Extreme Flood Research Articles

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.

Managing Extreme Climatic Variability: A Study of Rice Based Farming in Assam, India

Assam has been experiencing the adverse effects of climate change and its most common manifestation is the increasing frequency of extreme flood in the pre-monsoon, monsoon and post-monsoon seasons. Lakhimpur and Dhemaji are the two districts that have been subjected to severe flood every year and in recent years have been experiencing soil erosion, sand-silt deposition in rice growing areas. The present study was therefore attempted in these two districts to find out the impact of extreme events in agriculture and social activities of the farmers in flood affected areas and to find out their responses and coping mechanisms during extreme climate variability. The result of the study revealed that both men and women farmers have been experiencing changes in the weather patterns with longer duration of hotter days and shorter duration of cold days. They have been experiencing frequent drought and frequent flood accompanied by soil erosion, sand and silt deposition in their rice fields affecting their farming activities. The area and production of rice/pulses/oilseeds had decreased to a great extent due to sand-silt deposition. During extreme flood livestock and fish production also decreased due to diseases and decreasing swampy areas/ponds. Availability of food, fodder and fuel had been affected for which the time taken for collecting fodder and fuel has increased. The impact of extreme climate events resulted in increasing indebtedness, wage labour, migration of young men in search of employment and school dropout of children. In response to such extreme climate events farmers had learned to adapt themselves through diversified farming by branching out into livestock production, change in cropping pattern, increasing the area of rabi crops cultivation, adopting non-farm activities such as wage labour in neighbouring districts/states, shop keeping, fishing and selling dry fish etc. women have adopted non-farm activities such as weaving, fishing and selling local brew. The priority adaptation needs of these men and women farmers are stress tolerant varieties of rice in flood affected areas, stress tolerant crops in sand deposited areas, training on scientific crop and livestock production, poultry etc. Women need training on improved techniques of loom and designing so as to fetch good price of their products as well as livestock and poultry production.

A 7000-year record of extreme flood events reconstructed from a threshold lake in southern Norway

Recent decades have witnessed a shift in the seasonality and frequency of river floods in Norway, primarily attributed to contemporary global warming. Such changes necessitate a more comprehensive understanding of climate-flood dynamics across river systems. A significant challenge in flood risk assessment is that instrumental data records cover only the last few decades and do not capture low-frequency, high-magnitude extremes. Another challenge is the non-stationarity of the leading atmospheric pattern over Scandinavia, the North Atlantic Oscillation (NAO), which requires a spatial understanding of extreme events through time. To address this issue, we use lacustrine sediments from southernmost Norway, aiming to extend flood records beyond the scope of instrumental and historical data by exploring a threshold lake that accumulates flood deposits only when river discharge exceeds a critical level. A multi-proxy approach, which includes X-ray fluorescence (XRF), computed tomography (CT), and magnetic susceptibility (MS), was applied to fingerprint the sediments. We detected thin layers of minerogenic sediments, which we interpreted as slackwater deposits accumulated in a lake environment during extreme flood events. These fine-grained minerogenic bands were quantified using XRF and CT data to reconstruct the frequency of major floods over the past ∼7000 years. The minimum water discharge necessary for transporting sediments into the lake was assessed with the HEC-RAS hydraulic simulation software. Our record shows a high frequency of extreme floods from 7000 to 5000 cal yr BP before a significant decrease from 5000 to 2300 cal yr BP. The frequency has been high since 2300 cal yr BP, with a peak frequency after 1600 CE, coinciding with the Little Ice Age. There is a strong co-variability between a positive NAO index, winter precipitation, and major flood events over the past 120 years. A comparison between our flood record and other palaeorecords suggests that this relationship also holds on multi-millennial time scales.

