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Ensemble precipitation forecasts (EPFs) derived from single numerical weather predictions (NWPs) often miss extreme events, and individual hydrological models (HMs) often fail to accurately capture all types of flows, including flood peaks. To address these shortcomings, this study introduced four “EPF + HM” schemes for ensemble flood forecasting (EFF) by combining two EPFs and two HMs. A generator-based post-processing (GPP) method was applied to correct biases and under-dispersion within the raw EPF data. The effectiveness of these schemes in delivering high-quality flood forecasts was assessed using both deterministic and probabilistic metrics. The results indicate that, once post-processed by GPP, all proposed schemes show improvements in both deterministic and probabilistic performances, with skillful flood forecasts for 1–7 lead days. The deterioration in forecast performance with extended lead times is also lessened. Notably, the results indicate that uncertainty within hydrological models has a more pronounced impact on capturing flood peaks than uncertainty in precipitation inputs. This study recommends combining individual EPF with multiple hydrological models for reliable flood forecasting. In conclusion, effective flood forecasting necessitates employing post-processing techniques to correct EPFs and accounting for the uncertainty inherent in hydrological models, rather than relying solely on the uncertainty of the input data.

Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispersion in EPFs require post-processing for accurate application. It is imperative to explore the skillful lead time of post-processed EPFs for summer streamflow predictions, particularly in mountainous regions. In this study, four popular EPFs, i.e., the CMA, ECMWF, JMA, and NCEP, were post-processed by two state of art methods, i.e., the Bayesian model averaging (BMA) and generator-based post-processing (GPP) methods. These refined forecasts were subsequently integrated with the Xin’anjiang (XAJ) model for summer streamflow prediction. The performances of precipitation forecasts and streamflow predictions were comprehensively evaluated before and after post-processing. The results reveal that raw EPFs frequently deviate from ensemble mean forecasts, particularly underestimating torrential rain. There are also clear underestimations of uncertainty in their probabilistic forecasts. Among the four EPFs, the ECMWF outperforms its peers, delivering skillful precipitation forecasts for 1–7 lead days and streamflow predictions for 1–4 lead days. The effectiveness of post-processing methods varies, yet both GPP and BMA address the under-dispersion of EPFs effectively. The GPP method, recommended as the superior method, can effectively improve both deterministic and probabilistic forecasting accuracy. Moreover, the ECMWF post-processed by GPP extends the effective lead time to seven days and reduces the underestimation of peak flows. The findings of this study underscore the potential benefits of adeptly post-processed EPFs, providing a reference for streamflow prediction over mountain river basins.

Parameter regionalization of hydrological models is one of the most commonly used methods for hydrological prediction over ungauged catchments. Although there were many regional studies, there is no clear conclusion on the best-performing regionalization method for global hydrological modelling. The objective of this study is to determine an appropriate global-scale regionalization scheme (GSRS) for global hydrological modelling. To this end, the performance of five regionalization methods with two different average options, two weighting approaches, and seven efficiency thresholds (i.e. Kling-Gupta efficiency (KGE) values to measure hydrological model performances) was compared over thousands of catchments based on four conceptual hydrological models. Results of nine global models from the Global Earth Observation for Integrated Water Resource Assessment (EartH2Observe) project were selected to validate the accuracy of GSRS in estimating global runoff. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting method and the output average option offers the best regionalization result when using the KGE ≥ 0.5 as an efficiency threshold for all four hydrological models, (2) the regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, consistently performs better than the nine global models from EartH2Observe project in the estimation of runoff for most catchments, with varying degrees of improvement in the median, upper and lower quartiles, and whiskers of each performance metric, and (3) the global long-term annual water resources estimated by RGHSs range between 42,592 and 46,810 km3/yr. HIGHLIGHTS The performance of regionalization methods is evaluated with multiple hydrological models globally.; A global-scale regionalization scheme (GSRS) is proposed for global hydrological modelling.; The robust regionalization-based global hydrological simulation schemes (RGHSs), i.e. the proposed GSRS combining with four hydrological models, can produce reliable simulations of global water resources.;

