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The Qinghai‐Tibet Plateau (QTP) is highly susceptible to destructive rainstorm hazards and related natural disasters. However, the lack of sub‐daily precipitation observations in this region has hindered our understanding of rainstorm‐related hazards and their societal impacts. To address this data gap, a new approach is devised to estimate sub‐daily precipitation in QTP using daily precipitation data and geographical information. The approach involves establishing a statistical relationship between daily and sub‐daily precipitation based on data from 102 observation sites. This process results in a set of functions with six associated parameters. These parameters are then modeled using local geographical and climatic information through a machine learning algorithm called support vector regression. The results indicated that the temporal scaling characteristics of sub‐daily precipitation can be accurately described using a logarithmic function. The uncertainty of the estimates is quantified using the coefficient of variance and coefficient of skewness, which are estimated using a logarithmic and linear curve, respectively. Additionally, the six parameters are found to be closely linked to geographical conditions, enabling the creation of a 1‐km parameters data set. This data set can be utilized to quantitatively describe the probabilistic distribution and extract key information about maximum precipitation duration (from 1 to 12 hr). Overall, the findings suggest that the generated parameters data set holds significant potential for various applications, including risk analysis, forecasting, and early warning for rainstorm‐related natural disasters in QTP. The innovative method developed in this study proves to be an effective approach for estimating sub‐daily precipitation and assessing its uncertainty in ungauged regions. Plain Language Summary: As one of famous hotspots for natural disaster studies on Earth, the Qinghai‐Tibet Plateau (QTP) is highly vulnerable to destructive rainstorm hazard and related natural disasters, causing significant damage to property, infrastructure, agriculture, and resulting in extensive loss of life. Short‐duration heavy precipitation at sub‐daily scales is an important trigger for flash flood, debris flows and other disasters in QTP. However, it is a poorly gauged high mountain region, observed data for sub‐daily precipitation is extremely limited. Although there have been several satellite products and reanalysis data for sub‐daily precipitation in QTP, their quality has large bias and uncertainty compared to observations. It leaves a large data gap of sub‐daily precipitation, hindering the studies of rainstorm‐related natural disasters in the region. In this work, we develop a new strategy to quantify the temporal scaling characteristics of sub‐daily precipitation, as a basis of temporal downscaling. Then we use the new strategy to generate a parameters data set, to fill the data gap of sub‐daily precipitation in QTP. The parameters data set generated provides an effective way to estimate sub‐daily precipitation and its uncertainty, which can effectively serve for the rainstorm‐related natural disasters study in QTP. Key Points: A high‐resolution gridded parameters data set is generated to estimate sub‐daily precipitation and its uncertainty in QTPThe temporal scaling characteristics of sub‐daily precipitation in QTP is well described by a logarithmic functionSpatial heterogeneity in the temporal scaling characteristics of sub‐daily precipitation in QTP is closely related to geographical conditions [ABSTRACT FROM AUTHOR]

High Mountain Asia (HMA) is the headwater area for major Asian rivers, providing a vast amount of freshwater to billions of people in Asia. These rivers also make their surrounding areas highly vulnerable to destructive water‐related disasters. However, the complex spatiotemporal variability of runoff over HMA and its underlying mechanisms are poorly understood. This study investigates into the spatial heterogeneity of HMA's runoff variability at three timescales (interannual, interdecadal, and multidecadal) and the roles played by climate conditions and catchment properties. We find significant interannual and multidecadal variability of runoff in west and central HMA, and significant interdecadal variability in central and east HMA. At interannual and multidecadal timescales, the runoff variability tends to be more significant in dryer basins. The variability of runoff at the three timescales is largely controlled by climate variations, especially precipitation. The catchment properties, including groundwater storage and glacier‐snow meltwater, also play important roles in regulating the effect of precipitation. In particular, the high contributions of glacier‐snow meltwater in east HMA can weaken the response of runoff variability to precipitation at interannual and multidecadal timescales. The space‐time patterns of runoff variability over HMA are driven by atmospheric drivers including El Niño‐Southern Oscillation, Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation across timescales. The results of this study provide a better understanding of HMA's runoff variability and its physical mechanisms, which have critical implications for sustainable freshwater management and effective risk mitigation in this densely populated and ecologically vulnerable region. Key Points: Significant runoff variability at interannual and multidecadal timescales is detected in west and central High Mountain Asia (HMA)HMA's runoff variability is primarily controlled by precipitation, while catchment properties regulate precipitation effect in diverse waysSpatiotemporal variability in HMA's runoff is modulated by large‐scale atmospheric drivers including El Niño‐Southern Oscillation, Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation [ABSTRACT FROM AUTHOR]

