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Both El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are important indicators of the potential impact of climate at the global scale. The IOD-ENSO relationship at different timescales is temporally non-uniform, which is important to understand in order to evaluate its socioeconomic impacts. Here, we developed and applied a wavelet cross-correlation analysis approach for investigating the evolution of the IOD-ENSO relationship over a 150-year period. We found that the IOD-ENSO relationship was statistically significant at the dominant timescales of 1.5, 3, and 24 year, with the correlation degree varying with time. The hysteresis effect between IOD and ENSO and the evolution of their relationship obviously differ at the three dominant timescales. The positive (negative) IOD and El Niño (La Niña) events were mainly determined by the variability of IOD and ENSO at the timescale < 3 year. Strong positive IOD events in the September-October-November period would more likely be related to El Niño events 2 months later and La Niña events 14 months later, but negative IOD events would rarely co-occur with El Niño or La Niña events. The result achieved in this study can serve as a useful guide for the long-term forecast of IOD and ENSO. [ABSTRACT FROM AUTHOR]

De-noising is a substantial issue in hydrologic time series analysis, but it is a difficult task due to the defect of methods. In this paper an energy-based wavelet de-noising method was proposed. It is to remove noise by comparing energy distribution of series with the background energy distribution, which is established from Monte-Carlo test. Differing from wavelet threshold de-noising (WTD) method with the basis of wavelet coefficient thresholding, the proposed method is based on energy distribution of series. It can distinguish noise from deterministic components in series, and uncertainty of de-noising result can be quantitatively estimated using proper confidence interval, but WTD method cannot do this. Analysis of both synthetic and observed series verified the comparable power of the proposed method and WTD, but de-noising process by the former is more easily operable. The results also indicate the influences of three key factors (wavelet choice, decomposition level choice and noise content) on wavelet de-noising. Wavelet should be carefully chosen when using the proposed method. The suitable decomposition level for wavelet de-noising should correspond to series' deterministic sub-signal which has the smallest temporal scale. If too much noise is included in a series, accurate de-noising result cannot be obtained by the proposed method or WTD, but the series would show pure random but not autocorrelation characters, so de-noising is no longer needed. [ABSTRACT FROM AUTHOR]

The existence of several NDVI products in Qinghai-Tibetan Plateau (QTP) makes it challenging to identify the ideal sensor for vegetation monitoring as an important factor for landslide detection studies. A pixel-based analysis of the NDVI time series was carried out to compare the performances of five NDVI products, including ETM+, OLI, MODIS Series, and AVHRR sensors in QTP. Harmonic analysis of time series and wavelet threshold denoising were used for reconstruction and denoising of the five NDVI datasets. Each sensor performance was assessed based on the behavioral similarity between the original and denoised NDVI time series, considering the preservation of the original shape and time series values by computing correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and signal to noise ratio (SNR). Results indicated that the OLI slightly outperformed the other sensors in all performance metrics, especially in mosaic natural vegetation, grassland, and cropland, providing 0.973, 0.015, 0.022, and 27.220 in CC, MAE, RMSE, and SNR, respectively. AVHRR showed similar results to OLI, with the best results in the predominant type of land covers (needle-leaved, evergreen, closed to open). The MODIS series performs lower across all vegetation classes than the other sensors, which might be related to the higher number of artifacts observed in the original data. In addition to the satellite sensor comparison, the proposed analysis demonstrated the effectiveness and reliability of the implemented methodology for reconstructing and denoising different NDVI time series, indicating its suitability for long-term trend analysis of different natural land cover classes, vegetation monitoring, and change detection. [ABSTRACT FROM AUTHOR]

