Hydrological Observation Data Research Articles

Forecasting the design maxima water discharges of floods on the Latorica river according to the data of the Mukachevo gauging station using plotting position formulas

This article presents the results of forecasting design maxima discharges on the Latorica River within Mukachevo town based on hydrological observation data at the “Mukachevo” gauging station using plotting position formulas. While solving the task, a novel non-parametric method of forecasting using observation data is applied. The method includes extrapolating the discrepancy (divergence, disagreement) between the estimates of the statistical annual probabilities of exceedance obtained by different plotting position formulas. The task is considered in the frame of the stationarity hypothesis of the maximum river flow employing a time series of maximal discharges of the Latorica River observed at the “Mukachevo” gauging station from 1947 to 1999.We involved the thirteen plotting position formulas. There was no specific criterion for choosing them to solve the task. All applied formulas were considered admissible options, and results obtained after using them – expert judgments reflecting decision-makers’ predisposition to more cautious or less expensive decision options in flood management strategies.The epistemic uncertainty of the different plotting positions was reduced by employing the Fishburn rule. According to this rule, the significance of various plotting positions was given by arranging their estimates in descending order of importance of their values under decision-making. Depending on the selected significance option assignment of the different plotting position formulas, such rank-weighted estimates of the design peak discharges (each of them for annual exceedance probability 1%, 0.5%, and 0.2%) were computed: (1) the rank-weighted upper bound estimate (sup-estimate) corresponding to the predisposition to more cautious decision options; (2) the rank-weighted lower bound estimate (inf-estimate) corresponding to the predisposition to less expensive decision options. As possible control theoretical alternatives for forecasting design maximal discharges considered were five parametric probability distributions: 1) the Kritskyi-Menkel three-parameter gamma distribution; 2) Pearson’s type III distribution; 3) the Extreme value type I distribution (Gumbell’s type I distribution); 4) the Logarithmic Pearson type III distribution; and 5) the Two-parameters logarithmic-normal distribution. The population statistical parameters for these parametric probability distributions were estimated from the sample statistics by the method of moments.

Краткосрочное и среднесрочное прогнозирование уровней воды на реках бассейна Тобола

In order to provide the necessary hydrological information for operational decisions on the use of water resources and protecting the population from dangerous floods for the rivers of the Tobol river basin, a methodology for daily forecasting of average daily water levels with a lead time of 1 to 10 days has been developed. The hydrograph extrapolation method was used, which takes into account water levels for the date of the forecast and for the 5 previous days. The methodology was developed for 64 sections located on the Tobol River and its tributaries of various orders. To estimate the parameters of the scheme for obtaining forecasts and their verification, hydrological observation data for the period from 1985 to 2022 were used. For most rivers in the basin, the methodology provides satisfactory results. In general, the accuracy of forecasts and their lead time increase with increasing catchment area. The methodology can be used within an automated system for preparing and issuing forecasts.

Towards transient connectivity of river networks during rainfall events: Insight from hydrological observation and functional data analysis

River connectivity, which is critical for river networks to perform their functions, is deteriorating in highly urbanized areas. While it is well known about the structural and functional connectivity of river networks using traditional indicators, much less is known about the process connectivity and its influence factor. To this end, we propose a new approach based on hydrological observations and functional data analysis that has the advantage of capturing transient changes in process connectivity and responses to rainfall events. Applying the method to a typical river-dense plain in China, the results show that 1) the highest and the lowest values of process connectivity were concentrated in 0.6–0.95 and 0.04–0.61 during 2015–2020, respectively. The average values of process connectivity were lower in 2015, 2016, and 2020 with 0.49, 0.44, and 0.46, respectively, and higher in 2017 to 2019 with 0.52, 0.57, and 0.58, respectively. 2) There is a significant negative correlation between rainfall intensity and process connectivity, especially when heavy rainfall occurs. Under the same rainfall intensity, the process connectivity was weaker in the river network with lower structural connectivity, lower overflow capacity of main streams, strong concentration of rivers, more sluices and larger unit areas. 3) The main influencing factors on process connectivity were structural connectivity and rainfall, with contributions of 34.67% and 22.67%, respectively. Following closely are unit features and human regulation, contributing 14.07% and 12.61% to process connectivity, respectively. 4) Structural connectivity and unit features are the primary factors influencing process connectivity during low-intensity rainfall events. During high-intensity rainfall events, rainfall and unit features become the predominant factors affecting process connectivity. Our results provide a new insight for quantifying process connectivity in the plain and are expected to support the study of the changing mechanism of hydrological connectivity.