Quantification of the dependence characteristics in hydrological time series is essential for understanding hydrological variability and for managing water resources. However, how determining a suitable model for describing the dependent components is still a challenge. In this article, we proposed a correlation coefficient‐based information criterion (CCIC) to determine the optimal model order of regression‐based models for quantifying the dependence characteristics in hydrological time series. CCIC was developed by combining the index of correlation coefficient and the information entropy index. The former was used to reflect the fitting error of the model and the latter was used as a penalty term to reflect the model uncertainty. They have similar roles as the two terms of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) used commonly in hydrology. Results of Monte‐Carlo experiments verified higher and more stable accuracy of determining the true model order by CCIC than by AIC and BIC. Moreover, results indicated that the mean value of a time series had a big impact on the accuracy of CCIC. If the mean value of a time series was far from its initial value, the estimation of CCIC would have a big bias, causing low accuracy in the determination of suitable model order. The application of CCIC to annual precipitation from 520 stations in China further confirmed its advantages over AIC and BIC and indicated the significant short‐term dependence characteristics of annual precipitation in the Yangtze River basin. The proposed CCIC approach has potential for wider use in hydrometeorology. Key Points: Correlation coefficient‐based information criterion (CCIC) was proposed to determine the optimal order of regression models for quantifying dependence characteristics in hydrologic time seriesMC experiments verified higher and more stable accuracy of determining the true model order by CCIC than by Akaike Information Criterion and Bayesian Information Criterion used commonlyMean value of a time series had a big impact on the accuracy of CCIC for determining optimal order for regression models [ABSTRACT FROM AUTHOR]

Hot and dry extremes have co‐occurred with global warming, causing worse threats than single climate extremes. We analyse the compound hot and dry day (CHDD) and population exposure (Epop) across China based on observations and 11 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6. The bias‐corrected GCMs, particularly the two‐stage multivariate quantile mapping‐corrected ones, perform better than the raw ones in catching changes in CHDD but they all overestimate CHDD and Epop. More CHDDs occur in the southeast (SE), north (NC) and northwest (NW) China with increasing trends over regions stretching from the northeast (NE) to southwest (SW) China. Despite the declined CHDD, historical Epop has generally increased by the increased population. Future projections in the SSP5‐8.5 experiment indicate an overall increase of CHDD and Epop at 1.5°C warming and an increasing Epop to dangerous CHDD at 2°C warming. SW, NE and the Tibetan Plateau need effective measures of adaptation to reduce sufferings from CHDD under climate change. [ABSTRACT FROM AUTHOR]

Variability of water balance in closed lakes in the Tibetan Plateau (TP) can be a useful indicator of climate change. While most of lakes over the TP have been expanding, especially in the inner TP, the Yamzho Yumco in the Southern TP faced a significant water level decline in 1975–2012. This study focused on attribution of the water level changes to various factors with the help of a physically based and a statistical model for the water balance of the lake, along with observed precipitation and lake surface evaporation data. Results showed that climatic conditions dominated the changes in water level until 1997, and human activity through the construction and operation of a hydropower station started to play a strong role after 1998. The lake level has gone up and down due to changes in precipitation‐generated recharge and evaporation over the study period. The water level decline in 1975–1997 was mainly due to low level of the recharge from precipitation and land runoff. However, the drastic decline of water level in 2006–2012 was far from the reach of the climatic conditions. It was indeed caused by the intensified influence of the building and operation of a nearby hydropower station. It was also found that the water level change during the whole period (1975–2012) followed the aridity index for the basin, especially before 1989. This provides an effective way to predict future changes of lake level, if reliable projections for future climate change and human activity are available. Key Points: Decline of water level of YY during 1975–1997 was mainly attributed to decreased recharge from land runoffThe impacts of human activity on the water level decline of YY aggravated after 2005The aridity index over the drainage basin was closely linked to the changes of water level of YY [ABSTRACT FROM AUTHOR]