• Type of trend in observed surface air temperature during 1960–2019 in China was detected. • Long-range dependence of temperature highly influences the detection of its trend slopes and types. • Temperature over short periods in China was detected as having deterministic trends. • Temperature over long periods in China was detected as a combination of long-range dependence and deterministic trends. • Considering fractional integration and URTs together is more reliable to detect trend types in temperature. Detection of the type of trend in temperature data, by distinguishing between deterministic and stochastic trends, has important implications for understanding climate change. The Unit root tests (URTs) have been widely used for detecting the type of trend, but they do not consider the possibility of fractional integration and its influences. In this study, we detected the type of trends in observed surface air temperature during 1960–2019 at 558 stations across China, by considering fractional integration. The whole period was divided into three sub-periods by two structural breakpoints (denoted as SBP1 and SBP2). The fractional differencing parameter d was estimated by the Local Whittle (LW) function, and then three URTs (namely PP, KPSS, and ZA) were used to detect the type of trends in the temperature data during different sub-periods. The results indicated that the de-seasoned monthly temperature (DMT) series were fractionally integrated and, thus, exhibited long-range dependence characteristics, which significantly influenced the estimation of the slope of trend and detection of the type of trend. Compared with the LW function, the ordinary least squares method yielded biased estimation of the slope of trend, as it could not eliminate the influence of long-range dependence of the DMT series. The DMT series with weak long-range dependence or anti-persistent characteristics during 1960–SBP1, SBP1–SBP2, and SBP2–2019 were accurately detected as deterministic trends. However, the DMT series with strong long-range dependence during SBP1–2019 and 1960–2019 were detected to have stochastic trends by the KPSS test. Following the results of the fractional integration and URTs together, temperature over short periods in China were detected to have deterministic trends, but those at long periods were detected to have a combination of long-range dependence and deterministic trends. [ABSTRACT FROM AUTHOR]

A comprehensive integrated framework was designed to categorize individual streams and watersheds based on their temporal variation, and to determine the key drivers controlling watershed hydrology in the Qorveh-Dehgolan Basin. Four stream networks were extracted from multi-temporal Pan-Sharpened Landsat 7 and Landsat 8 imageries. Nineteen geomorphometric indices are measured and classified into the linear aspect, aerial aspect, and relief aspect. The results revealed a significant decline in stream networks from 2000 to 2017, reducing basin order. The factor analysis identified three principal factors including 'factor of basin magnitude' (affecting low-altitude watersheds), 'factor of steepness' (influencing high-altitude watersheds), and 'factor of texture'. The focal mechanism highlighted the complex fault mechanisms in the study area. Finally, RadViz clustering showed that for the lower order streams in the high-altitudes watersheds, stream width showed higher temporal variability in comparison to other parameters. In contrast, flow accumulation showed high variance in higher-order streams. [ABSTRACT FROM AUTHOR]

• Two key hydrology-related open issues on the "Sponge City" program in China are discussed. • The trade-off between the investment for the SCP and its potential benefits can be used to determine proper VCRAR. • Nonstationarity of rainfall data should be considered for estimating proper rainfall threshold, to achieve the VCRAR. For managing the worsening urban water disasters in China, the Government of China proposed the concept of " Sponge City " in 2013 and initiated the strategy in 30 pilot cities from 2015. Despite the promise of the concept, there have been many challenges in implementing the "Sponge City" program (SCP). In this manuscript, we discuss the hydrology-related challenges in implementing the SCP. In particular, we consider two key challenges: (1) Determination of the " Volume Capture Ratio of Annual Rainfall " (VCRAR), as controlling urban stormwater runoff is one of the core targets of the SCP; and (2) Estimation of a proper rainfall threshold, which influences the layout of green-infrastructures in the SCP to achieve the core VCRAR target. To discuss these challenges, we consider the city of Beijing, the capital of China, as a case study. Our analysis shows that the trade-offs between the investment for the SCP and its potential economic benefits should be considered by undertaking a proper determination of VCRAR. The VCRAR estimated for Beijing from the present analysis is 0.73. This value is more reasonable than the empirical value of 0.80 that is presently used, as it can guarantee the positive rate of return on the investment. We also find that the nonstationary characteristics of rainfall data and their spatiotemporal differences are important for the estimation of the rainfall threshold in SCP. For instance, even using the daily rainfall data over a period of 30 years (1983–2012) in Beijing, as required by the National Assessment Standard, the estimated rainfall threshold of 27.3 mm underestimates the reasonable rainfall threshold that should at least be larger than 30.0 mm. Thus, the former cannot ensure the VCRAR target of 0.80. Based on these results, we offer proper approaches and key suggestions towards useful guidelines for delivering better SCP in the Chinese cities. [ABSTRACT FROM AUTHOR]

