Extreme Hydrological Events Research Articles

Quantitative Evaluation of the Impact of Vegetation Restoration and Climate Variation on Runoff Attenuation in the Luan River Basin Based on the Extended Budyko Model

The occurrence of water scarcity and extreme hydrological events is becoming increasingly frequent worldwide. The river runoff process, as an important link in the water cycle of a basin, is an essential content of the study of hydrological processes in the basin. Vegetation is an influencing factor closely related to hydrological processes in a watershed. This article quantitatively analyzes the impact of vegetation restoration and climate variation on runoff in the Luan River Basin from 1982 to 2018. Firstly, trend analysis was conducted on runoff depth, precipitation, the Normalized Vegetation Index (NDVI), and reference evaporation. Then, Mann–Kendall mutation analysis and Pettitt mutation analysis were used to identify the year of runoff mutation. Finally, a Budyko model was constructed to quantitatively analyze the impact of vegetation and climate change on the runoff of the Luan River. The results showed that: (1) The runoff reduction rate of the Luan River was 1.2437 mm/a. The precipitation was increasing at a rate of 0.6977 mm/a. The reference evaporation rate decreased at a rate of 0.0977 mm/a. The annual growth rate of the Budyko parameter (n) was 0.0283. The annual growth rate of NDVI was 0.0022. (2) The coefficient of determination in the functional equation (a = 14.74, b = −3.4105) for NDVI and Budyko parameter (n) was 0.3297 (p < 0.01). (3) The contributions of Pr, ET0, NDVI, and (n) to reduced flow were −17.49%, 1.46%, 35.25%, and 80.78%, respectively. The increase in vegetation would lead to a decrease in runoff. This study can clarify the impact of vegetation restoration on water resource security in the Luan River Basin.

Assessing flash flood inundation from an extreme rainfall event: case study: Wadi Al Jizzi Oman

Abstract Flash floods present a significant risk to urbanized arid regions, and assessing their inundation patterns is crucial for effective disaster management. Extreme hydrologic events due to aridity and climate change are shaping human lives and major activities in numerous countries at an unprecedented pace. This study aims to assess flash floods from extreme storm events in an arid catchment using high-resolution data. The study applied two models on the event of a single storm, namely the IHACRES and AHP models. The observed flow was used for models' validation. The average flow output determined with the IHACRES model was approximately 0.47 m3/s while the flow output resulting from the AHP model was 0.45 m3/s. The efficiency showed that the IHACRES performed better in evaluating extreme events with an average of 0.88 while the AHP model showed an efficiency of 0.68. The quantitative simulation of both models is likely to have good applicability for simulating single storm events in arid catchments. The validated IHACRES and AHP models offer valuable tools for simulating flash flood. The study's outcomes have implications for flood management policy and infrastructure planning, ensuring a more resilient response to extreme flood events in arid regions globally.

Improvement of streamflow simulation by combining physically hydrological model with deep learning methods in data-scarce glacial river basin

Robust streamflow simulation at glacial basins is essential for the improvement of water sustainability assessment, water security evaluation, and water resource management under the rapidly changing climate. Therefore, we proposed a hybrid modelling framework to link the SWAT+ model considering glacial hydrological processes (GSWAT+) with Gated Recurrent Unit (GRU) neural networks to improve the model simulations and to establish a framework for the robust simulation and forecast of high and low flows in glacial river basins, which could be further used for the explorations of extreme hydrological events under a warming climate. The performance of different models (GSWAT+, GRU, and GRU-GSWAT+, respectively) were thoroughly investigated based on numerical experiments for two data-scarce glacial watersheds in Northwest China. The results suggested that the hybrid model (GRU-GSWAT+) outperformed both the individual deep learning (DL) model (GRU) and the conventional hydrological model (GSWAT+) in terms of simulation and prediction accuracy. Notably, the proposed hybrid model considerably enhanced the simulations of low and high flows that the conventional GSWAT+ failed to capture. Furthermore, utilizing suitable data integration (DI) schemes on feature and target sequences can substantially help to strengthen model stability and representativeness for monthly and annual streamflow sequences. Specifically, introducing one order differential method and decomposition approach, such as ensemble empirical signal decomposition (EEMD) and complete EEMD with adaptive noise (CEEMDAN), into feature and target sequences enriched the learnable ancillary information, which consequently strengthened the predictive performance of the proposed model. Overall, the proposed hybrid model with the suitable DI scheme has the potential to significantly enhance the accuracy of streamflow simulation in data-scarce glacial river basins. This hybrid model not only upheld the fundamental physical principles from the GSWAT+ model, but also considerably mitigated the accumulated bias errors, which caused by the shortage of climate data and inadequate hydrological principles, by using DL based model and DI schemes.

