River Basin Research Articles

Assessing the spatiotemporal dynamics of water and carbon fluxes in subtropical forest of Xin’an River Basin using an improved Biome-BGC model

Extreme climate occurred frequently in subtropical region, which seriously affects carbon and water fluxes such as evapotranspiration (ET) and gross primary productivity (GPP) of terrestrial ecosystems. The process-based biome biogeochemical cycles (Biome-BGC) model is widely used for simulating carbon and water fluxes of forest ecosystems. However, the lack of the interaction information of climate, vegetation and soil, such as the hysteresis effect, canopy stratification on photosynthesis, impedes better simulations of the ecohydrological processes. Here, we tended to improve the simulation accuracy of Biome-BGC model at a subtropical forest on the Xin’an River in Southeastern China by reconstructing the precipitation series, modifying the ET and canopy multilayers modules, and optimizing the parameters. The spatiotemporal patterns of GPP, ET, water use efficiency (WUE) and their response to environmental factors across the Xin’an River Basin from 1982 to 2018 were further explored. The results showed that the improved model performed well, with the determination coefficient, root means square error and mean absolute error being 0.730, 1.522 gC/m2/d and 1.218 gC/m2/d for GPP, 0.857, 1.082 mm/d and 0.838 mm/d for ET, respectively. Basin-averaged GPP, ET and WUE increased during 1982-2018 and these increasing trends were more pronounced during 1999-2018. Significant positive trends of WUE occurred in the northeast corner. The increasing air temperature and precipitation respectively dominated the increase in GPP and ET, the increasing CO2 concentration and NDVI mitigated the negative effect of extreme precipitation on WUE. Given that human activities such as afforestation have effectively reduced the extent of damage to forest ecosystems from extreme precipitation, we highlight an urgent need to formulate adaptation strategies aimed at reducing the risk of extreme climate in humid regions.

What are the dominant drivers and optimal thresholds for a healthy ecosystem in the Yellow River Basin, China? from a perspective of nonlinear nexus

Identifying the dominant factors influencing ecosystem health and their optimal thresholds is essential for the management and restoration of ecosystems. Most of the works, however, have less detected the importance level of individual factors, especially their changes over time and scale, and rarely determined their threshold effects on ecosystem health (EH), which seriously hinders the formulation of targeted policies for sustainable ecosystem management. To address these research gaps, we present an innovative visualization approach to reveal importance changes in natural and anthropogenic variables and their nonlinear coupling process with EH in the China’s Yellow River Basin (YRB) in the last two decades. Results show that (i) Although the EH in the YRB has improved slightly in the past 20 years, it is still not optimistic and shows distinct spatial heterogeneity. (ii) The impact of human activities on the ecosystem health of the YRB has increased and increasingly plays a decisive role, while that of the natural aspect has decreased recently. Specifically, environmental factors (slope, NDVI, and annual temperature) are the dominant factors affecting the upstream EH. Midstream EH is significantly affected by the synergistic effect of natural and anthropogenic factors, but the importance of the proportion of built-up land is intensified (ranked from 8th to 4th). Anthropogenic factors exert the greatest influence on downstream EH variation, especially for population density whose importance order rising from 7th to 2nd. (iii) By illustrating the nonlinear response curves of EH at multi-scale, the tipping point caused ecosystem deterioration in the YRB is explored and the optimal thresholds of leading factors oriented towards the most effective ecosystem conservation goal are obtained. These results help precisely identify the current health status of YRB's ecosystem, as well as providing a valuable reference for moderate restoration standards and sustainable ecosystem management.