• Non-monotonic trends of streamflow and its spatial variability in the continental United States are analyzed. • Considering only monotonic trends underestimates the long-term variability of streamflow in the Northeast zone. • The non-monotonic trend of streamflow in the region was mainly controlled by climatic conditions rather than landscapes. • Identification of non-monotonic trend is more desirable for the detection of long-term variability of streamflow. Analysis of the long-term variability of terrestrial streamflow is important for a wide range of studies in hydroclimatology and environment. However, the common practice of detecting only the monotonic trend cannot actually reflect the long-term variability and change in streamflow, thus resulting in an insufficient or biased understanding. This study focuses on the non-monotonic trends of streamflow and its spatial variability across the continental United States (US). Annual streamflow data observed during 1951–2002 at 530 stations across the continental US are studied. The discrete wavelet spectrum (DWS) is used to detect the non-monotonic trend of streamflow and evaluate its statistical significance. The results indicate significant non-monotonic trends in annual streamflow at 172 stations in the northeast Zone and the northern part of the southeast Zone (in the regions east of 100°W). However, considering only the monotonic trends (being significant at only 38 stations) causes a biased understanding of the long-term variability of streamflow in the northeast zone. The results also reveal that the non-monotonic trend of streamflow in the continental US was mainly controlled by the climatic conditions and that the effects of landscape conditions were weak. The average aridity index (AI) of 0.86–1.35 and its DWS-based significance level >0.1 can indicate the significant non-monotonic trend of streamflow at 95% confidence level. The regions that can meet the above two conditions account for 16% of the global terrestrial surfaces. By considering non-monotonic trends, the change ratio of streamflow is found to be double of that of AI, while similar change ratios are obtained from the monotonic trend results, as the reason of significant underestimation. Due to the partly equal roles of low-frequency oscillation and monotonic trend, identification of the non-monotonic trend should be more desirable for the detection and attribution of the long-term variability and change in streamflow. [ABSTRACT FROM AUTHOR]

• Summer rainfall has interannual/decadal periodicities only in four areas in China. • Effects of PDO and AMO on summer rainfall in the four areas have four types. • Rainfall keeps inverse phase between SNWF-water exporting and importing regions. • Rainfall in the next decade has "south drought-north flood" pattern in East China. Identification of the periodic variability of precipitation is of paramount importance for rainfall modeling and forecasting, but it is also a challenge. In this article, we (1) identify the periodic variability of summer precipitation in China at interannual and decadal scales; and (2) explore its physical connection to the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). The summer precipitation data in 1961–2013 from 520 meteorological stations across China and the empirical mode decomposition-based method are used for this study. Results show that at the interannual and decadal timescales, summer precipitation exhibits significant periodicities, which can be attributed to the variability of PDO and AMO, with high correlations, only in four small regions: northwest corner of China, most parts of the Songliao River Basin (SRB), the Huang-Huai-Hai Plain, and the south boundary of the Yangtze River Basin (YRB). However, their effects with spatial variability in strength and direction are distinguished as four types, due to the spatial variability in the changes in water vapor flux between warm and cold PDO (AMO) phases. Precipitation is mainly connected to: (1) AMO in the northwest corner of China, with the same phase; (2) PDO in most parts of SRB and lower reach of YRB, keeping the same phase; (3) PDO in the mid-reach of the Yellow River basin, eastward to the northern Huai River basin and the Southeast River basin, with the inverse phase; and (4) both PDO and AMO in the mid-reach of YRB, where a positive PDO and a negative AMO phase cause precipitation surplus. For the "four-horizontal-three-vertical" national water transfer framework in China, the decadal variability of summer precipitation between the water exporting and importing regions keeps an inverse phase, due to the PDO and AMO effects. Although allocation of water resources over a large region through huge water transfer projects in China (and elsewhere) often yield favorable results for water security, the periodic variability of precipitation, and especially the anthropogenic effects, needs to be carefully considered and evaluated, to maximize the benefits. [ABSTRACT FROM AUTHOR]

The precipitation pattern in the Taihu Lake basin in East China has significantly changed over the last decades, perhaps due to intensive urbanization. However, there is limited understanding of the effect of large-scale expansion of city groups on the precipitation variability, which causes a challenge for flood control in the basin. Considering the process of urbanization, we investigated in this study the variability of precipitation in different urban zones and characteristic scales and explored the influence of urban development on the rain island effect in the basin. The basin was divided into three zones (old urban area, new urban area, and suburbs) considering different degrees of urbanization. Results indicated that precipitation change and its response to urbanization had spatial differences at various characteristic scales. Urbanization exhibited a significant "precipitation enhancement effect" at the characteristic scales including annual, flood season, summer, autumn, and winter but indicated a "precipitation reduction effect" in spring. Annual (flood season) precipitation in 1990–2013 increased by 30.04 mm (16.94 mm), due to urbanization, compared with that in the preceding period. The rate of contribution of urbanization to precipitation increase was 15.6%, 41.1%, and 14.4% in summer, autumn, and winter. The enhancement effect of short duration rainfall extremes in old urban area was also discernible, and that in new urban areas would enhance along with urban expansion, complicating urban flood and waterlogging control. More effective adaptation strategies should be implemented to handle the unfavorable situation. [ABSTRACT FROM AUTHOR]