Testing heavy rainfall change in Jeju Island, Korea and its linkage to typhoon activity over East Asia during the Holocene

Understanding the spatiotemporal variability of extreme hydrologic events, especially those driven by typhoons over Asia, and their controlling factors is essential to testing and predicting their future changes under global warming. Here, we present a series of direct heavy-rainfall records over the past ca. 10,000 years based on lake sediments from a volcanic cone (Sara Oreum) on Jeju Island, Korea, where orographic amplification of precipitation is dominant and sensitive to present typhoon activity. The reconstructed heavy rainfall index based on the high-resolution elemental ratios (e.g., Ti/Al and Sr/Si) shows clear variability at centennial timescales and is similar to records of coastal flooding events along the southern coast of Korea, typhoon-driven overwash events along the south-western coast of Japan, indicating strong typhoon influence on the heavy rainfall events in Jeju Island during the Holocene. Furthermore, this heavy rainfall-typhoon linkage has been influenced by past El Niño–Southern Oscillation (ENSO) activity and sea surface temperature (SST) in the western Tropical Pacific, showing time-varying influences. This study suggests that on decadal to multi-centennial timescales, stronger ENSO activity is responsible for eastward shift in the location of typhoon genesis, resulting in more frequent recurving tracks of typhoon and increased landfalls in Korea and Japan, whereas SST affects the intensification rate of typhoon. The heavy rainfall index reconstructed from the volcanic cones of Jeju Island may be used as an indicator of past frequency and track changes of typhoon over highly populated East Asia.

Development and propagation of hydrologic drought from meteorological and agricultural drought in the Mekong River Basin

AbstractMekong River Basin (MRB) experiences extreme droughts and floods frequently, due to the precipitation deficit across the basin. The meteorological droughts will have a profound impact on the distribution of soil moisture and hence agricultural productivity, which will lead to the reduction of surface water resources. It is therefore important to evaluate the deficits in hydrometeorological extremes, that is, precipitation, soil moisture, and streamflow to help decipher how one drought results in the other. In this study, a streamflow deficit index (SDI) has been proposed and applied to understand the changes in hydrology due to the fluctuations in precipitation and soil moisture in the Mekong River Basin (MRB). The study used percent normal (PN), a precipitation deficit index, and soil moisture deficit index (SMDI) to identify the initiation and sustenance of hydrological drought using SDI. The SMDI was obtained from soil moisture simulated using the soil and water assessment tool (SWAT) for the periods of 1980–2008. The study results suggested that the proposed index was able to represent the historical river flow deficit that persisted in the basin in the year 1992. Increasing variation in the streamflow deficit in parts of Thailand, Lao PDR, Vietnam, and Cambodia from the year 1992 was also captured by SDI. The flooding year 2000, which resulted in an economic loss of over 200 million USD, was also effectively captured by the proposed index. The lengthening of the streamflow drought, a metric used to represent drought propagation, was shorter by at least 2 months in forested catchments compared with that of agricultural catchments, which implies that any deficit in precipitation and soil moisture, will have a severe impact on agricultural basins. Similarly, the attenuation of both agricultural and hydrological drought was found to be smaller in forested subbasins than those in agricultural subbasins. The findings of the study will be useful for the timely identification of extreme hydrological events and for planning mitigation measures.

Use of Mixed Methods in the Science of Hydrological Extremes: What Are Their Contributions?