Краткосрочное и среднесрочное прогнозирование стока рек бассейна Дона

For the rivers of the Don basin, the Hydrometcentre of Russia has developed a methodology for daily short- and medium-range forecasting of streamflow and water levels. Average daily discharges and water levels are predicted throughout the year with a lead time of 1 to 10 days based on the hydrograph extrapolation method, which takes into account their values for the date of the forecast and for the previous 5 days. To estimate the parameters of the forecasting scheme, hydrological observation data for each river section are used. The technique gives satisfactory results and can be used within the framework of an automated system for preparing and issuing forecasts in order to provide necessary forecast information for making operational decisions on the use of water resources of the Don basin rivers and protecting the population from dangerous floods.

Research on Optimization Method of Evaporation Duct Prediction Model

The sea surface roughness parameterization and the universal stability function are key components of the evaporation duct prediction model based on the Monin–Obukhov similarity theory. They determine the model’s performance, which in turn affects the efficiency and accuracy of electromagnetic applications at sea. In this study, we collected layered meteorological and hydrological observation data and preprocessed them to obtain near-surface reference modified refractivity profiles. We then optimized the sea surface roughness parameterization and the universal stability function using particle swarm optimization and simulated annealing algorithms. The results show that the particle swarm optimization algorithm outperforms the simulated annealing algorithm. Compared to the original model, the particle swarm optimization algorithm improved the prediction accuracy of the model by 5.09% under stable conditions and by 9.97% under unstable conditions, demonstrating the feasibility of the proposed method for optimizing the evaporation duct prediction model. Subsequently, we compared the electromagnetic wave propagation path losses under two different evaporation duct heights and modified refractivity profile states, confirming that the modified refractivity profile is more suitable as the accuracy criterion for the evaporation duct prediction model.

Impact of increasing vegetation coverage on gaining and losing streams in an arid endorheic river watershed

AbstractTo improve the ecology in northern China, the Chinese government has launched a series of revegetation projects. However, the impact of increasing vegetation coverage on gaining and losing streams remains largely unexplored, particularly in arid endorheic watersheds with frequent exchanges of water and other materials between surface water and groundwater. To address this, we modified the vegetation growth module of the soil and water assessment tool (SWAT) to simulate the vegetation coverage change proposed for the middle and lower reaches of the Bayin River. We then coupled the modified SWAT to a modular finite‐difference groundwater flow model to simulate the surface and subsurface water exchange. Hydrological, meteorological, and hydrogeological observation data were then used to build and calibrate the coupled model for the period 2001–2019. We set two vegetation coverage scenarios (non‐revegetation and vegetation) and two irrigation variants (non‐irrigation and irrigation) to investigate the impact of changes in vegetation coverage and the related irrigation variant on gaining and losing streams. The results showed that the vegetated land area and annual leaf area index both increased significantly between 2019 and 2001. The groundwater discharge to the gaining streams was more strongly affected by revegetation and related irrigation than the groundwater recharge from the losing streams. Irrigation related to revegetation emphasizes the effect of revegetation on streamflow. We suggest that in arid endorheic river basins, because of the related irrigation, revegetation has a substantial impact on groundwater level and streamflow, which is mainly composed of baseflow. Therefore, the irrigation efficiency of newly planted vegetation must be improved to stabilize the groundwater level and related ecological conditions.

