Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use, and irrigation. Although their monitoring is crucial in times of increased pressure on water resources by both climate change and human interventions, publically available datasets of lake and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 137 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh ∕ dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with a R2 > 0.8, an average R2 of 0.91 and a standard deviation of 0.05. For these lakes and for lakes with a nearly constant lake area (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage and (3) a predominant lack of data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the US, Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97 with a standard deviation of 0.041, and a normalized RMSE of 7.42 %. These results show a high potential for measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will not only provide a historical lake and reservoir volume variation record, but will also help to improve our understanding of the behaviour of lakes and reservoirs and their representation in (large-scale) hydrological models.

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Abstract. Lakes and reservoirs are crucial elements of the hydrological and biochemical cycle and are a valuable resource for hydropower, domestic and industrial water use and irrigation. Although their monitoring is crucial in times of increased 10 pressure on water resources by both climate change and human interventions, publically available datasets of lakes and reservoir levels and volumes are scarce. Within this study, a time series of variation in lake and reservoir volume between 1984 and 2015 were analysed for 135 lakes over all continents by combining the JRC Global Surface Water (GSW) dataset and the satellite altimetry database DAHITI. The GSW dataset is a highly accurate surface water dataset at 30 m resolution compromising the whole L1T Landsat 5, 7 and 8 archive, which allowed for detailed lake area calculations globally over a 15 very long time period using Google Earth Engine. Therefore, the estimates in water volume fluctuations using the GSW dataset are expected to improve compared to current techniques as they are not constrained by complex and computationally intensive classification procedures. Lake areas and water levels were combined in a regression to derive the hypsometry relationship (dh/dA) for all lakes. Nearly all lakes showed a linear regression, and 42 % of the lakes showed a strong linear relationship with an R 2 > 0.8 and an average R 2 of 0.91. For these lakes and for lakes with a nearly constant lake area 20 (coefficient of variation < 0.008), volume variations were calculated. Lakes with a poor linear relationship were not considered. Reasons for low R 2 values were found to be (1) a nearly constant lake area, (2) winter ice coverage, (3) small lake sizes and (4) a predominance of no data within the GSW dataset for those lakes. Lake volume estimates were validated for 18 lakes in the U.S., Spain, Australia and Africa using in situ volume time series, and gave an excellent Pearson correlation coefficient of on average 0.97, and a normalized RMSE of 7.42 %. These results show a high potential for 25 measuring lake volume dynamics using a pre-classified GSW dataset, which easily allows the method to be scaled up to an extensive global volumetric dataset. This dataset will help to validate large scale hydrological models, to improve regional and global flood and drought forecasting systems and to update hydropower estimations.Hydrol. Earth Syst. Sci. Discuss., https://doi

As a result of high-density urbanization and climate change, both the frequency and intensity of extreme urban rainfall are increasing. Drainage systems are not designed to cope with this increase, and as a result, floods are becoming more common in cities, particularly in the rapidly growing cities of China. To better cope with more frequent and severe urban flooding and to improve the water quality of stormwater runoff, the Chinese government launched the national Sponge City Construction (SCC) program in 2014. The current SCC design standards and guidelines are based on static values (e.g., return periods, rainfall intensities, and volume capture ratio (VCR)). They do not fully acknowledge the large differences in climate conditions across the country and assume that the hydraulic conditions will not change over time. This stationary approach stems from the traditional engineering approach designed for grey infrastructure (following a “one size fits all” approach). The purpose of this study was to develop a methodology to assess the VCR baseline (before construction in the pre-development stage) and changes in VCR (difference between the VCR of the pre- and post-development stage). The VCR of the post-development stage is one of the required indicators of the Assessment Standard for Sponge Cities Effects to evaluate SCC projects. In this study, the VCR was derived from remote-sensing-based land use land cover (LULC) change analysis, applying an unsupervised classification algorithm on different Landsat images from 1985 to 2015. A visualization method (based upon Sankey chart, which depicts the flows and their proportions of components) and a novel and practical partitioning method for built-up regions were developed to visualize and quantify the states and change flows of LULC. On the basis of these findings, we proposed a new indicator, referred to as VCRa-L, in order to assess the changes in urban hydrology after SCC construction. This study employed the city of Nanjing as a case study and analyzed detailed information on how LULC changes over time of built-up areas. The surface area of the urban and built-up areas of Nanjing quadrupled from 11% in 1985 to 44% in 2015. In the same period, neither the entire city nor its subregions reached the VCR target of 80%. The proposed new methodology aims to support national, regional, and city governments to identify and prioritize where to invest and implement SCC measures more effectively in cities across China.

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This paper explores the use of the JRC global surface water dataset, and the DAHITI satellite altimetry database to estimate hypsometry relationships for a reasonable number of lakes across the globe. The paper should be of interest to a people working in water resources, and potentially is a publishable paper.Thank you a lot for your time, effort and usefull feedback. We agree that our work should be of interest to many people working in water resources and hydrological modelling, as it improves on monitoring techniques of lakes and reservoirs currently available.At the moment however, the paper is a fairly simple data analysis with insufficient C1