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Study region: The Laohahe basin, north of China Study focus: In a changing environment, some river’s hydrological regimes have significantly changed and may threatened river ecosystem. Therefore, it is urgent to improve our understanding for the driving mechanism of the evolution of the hydrological regimes. In this study, a comprehensive comparison was carried out to assess the application of the Long Short-Term Memory (LSTM) and the Variable Infiltration Capacity (VIC) model in river ecohydrological regimes alteration attribution. Firstly, the ecohydrological indicators changes were evaluated, including streamflow, ecosurplus, ecodeficit, and hydrological health. Then, the VIC and LSTM models were applied to quantify the climatic and anthropic effects on river ecohydrological indicators, respectively. Finally, the attribution results were compared. New hydrological insights: The research results revealed that (1) the streamflow decreased since 1980 and dramatically decreased after 2000. The ecohydrological indicators also changed significantly (α < 0.05). (2) The alterations were mainly driven by human activities, contributed to more than 80 %. (3) The LSTM and VIC showed little performance difference in quantitative attribution at the monthly and yearly scale, while the LSTM performed a little bad in streamflow simulation at the daily scale. The convenient feature and better simulation capabilities of LSTM provides a new choice for the attribution of ecohydrological regimes evolution, providing a better service in managing local water resources and safeguarding river ecosystems.

Abstract Intense grazing may lead to grassland degradation on the Qinghai-Tibetan Plateau, but it is difficult to predict where this will occur and to quantify it. Based on a process-based ecosystem model, we define a productivity-based stocking rate threshold that induces extreme grassland degradation to assess whether and where the current grazing activity in the region is sustainable. We find that the current stocking rate is below the threshold in ~80% of grassland areas, but in 55% of these grasslands the stocking rate exceeds half the threshold. According to our model projections, positive effects of climate change including elevated CO2 can partly offset negative effects of grazing across nearly 70% of grasslands on the Plateau, but only in areas below the stocking rate threshold. Our analysis suggests that stocking rate that does not exceed 60% (within 50% to 70%) of the threshold may balance human demands with grassland protection in the face of climate change.

Study region: Global aridity zones. Study focus: Evaluating drought under the impact of climate change is crucial for ensuring sustainable development and ecological security, especially in global arid zones where the environment is vulnerable to global warming. However, there are large uncertainties in evaluating the future drought risk of arid zones due to uncertainties within climate projections and the use drought-related indices that may not be appropriate for all assessments. Therefore, under the precondition of bias correction applied to CMIP6 data, the drought detection capability of drought indices calculated from multimodel ensemble was evaluated. Based on the drought index with the strongest detection capability and the Reliability-Resilience-Vulnerability (RRV) framework, the future drought risk in global arid zones were predicted. New hydrological insights for the region: Among the drought indices calculated from CMIP6 historical simulations, SPEI offered the best representation of drought patterns. According to the CMIP6 future projections, SSP1–2.6 can effectively mitigate the impact of future drought risk compared with the historical period. In scenarios with higher emissions, the equatorial to northern latitude 30° region will experience more severe drought. For SSP5–8.5, the areas in the arid zones where drought risk deteriorates significantly are several times larger than in other scenarios. To mitigate drought risks, most nations should support a greener, low-emission approach in line with sustainable development goals, reducing the risk of drought-related damage.

Study region: Catchment area above the Huaxian station along the Wei River Basin, China. Study focus: This study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the region: The human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.

Abstract An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth.

