Hydrological Series Research Articles

Propagation from Meteorological to Hydrological Drought considering Nonstationarity in the Luanhe River Basin: Spatiotemporal Pattern and Driving Factors

Abstract Under the joint effects of climate change and human activities, the assumption of the stationarity of hydrological series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. To this end, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model was used to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability. The dynamic change of propagation characteristics was explored by utilizing the moving window, and the driving factors were revealed by using the variable importance in projection. The Luanhe River basin, which has a fragile ecological environment, was selected as a case study. The results indicated that 1) using a time-varying drought index was more reasonable than using a stationary assumption, and the latter relatively easily underestimated the propagation time, especially in summer; 2) drought propagation in summer and autumn was faster than that in spring and winter, and the propagation time showed a significant downward trend; 3) the trigger threshold (absolute value) of meteorological drought in spring was significantly higher than that in other seasons, and hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and 4) the precipitation P, decreasing runoff R, and increasing evaporation E were the main factors affecting the seasonal propagation characteristics. The findings of this study are of great significance for water resource management and further understanding of drought propagation mechanisms.

Effects of hydrological variability on the sustainable use of water in a regional economy. An application to Tuscany

Existing input-output (IO) models have mainly focused on water demand. Some studies have incorporated water supply (availability), but do not take into account its natural variability, an essential element when performing a water stress analysis. The present study integrates the hydrological variability of water availability into a hydroeconomic IO model, considering its exogenous effects on water supply and its exogenous effects on water demand. Two endogenous effects are considered: i) changes in blue water requirements in the agricultural industry due to variations in precipitation and evapotranspiration, and ii) changes in grey water requirements in all discharging industries due to variations in runoff and groundwater recharge. By means of a T-years hydrological series and Monte Carlo simulations, the model allows estimating T values of the Extended Water Exploitation Index (EWEI), obtaining its empirical probability distribution and confronting it with scarcity thresholds. Additionally, the model includes a methodology to incorporate intra-annual variation, obtaining the critical month EWEI and defining a more transparent and endogenous scarcity threshold. Empirically tested for the Italian region of Tuscany considering a multivariate hydrological model for the generation of a 100-year hydrological series, our results allow a more in-depth analysis of water scarcity in the region.

Time traveling through the floodscape: assessing the spatial and temporal probability of floods and susceptibility zones in the Lower Damodar Basin.

The flood of Damodar river is a well-known fact which is used to the whole riverine society of the basin as well as to the eastern India. The study aims to estimate the spatio-temporal probability of floods and identify susceptible zones in the Lower Damodar Basin (LDB). A flood frequency analysis around 90years hydrological series is performed using the Log-Pearson Type III model. The frequency ratio model has also been applied to determine the spatial context of flood. This reveals the extent to which the LDB could be inundated in response to peak discharge conditions, especially during the monsoon season. The findings indicate that 36.64% of the LDB falls under high to very high flood susceptibility categories, revealing an increasing downstream flood vulnerability trend. Hydro-geomorphic factors substantially contribute to the susceptibility of the LDB to high magnitude floods. A significant shift in flood recurrence intervals, from biennial occurrences in the pre-dam period to decadal or vicennial occurrences in the post-dam period, is observed. Despite a reduction in high-magnitude flood incidents due to dam and barrage construction, irregular flood events persist. The effect of flood in the LDB region is considered to be either positive as well as negative in terms of wholistic sense and impact. The analytical results of this research could serve to identify flood-prone zones and guide the development of flood resilience policies, thereby promoting sustainability within the LDB floodplain.

Statistical Analysis and Scenario Modeling of Non-Stationary Runoff Change in the Loess Plateau: A Novel Application of the Generalized Additive Model in Location, Scale and Shape