Unveiling climatic niches for deeper insights into invasion potential and enhanced distribution models of freshwater fishes

AbstractIntroduced species may exhibit variations in their preferred climatic niches between their native and introduced ranges, which can have important implications for the transferability of distribution models. In the Himalayan ecoregion, little is known about the geographic distribution and climatic niche overlap between native and introduced cold‐water species. Here, we used the COUE (centroid shift, overlap, unfilling, and expansion) framework to explore the invasive potential of rainbow (Oncorhynchus mykiss) and brown (Salmo trutta fario) trout and corresponding climatic niche overlap with native snow trout (Schizothorax plagiostomus and Schizothorax richardsonii) in the Indus and Ganges River basins. Although we found more stability in the climatic niche for O. mykiss (93%) than S. trutta (58%), both species do not conserve their climatic niches in this region (p > 0.05). S. trutta has expanded more toward new environmental conditions (42%) compared to O. mykiss (7%). However, there are still available environmental gaps that O. mykiss and S. trutta can potentially occupy in the future. There was a higher overlap in climatic niches between S. plagiostomus and O. mykiss and between S. richardsonii and S. trutta. Observed shifts in climatic niches of these introduced species can negatively affect the transferability of distribution models, which may underestimate the assessments of habitat suitability for introduced trout in the Himalayas. Our study demonstrates that the information on climatic niche dynamics can inform the model‐building process and improve the transferability and predictive performance to better assess vulnerability of sensitive habitats to introduced species in the Himalayas and elsewhere.

Comparative Assessment of the Mountainous River Basin in Kyrgyz-Kazakh Region of Central Asia with River Basins in Australia, Canada and USA

Central Asia is among the most heavily affected regions worldwide by climate change and water shortages. Impacts include changes in precipitation patterns, more frequent temperature extremes and increased aridity causing a negative impact on agricultural production, food availability, and environmental security. To combat this threat, it is important to enhance information literacy among all water users. This can be done through awareness campaigns, blended learning by providing the proper Technical and Vocational Education and Training (TVET) programs and utilizing all available facilities. This will address relevant issues, such as miscommunication, complexities of transboundary water sharing issues, overexploitation of water resources, and poor flood-drought mitigation techniques. Proper and user-friendly lifelong blended learning for scientific information dissemination focusing water issues can provide stronger support to increase awareness among water users and decision policy makers. Worldwide, especially in North America and Australia, information literacy campaigns have proven successful. This strategy can be replicated in the Mountainous Kyrgyz-Kazakh Chu-Talas transboundary river basin. The issues concerning the Mountainous Kyrgyz-Kazakh Chu-Talas transboundary river basin is elaborated and compared with Australian, Canadian, and US river basin management programs. The foresight analysis is presented, as to what would be a rationale to improve water resources more sustainably in Central Asia. Methodologies, programs, technologies, communities-based river basin committees, snow-water collection with agroforestry, and basin-based water market opportunities were analyzed to assess potential applications in Central Asia region.

Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins

Abstract. The increasing reliance on global models for evaluating climate- and human-induced impacts on the hydrological cycle underscores the importance of assessing the models' reliability. Hydrological models provide valuable data on ungauged river basins or basins with limited gauge networks. The objective of this study was to evaluate the reliability of 13 global models using the Gravity Recovery and Climate Experiment (GRACE) satellite's total water storage (TWS) seasonal cycle for 29 river basins in different climate zones. Results show that the simulated seasonal total water storage change (TWSC) does not compare well with GRACE even in basins within the same climate zone. The models overestimated the seasonal peak in most boreal basins and underestimated it in tropical, arid, and temperate zones. In cold basins, the modeled phase of TWSC precedes that of GRACE by up to 2–3 months. However, it lagged behind that of GRACE by 1 month over temperate and arid to semi-arid basins. The phase agreement between GRACE and the models was good in the tropical zone. In some basins with major underlying aquifers, those models that incorporate groundwater simulations provide a better representation of the water storage dynamics. With the findings and analysis of our study, we concluded that R2 (Water Resource Reanalysis tier 2 forced with Multi-Source Weighted Ensemble Precipitation (MSWEP) dataset) models with optimized parameterizations have a better correlation with GRACE than the reverse scenario (R1 models are Water Resource Reanalysis tier 1 and tier 2 forced with the ERA-Interim (WFDEI) meteorological reanalysis dataset). This signifies an enhancement in the predictive capability of models regarding the variability of TWSC. The seasonal peak, amplitude, and phase difference analyses in this study provide new insights into the future improvement of large-scale hydrological models and TWS investigations.

Response of Floods to the Underlying Surface Changes in the Taojiang River Basin Using the Hydrologic Engineering Center’s Hydrologic Modeling System

Due to underlying surface changes (USCs), the changes in the Taojiang River Basin’s flood generation conditions could impact the flooding process in the basin. However, most studies have typically focused on either land-use changes (LUCs) or soil and water conservation measures (SWCMs) to assess the impact of the USCs on floods, which may not provide a more comprehensive understanding of the response of floods to the USCs. To investigate how the USCs have altered the floods in the Taojiang River Basin, located upstream of Poyang Lake, China, the HEC-HMS model, which incorporates the influence of the USCs into the parameter calibration, is established in this study to investigate the flood processes on an hourly scale. The flood peak and the maximum 72 h flood volume are selected as two indexes and are applied to analyze the changes in floods caused by the USCs. The 1981–2020 period is divided into three sub-periods (i.e., 1981–1992, 1993–2007, and 2008–2020) based on the conditions of the USCs. It is found that the two indexes have exhibited decreasing trends, mainly due to the USCs during 1981–2020. Benchmarked against the baseline period of 1981–1992, the two indexes decreased by 3.06% (the flood peak) and 4.00% (the maximum 72 h flood volume) during 1993–2007 and by 5.92% and 7.58% during 2008–2020. Moreover, the impacts of the LUCs and SWCMs are separated through parameter adjustments in the model, revealing that the SWCMs played a dominant role in the USCs in the Taojiang River Basin. The quantification and assessment of the impact of the USCs on floods of different magnitudes revealed that the influence decreases with increasing flood magnitude. The results of this study improve our understanding of how USCs affect the flooding process and therefore provide support for flood control management under changing environments.

The Forecast of Streamflow through Göksu Stream Using Machine Learning and Statistical Methods

Forecasting streamflow in stream basin systems plays a crucial role in facilitating effective urban planning to mitigate floods. In addition to employing intricate hydrological modeling systems, machine learning and statistical techniques offer an alternative means for streamflow forecasts. Nonetheless, the precision and dependability of these methods are subjects of paramount importance. This study rigorously investigates the effectiveness of three distinct machine learning techniques and two statistical approaches when applied to streamflow data from the Göksu Stream in the Marmara Region of Turkey, spanning from 1984 to 2022. Through a comparative analysis of these methodologies, this examination aims to contribute innovative advancements to the existing methodologies used in the prediction of streamflow data. The methodologies employed include machine learning methods such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) and statistical methods such as Simple Exponential Smoothing (SES) and Autoregressive Integrated Moving Average (ARIMA) model. In the study, 444 data points between 1984 and 2020 were used as training data, and the remaining data points for the period 2021–2022 were used for streamflow forecasting in the test validation period. The results were evaluated using various metrics, such as the correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). Upon analyzing the results, it was found that the model generated using the XGBoost algorithm outperformed other machine learning and statistical techniques. Consequently, the models implemented in this study demonstrate a high level of accuracy in predicting potential streamflow in the river basin system.

Evaluation of prospective surface water potential zones and their suitability for drinking purposes in Mahanadi River Basin, Odisha (India)

Abstract This study presents the usefulness of the water quality index (WQI) based on Fuzzy (F)-analytic hierarchy process (AHP), multi-criteria decision-making technique, namely, weighted sum approach (WSA) and machine learning models such as Borda scoring algorithm (BSA) for its evaluation and were further applied to the datasets on water quality (WQ) of the Mahanadi River (Odisha), generated during 5 years (2018–2023) of monitoring at 19 different sites for 20 parameters. The results render two parameters, namely coliform and TKN, exceeding the WHO standards. The results revealed that 52.63% of surface water samples are excellent in terms of drinking WQ, 26.32% of the samples are categorized under medium, and rest 21.05% are grouped under poor/very poor/unsuitable in terms of the F-AHP WQI. According to the results of WSA, 10 samples (52.63%) are low polluted zones, 6 samples (31.58%) are medium-polluted zones, and around 15.79% (3 samples) are highly polluted. The graphic representations obtained by BSA underline that the calculated value ranged between 15 and 256, stating in a zone of good to poor WQ. The best WQ was observed in T-(1), (5), (14), (15), (16), (17), and (18) because there were no changes in land use.

Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria

Validation of selected gridded potential evapotranspiration datasets with ground-based observations over the Ogun-Osun River Basin, Nigeria

Impact of Urbanization on the Vegetation Dynamic in Tama River Basin

Impact of Urbanization on the Vegetation Dynamic in Tama River Basin

Quantifying the diffusion history of Yangmingism

AbstractYangmingism, a crucial component of Chinese Confucianism, has profoundly impacted the development of Eastern civilization throughout history and gained new significance within modern contexts. This study employs social network and GIS spatial analyses to investigate the spatio-temporal evolution and patterns of Yangmingism during the Ming and Qing dynasties. Conclusions drawn include: (1) Yangmingism spread during the Ming and Qing dynasties followed five phases (rise-peak-decline-revival-trough), consistent with general cultural diffusion processes. (2) The spatial structure of regions accepting Yangmingism changed from polycentric to localized ribbon-polycentric, then to monocentric, polycentric, and ultimately, fragmented distribution. Diffusion began with a point-axis structure along traffic routes, transitioned to a core-periphery spatial structure, and returned to a point-axis structure due to reduced diffusion potential energy. (3) Yangmingism diffusion exhibited distance decay characteristics, with the most extensive diffusion in the Han cultural region, primarily located within the middle and lower reaches of the Ganjiang River basin and the Yangtze River Delta. (4) Yangmingism diffusion reflected influences from local administrative hierarchies and cultural development. During the rise-to-peak phase, Yangmingism reached areas with higher administrative and cultural levels, while during the peak-to-decline phase, it disseminated from higher to lower hierarchy regions. (5) The spatial diffusion of Yangmingism is mainly relocation diffusion, followed by expansion diffusion. In the process of relocation diffusion, Yangmingism may reverse the diffusion from the “target regions” to the “source regions” due to the influence of the radiation of the high-ranking regions and the cultural innovation of the “target regions”.

Inequality in environmental risk exposure and procedural justice in the Matanza-Riachuelo River Basin

ABSTRACT Using a capabilities-based perspective on socioenvironmental justice, this paper aims to contribute to the understanding of the interconnection between social equity and care for the earth in the case of the Matanza-Riachuelo – the most industrialized and contaminated river basin in Argentina. First, it uses a spatial regression model to analyse the correlation between multidimensional poverty and potential exposure to three types of environmental risks: environmental hazards of productive establishments, open-air waste dumps, and proximity to contaminated rivers and streams. Second, it analyses inequities in the processes of resettlement of households living in conditions of extreme environmental risk in informal settlements. It shows that higher poverty areas face greater risk of exposure to open-air waste dumps but lower risk of exposure to industrial and commercial environmental hazards. It also demonstrates how enhanced participation of households in decision-making on resettlement contributed to improved outcomes.

Midsummer precipitation prediction over eastern China by the dynamic downscaling method

AbstractThis study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction.

Modeling the Impact of Climate Change on Streamflow in the Meghna River Basin: An Analysis Using SWAT and CMIP6 Scenarios