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

Floods are highly perilous and recurring natural disasters that cause extensive property damage and threaten human life. However, the paucity of hydrological observational data hampers the precision of physical flood models, particularly in ungauged basins. Recent advances in disaster monitoring have explored the potential of social media as a valuable source of information. This study investigates the spatiotemporal consistency of social media data during flooding events and evaluates its viability as a substitute for hydrological data in ungauged catchments. To assess the utility of social media as an input factor for flood prediction models, the study conducted time-series and spatial correlation analyses by employing spatial scan statistics and confusion matrices. Subsequently, a long short-term memory model was used to forecast the outflow volume in the Ui Stream basin in South Korea. A comparative analysis of various input factor combinations revealed that datasets incorporating rainfall, outflow models, and social media data exhibited the highest accuracy, with a Nash–Sutcliffe efficiency of 94%, correlation coefficient of 97%, and a minimal normalized root mean square error of 0.92%. This study demonstrated the potential of social media data as a viable alternative for data-scarce basins, highlighting its effectiveness in enhancing flood prediction accuracy.

Missing Rainfall Daily Data: A Comparison Among Gap-Filling Approaches

AbstractMissing data is a frequent problem in meteorological and hydrological temporal observation data sets. Finding effective solutions to this problem is essential because complete time series are required to conduct reliable analyses. This study used daily rainfall data from 60 rain gauges spatially distributed within Portugal's Guadiana River basin over a 30-year reference period (1976–2005). Gap-filling approaches using kriging-based interpolation methods (i.e. ordinary kriging and simple cokriging) are presented and compared to a deterministic approach proposed by the Food and Agriculture Organization (FAO method). The suggested procedure consists of fitting monthly semi-variogram models using the average daily rainfall from all available meteorological stations for each month in a reference period. This approach makes it possible to use only 12 monthly semi-variograms instead of one for each day of the gap period. Ordinary kriging and simple cokriging are used to estimate the missing daily precipitation using the semi-variograms of the month of interest. The cokriging method is applied considering the elevation data as the secondary variable. One year of data were removed from some stations to assess the efficacy of the proposed approaches, and the missing precipitation data were estimated using the three procedures. The methods were validated through a cross-validation process and compared using different performance metrics. The results showed that the geostatistical methods outperformed the FAO method in daily estimation. In the investigated study area, cokriging did not significantly improve the estimates compared to ordinary kriging, which was deemed the best interpolation method for a large majority of the rainfall stations.

Assessment of Water Conservation Services Based on the Method of Integrating Hydrological Observation Data According to Different Ecosystem Types and Regions

Water conservation is an essential indicator of the hydrological regulation capacity of terrestrial ecosystems. At the regional scale, the water conservation capacity of an ecosystem is typically assessed using the water balance model (WBM). However, the estimation of the runoff depth relies heavily on rainfall data and the ecosystem runoff coefficient look-up table, which introduces uncertainties in the assessment results. To address this issue, this study constructed a new method for quantifying the spatiotemporal distribution pattern of runoff depth based on the ecosystem type and regional spatial heterogeneity characteristics using runoff observation data from hydrological observation stations. We use this new method to evaluate the water conservation capacity of the ecosystem on a regional scale and compare and analyze the differences between the new and old methods in terms of connotation, data format, and evaluation results. Finally, we discuss the advantages and potential applications of the new method.

Methodology for surface water flood modelling based on analysis of digital elevation model and hydrological data

A methodology for surface water flood modelling is proposed, based on the use of small-scale mapping tools together with hydrological observation data. To reproduce the flooding surface during extreme floods, information on historical maximum water levels was selected, and a digital elevation model (DEM) was used as the cartographic basis. The proposed methodology is universal and provides possibility to determine the boundaries of the potential flooding area in river sections during extreme water levels, to identify objects in the potential flooding area, as well as to make operational decisions to prevent disastrous situations arising from floods and minimize adverse consequences. The results of application of the proposed method for certain river sections of the North Caucasus, a region characterized by a high degree of flood hazard, are presented.

