Flash floods have occurred frequently in the urban areas of southern China. An effective process-oriented urban flood inundation model is urgently needed for urban storm-water and emergency management. This study develops an efficient and flexible cellular automaton (CA) model to simulate storm-water runoff and the flood inundation process during extreme storm events. The process of infiltration, inlets discharge and flow dynamics can be simulated with little preprocessing on commonly available basic urban geographic data. In this model, a set of gravitational diverging rules are implemented to govern the water flow in a rectangular template of three cells by three cells of a raster layer. The model is calibrated by one storm event and validated by another in a small urban catchment in Guangzhou of southern China. The depth of accumulated water at the catchment outlet is interpreted from street-monitoring closed-circuit television (CCTV) videos and verified by on-site survey. A good level of agreement between the simulated process and the reality is reached for both storm events. The model reproduces the changing extent and depth of flooded areas at the catchment outlet with an accuracy of 4 cm in water depth. Comparisons with a physically based 2-D model (FloodMap) show that the model is capable of effectively simulating flow dynamics. The high computational efficiency of the CA model can meet the needs of city emergency management.

Three satellite rainfall products (3B42V7, IMERGV05/V04/V03, and China hourly Merged Precipitation Analysis product) are evaluated using measurements from a dense rain gauge network as reference in Guangdong Province, China from April 2014 to December 2016. The three products are compared with gauges in annual, monthly, daily and hourly accumulation, and at gridded, sub‐regional and regional scales. An error decomposition approach is employed to separate the total bias into Hit, Miss and False components. Overall, the CMPA estimate is the best in agreement with gauge observations. The improvement of IMERGV05 over 3B42V7 is notable, especially in reducing the hit bias and missed precipitation, resulting in better detection of the light rain and heavy rain. The enhancement of IMERG is more significant at the 3‐hr scale than daily scale. IMERGV05 shows better performance than its previous versions especially for IMERGV03, which had an abnormal blocky pattern mainly from July to September over the Pearl River Delta area. The three products have different error characteristics and show large spatial variations. 3B42V7 and IMERG have large areas of overestimation in the mountainous areas and underestimation in the coastal areas, while CMPA is characterized by an alternate distribution of small positive and negative values. The overestimation of precipitation is partially attributed to the positive hit biases that falsely estimate the precipitation from moderate intensity to heavy rainfall by the three products.

The storm hyetograph is critical in drainage design since it determines the peak flooding volume in a catchment and the corresponding drainage capacity demand for a return period. This study firstly compares the common design storms such as the Chicago, Huff, and Triangular curves employed to represent the storm hyetographs in the metropolitan area of Guangzhou using minute-interval rainfall data during 2008–2012. These common design storms cannot satisfactorily represent the storm hyetographs in sub-tropic areas of Guangzhou. The normalized time of peak rainfall is at 33 ± 5% for all storms in the Tianhe and Panyu districts, and most storms (84%) are in the 1st and 2nd quartiles. The Huff curves are further improved by separately describing the rising and falling limbs instead of classifying all storms into four quartiles. The optimal time intervals are 1–5 min for deriving a practical urban design storm, especially for short-duration and intense storms in Guangzhou. Compared to the 71 observed storm hyetographs, the Improved Huff curves have smaller RMSE and higher NSE values (6.43, 0.66) than those of the original Huff (6.62, 0.63), Triangular (7.38, 0.55), and Chicago (7.57, 0.54) curves. The mean relative difference of peak flooding volume simulated with SWMM using the Improved Huff curve as the input is only 2%, −6%, and 8% of those simulated by observed rainfall at the three catchments, respectively. In contrast, those simulated by the original Huff (−12%, −43%, −16%), Triangular (−22%, −62%, −38%), and Chicago curves (−17%, −19%, −21%) are much smaller and greatly underestimate the peak flooding volume. The Improved Huff curve has great potential in storm water management such as flooding risk mapping and drainage facility design, after further validation.

Extensive studies have highlighted the roles of rainfall, impervious surfaces, and drainage systems in urban pluvial flooding, whereas topographic control has received limited attention. This study proposes a depression-based index, the Topographic Control Index (TCI), to quantify the function of topography in urban pluvial flooding. The TCI of a depression is derived within its catchment, multiplying the catchment area with the slope, then dividing by the ponding volume of the depression. A case study is demonstrated in Guangzhou, China, using a 0.5 m-resolution Digital Elevation Model (DEM) acquired using Light Detection and Ranging (LiDAR) technology. The results show that the TCI map matches well with flooding records, while the Topographic Wetness Index (TWI) cannot map the frequently flooded areas. The impact of DEM resolution on topographic representation and the stability of TCI values are further investigated. The original 0.5 m-resolution DEM is set as a baseline, and is resampled at resolutions 1, 2, 5, and 10 m. A 1 m resolution has the smallest TCI deviation from those of 0.5 m resolution, and gives the optimal results in terms of striking a balance between computational efficiency and precision of representation. Moreover, the uncertainty in TCI values is likely to increase for small depressions.

Cloud contamination is one of the major barriers for wider applications of MODIS snow cover products. This study presents a cloud-removal approach, through multiday backward replacements based on Terra and Aqua daily MODIS snow cover products (MOD10A1 and MYD10A1), to generate a series of daily cloud-free snow cover products for advanced applications (MODMYD_MC). The products are evaluated using in situ snow depth data measured during 2000 to 2010 at 53 weather stations in the Heilongjiang Province, northeast China. The results show that the annual mean cloud covers of MOD10A1, MYD10A1, MODMYD_DC (the daily combination of MOD10A1 and MYD10A1), and MODMYD_MC are 50%, 54%, 35%, and 0%, mean snow covers are 6%, 6%, 10%, and 19%, and their mean agreements of snow cover mapping are 42%, 40%, 51%, and 91%, respectively. The snow-covered days (SCDs) derived from MODMYD_MC are also in good agreement (91%) with those obtained from in situ observations. The MODMYD_MC snow cover images are then used to investigate the detailed variation of snow cover in the XiaoXing’AnLing watershed. The snow-covered area in the watershed has an increasing trend in the recent decade, with the minimum present in the 2002 (hydrologic year) and the maximum present in 2010. The plains with lower elevation show shorter SCD but larger interannual variations than in the mountainous areas. This study indicates that MODMYD_MC can be applied to monitor the spatiotemporal variations of snow cover in northeast China and elsewhere in the world.