Detecting the spatial heterogeneity in the potential occurrence probability of water disasters is a foremost and critical issue for the prevention and mitigation of water disasters. However, it is also a challenging task due to the lack of effective approaches. In the article, the entropy index was employed and those daily rainfall data at 520 stations were used to investigate the occurrences of rainstorms in China. Results indicated that the entropy results were mainly determined by statistical characters (mean value and standard deviation) of rainfall data, and can categorically describe the spatial heterogeneity in the occurrence of rainstorms by considering both their occurrence frequencies and magnitudes. Smaller entropy values mean that rainstorm events with bigger magnitudes were more likely to occur. Moreover, the spatial distribution of entropy values kept a good relationship with the hydroclimate conditions, described by the aridity index. In China, rainstorms are more to likely occur in the Pearl River basin, Southeast River basin, lower-reach of the Yangtze River basin, Huai River basin, and southwest corner of China. In summary, the entropy index can be an effective alternative for quantifying the potential occurrence probability of rainstorms. Four thresholds of entropy value were given to distinguish the occurrence frequency of rainstorms as five levels: very high, high, mid, low and very low, which can be a helpful reference for the study of daily rainstorms in other basins and regions. [ABSTRACT FROM AUTHOR]

The jump points detection is critical to the understanding of hydrologic variability, especially in investigating the anthropogenic effects. Conventional methods are mainly statistical and cannot directly reflect the jump change degrees. This article proposes a moving correlation coefficient-based detection (MCCD) method for the detection of jump points (JPs) in hydroclimate data. The correlation coefficient (CC) between the potential jump component and the original data is calculated, and the CC series is realized by moving from the starting to the ending points of the original time series. Bigger CC value reflects higher jump degree; the position with the biggest absolute CC value is the JP that is the most expected. Its significance is evaluated by comparing its value with the CC threshold value at the relevant significance level. Monte-Carlo experimental results verify the MCCD method's higher efficiency compared with four commonly used conventional methods. It is especially noteworthy that the results indicate its stable efficiency, even when encountering the influences of some unfavorable factors. By applying the MCCD method to the Lancang River Basin, the JP of runoff in 2004 is detected at the Yunjinghong station in the lower reach. It is mainly attributed to the construction and operation of some major water hydropower projects, while the stable variations of areal precipitation and actual evapotranspiration, as well as the stable land-cover conditions, contribute little to the abrupt decrease in runoff. The MCCD method can be an effective alternative for the detection of JPs in hydroclimate data. [ABSTRACT FROM AUTHOR]

Abrupt changes are an important manifestation of hydrological variability. How to accurately detect the abrupt changes in hydrological time series and evaluate their significance is an important issue, but methods for dealing with them effectively are lacking. In this study, we propose an approach to evaluate the significance of abrupt changes in time series at five levels: no, weak, moderate, strong, and dramatic. The approach was based on an index of correlation coefficient calculated for the original time series and its abrupt change component. A bigger value of correlation coefficient reflects a higher significance level of abrupt change. Results of Monte-Carlo experiments verified the reliability of the proposed approach, and also indicated the great influence of statistical characteristics of time series on the significance level of abrupt change. The approach was derived from the relationship between correlation coefficient index and abrupt change, and can estimate and grade the significance levels of abrupt changes in hydrological time series. Application of the proposed approach to ten major watersheds in China showed that abrupt changes mainly occurred in five watersheds in northern China, which have arid or semi-arid climate and severe shortages of water resources. Runoff processes in northern China were more sensitive to precipitation change than those in southern China. Although annual precipitation and surface water resources amount (SWRA) exhibited a harmonious relationship in most watersheds, abrupt changes in the latter were more significant. Compared with abrupt changes in annual precipitation, human activities contributed much more to the abrupt changes in the corresponding SWRA, except for the Northwest Inland River watershed. [ABSTRACT FROM AUTHOR]

