Assessing the risk of a historical-level flood is essential for regional flood protection and resilience establishment. However, due to the limited spatiotemporal coverage of observations, the impact assessment relies on model simulations and is thus subject to uncertainties from cascade physical processes. This study assesses the flood hazard map with uncertainties subject to different combinations of runoff inputs, variables for flood frequency analysis and fitting distributions based on estimations by the CaMa-Flood global hydrodynamic model. Our results show that deviation in the runoff inputs is the most influential source of uncertainties in the estimated flooded water depth and inundation area, contributing more than 80 % of the total uncertainties investigated in this study. Global and regional inundation maps for floods with 1-in-100 year return periods show large uncertainty values but small uncertainty ratios for river channels and lakes, while the opposite results are found for dry zones and mountainous regions. This uncertainty is a result of increasing variation at tails among various fitting distributions. In addition, the uncertainty between selected variables is limited but increases from the regular period to the rarer floods, both for the water depth at points and for inundation area over regions. The uncertainties in inundation area also lead to uncertainties in estimating the population and economy exposure to the floods. In total, inundation accounts for 9.1 % [8.1 %–10.3 %] of the land area for a 1-in-100 year flood, leading to 13.4 % [12.1 %–15 %] of population exposure and 13.1 % [11.8 %–14.7 %] of economic exposure for the globe. The flood exposure and uncertainties vary by continent and the results in Africa have the largest uncertainty, probably due to the limited observations to constrain runoff simulations, indicating a necessity to improve the performance of different hydrological models especially for data-limited regions.

To develop a proper indicator which could predict water quality and trace pollution sources is critically important for the management of sustainable aquatic ecosystem. In our study, seven water samples collected from Wuliangsuhai Lake in Inner Mongolia were used. UV-visible spectra and synchronous fluorescence spectra were applied to investigate the humification degree and aromatic structure of dissolved organic matter (DOM) extracted from water samples. The results showed that both samples from W1 site and W3 site display lower humification degree and less aromatic structure, where industrial wastewater and domestic sewage, and reclaimed water of farmland irrigation, were accepted respectively. After computing the values of SUVA254, A280, A250/365, A253/203 and A226–400, we reached the conclusion that they have a consistent trend (W4> W6> W5> W2> W7> W1> W3). Fluorescence index (f450/500) was always utilised to interpret the origin of organic matter in a complex aquatic environment system. Values of f450/500 are closer to 1.60, indicating that humic substances derived from terrestrial sources and biological sources. Our study demonstrated that reclaimed water of farmland irrigation, industrial wastewater and domestic sewage will definitely influence the humification degree and amount of the aromatic structure of DOM.

Floods can be devastating in densely populated regions along rivers, so attaining a longer forecast lead time with high accuracy is essential for protecting people and property. Although many techniques are used to forecast floods, sufficient validation of the use of a forecast system for operational alert purposes is lacking. In this study, we validated the flooding locations and times of dike breaking that had occurred during Typhoon Hagibis, which caused severe flooding in Japan in 2019. To achieve the goal of the study, we combined a hydrodynamic model with statistical analysis under forcing by a 39-h prediction of the Japan Meteorological Agency's Meso-scale model Grid Point Value (MSM-GPV) and obtained dike-break times for all flooded locations for validation. The results showed that this method was accurate in predicting floods at 130 locations, approximately 91.6% of the total of 142 flooded locations, with a lead time of approximately 32.75 h. In terms of precision, these successfully predicted locations accounted for 24.0% of the total of 542 locations under a flood warning, and on average, the predicted flood time was approximately 8.53 h earlier than a given dike-break time. More warnings were issued for major rivers with severe flooding, indicating that the system is sensitive to extreme flood events and can issue warnings for rivers subject to high risk of flooding.