This study assesses future climate change impacts on the hydrologic response of the Upper Meghna River Basin (UMRB), a major river system in Bangladesh. Separate SWAT (Soil and Water Assessment Tool) hydrologic models were developed for the three major sub-basins of the UMRB, i.e., Barak, Meghalaya, and Tripura, considering their unique geographical, hydrological, and land-use characteristics. To evaluate the efficiency of multi-site modeling in providing better model performance, the SWAT models were calibrated at both single and multiple locations. Those models were then simulated to estimate future flows using climate projection data from thirteen CMIP6 General Circulation Models (GCMs) under moderate and extreme emission scenarios, SSP2-4.5, and SSP5-8.5. The results revealed that the annual maximum flow will keep increasing gradually with time. The outlets of the Meghalaya sub-basin will experience a more significant rise in future flow in the upcoming decades compared to the Barak and Tripura sub-basins. Results showed that dry season flows with increases of up to 31–50% would be less affected compared to the wet periods, which could experience increases of up to 47–66%) across the sub-basins by the end of the 21st century under extreme emission projections. Besides an increasing trend in the mean flow, future flows at several outlets also exhibited an escalation in the intensity and frequency of extreme flood events.

Climate-Driven Changes in Annual Flow Patterns: A Comprehensive Analysis of the Buktyrma River (Kazakhstan)

This article examines and calculates the annual flows of rivers in the Buktyrma River basin in connection with changing climatic conditions affecting runoff formation. An analysis of climatic characteristics, based on data from meteorological stations of the hydrometeorological service of Kazakhstan, indicates intense warming and a moderate increase in annual precipitation since the early 1980s. The more intense melting of high-mountain snow and glaciers has led to a reduction in glaciated areas. Alongside the noted trend of increasing precipitation, this melting contributes to an increase in surface runoff. According to Global Land Ice Measurements from Space data, there has been a reduction in the area of glaciers in the river basin and a complete disappearance of glaciers in the Sarymsakty River basin. Studying long-term water-flow data in the basin has helped identify periods with consistent patterns. These periods are characterized by different values of probability-distribution-curve parameters. An assessment of the homogeneity of the identified periods of runoff series over the past decades, using known parametric criteria, confirmed the reliability of the calculated values of annual runoff. The observed increase in the norm, depending on elevation, glacier presence, and the spatial orientation of watershed ridges, ranges from 5% to 52%. The observed trends in changes in climatic characteristics and runoff indicators of the rivers in the basin support water-management planning in this region. This includes using the approximation of empirical river-supply curves, which can aid in assessing the calculated hydrological characteristics.

Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Assessing water diversion impacts on the fish community size structure in headwater stream stretches

AbstractWater diversion for hydropower plants is one of the leading causes of habitat alteration and biotic homogenization. The impacts of small hydropower plants on fish communities are usually assessed using taxonomic composition and structure indicators. Size-based indicators are useful tools to evaluate the effects of environmental factors and anthropogenic perturbations on riverine ecosystems. In this study, we investigated the impact of water diversion on fish community body size structure in 16 small hydropower plants in the upper Ter River basin (NE Iberian Peninsula). We tested the use of multiple size-based metrics under different sampling intensities (one- vs. four-pass electrofishing) and compared control vs. diverted stream stretches. Results revealed that water diversion had a negative effect on fish community average and median length, while size spectrum and size diversity metrics showed no responses to the impact. Altitude was positively related with body size metrics and was the strongest natural driver affecting them. Moreover, we found that all the size-based metrics exhibited consistent values under different sampling efforts. Our findings suggest that size-based metrics could be useful indicators for bioassessment of river flow alteration and that one-pass electrofishing was robust enough to characterize the stream fish community size structure in our study.

Spatial–Temporal Pattern of Coordination between the Supply and Demand for Ecosystem Services in the Lhasa River Basin

Quantifying the spatiotemporal patterns of the coordination between ecosystem service supply and demand is vital for regional sustainable development. To reveal the dynamic pattern of the coordination of ecosystem service (ES) supply and demand in the Lhasa River Basin, we quantified the supply of the following four ESs using the InVEST model from 2000 to 2018: carbon sequestration (CS), water conservation (WC), habitat quality (HQ), and soil conservation (SC). Using socio-economic data, including land development degree, GDP, and population density, the ES demand was quantified. The ES supply–demand ratio (ESDR) and coupling coordination degree (CCD) model were used to evaluate the coupling relationship and coordination of ES supply and demand. The spatial autocorrelation analysis was used to determine the spatial correlation and changes in the ES supply–demand coupling coordination degree. The results indicate that the distribution of ESDR exhibited significant spatial heterogeneity. The area with ES supply far greater than demand was always in the upstream area of the Lhasa River, while the ES demand of Chengguan District far exceeded supply. Grasslands and forests were the main contributors to ESDRs, providing positive ESDRs for three services, SC, HQ, and WC, with a total proportion above 80%. From 2000 to 2018, the mismatch between ES supply and demand was gradually spreading upstream, while the upstream areas had a relatively high CCD. The spatial correlations of the CCD in the Lhasa River basin all showed statistically significant differences (p < 0.01). The high–high aggregation areas were concentrated in the northeast of the Lhasa River basin, while the low–low aggregation areas were centered around Chengguan District. This study provides reference values for optimizing the land use spatial patterns in ecologically vulnerable areas with the goal of sustainable development.

Spatiotemporal Evolution Features of the 2022 Compound Hot and Drought Event over the Yangtze River Basin

A rare compound hot and drought (CHD) event occurred in the Yangtze River Basin (YRB) in the summer of 2022, which brought serious social crisis and ecological disaster. The analysis of the causes, spatiotemporal characteristics and impacts of this event is of great significance and value for future drought warning and mitigation. We used the Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-On (GRACE-FO) data, meteorological data, hydrological data and satellite remote sensing data to discuss the spatiotemporal evolution, formation mechanism and the influence of the CHD event. The results show that the drought severity caused by the CHD event was the most severe during 2003 and 2022. The CHD event lasted a total of five months (from July to November), and there were variations in the damage in different sub-basins. The Wu River Basin (WRB) is the region where the CHD event lasted the longest, at six months (from July to December), while it also lasted four or five months in all the other basins. Among them, the WRB, Dongting Lake Rivers Basin (DLRB) and Mainstream of the YRB (MSY) are the three most affected basins, whose hot and drought severity values are 7.750 and −8.520 (WRB), 7.105 and −9.915 (DLRB) and 6.232 and −9.143 (MSY), respectively. High temperature and low precipitation are the direct causes of the CHD event, and the underlying causes behind this event are the triple La Niña and negative Indian Ocean Dipole event. The two extreme climate events made the Western Pacific Subtropical High (WPSH) unusually strong, and then the WPSH covered a more northerly and westerly region than in previous years and remained entrenched for a long period of time over the YRB and its adjacent regions. Moreover, this CHD event had a devastating impact on local agricultural production and seriously disrupted daily life and production. Our results have implications for the study of extreme disaster events.

A Comprehensive Flood Risk Inundation Mapping and Hybrid Model for Flood Forecasting in the Panam River Basin

The forecasting of flooding plays a pivotal role in the field of hydrology and serves as an essential measure for preventing any possible flood damage. This study presents an analysis of the utilization of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to prevent floods in river basins. ANFIS represents a hybrid approach that combines elements of neural networks and fuzzy logic methodologies for the purpose of analyzing and comparing input and output data. Performance evaluation of ANFIS is based on the ratio of Discrepancy Ratio (D) along with the determination coefficient (R2), Coefficient of correlation (R), and Root Mean Square Error (RMSE). Results: The best ANFIS model was the one that gave the best results during training. The RMSE was 622.43, with a correlation coefficient of 0.93 and an R2 of 0.86. Nevertheless, its validation performance demonstrated slightly elevated RMSE figures and lower correlation and R2 values, with RMSE at 2847.99, R at 0.91, and R2 at 0.83. An Artificial Neural Network (ANN) model with three transfer functions (SIGMOID, HYPERBOLIC TANGENT, and LINEAR) is developed and evaluated based on R = correlation and MSE = mean squared error. Furthermore, the study also entails the development of a flood model for the research area, consisting of 38 cross profiles for a detailed analysis of the river’s profile and flood inundation. This analysis aims to provide precise recommendations for flood protection measures.