Research in hydrological sciences is constantly evolving to provide adequate answers to address various water-related issues. Methodological approaches inspired by mathematical and physical sciences have shaped hydrological sciences from its inceptions to the present day. Nowadays, as a better understanding of the social consequences of extreme meteorological events and of the population’s ability to adapt to these becomes increasingly necessary, hydrological sciences have begun to integrate knowledge from social sciences. Such knowledge allows for the study of complex social-ecological realities surrounding hydrological phenomena, such as citizens’ perception of water resources, as well as individual and collective behaviors related to water management. Using a mixed methods approach to combine quantitative and qualitative approaches has thus become necessary to understand the complexity of hydrological phenomena and propose adequate solutions for their management. In this paper, we detail how mixed methods can be used to research flood hydrology and low-flow conditions, as well as in the management of these hydrological extremes, through the analysis of case studies. We frame our analysis within the three paradigms (positivism, post-positivism, and constructivism) and four research designs (triangulation, complementary, explanatory, and exploratory) that guide research in hydrology. We show that mixed methods can notably contribute to the densification of data on extreme flood events to help reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented, and to improving participatory decision making in water management and in handling extreme hydrological events.

Influence of snowpack on forest water stress in the Sierra Nevada

Higher global temperatures and intensification of extreme hydrologic events, such as droughts, can lead to premature tree mortality. In a Mediterranean climate like California, the seasonality of precipitation is out of sync with the peak growing season. Seasonal snowpack plays a critical role in reducing this mismatch between the timing of water input to the root zone and the peak forest water use. A loss of snowpack, or snow droughts, during warmer years, increases the asynchrony between water inputs and the peak of forest water use, intensifying water stress and tree mortality. Therefore, we hypothesize that the montane vegetation response to interannual climate variability in a Mediterranean climate is regulated by the snowpack. We tested this hypothesis using the 2012–2015 drought as a natural experiment. Regional Generalized Additive Models (GAMs) were used to infer and quantify the role of snowpack on forest water stress. The models simulate the Normalized Difference Infrared Index (NDII) as a proxy of forest water stress using water deficit (as seasonality index), location, slope, and aspect. The GAMs were trained using 75% of the data between 2001 and 2014. The remaining 25% of the data were used for validation. The model was able to simulate forest water stress for 2015 and 2016 across the northern, central, and southern Sierra Nevada with a range of R2 between 0.80 and 0.84. The simulated spatial patterns in forest water stress were consistent with those captured by the USDA Forest Service Aerial Detection Survey. Our findings suggest that the failure of a reduced snowpack in mitigating water deficit exacerbates forest water stress and tree mortality. Variations in water and surface energy budget across an elevational gradient play a critical role in modulating the vegetation response. These results provide insights into the importance of the Sierra Nevada snowpack under a warming climate. The models can aid forest managers to identify future forest water stress and tree die-off patterns.

Fractal characteristics of the middle reach of the Paraná River floodplain during extreme hydrological events

AbstractThe hydrological events that occur on floodplains are largely defined by precipitation and runoff inputs interacting with their geomorphological characteristics. In the middle Paraná River (MPR), hydrological events have traditionally been studied using water level time series and topographic charts or satellite images of certain floodplain sectors, using the same scale throughout the study area. Different zones of the floodplain were integrated using fractal dimension (FD) to better understand hydrological processes. FD is a useful tool used to characterize the geomorphological complexity of river networks in watersheds. The box‐counting method was applied in the MPR floodplain. The result showed that as water levels increase, FD decreases, thereby indicating the direct relation with the hydrological attributes of the floodplain (e.g., duration, magnitude, recurrence of the flood event, and connectivity). This relation constitutes the basis of a new approach of the analysis of hydrological events and their effect on the MPR floodplain. This research provides fundamental information obtained from two hydrological stages and different spatial scales which may complement the method designed to accurately understand the hydrogeomorphological processes that operate on the Paraná River floodplain. It provides a simple parameter that can be used to evaluate the role that each floodplain section plays in attenuating the hydrograph recorded at each gauge analyzed along the river. This information will be useful in the implementation of management policies directed to the mitigation of future hydrological threats generated by alterations in the hydrological regime due to hydroclimatic variations or watershed alterations. The results further strengthen the basis for integrated sustainable planning of the main channel of the Paraná River, its floodplain, and the resources provided by this ecosystem.