Entropy indexes are widely used to measure the complexity of precipitation and its spatiotemporal variability, due to their advantages in quantifying the complex and nonlinear interactions of climate variables. However, different entropy indexes may provide different outcomes on the complexity of precipitation, thus causing difficulties in the assessment of actual complexity. In this study, we identified and chose the optimal entropy index from six entropy indexes based on the reliability and stability of entropy values, to assess the complexity of precipitation. The six entropy indexes considered are: information entropy (IE), fuzzy entropy (FE), approximate entropy (AE), sample entropy (SE), wavelet entropy (WE), and permutation entropy (PE). We implemented the approach on the monthly precipitation data observed over the period 1968–2017 at 58 meteorological stations in Beijing, China. The results indicated that the SE index was the optimal index with the best reliability and stability and, thus, was used to examine the complexity of the 58 monthly precipitation series in Beijing. The results also suggested that the complexity of precipitation can be viewed in terms of four sub-regions in Beijing. The precipitation in the 'Northern Mountain Area', with high complexity, was more affected by the interactions between the terrain and climate variability, compared with that in the 'Southern Mountain Area' with low complexity. The precipitation in the 'Central Urban Area' was more significantly impacted by the extensive urbanization than that in the 'Northeast Suburban Area', causing higher complexity in the former. At temporal scale, the complexity of precipitation in the 'Southern Mountain Area' exhibited the largest increasing rate due to the highest impacts of climate variability, followed by the 'Central Urban Area' with a moderate increasing rate of complexity of precipitation due to the impacts of human activities. However, the complexity of precipitation in the 'Northern Mountain Area' and 'Northeast Suburban Area' exhibited small changes due to relatively weak sensitivity to climate variability and human activities. [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]

The Lowest navigable water level (LNWL) is an important indicator used for navigation design to balance the relationship between navigation safety and economic benefits of a waterway. However, it is a challenge of accurately estimating LNWLs due to the nonstationary characteristics of observed water level data series. In this study, a comprehensive framework was developed for handling this issue. In this framework, inter-annual variabilities in both the mean and variance of water level series were described by decomposing original series and were eliminated by composing new series. Intra-annual variability was determined by detecting indicators describing intra-annual water level distributions. Considerations of inter- and intra-annual variabilities were combined by designing annual water level processes for the past and current environments. Shipping risks during both annual and multi-annual periods were considered in the framework as well. The framework was demonstrated in estimating LNWLs at the Gaodao and Shijiao stations in the North River basin, southern China. The recommended LNWLs at the Gaodao station were 22.32 m for 95% guaranteed rate and 21.84 m for 98% guaranteed rate; LNWLs at the Shijiao station were 0.27 m for 95% guaranteed rate and 0.15 m for 98% guaranteed rate. The impact of variance variability on estimations of LNWLs was also evaluated. Results indicated that the recommended LNWLs would have errors of 0.11 ~ 0.48 m at the Gaodao station and 0.03 ~ 0.04 m at the Shijiao station if the variance variability was not considered. The proposed framework was then compared with Nonstationary synthetic duration curve (NSDC) method, and results illustrated that the duration curves plotted by NSDC method were unreasonable, leading to inaccurate design values. Overall, the developed framework is more reasonable and suitable for designing LNWLs of waterways where the variabilities of the water levels at different time scales are different or where the historical water level data contain various variations. [ABSTRACT FROM AUTHOR]

The application of multivariate statistical analysis (MSA) in analyzing drainage basin morphometry, in conjunction with seismic events (focal mechanism) and associated tectonic influences, has been discussed less in the framework of existing geomorphometric studies. Hence, in this study, an integrated approach was designed combining the geomorphometric analysis with MSA, focal mechanism and associated structural lineaments (using a remote sensing and GIS framework) to address the role of active tectonism on the drainage network characteristics in Qorveh-Dehgolan basin located in western part of Iran (Kurdistan province). Eight watersheds were delineated from burnt ASTER DEM using SWAT model and categorized into high-altitude watersheds and low-altitude watersheds. Four-sets of the drainage network were extracted from satellite imageries (Landsat 7 and 8) over a period of 17 years (2000–2017) to monitor the temporal stream network behavior. Overall, nineteen morphometric parameters were calculated (and categorized into linear, aerial, and relief aspects) exhibiting significant changes in the drainage network from 2000 to 2017 (the stream order declined from 5th to 4th order). The higher-altitude watersheds showed various anomalies in the calculated indices (indicating the potential influence of tectonic activity), while the lower altitude watersheds did not exhibit any specific anomalies. In addition, higher concentration of longer lineaments was observed in areas where the relatively resistant, fractured bedrock lithology (meta-volcanic and meta-sedimentary rocks) provided favorable conditions for origin and propagation of the lineaments. Further analysis of focal mechanism solutions (FMS, beachball diagrams) showed that the study area is primarily influenced by moderate earthquake events (3 ≤ Mw ≤ 6) and is influenced by a complex fault mechanism (primarily normal faults and strike slip faults). The morphometric parameters were further subjected to the factor analysis (principal axis factoring using Varimax rotation transformation) resulting in derivation of "factor of basin magnitude", "factor of steepness" and "factor of texture" as three principal factors impacting the drainage network. Finally, it was concluded that "factor of steepness" played a decisive role in defining hydrology of higher-altitude watersheds (an indication of tectonic influences in these watersheds) and thereby defining the overall hydrology of the study area as well. Overall, this study highlighted the significant role of the tectonic activity associated with structural lineaments and the seismic events in defining the nature and behavior of the drainage network and certain anomalies caused in the basin hydrology under these structural controls. [ABSTRACT FROM AUTHOR]

