The lack and inefficiency of urban drainage systems, as well as extreme precipitation, can lead to system overloading and, therefore, an urban pluvial flood. The study brings insights into this phenomenon from the perspective of the statistical relationship between precipitation and flooding parameters. The paper investigates the possibility of predicting sewer overloading based on the characteristics of the upcoming rain event using the Storm Water Management Model (SWMM) and statistical methods. Additionally, it examines the influence of precipitation resolution on the model sensitivity regarding floods. The study is set in a small urban catchment in Dresden (Germany) with a separated stormwater sewer system (SWSS). The flood-event-based calibrated model runs with observed and designed heavy rain events of various sums, durations, and intensities. Afterward, the analysis focuses on precipitation and model overloading parameters (total flood volume, maximum flooding time and flow rate, and maximum nodal water depth) with pairwise correlation and multi-linear regression (MLR). The results indicate that it is possible to define a certain threshold (or range) for a few precipitation characteristics, which could lead to an urban flood, and fitting MLR can noticeably improve the predictability of the SWSS overloading parameters. The study concludes that design and observed rain events should be considered separately and that the resolution of the precipitation data (1/5/10 min) does not play a significant role in SWSS overloading.

Water resources planning and management depend on the quality of climatic data, particularly rainfall data, for reliable hydrological modeling. This can be very problematic in transboundary rivers with limited disclosing of data among the riparian countries. Satellite precipitation products are recognized as a promising source to substitute the ground-based observations in these conditions. This research aims to assess the feasibility of using a satellite-based precipitation product for better hydrological modeling in an ungauged and riparian river in Pakistan, i.e., the Chenab River. A semidistributed hydrological model of The soil and water assessment tool (SWAT) was set up and two renowned satellite precipitation products, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were selected to assess the runoff pattern in Chenab River. The calibration was done from 2001–2006 with two years of a warmup period. The validation (2007–2010) results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations, IMERG-F (R² = 0.89, NSE = 0.82), 3B42 (R² = 0.85, NSE = 0.72), rather than daily timescale simulations, IMERG-F (R² = 0.66, NSE = 0.61), 3B42 (R² = 0.64, NSE = 0.54). Moreover, the comparison between IMERG-F and 3B42, shows that IMERG-F is superior to 3B42 by indicating higher R², NSE and lower percent bias (PBIAS) at both monthly and daily timescale. The results are strengthened by Taylor diagram statistics, which represent a higher correlation (R) and less RMS error between observed and simulated values for IMERG-F. IMERG-F has great potential utility in the Chenab River catchment as it outperformed the 3B42 precipitation in this study. However, its poor skill of capturing peaks at daily timescale remains, leaving a room for IMERG-F to improve its algorithm in the upcoming release.

Remote sensing precipitation or precipitation from numerical weather prediction (NWP) is considered to be the best substitute for in situ ground observations for flood simulations in transboundary, data-scarce catchments. This research was aimed to evaluate the possibility of using a combination of a satellite precipitation product and NWP precipitation for better flood forecasting in the transboundary Chenab River Basin (CRB) in Pakistan. The gauge-calibrated satellite precipitation product, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event in the Chenab River at the Marala Barrage gauging site in Pakistan. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model to predict the flood hydrograph in the Chenab River. The hydrologic simulations, with global GFS forecasts, were unable to predict the flood peak for all lead times. Then, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. In the WRF model, the CRB was centered in two domains of 25 km and 5 km resolutions. The downscaled precipitation forecasts were subsequently supplied to the IFAS model, and the predicted simulations were compared to obtain the optimal flood peak simulation in the Chenab River. It was found in this study that the simulated hydrographs, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R² and Nash–Sutcliffe efficiency (NSE) and the lowest percent bias (PBIAS) compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.

Numerical simulations of rainfall-runoff processes are useful tools for understanding hydrological processes and performing impact assessment studies. The advancements in computer technology and data availability have assisted their rapid development and wide use. This project aims to evaluate the applicability of a physically based, fully distributed rainfall-runoff model TOPKAPI-X for the simulation of flood events in two small watersheds of Saxony, Germany. The results indicate that the model was calibrated well for 4.88 km² Wernersbach catchment (NSE 0.89), whereas 276 km² Wesenitz catchment calibration was only satisfactory (NSE 0.7). The addition of the second soil layer improved the model’s performance in comparison to the simulations with only one soil layer for Wernersbach (NSE increase from 0.83 to 0.89). During the validation process, the model showed a variable performance. The best performance was achieved for Wernersbach for the year with the highest runoff (NSE 0.95) in the last decade. The lowest performance for the Wernersbach and Wesenitz catchments was 0.64 for both. The reasons for the model’s low performance in some years are discussed, and include: (i) input data quality and data insufficiency, (ii) methods used within the simulations (interpolation, ETP estimation, etc.), and (iii) assumptions made during the calibration (manual calibration, parameter selection, etc.).