Six latitude-longitude rectangular regions were selected (Four is on the 30°N line: around Ya'an, in the southwest of Hubei, in the souther of Jianghan Plain, west Hangzhou Bay.Others are Hainan Island and around Zhengzhou, one is in its south and another is in its north). The hourly rainfall data are subdivided into 42 grades and three kinds of "class-conditional probability density" are counted respectively. Its detailed curve graph and empirical fitting function which is in the simple form were given.The three kinds of conditional probability density curves are monotonic convex decreasing functions. Using the exponential function of three-parameter power function as the empirical fitting function, we can get a good fitting, which is universal to six sub-regions and the three kinds of class conditional probability density function. The results show that the frequency of rainfall in Ya'an is high, the first and second kinds of class conditional probability density are higher than that in other regions at the head and tail of the curve, indicating that the extreme short-term heavy rainfall occurs frequently. Due to the fact that the first and second kinds of class conditional probability density of heavy rainfall in Hainan Island are larger than that in other regions, the annual average rainfall is still larger even though there are many negative factors, such as low rainfall frequency, more isolated hourly rainfall, short duration of hourly continuous rainfall, fast decline of the third type of conditional probability density and strong skewness. In addition, by comparing the adjacent mountainous areas of Southwest Hubei and the Southern Jianghan Plain, the influence of the mountainous areas of Southwest Hubei on the rainfall structure is discussed. The increase of rainfall frequency caused by mountainous areas is mainly the increase of weak rainfall, but the weakly decrease of heavy rainfall frequency, so the mountain friction when there is no special terrain structure is negative factor for the occurrence of heavy rainfall.

Exploring the characteristics of uneven temporal and spatial distribution of precipitation in mountain watersheds can provide a reference for regional agricultural development and resource utilization, and contribute to the protection of the ecological environment. Based on the daily precipitation observation data of 40 meteorological stations in the Wujiang River Basin from 1963 to 2021, the temporal- and spatial-variation characteristics of the precipitation concentration degree (PCD) and precipitation concentration period (PCP) were analyzed using the Randall S analysis method, Mann–Kendall test method, Pettitt method, wavelet analysis and empirical orthogonal function (EOF). The results showed that the fluctuation range of PCD in Wujiang River Basin from 1963 to 2021 was 0.34–0.59, with a multi-year average of 0.47, which was obviously higher than the national average level and is showing a trend of slowly increasing. The fluctuation range of PCP was between 17.1 and 21.5 days, with a multi-year average of 19.0. The annual precipitation was mostly concentrated around the middle of July and showed a slowly decreasing trend. The abrupt change in PCD and PCP occurred around 1983 and 2001, respectively. There is an obvious Hearst phenomenon in PCP. In the future, the trend of precipitation concentration in the middle period will remain in advance, and the degree of precipitation concentration will continue to increase. The maximum precipitation in the flood season will continue to be delayed. The spatial pattern of the first mode of PCD and PCP in the Wujiang River basin was consistent and showed an opposite pattern between the upper reaches and the middle-lower reaches of the basin, which reflects the influence of the topography of the basin on the spatial distribution of precipitation. The distribution of precipitation is affected by topography. The elevation change in the basin was complex, and the leeward slope varied a lot. Therefore, it has a significant impact on precipitation. Areas with less precipitation are at higher elevations and on mountain leeward slopes, with a lack of moist air flow. The area with more precipitation was the summer monsoon mountain windward slope, and the topography blocks the increase in precipitation. The elevation of the central region is relatively uniform and the terrain is flat. Therefore, the distribution of precipitation is more uniform.

Aiming at the rainstorm days (≥50 mm/d) in the middle and lower reaches of the Yangtze River during 2010–2020, the obliquely rotated principal component in T-mode (PCT) method is used to classify the daily mean 850 hPa geopotential height, including Type 1 (vortex/shear line), Type 2 (frontal surface), Type 3 (warm shear line), Type 4 (warm inverse trough line), Type 5 (typhoon-westerly trough), and Type 6 (easterly wave). We studied the weather system configurations of different synoptic circulation patterns, their long-term trends, and their impacts on diurnal variations of heavy precipitation and drew the following conclusions: Type 1, Type 2, or Type 3 shows balanced double-peak frequencies of the start time of heavy precipitation during 06:00–08:00 BT and around 17:00 BT, respectively. For Type 1, dynamical lifting and thermal lifting play balanced roles, while for Type 2 and Type 3, dynamical lifting plays a key role. The number of rainstorm stations for Type 1 shows a slight increasing trend, while that for Type 2 or Type 3 shows a significant increasing trend. Type 4, Type 5, or Type 6 show a significant single peak frequency of the start time during 15:00–16:00. Type 5 and Type 6 are mainly affected by dynamical lifting along with favorable cape values, which can trigger rainstorms. The number of rainstorm stations for Type 4 or Type 6 shows a decreasing trend (that for Type 4 is more significant), while that for Type 5 shows a slightly increasing trend.

