Abstract. The Huaihe River Basin having China's highest population density (662 persons per km 2 ) lies in a transition zone between the climates of North and South China, and is thus prone to drought. Therefore, the paper aims to develop an appropriate drought assessment approach for drought assessment in the Huaihe River basin, China. Based on the Principal Component Analysis of precipitation, evapotranspiration, soil moisture and runoff, the three latter variables of which were obtained by use of the Xin'anjiang model, a new multivariate drought index (MDI) was formulated, and its thresholds were determined by use of cumulative distribution function. The MDI, the Standardized Precipitation Index (SPI) and the self-calibrating Palmer Drought Severity Index (sc-PDSI) time series on a monthly scale were computed and compared during 1988, 1999/2000 and 2001 drought events. The results show that the MDI exhibited certain advantages over the sc-PDSI and the SPI in monitoring drought evolution. The MDI formulated by this paper could provide a scientific basis for drought mitigation and management, and references for drought assessment elsewhere in China.

No abstract

Abstract. To investigate the agricultural land-use change on flood regime, the upper Huaihe River basin above the Dapoling station was selected as the case study site. Based on topography, land-use, hydrological and meteorological data in 1990s and 2010s, the improved distributed Xinanjiang model, with potential evapotranspiration being computed by coupling a dual-source evapotranspiration model with a simplified plant growth model, was adopted to simulate the daily and hourly rainfall-runoff processes over 1990s and 2010s, and then the effects of land-use change on flood volume, flood peak, occurring time of flood peak, the percentage of surface runoff component were investigated respectively. The results was interesting and indicated that impacts of land-use change on flood characteristics varied significantly with land-use types. The outputs could provide valuable references for flood risk management and water resources management in the Huaihe River basin.

Flood forecasting plays a pivotal role in preventing flood hazards. Hydrological models have been used extensively in a wide range of applications and have become ubiquitous in the hydrologic community (Beven &

Hydrological models are widely used for flood forecasting, and proper initialization of hydrological models is essential. Although the unscented Kalman filter (UKF) has shown promise in the context of operational forecasting, it is limited by its intrinsic instability caused by the loss of positive semidefiniteness of the covariance matrix. Effective methods for tackling this problem have not been seen in the literature. The cubature Kalman filter (CKF) is a powerful nonlinear filter for state estimation, which has received little attention in the hydrologic context. In this paper, to overcome the instability problem of the UKF, a new modified UKF (UKF‐M) is proposed by modifying the default UKF tuning strategy, and a novel nonlinear filter (CKF) is introduced to update the states of hydrological models. The methods are tested using a lumped hydrological model. The results of the stability experiment suggest that both the UKF‐M and CKF can overcome the instability problem, while the original UKF can fail due to the loss of positive semidefiniteness of the covariance matrix. The results of a reforecast experiment in an operational context show that both filters can improve the forecast performance by updating the model state based on the observed discharge, with the UKF‐M achieving better forecast performances. The UKF‐M and CKF can be used to overcome the instability problem of the original UKF and are valuable methods for improving forecast performances of hydrological models by state updating.