Particularity of hydrological processes under heavy ablation based on environmental isotopes in transition zones between endorheic and exorheic basins

Drastic changes in the cryosphere have a significant impact on the quantity and formation process of water resources in the Qilian Mountains. The present study focused on quantitative evaluation of runoff components and runoff formation processes during strong ablation periods (August), in 2018, 2020, and 2021, in the transition zone between endorheic and exorheic basins in China, based on 1906 stable isotope samples. The results revealed that as the altitude decreased, the contribution of glacier and snow meltwater and permafrost water to runoff decreased, whereas that of the precipitation increased. Precipitation is a major source of river runoff in the Qilian Mountains. Notably, the runoff yield and concentration of rivers that were greatly affected by the cryosphere exhibited the following characteristics: (1) The altitude effect of stable isotopes was not significant and even showed a reverse trend in some rivers. (2) The processes of runoff yield and composition were relatively slow; as such, precipitation, glacier and snow meltwater, and supra-permafrost water were first transformed into groundwater and then supplied runoff to upstream mountainous region. (3) Finally, stable isotope composition in such rivers were similar to those in glaciers and snow meltwater, with small fluctuations. Therefore, the water sources of rivers affected by the cryosphere are more uncertain than those of rivers unaffected by the cryosphere. In future study, a prediction model of extreme precipitation and hydrological events will be developed, and a prediction technology for runoff formation and evolution in glacier snow and permafrost will be developed to integrate short-and long-term forecasts.

Late Holocene paleoclimatic reconstruction inferred from El Bibane lagoon in southeast Tunisia

The reconstruction of paleofloods in the late Holocene has become an important research highlight in the context of the present climate change. The Tunisian Mediterranean coast is a region that is prone to experiencing severe weather. The flood events have a significant impact on the infrastructure and populations residing in coastal lowlands. Studies on lagoon sediments have recently revealed important information about the past extreme hydrological events as well as their relationship with past climate changes. This study aims to reconstruct the historical extreme flood-event record using sediment cores taken from the El Bibane coastal lagoon, southeast of Tunisia. Three sediment cores BL12–5, BL12–7 and BL12–10 were studied. Through a combination of analytical techniques such as the chronological, geochemical and sedimentological analyses, past flash floods were identified. Results display that the sedimentological signature of the paleoflood levels recognized within the lagoonal sequence is characterized by high values of the elemental ratios (Ti/Ca and Fe/Ca) and high content of fine material such as clay and silt. These floods are thought to have occurred as a result of intense precipitation that increased the terrigenous input to the El Bibane lagoon by the Fessi River. Chronologies of 210Pb, 137Cs, and 14C have been used to calculate the age of these floods. The records offer a good temporal correlation with historical floods recorded in the central Mediterranean region. In the southeast of Tunisia, periods with more frequent floods coincide with periods generally under alternation of wetter and drier conditions and minima of solar activity. The paleoflood record of the El Bibane lagoon can help researchers better understand the physical mechanisms responsible for changes in the intensity and/or frequency of extreme events in the south of Tunisia. Our research proves that the proxies utilized here have a lot of potential for reconstructing past floods, which is important knowledge for future flood prediction and mitigation in southeast Tunisia.

Expected reliability over a planning horizon of a hydraulic infrastructure and its uncertainty under hydroclimatic future projections

Extreme hydrological events have been recorded around the world, confirming the changing hydroclimate and therefore threatening the reliability over a planning horizon of the existing and projected hydraulic infrastructure. Unfortunately, there is a gap between climate science and engineering planning and design, which is partially filled in this study, whose aim is to evaluate the expected reliability over a planning horizon of a hydraulic infrastructure and its uncertainty for hydroclimatic projections. To achieve this goal, we implemented hydrological deep learning models that are driven by the Coupled Model Intercomparison Project Phase 6 (CMIP6). After validating and testing the hydrological deep learning models, sample series of the annual maximum flood (AMF) were generated for different mountain rivers of Chile and used for a standard frequency analysis that updates the exceedance probability of the flood events used for the hydraulic design. We found that the mean and the standard deviation of the sample series of the AMF are the key parameters that enable quantifying temporal changes in the exceedance probability of the design flow. The cumulative effects of these changes are summarized by the projected reliability over the planning horizon of the infrastructure, and the age of the infrastructure at which this projected reliability equates to the design reliability, quantifies the impact of climate change on the expected security of the hydraulic project. We show that the infrastructure that was designed for the smaller exceedance probability is more vulnerable to climate change, where the updated planning horizon can be as small as 45% of the planning horizon used for the design.

