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Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures, and parameters), raw forecasts are subject to systematic biases; hence, statistical postprocessing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation postprocessing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as postprocessors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21 ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibits the best performance, effectively reducing overestimation and achieving the best skill in forecasting precipitation. Eleven ensemble members and a 3-day forward-rolling time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies. [ABSTRACT FROM AUTHOR]

Cascade reservoirs were designed for the Yalong River basin, which ranks as the third-largest hydropower base in China. Reservoir impoundment has certain impacts on local climate, but few researches focus on this field. This paper integrates spatiotemporal analysis techniques and geostatistical methods to identify the reservoir projected meteorological indices (MIs) variations as well as the impact scope under different circumstances. Results show that the reservoir projected variations of moisture indexes were much more significant than temperatures. Anthropogenic disturbance has led to a dramatic decrease of relative humidity in the past decade, far beyond its periodic amplitude range. Moreover, dry valleys faced more serious drought risk, but reservoir impoundment of this region alleviated the local drought risk. The daily minimum temperature, relative to the maximum temperature, was more sensitive to catchment changes with an earlier appearance of temperature zone variation. Since impounding, the MIs' internal relationships in nondry valleys varied significantly more than that in dry valleys, with a positive correlation of 0.7 between the relative humidity and precipitation weakening greatly. The severe warming hotspots are distributed in the upper reach and have a probability of 0.95-1 to exceed the 1.5°C IPCC control target. This study provides references for the disturbance of reservoir impoundment to local climate at multiple temporal--spatial scales under different circumstances and contributes to the MIs variation pattern identification and quantitative risk assessment. [ABSTRACT FROM AUTHOR]