A hydro-climatic approach for extreme flood estimation in mountainous catchments

Prediction of rainfall-runoff process, peak discharges, and finally flood hydrograph is essential for flood risk management and river engineering projects. In most previous studies in this field, the precipitation rates have been entered into the models without considering seasonal and monthly distribution. In this study, the daily precipitation data of 144 climatology stations in Iran were used to evaluate the seasonal and monthly pattern of flood-causing precipitation. Then, by determining the rainy seasons and seasonal fit of precipitation with a probabilistic model and using regional precipitation, a semi-distributed conceptual model of rainfall-runoff (MORDOR-SD) was trained and validated using the observed discharge data. Flood prediction was performed using climatic data, modeling of hydrological conditions, and extreme flow data with high performance. According to the results, the Nash–Sutcliffe and Kling–Gupta coefficients were 0.69 and 0.82 for the mean daily streamflow, 0.98 and 0.98 for the seasonal streamflow, 0.98 and 0.94 for the maximum discharges, and 0.57 and 0.78 for low flows, respectively. Moreover, the maximum daily discharges in different return periods were estimated using the results of the MORDOR-SD model, considering the probability distribution function of the probabilistic model of central precipitation (MEWP), the probabilistic model of adjacent precipitation, and probability distribution function of the previous precipitation. Finally, the extreme flows were predicted and compared using different methods including the SCHADEX, regional flood analysis, GRADEX, and AGREGEE. The results showed that the methods GRADEX, AGREGEE, and SCHADEX have the highest performance in predicting extreme floods, respectively.

How extreme hydrological events correspond to climate extremes in the context of global warming: A case study in the Luanhe River Basin of North China

AbstractThe consensus on climate warming is well‐established, and extreme values inherently encapsulate more information than averages. Against the backdrop of frequent extreme climate events, studying extreme values holds profound significance. This study aims to reveal the characteristics of extreme climate events and their role in triggering extreme hydrological events in the typical North China basin, that is, the Luanhe River Basin. Trends of 25 extreme climate indices during 1960–2018 are analysed using the Sen's slope and MK significance test to study the changing characteristics of extreme climate. Characteristics of extreme flood and dry events are examined, encompassing trend analyses at different time scales (seasonal, interannual, decadal) and concentration analysis. Finally, and most significantly, correlation analysis is conducted on extreme climate indices and features of extreme hydrological events, followed by principal component analysis of extreme climate indices, to precisely quantify the impact of extreme climate on the occurrence of extreme hydrological events. The results indicate a warming trend in extreme temperature indices, with a more significant rise in minimum temperatures compared to maximum temperatures. There is a significant decrease in precipitation, but precipitation at higher magnitudes is less affected by the overall reduction in total precipitation. Extreme dry events have markedly increased, particularly concentrated in winter with delayed occurrences, primarily induced by extreme temperature events, that is, warming effects. Conversely, extreme flood events have significantly decreased, mainly concentrated in summer and early autumn, predominantly caused by extreme precipitation and extreme high‐temperature events. The climate and hydrological conditions in the study area have become more extreme and complex. Severe river droughts may occur more frequently in winter, while extreme flooding may happen in summer. Therefore, it is necessary to pay more attention to these developments.

On the Use of Weather Generators for the Estimation of Low-Frequency Floods under a Changing Climate

The present work presents a methodology based on the use of stochastic weather generators (WGs) for the estimation of high-return-period floods under climate change scenarios. Applying the proposed methodology in a case study, Rambla de la Viuda (Spain), satisfactory results were obtained through the regionalization of the bias-corrected EUROCORDEX climate projections and the integration of this information into the parameterization of the WG. The generated synthetic data series fed a fully distributed hydrological model to obtain the future flood quantiles. The results obtained show a clear increase in the precipitation extreme quantiles for the two analyzed projections. Although slightly reducing the annual amount of precipitation, variations between 4.3% for a return period of 5 years in the mid-term projection and 19.7% for a return period of 100 years in the long-term projection have been observed. In terms of temperatures, the results point to clear increases in the maximum and minimum temperatures for both projections (up to 3.6 °C), these increases being greater for the long-term projection, where the heat waves intensify significantly in both magnitude and frequency. Finally, although rivers may present, in general, with lower flows during the year, flood quantiles experience an increase of 53–58% for high return periods, which reach values of up to 145% when we move to smaller catchments. All this combined translates into substantial shifts in the river flow regimes, increasing the frequency and magnitude of extreme flood events.

Multiscale dynamics of transient merging between western disturbances and monsoonal lows: Connections to the July 2023 flood in Himachal Pradesh

The Himachal Pradesh region experienced an unprecedented 436% more rainfall than normal from July 7th to 10th, 2023, resulting in devastating landslides, flash floods, and significant socio-economic losses. Using rainfall observations and reanalysis data, this study investigates the dynamics of the extreme precipitation event that caused the disastrous 2023 flood in Himachal Pradesh, India. Prior to the flood, moderate rainfall events saturated the mountainsides, making them more vulnerable to landslides and flash floods. Local features such as rapid convergence, vertical velocity, relative humidity, relative vorticity, and moisture flux accumulation were observed to decrease following the extreme precipitation event, indicating a short-duration cloud burst event. Further, the study reveals a large-scale circulation pattern linking the extratropic and monsoonal low, which is favorable for extreme precipitation in the Himachal Pradesh region. This pattern includes a transient merging of eastward-propagating trough in the mid-upper-level, which extends southward and a monsoon low moving westward across India. The key elements of this transient merging feature consist of a Rossby wave train structure and a blocking high west of the features. The Rossby wave breaking effect occurred downstream of the blocking high two days prior to extreme precipitation event occurrence. This allowed high potential vorticity stratospheric air to intrude the equator and troposphere, causing surface pressure change and enhanced vertical motion, thereby advecting high amount of moisture causing extreme precipitation leading to catastrophic floods. Overall, the findings of this study hold direct implications for early warning systems and disaster preparedness in the face of extreme precipitation-induced flood situations triggered by such transient merging features.

Response of the microbial community structure to the environmental factors during the extreme flood season in Poyang Lake, the largest freshwater lake in China.

Poyang Lake is the largest freshwater lake in China, and there are several studies on the composition and diversity of bacteria in Poyang Lake, while few quantitative studies were carried out on the response of the bacterial community to environmental factors during the extreme flood season in Poyang Lake. The connected-lake heterogeneity of bacterial community composition (BCC) was investigated in Poyang Lake during the flood season in 2020. Illumina high-throughput sequencing technology was used in this study. The bacterial community structure in the water was different from that in the sediment of Poyang Lake during extreme flood seasons. The bacterial diversity in water was much lower than that in sediment. In the water column, the dominant phyla were Actinobacteriota, while the composition of bacteria in sediment was more complex than that in water, and the dominant phyla in sediment were Proteobacteria, Chloroflexi, Acidobacteriota, and Actinobacteriota. The bacterial diversity in the water of Poyang Lake showed seasonal dynamics, while no seasonal variation of bacterial communities in sediment was observed. The bacterial community structure in the sediment from the two bays and channel areas of Poyang Lake can be distinguished from each other. The microbial diversity in sediment gradually increased from the Sancha Bay to the Zhouxi Bay and then to the channel, but the total nitrogen (TN) concentration in sediment (STN) and the total phosphorus (TP) concentration in sediment (STP) showed opposite trends. This might be due to the anthropogenic disturbances from the extreme flood. The bacterial community structure in, water column was significantly correlated with WT, NH4-N, STP, SOM, Chl a, DO, TP, and Eh, while the bacterial community structure in sediment was significantly correlated with SOM and STP. The bacterial community structure in water was greatly different from that in sediment in Poyang Lake during extreme flood seasons. The bacterial community structure in the water column was not only sensitive to the geochemical characteristics of the water but also affected by some nutrient concentrations in the sediment. During the wet seasons, bacterial diversity was only affected by SOM and STP.

Floods, droughts, and environmental circumscription in early state development: the case of ancient Egypt

AbstractWhat explains the origins and survival of the first states around 5000 years ago? In this research, we focus on the role of weather-related productivity shocks for early state development in ancient Egypt. We present a framework of extractive state consolidation predicting that political stability should be high whenever environmental circumscription is high, i.e., whenever there is a large gap between the productivity of the area under state control (core) and that of the surrounding areas (hinterland). In such periods, the elite can impose high levels of taxation that the population will be forced to accept as exit to the hinterland is not a feasible option. In order to test this hypothesis, we develop novel proxies for both the historical productivity of the Nile banks and of the Egyptian hinterland on the basis of high-resolution paleoclimate archives. Our empirical analysis then investigates the relationship between these proxies for environmental circumscription and political outcomes such as ruler and dynastic tenure durations, the area under state control and pyramid construction during 2685–1140 BCE. Our results show that while extreme Nile floods are associated with a greater degree of political instability, periods with a greater rainfall in the hinterland (i.e., a lower effective environmental circumscription) causes a decline in state capacity and a delayed increase in political instability.

Flood Risk Mapping during the Extreme February 2021 Flood in the Juruá River, Western Brazilian Amazonia, State of Acre

Cruzeiro do Sul, a municipality in Northwestern Brazil is recurrently impacted by floods, particularly along the Juruá River. This study presents a comprehensive flood risk analysis by integrating geoprocessing, remote sensing, and hydraulic modeling techniques. Our objectives are to simulate flood extents, identify high-risk areas, and guide sustainable territorial management. Our findings illustrate that the flood impacts are distributed across urban (27%), agricultural (55%), and forest/grassland (17%) landscapes. Historical records and literature reviews also underscore a recurring pattern of extreme floods in the municipality, notably during February’s La Niña events. Some vulnerable urban neighborhoods were identified: Vila Cruzeirinho, Centro, Miritizal, and Da Várzea. These areas are especially susceptible due to their proximity to the river and increased surface runoff during high flood events. By amalgamating various data sources and methods, this research aids decision making for flood mitigation and urban development, fostering resilience against recurrent flooding events in Cruzeiro do Sul.

A National‐Scale Coastal Flood Hazard Assessment for the Atoll Nation of Tuvalu

AbstractAtoll nations such as Tuvalu are considered to be amongst those most vulnerable to the effects of climate change. Here we present a national‐scale coastal flood hazard assessment for Tuvalu based on high‐resolution Light Detection and Ranging (LiDAR) topography and bathymetry. We follow a fully probabilistic approach, considering sea level anomalies, tides, and extreme wave conditions from a mixed climate (i.e., from distant extra‐tropical storms and local tropical cyclones). Nearshore processes such as wave setup and runup are also accounted for. Hazard maps were calculated for the present sea level, as well as for sea level rise projections corresponding to different shared socioeconomic pathways (SSP2 4.5 and SSP5 8.5) and time horizons (2060 and 2100). With a mean elevation of 1.55 m above mean sea level (1.37 m above mean high water spring) >25% of land area is inundated once every 5 years and >50% of land area floods once every 100 years nationally. Results indicate that present day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 (annual exceedance probability >20%), even under the moderate SSP2 4.5 sea level rise projections. Results of this study highlight the pressing need for ambitious and large‐scale adaptation solutions which are commensurate with projected sea level rise and marine hazard impacts. The methodologies presented in this paper can easily be applied to other low‐lying islands in the tropical Pacific, where mixed climates (i.e., regular and TC conditions) and non‐linear nearshore processes dominate extreme water levels and flooding.

Numerical Modeling of Potential Large Wood Entrainment in Rivers: Application of Hybrid Modeling in the Inter‐Dam Reach of the Dyje River, Czechia

AbstractMobilization of large wood in river channels during floods represents a hazardous factor, augmenting flood risk and endangering infrastructures such as bridges, weirs, and reservoir dams. A hybrid modeling approach combining numerical models with field‐based surveys has been recently used to elucidate the processes of LW entrainment and deposition in rivers. We used two‐dimensional hydraulic modeling performed in HEC‐RAS to simulate LW entrainment in two valley bottom segments of the Dyje River, Czechia, where LW deposition is a significant hazard to the dam of the downstream Znojmo reservoir. We surveyed all LW pieces in the inundation area of the 2002 extreme (>Q100) flood and simulated their entrainment for eight flood scenarios (1–100‐year recurrence interval). We used the equations of Braudrick and Grant (2000, https://doi.org/10.1029/1999WR900290) to calculate the LW entrainment threshold; we introduced coefficient k accounting for the incomplete submersion of LW pieces resting on an inclined surface into the original equations. Four entrainment categories — stable, wetted stable, wetted buoyant, and wetted entrained — were defined, and the proportion of LW pieces in each category was calculated for the flood scenarios. We found marked differences in entrainment categories for respective flood scenarios between the two valley segments. These were attributed to the differences in pieces' dimensions, their spatial distribution within the inundation area, and valley‐bottom topography, which affects the hydraulic conditions for a given discharge. The presented approach enables the calculation of the LW quantity potentially mobilized by the flood of a certain magnitude indicating the degree of potential risk for the infrastructures located downstream.