In order to improve the calculation accuracy of the rainfall probability distribution in related applications, this study aimed to select a theoretical function from applicable functions for three classes of the class-conditional probability density function (CCPD) of hourly rainfall series. The three applicable functions are generalized gamma distribution (GΓD), generalized normal distribution (GND), and Weibull distribution. For the reason that it is hard to distinguish the advantages and disadvantages of the three functions by the probability plot and error analysis of fitted values, optimization criteria are proposed, which are the Bayesian information criterion (BIC) and the estimated accuracy of both the annual average rainfall (AAR) and the annual average continuous rainfall (AACR). The results show that by using three applicable functions in 15 regions, the relative fitting deviations for CCPD1 were less than 2.3% and less than 3.3% for ln(CCPD1). The goodness-of-fit values were all above 0.98 for CCPD1 and greater than 0.94 for ln(CCPD1). The fitting effect of the Weibull distribution was relatively poor from the perspective of the probability plot and error analysis of the fitted values, while the three applicable functions could be used to fit CCPD. GΓD had the highest fitting accuracy for the three classes of CCPDs, but there is concern about overfitting due to its broad spectrum. GND, with fewer parameters, had comparable performance to GΓD, and when fitting CCPD1 using GND, the mean relative fitting deviation was 0.6%, the coefficient of determination was 0.999, and for ln(CCPD1), they were 1.45% and 0.989. At the same time, GND performed well in estimating the AARs, with an 8.6% relative error and a 0.92 correlation coefficient in the fifteen regions, indicating that GND can well reflect the spatial variation characteristics of the AAR. Moreover, the function form of GND is simple. GND follows the parsimonious principle, and it is suitable for the whole domain. Therefore, GND is recommended as the theoretical density function based on the optimization criteria. The genetic algorithm was adopted to obtain the approximate solution of the parameters through optimization, which can simplify the derivation and calculation steps. The multiplicative and additive fitting errors were both used in the objective functions, which gave comprehensive consideration to both ends of the fitting curve.

The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP centers in the Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE), the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP), in EPF and ESP over a mountain river basin in China. This evaluation was carried out based on both deterministic and probabilistic metrics at a daily temporal scale. The effectiveness of two postprocessing methods, the Generator-based Postprocessing (GPP) method, and the Bayesian Model Averaging (BMA) method were also investigated for EPF and ESP. Results showed that: (1) The ECMWF shows better performances than NCEP in both EPF and ESP in terms of evaluation indexes and representation of the hydrograph. (2) The GPP method performs better than BMA in improving both EPF and ESP performances, and the improvements are more significant for the NCEP with worse raw performances. (3) Both ECMWF and NCEP have good potential for both EPF and ESP. By using the GPP method, there are desirable EPF performances for both ECMWF and NCEP at all 7 lead days, as well as highly skillful ECMWF ESP for 1~5 lead days and average moderate skillful NCEP ESP for all 7 lead days. The results of this study can provide a reference for the applications of TIGGE over mountain river basins.

The number of global precipitation datasets (PPs) is on the rise and they are commonly used for hydrological applications. A comprehensive evaluation on their performance in hydrological modeling is required to improve their performance. This study comprehensively evaluates the performance of eight widely used PPs in hydrological modeling by comparing with gauge-observed precipitation for a large number of catchments. These PPs include the Global Precipitation Climatology Centre (GPCC), Climate Hazards Group Infrared Precipitation with Station dataset (CHIRPS) V2.0, Climate Prediction Center Morphing Gauge Blended dataset (CMORPH BLD), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN CDR), Tropical Rainfall Measuring Mission multi-satellite Precipitation Analysis 3B42RT (TMPA 3B42RT), Multi-Source Weighted-Ensemble Precipitation (MSWEP V2.0), European Center for Medium-range Weather Forecast Reanalysis 5 (ERA5) and WATCH Forcing Data methodology applied to ERA-Interim Data (WFDEI). Specifically, the evaluation is conducted over 1382 catchments in China, Europe and North America for the 1998-2015 period at a daily temporal scale. The reliabilities of PPs in hydrological modeling are evaluated with a calibrated hydrological model using rain gauge observations. The effectiveness of PPs-specific calibration and bias correction in hydrological modeling performances are also investigated for all PPs. The results show that: (1) compared with the rain gauge observations, GPCC provides the best performance overall, followed by MSWEP V2.0; (2) among the eight PPs, the ones incorporating daily gauge data (MSWEP V2.0 and CMORPH BLD) provide superior hydrological performance, followed by those incorporating 5-day (CHIRPS V2.0) and monthly (TMPA 3B42RT, WFDEI, and PERSIANN CDR) gauge data. MSWEP V2.0 and CMORPH BLD perform better than GPCC, underscoring the effectiveness of merging multiple satellite and reanalysis datasets; (3) regionally, all PPs exhibit better performances in temperate regions than in arid or topographically complex mountainous regions; and (4) PPs-specific calibration and bias correction both can improve the streamflow simulations for all eight PPs in terms of the Nash and Sutcliffe efficiency and the absolute bias. This study provides insights on the reliabilities of PPs in hydrological modeling and the approaches to improve their performance, which is expected to provide a reference for the applications of global precipitation datasets.

The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment.