Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

Flow forecasting is an essential topic for flood prevention and mitigation. This study utilizes a data-driven approach, the Long Short-Term Memory neural network (LSTM), to simulate rainfall–runoff relationships for catchments with different climate conditions. The LSTM method presented was tested in three catchments with distinct climate zones in China. The recurrent neural network (RNN) was adopted for comparison to verify the superiority of the LSTM model in terms of time series prediction problems. The results of LSTM were also compared with a widely used process-based model, the Xinanjiang model (XAJ), as a benchmark to test the applicability of this novel method. The results suggest that LSTM could provide comparable quality predictions as the XAJ model and can be considered an efficient hydrology modeling approach. A real-time forecasting approach coupled with the k-nearest neighbor (KNN) algorithm as an updating method was proposed in this study to generalize the plausibility of the LSTM method for flood forecasting in a decision support system. We compared the simulation results of the LSTM and the LSTM-KNN model, which demonstrated the effectiveness of the LSTM-KNN model in the study areas and underscored the potential of the proposed model for real-time flood forecasting.

The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

Free water storage capacity, an important characteristic of land surface related to runoff process, has a significant influence on runoff generation and separation. It is thus necessary to derive reasonable spatial distribution of free water storage capacity for rainfall-runoff simulation, especially in distributed modeling. In this paper, a topographic index based approach is proposed for the derivation of free water storage capacity spatial distribution. The topographic index, which can be obtained from digital elevation model (DEM), are used to establish a functional relationship with free water storage capacity in the proposed approach. In this case, the spatial variability of free water storage capacity can be directly estimated from the characteristics of watershed topography. This approach was tested at two medium sized watersheds, including Changhua and Chenhe, with the drainage areas of 905 km² and 1395 km², respectively. The results show that locations with larger values of free water storage capacity generally correspond to locations with higher topographic index values, such as riparian region. The estimated spatial distribution of free water storage capacity is also used in a distributed, grid-based Xinanjiang model to simulate 10 flood events for Chenhe Watershed and 17 flood events for Changhua Watershed. Our analysis indicates that the proposed approach based on topographic index can produce reasonable spatial variability of free water storage capacity and is more suitable for flood simulation.

As the most important input for rainfall-runoff models, precipitation is usually observed at specific sites on a daily or sub-daily time scale and requires interpolation for further application. This study aims to explore that for a given objective function, whether a higher temporal and spatial resolution of precipitation could provide an improvement in model performance. Four different gridded hourly and daily precipitation datasets, with a spatial resolution of 1 km * 1 km for the Baden-Wurttemberg state of Germany, were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of pluviometers with high temporal-resolution rainfall observations. Two different flavors of HBV models with different model structures, lumped and spatially distributed, were used to test the sensitivity of model performance on the spatial resolution of precipitation. For four selected mesoscale catchments located at the upstream region of Baden-Wurttemberg, these four precipitation datasets were used to simulate the daily discharges using both lumped and semi-distributed HBV models. Different possibilities of improving the accuracy of daily streamflow prediction were investigated. Three main results were obtained from this study: (1) a higher temporal resolution of precipitation improved the model performance if the observation density was high; (2) a combination of observed high temporal-resolution observations with disaggregated daily precipitation leads to a further improvement in the model performance; (3) for the present research, the increase of spatial resolution improved the performance of the model insubstantially or only marginally for most of the study catchments.