Aprimorando a previsão de vazão para o setor elétrico brasileiro: uma estratégia baseada em um hiper-multimodelo

ABSTRACT Streamflow forecasting plays an important role in ensuring the reliable supply of electricity in countries heavily reliant on hydropower. This paper proposes a novel framework that integrates various hydrological models, climate models, and observational data to develop a comprehensive forecasting system. Three families of models were employed: seasonal forecasting climate models integrated with hydrological rainfall-runoff models; stochastic or machine learning models utilizing endogenous variables, and stochastic or machine learning models that consider exogenous variables. The hyper-multimodel framework could successfully increase the overall performance of the scenarios generated through the use of the individual models. The quality of the final scenarios generated was directly connected to the performance of the individual models. Therefore, the proposed framework has potential to improve hydrological forecast for the Brazilian electricity sector with the use of more refined and calibrated individual models.

Runoff Estimation of Jiulong River Based on Acoustic Doppler Current Profiler Online Monitoring Data and Its Implication for Pollutant Flux Estimation

The runoff of the Jiulong River (JLR) is a key parameter that affects the estimation of pollutant flux into Xiamen Bay (XMB). The precise runoff estimation of the JLR can be used to determine the accuracy of the pollutant flux estimation flowing into XMB. In this study, to analyze the hydrological dynamic characteristics and identify the correlation between fixed-site real-time ocean current observations and cross-sectional navigation flow observations, we conducted six navigation observations on two cross-sections of the JLR estuary during the spring tide and neap tide in the normal season, wet season, and dry season in 2020. Simultaneously, we measured hydrological observation data by a fixed-site buoy located in the JLR estuary and collected runoff data that were measured upstream of the JLR. The results showed that the average correlation coefficient between the average velocity of the fixed-point buoy and average velocity of the section was more than 0.90, higher than expected, the minimum average deviation was 4%, and the minimum sample standard error was 5.7%, which was a good result. In this study, we constructed a model for estimating the runoff of the JLR into the sea. The findings demonstrated that Acoustic Doppler Current Profiler (ADCP) online monitoring data were useful to estimate runoff of the JLR with high accuracy, could promote the accuracy of estimated pollutant flux of the JLR's discharge into XMB, and could provide more scientific and reliable basic data for future load flux estimation research.

Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms

The article presents the results of study of the application of machine learning methods to the problem of classification and identification of different river water regimes in a large region – the European territory of Russia. An accumulation of hydrological observation data for the 60 – 80 years makes it possible to create an information basis for such studies. The article uses information on the average monthly runoff at 351 hydrological gauges during the period from 1945 to 2018. The most widely used data clustering approaches were used as analysis methods – K-means, EM-method, agglomerative hierarchical clustering, DBSCAN algorithms and the application of gradient boosting methods (CATBUST). Clustering and classification algorithms were given eight parameters as a basis for prediction. It was found that the most distinct and stable clusters are formed with three parameters, and the highest silhouette coefficient (SS = 0,3-0,5) is obtained using the numbers for months of the maximum and minimum runoff and the ratio of the maximum to the minimum water flow. The best result gives DBSCAN (SS = 0,6 – 0,7). Supervised classification models also show high correspondence with the reference classification, with an accuracy of 87%. Both clustering methods and classification methods showed a shift of clusters representing southern water regimes. In the central region these regimes expanded by a 1000 km to the north. Furthermore, results demonstrate that currently available data already makes it possible to apply machine learning methods to the analysis of hydrological data. Clusters corresponding to different types of water regime can be obtained by utilizing contemporary clustering algorithms. The study shows that over the past 40 years, the southern types of water regimes have noticeably shifted to the north.