The hydrological series in the Loess Plateau region has exhibited shifts in trend, mean, and/or variance as the environmental conditions have changed, indicating a departure from the assumption of stationarity. As the variations accumulate, the compound effects caused by the driving variables on runoff variations grow complex and interactive, posing a substantial risk to water security and the promotion of high-quality development in regions or river basins. This study focuses on the Tuwei River Basin in the Loess Plateau, which experiences significant changes in vegetation coverage and minimal human disturbance, and examines the cross-driving relationship between the runoff change and its driving variables (including hydrometeorological and environmental variables). A quantitative statistical analysis method based on the GAMLSS is then developed to estimate the interacting effects of changes in the driving variables and their contribution to runoff changes. Finally, various anticipated scenarios are used to simulate the changes in driving variables and runoff disturbances. The findings indicate the following: (1) The developed GU, LO, and NO distribution-based GAMLSSs provide a notable advantage in effectively capturing the variations in groundwater storage variables, actual evapotranspiration, and underlying surface parameters, as well as accurately estimating the impacts of other relevant variables. (2) The precipitation and groundwater storage variables showed predominantly positive contributions to the runoff change, but actual evapotranspiration had an adverse effect. The changes in underlying surface parameters, particularly since 2000, increase actual evapotranspiration, while decreasing groundwater storage, resulting in a progressive decrease in runoff as their contribution grows. (3) The scenario simulation results reveal that alterations to the underlying surface have a substantial influence on the evolution of runoff in the Tuwei River Basin. Additionally, there are cross-effects between the impact of various driving variables on runoff, potentially compounding the complexity of inconsistent changes in runoff sequences.

Оптимизационный алгоритм управления водными ресурсами водохранилищ для обоснования диспетчерских графиков

The rules for the use of reservoir water resources need to be constantly updated, considering a significant number of factors affecting the effectiveness of management. Anthropogenic changes in morphometric functions, partial siltation of dead volume, and changes in basic hydrological characteristics have a significant impact on reservoir parameters and flow regulation regime. Justification of the regulatory regime remains an urgent and traditionally difficult engineering task of the IRP, discussed at professional meetings and in scientific publications. The article discusses a methodological approach based on the introduction of a given control algorithm into the process of assigning an interval request to the reservoir. In this case, the request is formulated as a two-parameter power function of the predicted resource for the estimated time interval (month). The degree indicator and the relative length of the range of uninterrupted water requirements coverage are the arguments of the query function. Nomograms illustrating the influence of the degree indicator on the outline of the dependence of requirements on the reservoir are constructed. In our opinion, this approach significantly reduces the Pareto set, excluding a significant part of the set of formal optima. The algorithm of the proposed model is focused on two-criteria optimization, where the relative values of the total annual water output and the sum of the relative interval ordinates of water consumption are considered as criteria. The unified functional is formed by linear convolution of criteria. The model was tested on the example of the resources of the Zhizdra River for a series of two low-water years. In the process of balance modeling and optimization, Excel tools are used, including “solution search” and VBA procedures. It is noted that it is necessary to use more powerful optimization algorithms for calculations based on multiyear hydrological series.

Особенности формования и трансформации максимального стока реки Миус

Purpose: to study the features of the formation and transformation of the maximum river runoff based on empirical data. Materials and methods. The series of observations of water levels and flows in two existing gauging stations Kuibyshevo (hydrological station 1) and Matveev Kurgan (hydrological station 2) with a duration of 51 years, were used as source materials. Statistical and theoretical methods were used as information processing methods, including system analysis and hydrological and geographical generalizations. Results and discussions. A study consisting of four stages: analysis of hydrographic characteristics, construction of dependency graphs of maximum flow rates Q = f(H), analysis of hydrological series and calculated data, was carried out. As a result of the analysis of hydrographic characteristics, a significant difference in the average heights of the watershed area and the bed slope was revealed. When comparing the maximum parameters by year, it was revealed that the maximum level recorded at the gauging station does not correspond to the maximum flow rate: at the hydrological station 1, the maximum water level was observed in 1979 and corresponded to 2 % availability, while the same year discharge of 151 cubic m/s corresponded to 6 % availability, the highest discharge was recorded in 1970 and was higher by 30 cubic m/s, the water level corresponded to 4 % availability and was lower by 0.49 m; at hydrological station 2, the maximum water level was recorded in 1985 with a flow rate of 202 cubic m/s, corresponding to 6 % probability, the maximum flow rate was observed in 1964 at a water level 0.38 m below the maximum, which corresponds to 12 % availability. Conclusions. The materials presented in the article confirm that the use of the coefficients of variation and asymmetry of one gauging station for the long-term average values of another gauging station is not entirely correct, despite the fact that they are located on the same river at a fairly close distance.