Investigating extreme hydrological risk impact on water quality; evidence from Buffalo catchment headwater, Eastern Cape, South Africa

Evidence from increasing mineralization, micropollutant concentrations, waterborne epidemics, an algal boom, and dissolved organic matter has provided substantial evidence that climate change impacts water quality. While the impact of the extreme hydrological event (EHE) on water quality (WQ) has aroused considerable research interest, research uncertainty has been premised on WQ data scarcity, a short time frame, data non-linearity, data structure, and environmental biases on WQ. This study conceptualized a categorical and periodic correlation using confusion matrices and wavelet coherence for varying standard hydrological drought index (SHDI; 1971–2010) and daily WQ series (1977–2011) of four spatially distinct basins. By condensing the WQ variables using chemometric analyses, confusion matrices were assessed by cascading the SHDI series into 2-, 3-, and 5-phase scenarios. 2-phase revealed an overall accuracy (0.43–0.73), sensitivity analysis (0.52–1.00), and Kappa coefficient (− 0.13 to 0.14), which declines substantially with the phase increase, suggesting the disruptive impact of EHE on WQ. Wavelet coherence depicted the substantial ({R}_{n}^{2}left(u,sright)ge 0.5) mid- and long-term (8–32 days; 6–128 days) co-movement of streamflow over WQ, confirming the varying sensitivity of WQ variables. Land use/land cover mapping and the Gibbs diagram corroborate the eventful WQ evolution by EHE and their spatial variability concerning landscape transformation. Overall, the study deduced that hydrologic extreme triggers substantial WQ disruption with dissimilar WQ sensitivity. Consequently, suitable chemometric indicators of EHE impacts such as WQ index, nitrate-nitrogen, and Larson index at designated landscapes were identified for extreme chemodynamics impact assessment. This study proffers a recommendation for monitoring and managing the impact of climate change, floods, and drought on water quality.

Phytoplankton functional responses induced by extreme hydrological events in a tropical reservoir.

The online version contains supplementary material available at 10.1007/s10750-023-05241-3.

A framework approach to address the trend and causes of flood stage change in a river reach downstream of a dam influenced by tributaries

The evaluation of the trend of flood stage changes in alluvial rivers downstream of dams is important for flood management. However, the flood stage associated with a given discharge generally is nonstationary in river reaches with multiple tributaries. This is not only because of the dam-induced shifting in the cross-sectional area and/or channel roughness but also because of the backwater induced by high flows from the tributaries. To determine the total trend of the flood stage and quantify the separate contributions of hydrological and geomorphic effects, the current study proposed a framework approach consisting of hydrological analysis and multiscenario numerical modeling. By this means, the trend in the flood stage could be distinguished from the stage oscillation driven by varying factors, including extreme hydrologic events. The effects of chronic changes, including channel incision and flow resistance increase, also were quantitatively separated. This framework was applied to the Chenglingji–Datong (CD) reach downstream of the Three Gorges Dam (TGD) in the Yangtze River, China. The results indicated that the effect of the roughness increase counterbalanced the effect of channel incision when the flow discharge was beyond the bankfull level. The backwater effect induced by tributary inflow was the major cause of the flood stage rise in recent years. The method presented in the current study provides a useful tool for managers and engineers to obtain better insight into the driving mechanisms of flood stage changes in river reaches that are downstream of dams. These findings indicate that the flood stage may not decline or may even occasionally increase, although the cross-sectional area was enlarged by channel incision. Special attention should be given to the flood risk situation in the study reach after the TGD began operation.

