Xiuqin Fang

This study assessed risk of major flooding across the globe based on data in the Emergency Events Database spanning 1980 to 2023 and two machine learning methods, extreme gradient boosting and random forest. A flood disaster index was calculated for politically defined provinces around the world using a combination of analytic hierarchy processing and entropy weighting. The resulting indices, together with hydro-meteorological, topographic, vegetation and economic variables, were used to train two machine learning algorithms, which ranked 20 variables according to their relative contribution to flood risk in areas differing in climate zones or levels of socio-economic development. The two algorithms did not substantially differ from each other in their rankings. The modeling suggests that low and middle latitudes are at greater risk of flooding than high latitudes, and it identified the following areas as particularly vulnerable: China, South Asia, western Arabian Peninsula, western Germany, Java (Indonesia), Zulia (Venezuela), and eastern Australia. Around the world, risk of flooding depends much more on river network density than on surface runoff. Other major determinants of major flood risk depend on the climate zone: in the tropics, economy and precipitation are major determinants; in arid regions, vegetation cover; in temperate regions, population and prolonged heavy rainfall; in cold regions, precipitation and surface soil moisture; and in polar regions, topographic factors. In the socio-economically defined "Global North", precipitation may be the primary determinant, while in the "Global South", economic factors may be more crucial.

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm⁻²yr⁻¹ across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

Soil moisture (SM) is a bridge between the atmosphere, vegetation and soil, and its dynamics reflect the energy exchange and transformation between the three. Among SM at different soil profiles, root zone soil moisture (RZSM) plays a significant role in vegetation growth. Therefore, reliable estimation of RZSM at the regional scale is of great importance for drought warning, agricultural yield estimation, forest fire monitoring, etc. Many satellite products provide surface soil moisture (SSM) at the thin top layer of the soil, approximately 2 cm from the surface. However, the acquisition of RZSM at the regional scale is still a tough issue to solve, especially in the semi-arid areas with a lack of in situ observations. Linking the dynamics of SSM and RZSM is promising to solve this issue. The soil moisture analytical relationship (SMAR) model can relate RZSM to SSM based on a simplified soil water balance equation, which is suitable for the simulation of soil moisture mechanisms in semi-arid areas. In this study, the Xiliaohe River Basin is the study area. The SMAR model at the pixels where in situ sites were located is established, and parameters (a, b, s_w2, s_c1) at these pixels are calibrated by a genetic algorithm (GA). Then the spatial parameters are estimated by the random forest (RF) regression method with the soil, meteorological and vegetation characteristics of the study area as explanatory variables. In addition, the importance of soil, climatic and vegetation characteristics for predicting SMAR parameters is analyzed. Finally, the spatial RZSM in the Xiliaohe River Basin is estimated by the SMAR model at the regional scale with the predicted spatial parameters, and the variation of the regional SMAR model performance is discussed. A comparison of estimated RZSM and in-situ RZSM showed that the SMAR model at the point and regional scales can both meet the RMSE benchmark from NASA of 0.06 cm³·cm⁻³, indicating that the method this study proposed could effectively estimate RZSM in semi-arid areas based on remotely sensed SSM data.

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

We chose Jiangxi Province in China as the case study, which frequently suffered from mountain torrents disaster. We selected 14 indexes of flood risk assessment and random forest algorithm to construct a mountain torrents disaster risk assessment model. The optimal model which has overall accuracy of about 90.0% was used to map the risk zoning of mountain flood disaster in Jiangxi Province. Our mapping results indicated that the area of high risk and very high risk zones accounts for 33.0% of the total area of Jiangxi Province, and the regions with high risk are mainly distributed in that vicinity of mountains with high rainfall, heavy rainstorm and high potential of farmland production, etc.

Water scarcity in Huaihe River, the sixth largest river in China, is stressed by the changing climate and intense human activities, especially in the headwater area. Xixian, situated in the upper reaches of the Huai River, is an important agricultural county with a population of more than one million people, and nearly one billion kg of crop yield every year. The projected climate changes and increasing population are expected to further complicate the utilization of already stressed water resources, endangering the agricultural activities in this area. There is pressing need for a watershed model to better understand the interaction between land use activities and hydrologic processes and to support sustainable water use. This study evaluated the performance of the Soil and Water Assessment Tool (SWAT) for hydrologic modeling in the Xixian basin; three methods of calibration and uncertainty analysis (sequential uncertainty fitting, generalized likelihood uncertainty estimation, and parameter solu...