Hydrological complexity analysis using multiscale entropy: Methodological explorations and insights

Multiscale entropy is a popular and valuable methodology for analyzing hydrological complexity. However, some errors and limitations remain in its use and interpretation. This study identified three key issues, incorrect mapping of entropy values with frequency signals, an insufficient time scale range, and an unclear relationship between features in the multiscale entropy curve and hydrological interpretation. To address these issues, we focused on the sediment concentration and runoff in the Yanhe Watershed and conducted in-depth explorations using refined composite multiscale entropy (RCMFE) and three other methods, power spectrum density analysis, wavelet transform, and surrogate analysis.The results indicate that the entropy at each scale is influenced by signals with time scales equal to or greater than its own scale and that its variation is controlled by the overall signal dispersion. The RCMFE method demonstrated suitability for the complexity analysis of sediment and runoff sequences with short-length data. To effectively capture hydrological complexity, attention to the scale range issue is crucial, ensuring that the richness of complex features at larger scales in multiscale entropy curves is not overlooked. Interpretation of the local minima and maxima in the multiscale entropy curve is highlighted. Local minima signify high periodic patterns and predictability, whereas the first peak, typically occurring at approximately 2–3 months, is a valuable indicator of the overall complexity of the hydrological series. Linear characteristics were found to contribute significantly to the hydrological complexity, with stronger linear correlations resulting in an overall increase, lower local minima, and increased volatility in the multiscale entropy curve. The notable reduction in the hydrological complexity of the Yanhe Watershed calls for ecological sustainability and sustainable soil and water conservation efforts.

A new method for detecting abrupt changes in the dependence among multivariate hydrological series based on moving cut total correlation

A new method for detecting abrupt changes in the dependence among multivariate hydrological series based on moving cut total correlation

Comparison of the minimum discharge of the Instruch river at the beginning of the 20th and 21st century

The hydrological series of the minimum annual discharges of the Instruch River (hydrological gauging station Ulyanovo, Kaliningrad region) was studied. The values of the minimum discharge rates were processed in the Mathcad environment. The characteristics of the minimum discharges (average and minimum annual, minimum 5-, 10- and 30-day discharges) of the Instruch River in 1901-1912 and 2008-2020 are determined. A comparative analysis of the characteristics of the minimum annual discharges of the river Instruch for the indicated periods.

Hydrological response under CMIP6 climate projection in Astore River Basin, Pakistan

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes. Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes. Coupled Model Intercomparison Project phase 6 (CMIP6) dataset of three GCM’s (BCC-CSM2-MR, INM-CM5-0, and MPI-ESM1-2-HR) with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020–2070. Bias correction method was used to reduce errors. In this study, statistical significance of trends was performed by using the Man- Kendall test. Sen’s estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations. MPI-ESM1-2-HR showed better results with coefficient of determination (COD) ranging from 0.70–0.74 for precipitation and 0.90–0.92 for maximum and minimum temperature at Astore, Rama, and Rattu followed by INM-CM5-0 and BCC-CSM2-MR. University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change. Results revealed that by the end of the 2070s, average annual precipitation is projected to increase up to 26.55% under the SSP1–2.6, 6.91% under SSP2–4.5, and decrease up to 21.62% under the SSP5–8.5. Precipitation also showed considerable variability during summer and winter. The projected temperature showed an increasing trend that may cause melting of glaciers. The projected increase in temperature ranges from - 0.66°C to 0.50°C, 0.9°C to 1.5°C and 1.18°C to 2°C under the scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. Simulated streamflows presented a slight increase by all scenarios. Maximum streamflow was generated under SSP5–8.5 followed by SSP2–4.5 and SSP1–2.6. The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand. The projected streamflows (2020–2070) compared to the control period (1990–2014) showed a reduction of 3%–11%, 2%–9%, and 1%–7% by SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. The results revealed detailed insights into the performance of three GCMs, which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management

AbstractHydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long‐term variability of hydrological variables: as an overall trend, shifting mean, or as a long‐term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD‐NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain‐River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD‐NSOR model can be an appropriate option by reproducing long‐term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long‐term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water‐related risks.

Statistical and Water Management Assessment of the Impact of Climate Change in the Reservoir Basin of the Volga–Kama Cascade on the Environmental Safety of the Lower Volga Ecosystem