Soil moisture (SM) is a vital variable controlling water and energy exchange between the atmosphere and land surface. Spatiotemporally continuous SM information is urgently needed for large-scale meteorological and hydrological applications. Considering the weakness of the penalized least square regression based on the discrete cosine transform (DCT-PLS) method when the missing data are not evenly distributed in the original data set, this study proposes an in situ observation-combined DCT-PLS (ODCT-PLS) to reconstruct missing values of daily surface SM from the Climate Change Initiative program of the European Space Agency (ESA CCI). The result of the reconstruction for ESA CCI SM data in the Xiliaohe River Basin from 2013 to 2020 showed that the SM reconstructed by ODCT-PLS was in better agreement with in situ soil moisture compared with that reconstructed by DCT-PLS, with the average correlation coefficient (CORR) increasing by 0.3636, the average root mean squared error (RMSE) decreasing by 0.0109 m3/m3 and the average BIAS decreasing by 0.0047 m3/m3. Compared with the original ESA CCI SM, DCT-PLS and ODCT-PLS can both restore the spatial variation of SM in the study area. The reconstruction method proposed in our study provides a valuable alternative to reconstruct the three-dimensional geophysical dataset with spatially or temporally continuous data gap. HIGHLIGHTS This paper proposed a new reconstruction method based on the measured observation data and the DCT-PLS method.; This paper utilized the measured observation data by using the CDF matching method.; The new method this paper proposed can be applied to reconstruct three-dimensional geophysical datasets whose data gaps are spatial-temporally continuous.;

IntroductionConductance-photosynthesis (Gs-A) models, accompanying with light use efficiency (LUE) models for calculating carbon assimilation, are widely used for estimating canopy stomatal conductance (Gs) and transpiration (Tc) under the two-leaf (TL) scheme. However, the key parameters of photosynthetic rate sensitivity (gsu and gsh) and maximum LUE (ϵmsu and ϵmsh) are typically set to temporally constant values for sunlit and shaded leaves, respectively. This may result in Tc estimation errors, as it contradicts field observations.MethodsIn this study, the measured flux data from three temperate deciduous broadleaved forests (DBF) FLUXNET sites were adopted, and the key parameters of LUE and Ball-Berry models for sunlit and shaded leaves were calibrated within the entire growing season and each season, respectively. Then, the estimations of gross primary production (GPP) and Tc were compared between the two schemes of parameterization: (1) entire growing season-based fixed parameters (EGS) and (2) season-specific dynamic parameters (SEA).ResultsOur results show a cyclical variability of ϵmsu across the sites, with the highest value during the summer and the lowest during the spring. A similar pattern was found for gsu and gsh, which showed a decrease in summer and a slight increase in both spring and autumn. Furthermore, the SEA model (i.e., the dynamic parameterization) better simulated GPP, with a reduction in root mean square error (RMSE) of about 8.0 ± 1.1% and an improvement in correlation coefficient (r) of 3.7 ± 1.5%, relative to the EGS model. Meanwhile, the SEA scheme reduced Tc simulation errors in terms of RMSE by 3.7 ± 4.4%.DiscussionThese findings provide a greater understanding of the seasonality of plant functional traits, and help to improve simulations of seasonal carbon and water fluxes in temperate forests.

The response of blue and green water to climate and land-use change in the Ganjiang River Basin (GRB) is evaluated via the SWAT model that combines three scenarios (the land-use/land-cover (LULC), climate change, and integrated climate and LULC change scenarios) in the 2040s (2031–2050) and 2060s (2051–2070). The results indicate that, for the GRB, cropland, woodland, and grassland show a decreasing trend, while build-up and water areas show an increasing trend in terms of future land-use change. The climatic conditions projected using NORESM1-M model data under the RCP4.5 and RCP8.5 scenarios suggest, respectively, increases in precipitation (31.17 and 27.24 mm), maximum temperature (2.25 and 2.69 °C), and minimum temperature (1.96 and 2.58 °C). Under climate change conditions, blue water is estimated to decrease by up to 16.89 and 21.4 mm under RCP4.5 and RCP8.5, while green water is estimated to increase up to 19.14 and 20.22 mm, respectively. Under the LULC changes, blue water is projected to increase by up to 5.50 and 7.57 mm, while green water shows decreases of 4.05 and 7.80 mm for the LULC2035 and LULC2055 scenarios, respectively. Under the four combined LULC and climate change conditions (RCP4.5_2040s, RCP4.5_2060s, RCP8.5_2040s, and RCP8.5_2060s), blue water tends to decrease by 0.67, 7.47, 7.28, and 9.99 mm, while green water increases by 19.24, 20.8, 13.87, and 22.30 mm. The influence of climate variation on blue and green water resources is comparatively higher than that of the integrated impacts of climate and land-use changes. The results of this study offer a scientific reference for the water resources management and planning department responsible for scheduling water resource management plan in the GRB. HIGHLIGHTS This study investigates the effects of climate changes and land-use changes on blue/green water.; The NORESM1-M model data from the CMIP5 are combined with the SWAT model to analyse the influence of future climate changes on blue/green water.; The CA–Markov model is applied to generate future land-use scenarios.; Multiple climate and land-use scenarios are set to quantitatively analyse the changes of blue/green water.;