The lower Yellow River (YR) reach is one of the most typical and complex river channels in the world. The impact of human activities on the evolution of its channels is an important scientific issue that needs to be explored. Based on cross-sectional channel data measured at four typical hydrological stations in the lower YR during the period from 1965 to 2015 and the accompanying annual runoff and sediment data, the variation characteristics of the longitudinal slopes of the riverbeds, the width-depth ratio of the cross-sections, and the lateral migration of thalweg points were analyzed. The river phase coefficients (ξ = W 1/2/ H) of the YR channel across different years were further evaluated. Furthermore, the correlation relationship between the river phase coefficient, annual discharge, and sediment concentration was established. The results show that over the last five decades, the fluctuation of the longitudinal profile of the upper and lower parts of the lower YR was more obvious than that of the middle part, and the variation range of the width of the channel cross sections and the fluctuation of the mainstream line differed in 1965–1973, 1974–1980, 1981–1985, 1986–1999, and 2000–2015. The morphological parameters of the channel cross-section (ξ) were mainly affected by the annual average discharge and sediment concentration, and a good fitting relationship was found through quantitative analyses. Large reservoirs have the function of redistributing water and sediment yields and reducing peak discharges, thereby leading to changes in the erosion-deposition patterns and river morphologies of channels. • Reservoir operation greatly controls flow regime and sediment transport. • Thalweg points record the channel a cyclic process of siltation and erosion. • Channel dimensions under bank-full level respond to water-sediment changes. • River phase coefficient is a key parameter indicating channel morphological feature. • Construction of Xiaolangdi Reservoir has mitigated further channel shrinkage. [ABSTRACT FROM AUTHOR]

• Declining water storage trends in the GRACE and GHMs were observed over Central Asia. • TWSA can be well simulated and predicted by climate variables. • Potential strategies are provided to address the SDGs of water resource over Central Asia. Arid regions of Central Asia have sensitive ecosystems that rely heavily on terrestrial water storage which is composed of surface water storage, soil moisture storage and groundwater storage. Therefore, we employed three Gravity Recovery and Climate Experiment (GRACE) satellite datasets and five global hydrological models (GHMs) to explore the terrestrial water storage (TWS) changes over arid regions of Central Asia from 2003 to 2014. We observed significantly decreasing water storage trends in the GRACE data, which were underestimated by the GHMs. After averaging the three GRACE satellite datasets, we found that the water storage was decreasing at a rate of −4.74 mm/year. Contrary to the prevailing declining water storage trends, northeastern Kazakhstan (KAZ), and southern Xinjiang increased their water storage over the same period. The GRACE data showed that Turkmenistan (TKM), Uzbekistan (UZB) and KAZ experienced the most severe water depletions, while Tajikistan (TJK) and northwest China (NW) experienced the least significant depletions. With respect to the major river and lake basins, the Aral Sea Basin exhibited the most serious water loss (-0.60 mm/month to −0.38 mm/month). The water storage positively correlates with the precipitation; and negatively correlates, with a three-month lag, with temperature and potential evapotranspiration (PET). Partial least square regression (PLSR) had the high capability in simulating and predicting the TWS. These results provide scientific evidence and guidance for local policy makers working toward sustainable water resource management, and the resolution of international water resource disputes among Central Asian countries. [ABSTRACT FROM AUTHOR]

• Among the 12 methods that are used widely for detecting change points, eight of them that perform better are recommended. • Based on MC experiments, the efficiency of each method was quantified and its capability was quantitatively clarified. • Indian monsoon controlled precipitation process in the regions south of 30°N and west of 110°E in Southwest China. • Results of this study can be a useful reference for choosing a method to detect change points in hydroclimatic time series. An abrupt change is an important manifestation of hydroclimatic variability. Accurate detection of change points is a critical issue in hydroclimatic and climate change studies. In the article, we evaluated the performances of 12 methods (including both parametric and non-parametric) for detecting change points in hydroclimatic time series by considering the influences of eight major factors. Different methods exhibited different efficiencies and eight of the methods performed better which are recommended for application. Furthermore, the mean values of series and locations of change points were found to have little influence on the detection of change points. However, for a time series with smaller variance but bigger skewness and larger difference in the mean values before and after the change point, the abrupt changes can be more easily and accurately detected. Detection of change points in shorter series would have larger uncertainty. Based on the Monte-Carlo experiments, the efficiency of each method was quantified and its capability was quantitatively clarified. Detection of abrupt changes in precipitation over Southwest China showed that the Indian monsoon had a dominant influence on precipitation in the regions south of 30°N and west of 110°E. Since 2007 the Indian monsoon has maintained a weakening pattern, causing a decrease in precipitation on the Yunnan-Guizhou Plateau, which is one of the main causes of frequently occurring droughts. Results of this study can be a useful reference for choosing a method to detect change points in hydroclimate time series, and be an important complement for the detection and attribution of hydroclimatic variability. [ABSTRACT FROM AUTHOR]

This study proposes an improved nonstationary model for flood frequency analysis by investigating the relationship between flood peak and flood volume, using the Three Gorges Dam (TGD), China, for verification. First, the generalized additive model for location, scale and shape (GAMLSS) is used as the prior distribution. Then, under Bayesian theory, the prior distribution is updated using the conditional distribution, which is derived from the copula function. The results show that the improvement of the proposed model is significant compared with the GAMLSS-based prior distribution. Meanwhile, selection of a suitable prior distribution has a significant effect on the results of the improvement. For applications to the TGD, the nonstationary model can obviously increase the engineering management benefits and reduce the perceived risks of large floods. This study provides guidance for the dynamic management of hydraulic engineering under nonstationary conditions. [ABSTRACT FROM AUTHOR]

Conventional methods to design the lowest navigable water level (LNWL) in inland waterways are usually based on stationary time series. However, these methods are not applicable when nonstationarity is encountered, and new methods should be developed for designing the LNWL under nonstationary conditions. Accordingly, this article proposes an approach to design the LNWL in nonstationary conditions, with a case study at the Yunjinghong station in the Lancang River basin in Southwest China. Both deterministic (trends, jumps and periodicities) and stochastic components in the hydrological time series are considered and distinguished, and the rank version of the von Neumann's ratio (RVN) test is used to detect the stationarity of observed data and its residue after the deterministic components are removed. The stationary water level series under different environments are then generated by adding the corresponding deterministic component to the stationary stochastic component. The LNWL at the Yunjinghong station was estimated by this method using the synthetic duration curve. The results showed that the annual water level series at the Yunjinghong station presented a significant jump in 2004 with an average magnitude decline of − 0.63 m afterwards. Furthermore, the difference of the LNWL at certain guaranteed rate (90%, 95% and 98%) was nearly − 0.63 m between the current and past environments, while the estimated LNWL under the current environment had a difference of − 0.60 m depending on nonstationarity impacts. Overall, the results clearly confirmed the influence of hydrological nonstationarity on the estimation of LNWL, which should be carefully considered and evaluated for channel planning and design, as well as for navigation risk assessment. [ABSTRACT FROM AUTHOR]

Due to the rapid urbanization development, the precipitation variability in the Taihu Lake basin (TLB) in East China has become highly complex over the last decades. However, there is limited understanding of the spatiotemporal variability of precipitation complexity and its relationship with the urbanization development in the region. In this article, by considering the whole urbanization process, we use the SampEn index to investigate the precipitation complexity and its spatial differences in different urbanization areas (old urban area, new urban area and suburbs) in TLB. Results indicate that the precipitation complexity and its changes accord well with the urbanization development process in TLB. Higher urbanization degrees correspond to greater complexity degrees of precipitation. Precipitation in old urban areas shows the greatest complexity compared with that in new urban areas and suburbs, not only for the entire precipitation process but also the precipitation extremes. There is a significant negative correlation between the annual precipitation and its SampEn value, and the same change of precipitation can cause a greater complexity change in old urbanization areas compared with the new urban areas and suburbs. It is noted that the enhanced precipitation complexity in a new urban area during recent decades cannot be ignored facing the expanding urbanization. [ABSTRACT FROM AUTHOR]