When managing water resources in order to provide water to consumers, a number of consequences arise related to the violation of the hydrological regime due to the regulation of flow by reservoirs. The second factor is possible climate change. These changes can negatively (or positively) affect the functioning of aquatic ecosystems. To reduce the impact on the environment, it is necessary to determine the nature and indicators of changes in the hydrological regime, calculate quantitative estimates of these indicators and ranges of acceptable values, and develop release rules that ensure compliance with these ranges with a given probability. To manage the water resources of the Volga and Kama Rivers, the main ecological task is to flood the floodplain meadows, to maintain the conditions of natural reproduction of fish on the Lower Volga, including the Volga River delta and the Volga–Akhtuba floodplain. In addition, it is necessary to meet with sufficient reliability the requirements of energy in the summer–autumn and winter low-water periods and water transport during the navigation period. The task of optimal management is to find such solutions in years of different water content that ensure the well-being of the main water users with a given probability and do not disturb the Lower Volga ecosystem. This article presents the research of the water resources state of the water resource system of the Volga and Kama river basins. A statistical analysis of the hydrological series of the observed inflow for 1916–2020 was performed, and the inflow change point (1979) was found by the Bayesian method of estimation. A statistically significant difference between the average inflow values of two series (1916–1978, 1979–2020) was proved using a two-sample Student’s test. The seasonal parameters of the reliability curves were calculated based on the three-parameter Kritsky and Menkel distribution. For these two series, water resource optimization calculations (using Excel Solver) were performed, and the reliability of fulfilling the requirements of water users was determined; for the series 1916–1978, an alternative solution was found in favor of fisheries, and an analysis of the results was also performed. The methodology used in the research allows finding trade-off solutions in the favor of different water users (ecology, agriculture and fisheries, water supply, hydropower, navigation, etc.) and is based on the use of multi-criteria optimization methods and the trade-offs theory. As a result of the research, new knowledge was obtained about the hydrological situation in the basin of the Volga–Kama reservoir cascade in connection with climate change.

A Decomposition-based Multi-model and Multi-parameter ensemble forecast framework for monthly streamflow forecasting

Hydrological ensemble forecasting plays a critical role in decision-making and water resource management. The skill of an ensemble forecasting system is limited by input data, model parameters and model structure, and the precision of the system depends mainly on the ensemble size. The main contradiction is to ensure the forecast performance and ensemble size under considering the uncertainties of inputs and models. In this study, a hybrid decomposition-based multi-model and multi-parameter (DMP) ensemble streamflow forecast method is proposed. The proposed method couples the signal decomposition and Artificial Intelligence (AI) forecast method in order to provide a more efficient and effective streamflow forecast. The decomposition method is used to reduce the uncertainty of inputs for the forecast model, and AI models are used to improve the performance of forecasting. The results illustrate that the DMP ensemble streamflow forecast method not only extracts the characteristic periodic term and trend term of hydrological series but also improves the forecasting accuracy and reduces the ensemble forecast uncertainty. At the same time, it greatly expands the ensemble size which solves the problem of insufficient ensemble size in order to be convenient for further water resources analysis and decision-making. The findings also show that high-frequency subseries are highly sensitive to ensemble forecast, and it is recommended that more than 2 layers of high-frequency series should be used for ensemble forecast. The proposed approach in this study is more suitable for nonlinear and non-stationary hydrological series forecasting, and it is of reference significance for real reservoir operation.

Statistical analysis of hydrological/meteorological parameters – Bosna River Basin in Federation of Bosnia and Herzegovina

According to the instructions of the World Meteorological Organization and the legal obligations of conducting hydrological monitoring in the Sava River Basin District in the Federation of Bosnia and Herzegovina, the "Sava River Watershed Agency Sarajevo" has been conducting, planning and organizing hydrometric measurements at its hydrological stations for many years. Additionally, the Federal Hydro-Meteorological Institute performs hydrological and meteorological monitoring at its stations. Taking into account the above information, and the fact that the World Meteorological Organization (WMO) has defined "basic" climatic and hydrological series in the interval of 30 years (1961-1990 and 1991-2020), hydrological/meteorological data for the last two basic series (1961-1990 and 1991-2020) were completed for Sava River Basin, with an exception with regard to the period 1991-2000. The study includes statistical analyses that are typical for the analysis of climatological and hydrological parameters. The analysis was performed at three meteorological stations (MS Sarajevo, MS Zenica and MS Tuzla) and three hydrological stations (HS Maglaj, river Bosna; HS Zavidovići, river Krivaja and HS Olovo, river Krivaja), which have the longest series of observations in the Bosna River Basin. The analysis results obtained for the series 1991-2020 will serve as the basis for analyzing the new series 2021-2050, just as the series from 1961-1990 served as the basis for analyzing the series 1991-2020 in this study.