Estimations of Water Volume and External Loading Based on DYRESM Hydrodynamic Model at Lake Dianchi

There are many rivers flowing from complex paths into Lake Dianchi. At present, there is a lack of inflow and water quality monitoring data for some rivers, resulting in limited accuracy of statistical results regarding water volume and external loading estimations. In this study, we used DYRESM to estimate the water volume entering Waihai of Lake Dianchi from 2007 to 2019 without historical hydrological observation data. Then, we combined this information with the monthly monitoring data of water quality to calculate the annual external loading. Our results showed that: (1) DYRESM could effectively capture the extreme changes of water level at Waihai, showing its reliable applicability to Lake Dianchi. (2) The average annual inflow of rivers entering Waihai was about 6.69 × 108 m3. The fitting relationship between river inflow and precipitation was significant on annual scale (r = 0.74), with a higher inner-annual fitting coefficient between them (r = 0.98), thus suggesting that precipitation and its caused river inflows are the main water source for Waihai. (3) From 2007 to 2010, the river loadings remained at a high level. They decreased to 2445.44 t (total nitrogen, TN) and 106.53 t (total phosphorus, TP) due to a followed drought in 2011. (4) The river loading had annual variation characteristics. The contribution rates of TN and TP loading in the rainy season were 63% and 67% respectively. (5) Panlong River, Daqing River, Jinjia River, Xinbaoxiang River, Cailian River and Hai River were the main inflow rivers. Their loadings accounted for 81.3% (TN) and 80.3% (TP) of the total inputs. (6) River loadings have gradually reduced and the water quality of Waihai has continually improved. However, Pearson analysis results showed that the water quality parameters were not significantly correlated with their corresponding external loading at Waihai, indicating that there might be other factors influencing the water quality. (7) The contribution rates of internal release to the total loads of TN and TP at Waihai were estimated to be 7.6% and 8.9% respectively, suggesting that the reductions of both external and internal loading should be considered in order to significantly improve the water quality at Waihai of Lake Dianchi.

Effects of climate change, crop planting structure, and agricultural management on runoff, sediment, nitrogen and phosphorus losses in the Hai-River Basin since the 1980s

Investigating the spatiotemporal changes in the eco-hydrological processes and environment, as well as their drivers, is essential to manage the trade-offs among water supply, agricultural production, and environment at a basin scale. In this study, we developed a framework based on the Soil and Water Assessment Tool (SWAT), together with the historical data, to evaluate the effects of changes in climate, crop planting structure, and agricultural managements on the eco-hydrological processes and environment in the Hai-River basin during 1985–2015. The results showed that an increase in temperature not only reduced runoff and sediment but also increased nitrogen and phosphorous losses in the river basin. By contrast, an increase in precipitation increased runoff directly and increased sediment and nitrogen and phosphorous losses indirectly. Runoff caused directly the difference between sediment and losses of nitrogen and phosphorous. Changes in crop planting area and fertilizer application rate affected positively the losses of nitrogen and phosphorous. Changes in irrigation area affected negatively runoff. Different crop species had different impacts on runoff and sediment. Increase in crop planting areas of maize and vegetable generally reduced runoff and sediment, while increase in crop planting areas of wheat and beans generally increased both runoff and sediment. The findings gain insights into the impacts and driving mechanisms of the changes in climate, land use, crop planting structure and agricultural management on runoff, sediment, and losses of nitrogen and phosphorus in the Hai-River basin. • Impact of crop planting structure and managements on runoff, sediment, N and P loss were assessed. • Long-term hydrological observation data, statistics data and SWAT model were used. • Increase in crop planting area could increase N and P loss. • Different increases in crop planting area have different effects on runoff and sediment. • Temperature increase reduced runoff and sediment, but increased N and P loss.