Abstract: Evaluation of drought and its spatial distribution is essential to develop mitigation measures. In this study, we employed the entropy index to investigate the spatiotemporal variability of meteorological droughts over China. Entropy values, with a reliable hydrological and geographical basis, are closely related to the months of precipitation deficit and its mean magnitude and can thus represent the physical formation of droughts. The value of entropy index can be roughly classified as <0.35, 0.36–0.90, and >0.90, reflecting high, middle, and low occurrence probabilities of droughts. The accumulated precipitation deficits, based on the standardized precipitation‐evapotranspiration index at the 1, 3, 6, and 12 month scales, consistently increase with entropy decrease, no matter considering the moderately, severely, or extremely dry conditions. Therefore, Northwest China and North China, with smaller entropy values, have higher occurrence probability of droughts than South China, with a break at 38°N latitude. The aggravating droughts in North China and Southwest China over recent decades are represented by the increase in both the occurrence frequency and the magnitude. The entropy, determined by absolute magnitude of the difference between precipitation and potential evapotranspiration, as well as its scatter and skewness characteristics, is easily calculated and can be an effective index for evaluating drought and its spatial distribution. We therefore identified dominant thresholds for entropy values and statistical characteristics of precipitation deficit, which would help evaluate the occurrence probability of droughts worldwide. [ABSTRACT FROM AUTHOR]

In this article, we investigated the variability of precipitation conditions in the Haihe River basin (HRB) during 1961-2010 by analyzing four daily precipitation scenarios. These scenarios were set with the values of, equal to 0 mm/day, 10-20 mm/day, 20-50 mm/day, and greater than 50 mm/day, which were denoted as P0, P10, P20, and P50, respectively. Results indicate that the mean values of daily precipitation decline, and its fluctuation becomes weak with years in HRB. The contour of daily precipitation with the mean value of 1.4 mm/day moves more than 100 km toward southeast in the basin from 1960s to 2000s. The variations of four precipitation scenarios show difference. The Tianjin and Langfang cities were the P0 drought center in HRB after 1980s, and the days and regions without precipitation increase with years. The magnitude of P10 extrema shows no significant changes over the last 50 years, but the rainfall centers vary with areas in HRB. The magnitude of P20 extrema shows no obvious changes in 1961-2000 but increases in 2000s. The magnitude of P50 extrema obviously declines in the last 50 years, with the rainfall center moving from northeast to south of HRB. Urbanization impacts are reflected in some cities in 1980s and 1990s, but after 2000, the urbanization impacts were not clearly detected due to the significant precipitation decreases in HRB. In summary, precipitation decrease is caused by the decreases of P50 extrema rather than P10 and P20 extrema in HRB, which would be favorable for the flood resources utilization through ample-low flow operations over space. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficult task in practice due to the confusing concept of trend and disadvantages of methods. In this article, an improved definition of trend was given as follows: 'a trend is the deterministic component in the analysed data and corresponds to the biggest temporal scale on the condition of giving the concerned temporal scale'. It emphasizes the intrinsic and deterministic properties of trend, can clearly distinguish trend from periodicities and points out the prerequisite of the concerned temporal scale only by giving which the trend has its specific meaning. Correspondingly, the discrete wavelet-based method for trend identification was improved. Differing from those methods used presently, the improved method is to identify trend by comparing the energy difference between hydrologic data and noise, and it can simultaneously separate periodicities and noise. Furthermore, the improved method can quantitatively estimate the statistical significance of the identified trend by using proper confidence interval. Analyses of both synthetic and observed series indicated the identical power of the improved method as the Mann-Kendall test in assessing the statistical significance of the trend in hydrologic data, and by using the former, the identified trend can adaptively reflect the nonlinear and nonstationary variability of hydrologic data. Besides, the results also showed the influences of three key factors (wavelet choice, decomposition level choice and noise content) on discrete wavelet-based trend identification; hence, they should be carefully considered in practice. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this article, by using the daily precipitation data measured at 58 meteorological stations, spatial and temporal variability of daily precipitation including zero rainfall values (called 'precipitation') and without zero rainfall values (called 'rain') and four precipitation extrema (P0, P20, P50, and P100 representing the daily precipitation with the magnitude smaller than 0.1 mm, bigger than 20 mm, 50 mm, and 100 mm per day, respectively) in the Yangtze River Delta (YRD) during 1958-2007 were analyzed, and the effects of urbanization were further investigated. Results indicate that (i) differing from the downward trends in 1958-1985, daily precipitation and rain in 1986-2007 show slowly downward trends in the mid YRD but show upward trends in the northern and southern YRD. (ii) Spatial and temporal variability of the rain is more complex than daily precipitation. Both of them become smaller but show more obvious fluctuations in 1986-2007. (iii) Urbanizations cause not only the urban rainfall island problem but also more obvious fluctuations of rain intensity in the mid YRD, reflecting more uncertainty of daily precipitation variability. (iv) Urbanizations have little effects on the variability of P0 and P100 but cause notable increases of P20 and P50. (v) The spatial variability of daily precipitation and precipitation extrema in 1958-1985 clearly shows a breakpoint at 30°20′N latitude, but the breakpoint disappears afterward because of the effects of urbanization. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]