Calculation of Total Erosion in the Lower Reaches of Mezhdurechensk Reservoir Using Empirical Morphometric Dependencies

The research aims are to develop a methodology and the choice of initial formulas for calculating channel deformations in the lower reaches of waterworks on rivers composed of easily eroded soils (like the Amu Darya river). To calculate the total erosion below the Mezhdurechensk reservoir, both theoretical equations of channel deformation (Saint-Venant equations) and empirical dependences of the transporting capacity of the flow (turbidity) and morphometric dependences of the connection of the width of the deformable channel from the average depth obtained for the lower reaches of the Takhiatash hydrounit, the stem of which is located slightly above the Mezhdurechensk hydrounit, were used. A hydrological series of years was formulated for the calculation (considering the actual frequency of low-water, medium-water, and high-water years). The results of calculating the total erosion of the bottom in the first ten years of the simulated hydrological series of years, provided that the width of the channel in the lower reaches is constant, is expected to be 0.17-0.41 m (with full flow clarification) and 0.16-0.38 m (with partial flow clarification ε = 0.8). The change in the cross-sectional area of the riverbed will be 304(292)m2. Provided that the width narrows to a stable width Bstable =kha : Δz = 0.41 m (with the concentration of the flow at a narrower width, the decrease will be correspondingly large). The proposed methodology can be applied to calculate the total erosion on rivers composed of easily eroded soils and having average slopes of the longitudinal profile of the river within 0.0001 (similar to the Amu Darya River).

A comprehensive ecological flow calculation for a small hydropower development river: A case study

AbstractSmall hydropower projects play a significant role in supplying clean energy and promoting economic development all around the world. The calculation of a river's ecological flow, that is, not only the quantity but also the quality of flowing water, in the watershed where small hydropower projects are located play an important role in scientifically guiding the discharge of small hydropower stations in the upper reaches of the river. By combining Gini coefficient with hydrological variation diagnosis, this study provided a novel approach to test the randomness of hydrological series and assess the evenness of runoff distribution. Furthermore, this study is the first attempt to calculate ecological flow for two separate periods (flood and low‐water periods), and thus is able to better reflect annual runoff variations. In the meantime, the Tennant method was improved by introducing of monthly runoff coefficient and median substitution, and integrating four other hydrological methods (empirical method, Qp method, annual distribution method and minimum monthly average measured runoff method) into the comprehensive calculation and analysis. The above methods were applied to the ecological base flow calculation for a small hydropower project in Dongjiang River basin of China. Through comprehensive and comparative analysis, better applicability was demonstrated by the improved Tennant method and the annual distribution method. The recommended values of ecological base flow for each month were derived at the range of 407–431 m3/s during the flood period and 179–239 m3/s during the low‐water period. This study demonstrated that the method could meet the ecological flow calculation requirements for river ecosystem and provide scientific basis for the delineation of ecological flow thresholds, and thereby ensure the healthy and sustainable development of small hydropower and the healthy river aquatic ecosystem.

Analysis of the Runoff Component Variation Mechanisms in the Cold Region of Northeastern China under Climate Change

Climate change alters hydrological processes in cold regions. However, the mechanisms of runoff component variation remain obscure. We implemented a WEP-N model to estimate monthly runoff in the Songhua River Basin (SRB) between 1956 and 2018. All flow simulations were accurate (NSE > 0.75 and RE < 5%). The annual runoff was attenuated in 1998, and the hydrological series (1956–2018) was divided into base and change periods in that year. Relative to the BS (base scenario), annual production flow reduction was −28.2% under climate change and water use. A multifactor attribution analysis showed that climate change and water use contributed 77.0% and 23.0% to annual runoff reduction, respectively. Decreases in annual surface and base flow explained 62.1% and 35.7% of annual production flow reduction, respectively. The base flow increased by 8.5% and 6.5% during the freezing and thawing periods, respectively. Relative to the BS, groundwater recharge increased by 9.2% and 4.1% during the freezing and thawing periods, respectively, under climate change conditions. Climate change was the dominant factor attenuating production flow. The change in production flow occurred mainly during the non-freeze-thaw period. The decrease in total production flow in the SRB was caused mainly by the decrease in the surface flow, where the reduction in base flow accounted for a relatively small proportion. Production flow attenuation aggravated water shortages. The utilization rate of groundwater resources is far below the internationally recognized alarm line. Therefore, attention should be directed towards certain areas of the SRB and other regions with minimal groundwater exploitation.