The Benefits of Continental-Scale High-Resolution Hydrological Modeling in the Detection of Extreme Hydrological Events in China

High-resolution hydrological modeling is crucial for detecting extreme hydrological events and understanding fundamental terrestrial processes. However, spatial resolutions in current hydrological modeling studies have been mostly constrained to relatively coarse resolution (~10–100 km), and they therefore have a difficult time addressing flooding or drought issues with fine resolutions. In this study, a continental-scale high-resolution hydrological modeling framework (0.0625°, ~6 km) driven by remote sensing products was used to detect extreme hydrological event occurrences in China and evaluated based on the Variable Infiltration Capacity (VIC) model. The results showed that the developed model provided more detailed information than the coarser resolution models (a 0.25° and 1°), thereby capturing the timing, duration, and spatial extent of extreme hydrologic events regarding the 2012 Beijing flood and 2009/10 drought in Hai River Basin. Here, the total water storage changes were calculated based on the VIC model (−0.017 mm/year) and Gravity Recovery and Climate Experiment (GRACE) satellite (−0.203 mm/year) to reflect the water availability caused by climate change and anthropogenic factors. This study found that the 0.0625° dataset could capture detailed changes, thereby providing reliable information during occurrences of extreme hydrological events. The high-resolution model integrated with remote sensing products could be used for accurate evaluations of continental-scale extreme hydrological events and can be valuable in understanding its long-term occurrence and water resource security.

Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Automated flood detection using earth observation data is a crucial task for efficient flood disaster management. Current solutions to identify flooded areas usually rely on calculating the difference between new observations and static, pre-calculated water extents derived by either single acquisitions or timely aggregated products. Such pre-calculated datasets, however, lack representation of real-world seasonality and short-term changes in trend. In this paper we present a complete workflow to automatically detect hydrological extreme events and their spatial extent, which automatically adapts to local seasonality and trend. For that we rely on a novel combination of well-established algorithms and tools to detect anomalies in time-series of water extent across large study areas. The data is binned into a discrete global grid system H3, which greatly simplifies aggregation across spatial and temporal resolutions. For each grid cell of an H3 resolution we perform a time-series decomposition using Seasonal and Trend decomposition using Loess (STL) of the cell’s proportion which is covered with surface water. All cells receive an anomaly score, calculated with extended isolation forest (EIF) on the residuals for each step in time. A burst of anomalies represents a hydrological extreme event like a flood or low water level. The presented methodology is applied on Sentinel-1/2 data for two study areas, one near Sukkur, Pakistan and the other one in Mozambique. The detected anomalies correlate with reported floods and seasonal variations of the study areas. The performance of the process and the possibility to use different H3 resolutions make the proposed methodology suitable for large scale monitoring.

Impacts of climate change on glacial retreat during 1990–2021 in the Chinese Altay Mountains

Melting glaciers have changed the spatial–temporal distribution of water resources in inland river basins and are one of the important inducements of extreme hydrological events. Most previous studies on glacier changes in the Chinese Altay Mountains have focused on the period 1960–2011; however, there is a lack of glacier data reflecting recent changes (1990–2021) over the past decades, and the response of glaciers to regional climate change is still unclear. In this study, Landsat images and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to map glacier outlines from 1990 to 2021 for the whole region and its surrounding areas, and the impacts of temperature, precipitation, and solar radiation variation on glacier retreat were investigated. The results indicate that (1) the glacier area retreat rate of 0.55 %∙a-1 in the Chinese Altay Mountains during 1990–2021 is obviously lower than the glacier area shrinkage rate of 0.75 %∙a-1 from 1960 to 2009, and the retreat rate of glacier volume is 0.60 %∙a-1. (2) More than 80 % of the total glacier area located at elevation ranged from 2600 to 3400 m, and the greatest glacier area loss occurred within the elevation range of 2800–3000 m. Although the glacier area was retreating in all orientations, the south-facing glacier has the largest retreat rate. (3) The rate of change of glacier area was −1.26 %∙a-1, −0.71 %∙a-1 and −0.51 %∙a-1 in the Haba sub-basin, Sawir Mountains region, and Burqin sub-basin, respectively. (4) Owing to the slowdown in the warming rate since 1990, the glacier shrinkage rate was moderate during 1990–2021 compared to the 1960 s–1980 s, and contribution of the increase of solar radiation (35.72 %) and summer temperature (25.3 %) to glacier retreat from 1990 to 2021 were more prominent than other climate factors.

