At present, the Sponge City Concept (SCC) is gaining ground, Sponge Cities technologies are becoming more and more accepted by Chinese city governments, and the first best practices are being shared. However, there are still many challenges ahead which hamper effective implementation and upscaling. This paper presents an overview of some opportunities and constraints for the take up of this approach and has drawn upon international experiences. In China at the national level, the State Council has set a progressive target for the SCC initiative to be achieved in 2030. This target seems to be ambitious as the time needed for integrative planning and design and implementation is much longer than traditional sectoral approaches often omitting to address social well-being, the (local) economy, and ecosystem health. This particularly holds true for the existing building stock. Transforming the existing building stock requires a long-term planning horizon, with urban restoration, regeneration, and modernization being key drivers for adapting the city to become a sponge city. A key challenge will be to align the sponge city initiative (SCI) projects with infrastructure and urban renovation portfolios. Moreover, substantial investment needs and a lack of reliable financing schemes and experience also provide a huge challenge for China. This calls for an integrative opportunistic strategy that creates enabling conditions for linking the SCI investment agenda with those from other sectors. These transformations cannot be made overnight: completing the transformation process will typically take a life time of one generation. The progress in sustainable urban water management is also impacted by innovations in technologies as well as in management strategies. These technological innovations create fertile ground for businesses to adapt state-of-the-art developments from around the world and contextualize them into fit-for-purpose products. China is well-placed to play a leading role in this process in the coming decade.

Satellite based rainfall estimation techniques have emerged as a potential alternative to ground based rainfall measurements. The Tropical Rainfall Measuring Mission (TRMM) precipitation, in particular, has been used in various climate and hydrology based studies around the world. While having wide possibilities, TRMM rainfall estimates are found to be inconsistent with the ground based rainfall measurements at various locations such as the southwest coast and Himalayan region of India, northeast parts of USA, Lake Victoria in Africa, La Plata basin in South America, etc. In this study, the applicability of TRMM estimates is evaluated over the Upper Ganga Basin (Himalayan catchment) by comparing against gauge-based India Meteorological Department (IMD) gridded precipitation records. Apart from temporal evaluation, the ability of TRMM in capturing spatial distribution is also examined using three statistical parameters namely correlation coefficient (r), mean absolute error (MAE) and relative bias (RBIAS). In the results, the dual nature of bias is evident in TRMM precipitation with rainfall magnitude falling in the range from 100 to 370 mm representing positive bias, whereas, rainfall magnitude above 400 mm, approximately, representing negative bias. The Quantile Mapping (QM) approach has been used to correct the TRMM dataset from these biases. The raw TRMM precipitation is found to be fairly correlated with IMD rainfall for post-monsoon and winter season with R² values of 0.65 and 0.57, respectively. The R² value of 0.41 is obtained for the monsoon season, whereas least correlation is found for the pre-monsoon season with an R² value of 0.24. Moreover, spatial distribution of rainfall during post-monsoon and winter season is captured adequately; however, the limited efficiency of TRMM is reflected for pre-monsoon and monsoon season. Bias correction has satisfactorily enhanced the spatial distribution of rainfall obtained from TRMM for almost all the seasons except for monsoon. Overall, the corrected TRMM precipitation dataset can be used for various climate analyses and hydrological water balance based studies in the Himalayan river basins.