Urban flooding exposure is generally investigated with the assumption of stationary disasters and disaster-hit bodies during an event, and thus it cannot satisfy the increasingly elaborate modeling and management of urban floods. In this study, a comprehensive method was proposed to simulate dynamic exposure to urban flooding considering human mobility. Several scenarios, including diverse flooding types and various responses of residents to flooding, were considered.

The effects of WRF domain configurations and spin-up time on rainfall were evaluated at high temporal and spatial scales for simulating an extreme sub-daily heavy rainfall (SDHR) event. Both objective verification metrics and subjective verification were used to identify the likely best set of the configurations. Results show that re-evaluation of these WRF settings is of great importance in improving the accuracy and reliability of the rainfall simulations in the regional SDHR applications.

This study introduces a new QM approach based on a composite distribution of a generalized Pareto distribution for the upper tail and a gamma distribution for the interior part of the distribution. The proposed composite distributions provide a significant reduction of the biases compared with that of the conventional method for the extremes. The proposed approach can provide a useful alternative for the bias correction of a regional-scale modeled data with a limited network of rain gauges.

This study compared the hydrological model performance of different sub-annual calibration schemes, which take into account intra-annual variations of climate. Two methods recognizing climatic patterns were applied to partition sub-periods with hydroclimatic similarities. The effect of time scales on sub-annual calibration schemes was also studied. Results indicate when using sub-annual calibration schemes, the selection of partitioning method and time scale is important to model performances.

Reliable estimation of hydrological soil moisture state is of critical importance in operational hydrology to improve the flood prediction and hydrological cycle description. This paper attempts for the first time to build a soil moisture product directly applicable to hydrology using multiple data sources retrieved from remote sensing and land surface modelling. The result shows a significant improvement of the soil moisture state accuracy; the method can be easily applied in other catchments.

We explore unit hydrograph (UH) affected by geomorphology that could be used in ungauged catchments. Virtual catchments approach (VCA) is used instead of gauged catchments in runoff modelling. Catchment shape is newly introduced and the agreement of the results with the hydrological principles verifies the reliability of VCA. With the robust VCA, a large amount of catchments can be created with desirable features to explore a more comprehensive equation that can be used in ungauged catchments.

The core component of many hydrological systems, the moisture storage capacity available to vegetation, is typically treated as a calibration parameter in hydrological models and often considered to remain constant in time. In this paper we test the potential of a recently introduced method to robustly estimate catchment-scale root-zone storage capacities exclusively based on climate data to reproduce the temporal evolution of root-zone storage under change (deforestation).

Surat, India has a population of 4.5 million and lies on the banks of the river Tapi and is located downstream from a dam that repeatedly floods the city. Floods in Surat may increase in occurrence due to urbanization and climate change. We have developed a model that floods 50 % of the city and exposes > 60 % of the population and critical infrastructure. We highlight how modeling has contributed to changes in flood risk management and resulted in actions that increase city resilience.

A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.

We present the outcomes of a collaborative hydrological experiment undertaken by five different international research groups in a virtual laboratory. Moving from the definition of accurate protocols, a rainfall-runoff model was independently applied by the research groups, which then engaged in a comparative discussion. The results revealed that sharing protocols and running the experiment within a controlled environment is fundamental for ensuring experiment repeatability and reproducibility.