Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the Great Himalayas region. High-quality precipitation data are difficult to obtain due to a sparse network, cold climate and high heterogeneity in topography. In this paper, we examine four datasets in northern India of the Western Himalayas: interpolated gridded data based on gauge observations (IMD, 1° × 1°, and APHRODITE, 0.25° × 0.25°), reanalysis data (ERA-Interim, 0.75° × 0.75°) and high-resolution simulation by a regional climate model (WRF, 0.15° × 0.15°). The four datasets show a similar spatial pattern and temporal variation during the period 1981–2007, though the absolute values vary significantly (497–819 mm year⁻¹). The differences are particularly large in July and August at the windward slopes and high-elevation areas. Overall, the datasets show that the summer is getting wetter and the winter is getting drier, though most of the trends in monthly precipitation are not significant. Trend analysis of summer and winter precipitation at every grids confirms the changes. Wetter summers will result in more and bigger floods in the downstream areas. Warmer and drier winters will result in less glacier accumulation. All the datasets show consistency in the period 1981–2007 and can give a spatial overview of the precipitation in the region. Comparing with the Bhuntar gauge data, the WRF dataset gives the best estimates of extreme precipitation. To conclude, we recommend the APHRODITE dataset and the WRF dataset for hydrological studies for their improved spatial variation which match the scale of hydrological processes as well as accuracy in extreme precipitation for flood simulation.

Cities are challenged by climate change impacts, such as extreme rainfall events that affect conventional urban water management systems via increased sewage water overflows resulting in water quality deterioration and urban floods causing infrastructure damage. Investments in blue–green infrastructure (BGI) are increasingly considered to address these issues. However, these should be cost-effective. In this study, the effectiveness of five different BGI strategies and one grey strategy are assessed for a peri-urban catchment area in Oslo (Grefsen) using a cost–benefit analysis. The strategies include (i) wadis; (ii) green roofs; (iii) raingardens, rain barrels and wadis; (iv) infiltration crates; (v) water squares, and (vi) a separate sewage system. Besides economic effectiveness, the study also aims to identify the proper protection level by comparing cost–benefit ratios and net benefits for 60-min rainfall events occurring once every 5, 20, and 100 years (M5, M20, and M100), concerning both the current situation and under future climate change (using the Representative Concentration Pathway 8.5). The analyses revealed the highest BC ratios for wadis (12.0–17.3), separate sewage systems (7.7–15.1), and a combination of raingardens, rain barrels, and wadis (1.6–2.3). Strategies dimensioned for less frequent but more intensive rainfall events yielded higher BC ratios. Results for infiltration crates were difficult to interpret and were found to be very sensitive to input parameters. The other strategies implied a negative BC ratio. The study concludes that investments in BGI in Grefsen, Oslo, can be positively judged from a social–economic perspective and provide suitable information for water-related decision makers to decide upon the strategy selection and the appropriate flood protection level.

There has been a surge of interest in the field of urban flooding in recent years, due to the growth of cities and the increase in frequency and magnitude of extreme rainfall events. Hydrological modeling is a useful tool to understand urban floods and compare different stormwater management solutions. In this study, we use the Storm Water Management Model (SWMM) in an urban catchment, Grefsen in Norway, to analyze the effects of different Low Impact Development (LID) methods to reduce combined sewer overflow (CSO). Additionally, we examine the cost of these solutions and find an optimized solution in terms of maximum effects and minimum cost. The results are useful for decision-makers to achieve sustainable stormwater management.  Acknowledgement:This research is funded by the Norwegian Research Council via the project New Water Ways.

The Storm Water Management Model (SWMM) has been globally used for stormwater management. However, the calibration and evaluation of SWMM for historical rainfall–runoff events in partially separated and combined drainage systems is rarely reported in Norway. In this study, we employed SWMM for the Grefsen catchment in Oslo, Norway. The main problem in the Grefsen basin is combined sewer overflow. We calibrated the model parameters based on 32 rainfall–runoff events and evaluated the calibrations using four indicators: Nash–Sutcliffe efficiency, percentage bias, and continuity errors for runoff and flow. There were 32 successful calibrations using Nash–Sutcliffe efficiency, 30 successful calibrations using percentage bias, 32 successful calibrations using continuity error runoff, and four successful calibrations using continuity error flow. SWMM can well simulate the dynamics of hydrological and hydraulic systems in this catchment. Among the 124 validations, there were 88 successful simulations using Nash–Sutcliffe efficiency, 35 successful simulations using percentage bias, 124 successful simulations using continuity error for runoff, and 62 successful simulations using continuity error for flow. The results show that percentage bias and continuity error flow are the critical indicators for model calibration. This study reveals the large uncertainty caused by calibration and validation criteria, and highlights the importance of considering model computation error.

Data scarcity is the biggest problem for scientific research related to hydrology and climate studies in the great Himalayas Region. High quality precipitation data are among the most difficult to obtain due to sparse network, cold climate and high heterogeneity in topography. This paper examines four different types of datasets, including interpolated gridded data based on ground observations (IMD, 1° × 1° and APHRODITE, 0.25° × 0.25°), reanalysis data (ERA-interim, 0.75° × 0.75°) and high resolution simulation by a regional climate model (WRF, 0.15° × 0.15°). In Northern India of the Western Himalayas, the four datasets show a similar spatial pattern and temporal variation during the period 1981–2007, though the absolute values vary significantly (497–819 mm/year) mainly due to the data source and the methods of data generation. The differences are particularly large in July and August at the windward slopes and the high elevation area. All the datasets show a wetter summer and drier winter during the period, though most of the trends in monthly precipitation are not significant. The comparison between two periods of 1981–1985 and 2003–2007 shows an increase in summer and a decrease in winter with large variations. Between the periods, the runoff is expected to increase which is likely to result in more and bigger floods in the downstream areas according to the IMD, APHRODITE and WRF datasets, whereas the ERA-interim dataset reveals a tendency toward longer low flow periods and more droughts. All the datasets can give a good overview of the precipitation, but because of coarse spatial resolution and small size of basins in this area, future work such as local correction is necessary for hydro-glacial modelling.