Contribution of the cryosphere to runoff in “Chinese water tower” based on environmental isotopes

Cryospheric meltwater is an important runoff component and it profoundly influences changes in water resources in the Tibetan Plateau. Significant changes in runoff components occur in the three-river headwater region (TRHR), which is an important part of “Chinese Water Tower” due to climate warming. However, these effects remain unclear owing to the sparse and uneven distribution of monitoring sites and limited field investigations. Quantifying the contribution of cryospheric meltwater to outlet runoff is a key scientific question that needs to be addressed. In this study, we analyzed 907 precipitation, river water, ground ice, supra-permafrost water, and glacier snow meltwater samples collected from October 2019 to September 2020 in the TRHR. The following results were obtained: (1) There was significant spatio-temporal variation in stable isotopes in different waters; (2) The seasonal trends of stable isotopes for different waters, the relationship between each water body and the local meteoric water line (LWML) confirmed that river water was mainly recharged by precipitation, supra-permafrost water, and glacier snow meltwater; (3) Precipitation, supra-permafrost water, and glacier snow meltwater accounted for 52%, 39%, and 9% of river water, respectively, during the ablation period according to the end-member mixing analysis (EMMA); (4) In terms of future runoff components, there will be many challenges due to increasing precipitation and evaporation, decreasing snow cover, glacier retreat, and permafrost degradation. Therefore, it is crucial to establish the “star-machine-ground” observation networks, forecast extreme precipitation and hydrological events, build the “TRHE on the Cloud” platform, and implement systematic hydraulic engineering projects to support the management and utilization of water resources in the TRHR. The findings of environmental isotope analysis provide insights into water resources as well as scientific basis for rational use of water resources in the TRHR.

FLOODING IMPACTS AND FARMING MOTIVATION IN ANAM COMMUNITY OF ANAMBRA STATE, NIGERIA: IMPLICATION FOR FOOD SECURITY

Climate change projections that show an increase in the frequency and intensity of climatic hazards like flooding highlight the significance of understanding community vulnerability to extreme hydrological events. The current study looked at how farmers' motivation is impacted by flooding. Two hundred and twenty-five farmers recruited from farming communities in Anam, Anambra State, Nigeria, completed a self-report instrument to ascertain their motivations relative to continuing farming activities in the era of flooding. A cross-sectional design was employed in the study. Data from the respondents were analyzed using the statistical package for social sciences (SPSS, Version 23). Simple regression was run to test the primary hypothesis that flooding would predict farmers' motivation. The analysis demonstrated a statistically significant effect of flooding concerns on the farmers' motivation F (1,223), 21.31 P< .05, with the R2 indicating the independent variable accounted for 22.3% of the variation in farmers' motivation. The present finding contributes to the agricultural literature by revealing the flooding trend as a potential determinant of farmers' low motivations and the increasing low food production.

Changes in Nutrient Concentration Resulting From Floods and Their Impact on the Estuary–Sea Continuum

AbstractEvaluating the contribution of rivers to coastal seas in the Earth system and accurately predicting marine environmental responses to climate change are crucial for mitigating and addressing future natural disasters, especially those related to extreme hydrological events, such as fluvial floods. The Changjiang (Yangtze) River Estuary (CRE) and the East China Sea continuum, known for high environmental stress, complex marine dynamic processes, and frequent flood events, were selected to determine the compound impacts of a severe flood that persisted for the whole of July 2017. We conducted three cruise surveys before and after this flood event. Our surveys revealed that the phosphate concentration in the channel of the CRE declined from 2 to <1 μM during the flood, while the nitrate concentration remained stable. We applied a coupled physical‐biogeochemical model to determine the spatio‐temporal influence of floodwater (freshwater from upstream during flood events). During the initial rising discharge stage, surface phytoplankton blooms triggered by enhanced stratification consumed phosphate and significantly increased the N:P ratio in the sea. The ratio was restored as the blooms diminished, but remained elevated due to lower phosphate floodwater in the northeastward area within the low‐salinity river plume. Model experiments also showed that phytoplankton biomass and N:P ratio in the flood plume region were sensitive to the riverine phosphate concentration inputs during the flood event.