The meteorology of the 2019 North Queensland floods

AbstractIn January–February 2019, a monsoon low developed over northeastern Australia and brought extreme flooding to much of tropical Queensland. The European Centre for Medium Range Weather Forecasts reanalyses are used to explain the formation and severity of the event. Strong anticyclonic Rossby wave breaking to the southeast of Australia produced weak steering winds over northern Australia, and consequently the low remained nearly stationary for over a week, contributing to the extreme rainfall. Furthermore, the tropical moist margin was deformed by a series of disturbances along the monsoon trough, which drew moist maritime air over land. The event examined has much in common with the composite mean of slow‐moving potential vorticity anomalies in North Queensland, including weak background winds and heavy precipitation. An ensemble subseasonal forecast with the Australian Bureau of Meteorology's Australian Community Climate and Earth System Simulator Seasonal prediction version system 1 shows the same relationship between the background flow, the steering, and the subsequent rainfall. Hence, accurately forecasting the background winds is a prerequisite to forecasting such extreme rainfall.

Enhancing flood risk assessment in the Johor River Basin through trivariate copula

ABSTRACT Copulas are a vital statistical tool, particularly in hydrology, for understanding complex relationships among flood characteristics. This study focuses on three key flood features: peak discharge, flood volume, and flood duration, using trivariate copulas to capture their interdependencies. This is crucial because bivariate and univariate analyses fall short in considering all three factors simultaneously. To handle extreme flood values, L-moment is proposed over maximum likelihood estimation and inference function margin due to its enhanced reliability and susceptibility to outliers and extreme values. Akaike information criterion was employed to identify the best-fit marginal distribution and copula. The Lognormal distribution effectively models peak discharge, while Weibull and generalized extreme value distributions fit flood volume and duration best, respectively. Various copula families, including elliptical and Archimedean, are assessed, where Clayton copula emerge as the most suitable. This analysis demonstrates that when more flood features are considered together, the return period increases, indicating the reduced likelihood of occurrence. The trivariate case of the AND-joint return period surpasses the trivariate case of the OR-joint return period where the TP, V, DAND=5, 405.93 years, while TP, V, DOR=500.46 years. This comprehensive approach enhances hydrological modeling and decision-making for water resource management and flood mitigation projects.

Compounding effects in flood drivers challenge estimates of extreme river floods.

Estimating river flood risks under climate change is challenging, largely due to the interacting and combined influences of various flood-generating drivers. However, a more detailed quantitative analysis of such compounding effects and the implications of their interplay remains underexplored on a large scale. Here, we use explainable machine learning to disentangle compounding effects between drivers and quantify their importance for different flood magnitudes across thousands of catchments worldwide. Our findings demonstrate the ubiquity of compounding effects in many floods. Their importance often increases with flood magnitude, but the strength of this increase varies on the basis of catchment conditions. Traditional flood analysis might underestimate extreme flood hazards in catchments where the contribution of compounding effects strongly varies with flood magnitude. Overall, our study highlights the need to carefully incorporate compounding effects in flood risk assessment to improve estimates of extreme floods.

The influence of microtopography to road inundation caused by extreme flood

Microtopography plays a critical role in road inundation during urban flood events. The microtopography in this paper was defined as terrain-scale features that encompass surface roughness, slope, road network and urban building layout. This paper aims to explore the mechanism of depression storage and road inundation under different microtopography. Simulations under 4 rainfall intensities (144.0– 182.88 mm/h) and 14 slope combinations (four transverse slope and five longitudinal slope) were implemented in an 800 by 70 cm local model. The correlation heat map directly reflected that longitudinal slope had higher influence on drainage than other factors. Then real topographical and hydrological data was applied to predict road inundation with five different extreme rainfall events in Jiangning District (Nanjing City, China). The microtopography characteristics of frequent inundation road were extracted, which further verified the conclusions of the local model. Results show that: the microtopography depressions drainage process could be divided into six main stages: filling stage, interaction stage, unstable drainage stage, stable flow stage, drainage stage and stage of drainage end. Water was stored on depressions of road, and the storage volume and discharge efficiency were affected by the surface relief and slope. The emergence of slope provided an altered path and power for water drainage. Only 0.3 % slope could contribute a 28.4 % to discharge efficiency. Upon comparation, the best combination for drainage was 2.0 % transverse slope with 3.0 % longitudinal slope. These findings provided meaningful insights and perspectives for urban flood hazard mitigation and were a more detailed reference for road design.