Real-time de-noising of satellite-derived soil moisture observations presents opportunities to deliver more accurate and timely satellite data for direct satellite users. So far, the most commonly used techniques for reducing the impact of noise in the retrieved satellite soil moisture observations have been based on moving average filters and Fourier based methods. This paper introduces a new alternative wavelet based approach called Wiener-Wavelet-Based Filter (WiW), which uses an entropy based de-noising method to design a causal version of the filter. WiW is used as a post-retrieval processing tool to enhance the quality of observations derived from one active (the Advanced Scatterometer, ASCAT) and one passive (the Advanced Microwave Scanning Radiometer for Earth Observing System, AMSRE) satellite sensors. The filter is then compared with two candidate de-noising techniques, namely: i) a Wiener causal filter introduced by Su et al. (2013) and ii) a conventional moving average filter. The validation is carried out globally at 173 (for AMSRE) and 243 (for ASCAT) soil moisture stations. Results show that all the three de-noising techniques can increase the agreement between satellite and in situ measurements in terms of correlation and signal-to-noise ratio. The Wiener-based methods show least signal distortion and demonstrate to be conservative in retaining the signal information in de-noised data. Importantly, the Wiener filters can be calibrated with the data at hand, without the need for auxiliary data. [ABSTRACT FROM AUTHOR]

• The variations in maximum infiltration rate and cumulative infiltration for three green infrastructures with layer thickness and impervious barrier depth were analyzed. • Results support the importance of infiltration potential for improving the effectiveness of green infrastructures to reduce overland flow. • Urban hydrological simulations should incorporate the layered heterogeneity and infiltration potential assessment of green infrastructures. Spatial heterogeneity in hydrological responses is an important element in urbanized watersheds. One of the main challenges in simulating urban hydrology is the lack of layered information and knowledge of the true infiltration potential of stormwater infrastructure systems. In this study, changes in the infiltration characteristics of three green infrastructures (GIs), with and without underground barriers, were simulated during a designed rain event in Xiamen, China, using a layered, one-dimensional infiltration model. The variations in the maximum infiltration rate (MIR) and cumulative infiltration (CI) for permeable pavements, vegetative swales, and rain gardens were found to differ in response to 204 scenarios of pervious material thickness, even though they were linearly correlated. Comparisons between the GIs, with and without barriers, revealed that the MIR was almost unchanged, while CI varied considerably with the depth to the impervious layer. To explore the impacts of barriers on infiltration potential, simulations with depths to the impervious layer from 700 to 1000 mm were compared. We statistically evaluated the infiltration potential with all possible scenarios for the GIs. Identifying the potential impacts of infiltration processes beneath GI can be beneficial in urban stormwater management. [ABSTRACT FROM AUTHOR]

Summary Evapotranspiration (ET) plays a critical role in linking the water and energy cycles but is difficult to estimate at regional and basin scales. In this study, we present a worldwide evaluation of nine ET products (three diagnostic products, three land surface model (LSM) simulations and three reanalysis-based products) against reference ET ( ET wb ) calculated using the water balance method corrected for the water storage change at an annual time scale over the period 1983–2006 for 35 global river basins. The results indicated that there was no significant intra-category discrepancy in the annual ET estimates for the 35 basins calculated using the different products in 35 basins, but some products performed better than others, such as the Global Land surface Evaporation estimated using the Amsterdam Methodology (GLEAM_E) in the diagnostic products, ET obtained from the Global Land Data Assimilation System version 1 (GLDAS 1) with the Community Land Model scheme (GCLM_E) in LSM simulations, and ET from the National Aeronautics and Space Administration (NASA) Modern Era Retrospective-analysis for Research and Applications reanalysis dataset (MERRA_E) in the reanalysis-based products. Almost all ET products (except MERRA_E) reasonably estimated the annual means (especially in the dry basins) but systematically underestimated the inter-annual variability (except for MERRA_E, GCLM_E and ET simulation from the GLDAS 1 with the MOSAIC scheme – GMOS_E) and could not adequately estimate the trends (e.g. GCLM_E and MERRA_E) of ET wb (especially in the energy-limited wet basins). The uncertainties in nine ET products may be primarily attributed to the discrepancies in the forcing datasets and model structural limitations. The enhancements of global forcing data (meteorological data, solar radiation, soil moisture stress and water storage changes) and model physics (reasonable consideration of the water and energy balance and vegetation processes such as canopy interception loss) will undoubtedly improve the estimation of global ET in the future. [ABSTRACT FROM AUTHOR]