ОБОСНОВАНИЕ ОРОШЕНИЯ ДОПОЛНИТЕЛЬНЫХ ПЛОЩАДЕЙ НА МЕСТНОМ СТОКЕ ЗА СЧЕТ АВТОМАТИЗАЦИИ ГИДРОЛОГИЧЕСКИХ РАСЧЕТОВ

Purpose: involvement of lands in irrigation using local runoff sources by simplifying and automating hydrological computations of spring floods according to the methodology in the absence of observational data with the possibility of minimal use of reference literature. Materials and methods. The program was developed in MS Excel and is designed to calculate the water body flow rates and build estimated flood hydrographs in the absence of hydrological observation data. Normative and methodological sources were used as initial reference materials. Results. The structure of the program, which consists of four interconnected blocks, in which sequential calculations are performed is given. Block 1 contains reference materials from the methodological literature that are necessary for the calculation. In block 2, the initial data is entered and the maximum instantaneous costs are calculated. Block 3 is intermediate, as it does not contain output data, but serves as a mathematical basis for further graphical construction of hydrographs. For their construction, data on analogue rivers are used. Block 4 displays the main calculated data: maximum discharges, runoff volumes and hydrographs for given reproducibility. Calculation algorithms for blocks 2 and 3 and output data for block 4 are presented. Conclusions. The possibility of involving agricultural lands in irrigation at the expense of local runoff reserves can be achieved thanks to the algorithm proposed by the authors and the program for calculating the maximum spring flood water discharges for various provision. This approach can significantly reduce the calculation of specified parameters by automating the process of selecting reference data and improve their accuracy by eliminating errors. All formulas used in this work comply with regulatory and methodological documents.

A new method for describing the inundation status of floodplain wetland

The inundation state is one of the main factors affecting the development of river floodplain wetlands, and the water inundate frequency (WIF) is a commonly used characterisation tool. This study combines remote sensing data with ground hydrological observation data to propose the water inundate guarantee rate (WIGR) as a new tool to characterise the inundation probability in river floodplain areas. It further conducts a comparative study between WIGR and WIF in the Yimin River, located in China's Inner Mongolia Autonomous Region. The results show that the inundation state maps of the study area are generally consistent using WIF and WIGR. However, there are observed differences in specific regions. In regions with very low assurance rates, it covers a larger area of 2.90 km2 using WIGR relative to WIF. Meanwhile, The area of other low, medium, high and very high areas is relatively lower. The reason for the difference is that the selected 52 phase remote sensing data corresponds to a low flow guarantee rate, which is generally medium and high. Further, the flow guarantee rate introduced in the WIGR analysis process plays a corrective role in the final analysis results. Compared with WIF, the WIGR algorithm is systematic, continuous, and has certain statistical significance, which has higher scientific credibility and accuracy. Specifically, the simple operation of the WIGR algorithm shows great potential to be applied to other related studies. Nevertheless, further improvement of the WIGR algorithm should focus on the influence of the atmospheric environment on remote sensing data and the acquisition of hydrological data. The WIGR proposed in this study can provide technical support for the conservation of river floodplain wetlands and open new ideas for studying river floodplain wetland ecosystems.

Long time series of daily evapotranspiration in China based on the SEBAL model and multisource images and validation

Abstract. Satellite observations of evapotranspiration (ET) have been widely used for water resources management in China. An accurate ET product with a high spatiotemporal resolution is required for research on drought stress and water resources management. However, such a product is currently lacking. Moreover, the performances of different ET estimation algorithms for China have not been clearly studied, especially under different environmental conditions. Therefore, the aims of this study were as follows: (1) to use multisource images to generate a long-time-series (2001–2018) daily ET product with a spatial resolution of 1 km × 1 km based on the Surface Energy Balance Algorithm for Land (SEBAL); (2) to comprehensively evaluate the performance of the SEBAL ET in China using flux observational data and hydrological observational data; and (3) to compare the performance of the SEBAL ET with the MOD16 ET product at the point scale and basin scale under different environmental conditions in China. At the point scale, both the models performed best in the conditions of forest cover, subtropical zones, hilly terrain, or summer, respectively, and SEBAL performed better in most conditions. In general, the accuracy of the SEBAL ET (rRMSE = 44.91 %) was slightly higher than that of the MOD16 ET (rRMSE = 48.72 %). In the basin-scale validation, both the models performed better than in the point-scale validation, with SEBAL obtaining results superior (rRMSE = 13.57 %) to MOD16 (rRMSE = 32.84 %). Additionally, both the models showed a negative bias, with the bias of the MOD16 ET being higher than that of the SEBAL ET. In the daily-scale validation, the SEBAL ET product showed a root mean square error (RMSE) of 0.92 mm d−1 and an r value of 0.79. In general, the SEBAL ET product can be used for the qualitative analysis and most quantitative analyses of regional ET. The SEBAL ET product is freely available at https://doi.org/10.5281/zenodo.4243988 and https://doi.org/10.5281/zenodo.4896147 (Cheng, 2020a, b). The results of this study can provide a reference for the application of remotely sensed ET products and the improvement of satellite ET observation algorithms.

CMIP5-Based Projection of Decadal and Seasonal Sea Surface Temperature Variations in East China Shelf Seas

The East China Shelf Seas, comprising the Bohai Sea, the Yellow Sea, and the shelf region of East China Sea, play significant roles among the shelf seas of the Western North Pacific Ocean. The projection of sea surface temperature (SST) changes in these regions is a hot research topic in marine science. However, this is a very difficult task due to the lack of available long-term projection data. Recently, with the high development of simulation technology based on numerical models, the model intercomparison projects, e.g., Phase 5 of the Climate Model Intercomparison Project (CMIP5), have become important ways of understanding climate changes. CMIP5 provides multiple models that can be used to estimate SST changes by 2100 under different representative concentration pathways (RCPs). This paper developed a CMIP5-based SST investigation framework for the projection of decadal and seasonal variation of SST in East China Shelf Seas by 2100. Since the simulation results of CMIP5 models may have degrees of errors, this paper uses hydrological observation data from World Ocean Atlas 2018 (WOA18) for model validation and correction. This paper selects seven representative ones including ACCESS1.3, CCSM4, FIO-ESM, CESM1-CAM5, CMCC-CMS, NorESM1-ME, and Max Planck Institute Earth System Model of medium resolution (MPI-ESM-MR). The decadal and seasonal SST changes in the next 100 years (2030, 2060, 2090) are investigated by comparing with the present analysis in 2010. The experimental results demonstrate that SST will increase significantly by 2100: the decadal SST will increase by about 1.55 °C, while the seasonal SST will increase by 1.03–1.95 °C.

Scenario‐based extreme flood risk analysis of Xiong'an New Area in northern China

AbstractWeather disasters, with floods amongst the most destructive, are increasingly being correlated with climate change. Flood warning measures, for which accurate simulation of flood evolution processes is required, are crucial to avoid human and economic losses. Xiong'an New Area was developed as a hub in northern China, implying an influx of residents and increased economic activity. However, this area is located downstream of the Baiyangdian Lake Basin; therefore, evaluating the flood risk here is vital. We applied a hydrodynamic model in our study and, to simulate the flood inundation process, we employed terrain spatial resolution; we calibrated and validated the model parameters using AnXin hydrological observation data. The results showed a relative water‐level error of 5.25%, indicating our model was largely reliable. We calculated the annual exceedance probability of peak discharge against the magnitude and frequency of historical flood‐peak streamflow in the basin. We simulated the flood submerged area under three scenarios, that is, return periods of 10, 50, and 100 years, and compared the effects of floods under these scenarios against the urban development planning of Xiong'an New Area. Our results provide practical guidance for flood control planning and crucial insight for urban planning in Xiong'an New Area.