Optimizing future mortality rate prediction of extreme temperature-related cardiovascular disease based on skewed distribution in peninsular Malaysia

Abstract The bias correction method (BCM) is useful in reducing the statistically downscaled biases of global climate models’ (GCM) outputs and preserving statistical moments of the hydrological series. However, BCM is less efficient under changed future conditions due to the stationary assumption and performs poorly in removing bias at extremes, thereby producing unreliable bias-corrected data. Thus, the existing BCM with normal distribution is improved by incorporating skewed distributions into the model with linear covariate (BCM-QMskewed). In this study, BCM-QMskewed is developed to reduce biases in the extreme temperature data of peninsular Malaysia. The input is the MIROC5 model output gridded data and observations sourced by the Malaysian Department of Irrigation and Drainage (1976–2005). BCM-QMskewed with lognormal (LGNORM) and Gumbel (GUM) has shown considerable skill in correcting biases, capturing extreme and nonstationarity of current and future extreme temperatures data series corresponding to the representative concentration pathways (RCPs) for 2006–2100 based on model diagnostics and precision analysis. Higher projection of extreme temperatures is more pronounced under RCP8.5 than RCP4.5 with precise estimates ranging from 33 to 42 °C and 30 to 32 °C, respectively. Finally, the projection of extreme temperatures is used to calculate cardiovascular disease (CVD) mortality rate which coincides with high extreme temperatures ranging between 0.002 and 0.014.

Two-stage hybrid model for hydrological series prediction based on a new method of partitioning datasets

• A new method of dividing datasets based on PSO is proposed. • BNN incorporates Langevin dynamics in the parameter update step (LDBNN). • Integration of LDBNN, CEEMDAN, VMD and the proposed method yield the hybrid model. • The new data dividing method performs better than the traditional dividing method. • The hybrid model yields excellent point and interval predictions. Obtaining accurate point estimates and reliable interval prediction results for rainfall and runoff series is important to aid in water resource decision-making and planning management in a changing environment. In this paper, we propose a two-stage hybrid model (P-CVEE-LDBNN). The model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods for data preprocessing and a Bayesian neural network (LDBNN) based on Langevin dynamics as a prediction model. Then, a new dataset partitioning method based on a particle swarm optimization algorithm, which is different from the traditional dataset partitioning method, is applied. A one-stage hybrid model (T-CEE-LDBNN) that integrates the CEEMDAN method and the traditional method of partitioning datasets, a one-stage hybrid model (P-CEE-LDBNN) that integrates the CEEMDAN method and the new method of partitioning datasets, and the two-stage hybrid P-CVEE-LDBNN model were compared. These models were applied to monthly runoff and monthly precipitation series from seven hydrological and meteorological stations in the Yellow River basin. The mean absolute error (MAE), absolute root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (R) were used to evaluate the predictive ability of the models. The containment ratio (CR), the average bandwidth (IW), and the average asymmetry degree (S) were used to evaluate the interval prediction results of the models. The results show that (1) the subset obtained with the new method of data partitioning considering statistical properties is more favorable for model prediction. (2) The second decomposition approach based on VMD is a reliable method that can significantly improve the prediction accuracy of the final model. (3) Compared with traditional neural networks that can only obtain deterministic point prediction results, Bayesian methods can provide intervals with prediction results, making the results of the two-stage hybrid P-CVEE-LDBNN model highly reliable.

Regional Flood Frequency Analysis of the Sava River in South-Eastern Europe

Regional flood frequency analysis (RFFA) is a powerful method for interrogating hydrological series since it combines observational time series from several sites within a region to estimate risk-relevant statistical parameters with higher accuracy than from single-site series. Since RFFA extreme value estimates depend on the shape of the selected distribution of the data-generating stochastic process, there is need for a suitable goodness-of-distributional-fit measure in order to optimally utilize given data. Here we present a novel, least-squares-based measure to select the optimal fit from a set of five distributions, namely Generalized Extreme Value (GEV), Generalized Logistic, Gumbel, Log-Normal Type III and Log-Pearson Type III. The fit metric is applied to annual maximum discharge series from six hydrological stations along the Sava River in South-eastern Europe, spanning the years 1961 to 2020. Results reveal that (1) the Sava River basin can be assessed as hydrologically homogeneous and (2) the GEV distribution provides typically the best fit. We offer hydrological-meteorological insights into the differences among the six stations. For the period studied, almost all stations exhibit statistically insignificant trends, which renders the conclusions about flood risk as relevant for hydrological sciences and the design of regional flood protection infrastructure.