuga-nada region, southwest Japan, and estimated its spatiotemporal slip distribution. We performed such analysis considering the piecewise evolution of slip over time with time windows of 0.8 years to ensure a good signal-to-noise ratio in the horizontal displacements in each time window. The results showed that a slip of more than 2 cm occurred in the central part of Miyazaki Prefecture from 2018.5 to 2019.3 (the units were 0.1 years = 36.5 days). Then, the amount of slip increased and expanded slightly to the southern part of Miyazaki Prefecture from 2019.3 to 2020.1, and the amount of slip reached a maximum of 3.9 cm in the subsequent period (2020.1–2020.9). A smaller slip occurred at almost the same location in the following 0.8 years. Therefore, the duration of the L-SSE was approximately 3.2 years from approximately 2018.5 to 2021.7. The annual average maximum slip rate was approximately 4.9 cm/yr (3.9 cm/0.8 yr) during the period from 2020.1 to 2020.9. The maximum total slip was estimated to be approximately 12.9 cm, and the equivalent release moment was 4.9 × 1019 Nm, corresponding to Mw7.1. Compared to previous L-SSEs that occurred in the Hyuga-nada region, the annual average maximum slip rate was relatively low, and the main total slip was estimated at almost the same location on the plate interface. On the other hand, the slip duration of this L-SSE was the longest, and the release moment and moment magnitude were the greatest. The total slip area of more than approximately 10 cm in the Hyuga-nada L-SSE estimated in this study almost overlapped with the afterslip area of the December 3, 1996 Hyuga-nada earthquake, with a depth range of approximately 30–40 km. Short-term SSEs also occurred in the same depth range. These coincidental same depth ranges of interplate seismic events stem from large thermal gradients. The large thermal gradients play an important role in narrowing the depth range of frictional parameters (a–b) at the plate interface. In addition, the pore pressure and normal stress are closely related to the critical stiffness. In particular, contrasting values of pore pressure and normal stress at the shallow and deep sides of the mantle wedge corner may also contribute to transient aseismic slip within the same depth range.

Modelling multidecadal variability in flood frequency using the Two-Component Extreme Value distribution

The Two-Component Extreme Value (TCEV) distribution is traditionally known as the exact distribution of extremes arising from Poissonian occurrence of a mixture of two exponential exceedances. In some regions, flood frequency is affected by low-frequency (decadal) climate fluctuations resulting in wet and dry epochs. We extend the exact distribution of extremes approach to such regions to show that the TCEV arises as the distribution of annual maximum floods for Poissonian occurrences and (at least two) exponential exceedances. A case study using coastal basins in Queensland and New South Wales (Australia) affected by low-frequency climate variability, shows that the TCEV produces good fits to the marginal distribution over the entire range of observed values without the explicit need to resort to climate covariates and removal of potentially influential low values. Moreover, the TCEV reproduces the observed dog-leg, a key signature of different flood generation processes. A literature review shows that the assumptions underpinning the TCEV are conceptually consistent with available evidence on climate and flood mechanisms in these basins. We provide an extended domain of the TCEV distribution in the L-moment ratio diagram to account for the wider range of parameter values encountered in the case study and show that for all basins, L-skew and L-kurtosis fall within the extended domain of the TCEV.

Projection of climate change effects on flood inundation in the Gin River basin, Sri Lanka

Abstract. Human-induced climate change has altered the frequency and severity of heavy precipitation, droughts, and floods worldwide. Sri Lanka, a developing country in the Indian Ocean, has suffered frequent floods, affecting over 64 % of the population in the past decade. The Gin River, the primary drinking water source of the densely populated Galle district with over 1 million residents, faces recurrent flood disasters. Therefore, this study employed a multi-model ensemble to simultaneously simulate the impact of climate change on both river flow and inundation extent in the frequently flooded Gin River basin located in humid tropical monsoon region. Bias-corrected climate projections of an ensemble of five General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) under Representative Concentration Pathway (RCP) 4.5 emission scenario were used to project future river flow and flood inundation in the Gin River basin, Sri Lanka. The validated Rainfall–Runoff–Inundation (RRI) model projected a 5 % increase in high flows (flows exceeding a 5 % probability of exceedance (<Q5)) at the downstream (Baddegama) gauging station and a 16 % increase at the upstream (Thawalma) gauging station in the future (2040–2060) in comparison to the past (1980–2000), which can be attributed to the elevated rainfall projected during the two monsoon seasons (southwest monsoon (May–September) and northeast monsoon (December–February)) by the GCM ensemble. In addition to the projected changes in high flows revealed by the RRI simulations, the model also yielded critical information about the expansion of annual maximum inundated extent in the basin by 3.5 % that will aid flood risk projection and sustainable management in the basin.

Developing Non-Stationary Frequency Relationships for Greater Pamba River basin, Kerala India incorporating dominant climatic precursors

Abstract. Global climate changes significantly contribute to increased frequency of hydrologic extremes. This significantly underestimates the hydrologic design parameters, bringing of hydro systems to increased failure risk. In order to address this concern, the current practice of development of hydrologic frequency tools need to be updated accounting for non-stationarity. This study first considered a diverse set of statistical tests to examine the trend, change points, non-stationarity and randomness of streamflow, rainfall and temperature time series of scales ranging from daily to annual. The annual maxima time series indicated non stationarity against the stationary behaviour of daily series of hydro-meteorological datasets of the basin. Subsequently, this study developed the Temperature Duration Frequency (TDF), Rainfall Intensity Duration Frequency (IDF) and Flood Frequency (FF) curves of Greater Pamba river basin in Kerala India, the part of which was most severely affected by the near century return period flood event of 2018. The analysis was performed for a multitude of combinations of variations in distribution parameters with time and climatic drivers as physical covariates in the extreme value formulations. The study proposed a novel wavelet coherence (WC) based driver selection of most dominant combination of climatic precursors in developing FF and IDF relations of three locations of Kalloopara, Malakkara and Thumpamon and TDF curve of Kuttanad region in the basin, considering data of 1985–2015 period. The proposed WC framework considers bi-multi-and partial effects of climatic oscillations (COs) like El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) and North Atlantic Oscillation (NAO) in identifying potential drivers. The different WC formulations captured in-phase relationships of streamflows and rainfall with COs at intra-annual, annual and inter annual scales up to 4 years. The methods showed that addition of climatic precursors improved the NS estimates of flood and rainfall quantiles by more accurately capturing the magnitudes of extreme streamflows and rainfalls of 2018, 2021 than the time covariate formulations. However, the role of COs on extreme temperature is not found to be influential in developing TDF relationships, which needs further investigation.