Ensemble Forecasts of Extreme Flood Events with Weather Forecasts, Land Surface Modeling and Deep Learning

Integrating numerical weather forecasts that provide ensemble precipitation forecasts, land surface hydrological modeling that resolves surface and subsurface hydrological processes, and artificial intelligence techniques that correct the forecast bias, known as the “meteo-hydro-AI” approach, has emerged as a popular flood forecast method. However, its performance during extreme flood events across different interval basins has received less attention. Here, we evaluated the meteo-hydro-AI approach for forecasting extreme flood events from headwater to downstream sub-basins in the Luo River basin during 2010–2017, with forecast lead times up to 7 days. The proposed meteo-hydro approach based on ECMWF weather forecasts and the Conjunctive Surface-Subsurface Process version 2 land surface model with a spatial resolution of 1 km captured the flood hydrographs quite well. Compared with the ensemble streamflow prediction (ESP) approach based on initial conditions, the meteo-hydro approach increased the Nash-Sutcliffe efficiency of streamflow forecasts at the three outlet stations by 0.27–0.82, decreased the root-mean-squared-error by 22–49%, and performed better in reliability and discrimination. The meteo-hydro-AI approach showed marginal improvement, which suggested further evaluations with larger samples of extreme flood events should be carried out. This study demonstrated the potential of the integrated meteo-hydro-AI approach for ensemble forecasting of extreme flood events.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

Post-Analysis of Daniel Extreme Flood Event in Thessaly, Central Greece: Practical Lessons and the Value of State-of-the-Art Water-Monitoring Networks

Storm Daniel initiated on 3 September 2023, over the Northeastern Aegean Sea, causing extreme rainfall levels for the following four days, reaching an average of about 360 mm over the Peneus basin, in Thessaly, Central Greece. This event led to extensive floods, with 17 human lives lost and devastating environmental and economic impacts. The automatic water-monitoring network of the HIMIOFoTS National Research Infrastructure captured the evolution of the phenomenon and the relevant hydrometeorological (rainfall, water stage, and discharge) measurements were used to analyse the event’s characteristics. The results indicate that the average rainfall’s return period was up to 150 years, the peak flow close to the river mouth reached approximately 1950 m3/s, and the outflow volume of water to the sea was 1670 hm3. The analysis of the observed hydrographs across Peneus also provided useful lessons from the flood-engineering perspective regarding key modelling assumptions and the role of upstream retentions. Therefore, extending and supporting the operation of the HIMIOFoTS infrastructure is crucial to assist responsible authorities and local communities in reducing potential damages and increasing the socioeconomic resilience to natural disasters, as well as to improve the existing knowledge with respect to extreme flood-simulation approaches.

Flood dynamics on the upper Letaba River, South Africa, deduced from luminescence dating

ABSTRACT The Letaba River is one of several east-flowing rivers in the semiarid region of northeast South Africa. There is strong seasonality of river discharge and patterns of geomorphic behaviour along bedrock- and sediment-dominated reaches in these rivers, and in response to extreme seasonal flood events. This paper presents new luminescence ages on sediment samples from five sites in upper (headwater) reaches of the Letaba River catchment outside of Kruger National Park, South Africa, combined with evidence from reach-scale geomorphology and sediment sample analysis. River reaches are mainly mixed bedrock-alluvial with a patchwork of poorly sorted coarse sand bars overlying an abraded extended bedrock channel system. Luminescence ages from river sediment deposits (n = 13) cluster around three time periods of the last 400 years, 500–1100 BP, and 1400 BP. This suggests different reworked populations are present, which are a result of the partial bleaching of quartz grains and, thus, a mixed luminescence signal as flood-transported sediments are progressively moved from one depositional sink to another. This pattern of luminescence ages is quite different to lowland river systems in the same region where ages on the whole are significantly younger. Flood processes and dynamics in headwater reaches of semiarid rivers are often not considered but can yield a better understanding of system sensitivity to climate and event forcing.