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The Hydrologic Modeling System (HEC-HMS) and statistical analysis method were used to analyze the relationship between flood eigenvalues (i.e., flood volume and peak flow) and landscape pattern metrics. Then, the flood-landscape ecological risk index (ERI_FL) was proposed and constructed to quantitatively assess the flood-landscape ecological risk (FLER). The semivariogram method was used to spatialize the ERI_FL values. Lastly, this study analyzed the spatial–temporal change of FLER at watershed scale and at sub-basin scale, respectively. Two historical landscape distributions (i.e., 2003 and 2017) of Qinhuai River basin were used to perform this study. The results showed that there were certain relationships between landscape pattern and flood eigenvalues, and for different landscapes, the response metrics and degrees were different. FLER increased as urbanization increased. FLER change magnitude had a positive relationship with urban land percentage change magnitude. The distribution of FLER and the distribution of FLER change both showed spatial differences at watershed scale. The structural features of landscape pattern had significant effects on regional floods. In the urbanization process, avoiding forming large-scale landscape patches, improving landscape abundance, and increasing contact area between different types of landscape patches were helpful to reduce the negative effects caused by the increase of urban landscape area on flood.

Satellite precipitation estimation provides crucial information for those places lacking rainfall observations from ground–based sensors, especially in terrestrial or marine areas with complex climatic or topographic conditions. This is the case over much of Western China, including Upper and Middle Lancang River Basin (UMLRB), an extremely important transnational river system in Asia (the Lancang–Mekong River Basin) with complex climate and topography that has limited long–term precipitation records and high–elevation data, and no operational weather radars. In this study, we evaluated three GPM IMERG satellite precipitation estimation (IMERG E, IMERG L and IMERG F) over UMLRB in terms of multi–year average precipitation distribution, amplitude consistency, occurrence consistency, and elevation–dependence in both dry and wet seasons. Results demonstrated that monsoon and solid precipitation mainly affected amplitude consistency of precipitation, aerosol affected occurrence consistency of precipitation, and topography and wind–induced errors affected elevation dependence. The amplitude and occurrence consistency of precipitation were best in wet seasons in the Climate Transition Zone and worst in dry seasons in the same zone. Regardless of the elevation–dependence of amplitude or occurrence in dry and wet seasons, the dry season in the Alpine Canyon Area was most positively dependent and most significant. More significant elevation–dependence was correlated with worse IMERG performance. The Local Weighted Regression (LOWERG) model showed a nonlinear relationship between precipitation and elevation in both seasons. The amplitude consistency and occurrence consistency of both seasons worsened with increasing precipitation intensity and was worst for extreme precipitation cases. IMERG F had great potential for application to hydroclimatic research and water resources assessment in the study area. Further research should assess how the dependence of IMERG’s spatial performance on climate and topography could guide improvements in global precipitation assessment algorithms and the study of mountain landslides, floods, and other natural disasters during the monsoon period.

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With the implementation of China’s “Going Out” strategy and “Belt and Road Initiative” (BRI) as well as the shortage of domestic hydropower market, the scale of hydropower investment along BRI by Chinese companies has expanded rapidly. However, these countries have great differences in politics, laws, economy, hydropower potential, social development and environmental constraints. Due to the inappropriate choice of countries for investment, many failure cases have also occurred. To specifically evaluate hydropower investment in these countries, this paper proposed a six-dimensional indicator system which can represents the characteristics of hydropower investment along BRI based on the analysis of the typical cases of overseas investment by Chinese enterprises. Furthermore, a fuzzy optimal model based on the Delphi-Entropy weight was constructed to evaluate the hydropower investment of 65 countries along BRI as well as a list of countries and corresponding investment grades are proposed. The result indicates that politics and hydropower industry factors are the key determinants of choosing the countries for conducting investment while legal, economic, social and environmental factors should also be covered. In conclusion, the optimal choices for China’s hydropower investment along BRI are Russia, Pakistan, Malaysia, Kazakhstan and Indonesia and the strategy has been given accordingly. Moreover the policy recommendations from the perspective of nation and enterprise level have also been proposed.