Water is one of the most critical natural resources that maintain the ecosystem and support people’s daily life. Pressures on water resources and disaster management are rising primarily due to the unequal spatial and temporal distribution of water resources and pollution, and also partially due to our poor knowledge about the distribution of water resources and poor management of their usage. Remote sensing provides critical data for mapping water resources, measuring hydrological fluxes, monitoring drought and flooding inundation, while geographic information systems (GIS) provide the best tools for water resources, drought and flood risk management. This special issue presents the best practices, cutting-edge technologies and applications of remote sensing, GIS and hydrological models for water resource mapping, satellite rainfall measurements, runoff simulation, water body and flood inundation mapping, and risk management. The latest technologies applied include 3D surface model analysis and visualization of glaciers, unmanned aerial vehicle (UAV) video image classification for turfgrass mapping and irrigation planning, ground penetration radar for soil moisture estimation, the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) satellite rainfall measurements, storm hyetography analysis, rainfall runoff and urban flooding simulation, and satellite radar and optical image classification for urban water bodies and flooding inundation. The application of those technologies is expected to greatly relieve the pressures on water resources and allow better mitigation of and adaptation to the disastrous impact of droughts and flooding.

This study analyzes diurnal and seasonal variations of snow albedo at four Baseline Surface Radiation Network stations in the Arctic and Antarctica from 2003 to 2008 to elucidate similarities and differences in snow albedo diurnal cycles across geographic zones and to assess how diurnal changes in snow albedo affect the surface energy budget. At the seasonal scale, the daily albedo for the perennial snow at stations South Pole and Georg von Neumayer in Antarctica has a similar symmetric variation with solar zenith angle (SZA) around the austral summer; whereas the daily albedo for the seasonal snow at stations Barrow, Alaska, and Ny-Ålesund, Spitsbergen, in the Arctic tends to decrease with SZA decrease from winter to spring before snow starts melting. At the hourly scale, each station shows unique diurnal cycles due to different processes that affect snow albedo such as cloud cover, snow metamorphism, surface hoar formation, SZA, solar azimuth angle, and surface features. Cloud escalates the snow albedo at all four stations by shifting solar radiation to visible wavelengths and diminishes the diurnal variation by diffusing incident solar radiation. The 24 h mean snow albedo is higher on cloudy than clear days by 0.02 at the South Pole (December) and Barrow (May), 0.05 at Neumayer (December), and 0.07 at Ny-Ålesund (April). Snow surface structures, for example, wind-channeled sastrugi, appear to be a controlling factor in the diurnal variation of clear-sky snow albedo at the South Pole and Ny-Ålesund. The surface hoar formation cycles and snow metamorphism are consistent with the asymmetric diurnal variation of snow albedo at Neumayer and Barrow. Near the melting point temperature, melt-freeze cycles exceed cloud and surface structure impacts and dominate the diurnal variation of snow albedo at stations Barrow and Ny-Ålesund. The satellite-measured clear-sky snow albedo usually underestimates the average all-sky snow albedo at these stations. These results illustrate the potential biases in daily and monthly albedo products constructed from sun-synchronous satellite daily instantaneous observations which inevitably undersample the diurnal variation of snow albedo.

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.