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

Although various studies have investigated the impacts of climate variability and human activities on drought, researches specifically analysing the impact on ecological drought are still limited. A deep understanding of the climatic and anthropogenic effects on ecological drought processes is crucial for ecological regulation and management in the changing environments. In the present study, an integrated approach for comprehensive understanding and quantification of ecological drought in rivers was proposed which first applied the nonparametric kernel density estimation (KDE) method to calculate the most suitable ecological streamflow (MSES) for a river ecosystem. Then, the variable threshold level method based on the MSES for each month and the run theory method were applied to identify the ecological drought duration and deficit volumes. Finally, a quantification approach based on hydrological model simulation was proposed to attribute the impacts of climate variability and human activities on ecological drought. The proposed approach was applied on two catchments, Xianyang (XY) and Huaxian (HX) within the Weihe River Basin (WRB) in northern China. Comparison results obtained using the two empirical methods revealed that the MSES calculated using the KDE method was reasonable and can be used for ecological drought identification. The identification results showed that both the median and upper quartile values of the drought duration and deficit volumes during the disturbed period (1991–2017) were greater than those during the undisturbed period (1961–1990). Quantification results showed that human activities were the dominant factor aggravating ecological drought in the WRB after 1990. The contribution rates of climate variability and human activities toward ecological drought variations were 25.6% and 74.4%, respectively, for the XY station and 42.7% and 57.3%, respectively, for the HX station. Although the WRB was selected as a case study, the proposed approach can also be applied to other regions to provide scientific guidance for regional ecological management.

Study region: Three Gorges Reservoir area, the largest reservoir area in China Study focus: The frequency and intensity of extreme weather events increase with climate warming, and the local climate effects in the large reservoir area may also change the local precipitation structure. Therefore, based on the precipitation sequence data in the Three Gorges Reservoir (TGR) area from 1959 to 2019, the proposed multi-time-scale mutual information entropy (MTSMIE) method was used to reveal the cyclic synchronous changes of drought-flood abrupt alternation and precipitation concentration. New hydrological insights: The results demonstrate that (1) the spatial distribution of the precipitation concentration index in the TGR area has obvious regional variability. On the temporal scale, the precipitation concentration increased by 5.5% and decreased by 8.2% after impoundment at the head and tail of the TGR, respectively. (2) The frequency of drought-to-flood (DTF) events from the head of the reservoir area to the tail of the reservoir area presents a ''more-less'' feature. The frequency of DTF events after impoundment was less than that before the impoundment of the reservoir. However, the flood-to-drought (FTD) events are the opposite. (3) The period of reliable research and monitoring of DFAA and precipitation concentration in the Three Gorges region is about 15–17 years through the MTSMIE method.

Flash floods are a type of catastrophic disasters which cause significant losses of life and property worldwide. In recent years, machine learning techniques have become powerful tools for evaluating flash flood susceptibility. This research applies stacking and blending ensemble learning approaches to assess the flash flood potential in Jiangxi, China. Four base models – linear regression, K-nearest neighbours, support vector machine, and random forest – are adopted to build the two ensemble models. All models are evaluated by three metrics (accuracy, true positive rate, and the area under the receiver operating characteristic curve) and compared with a Bayesian approach. The results suggest that the blending approach is superior to all the other models, which has then been selected to evaluate the vulnerability of flash floods for all the catchments in Jiangxi. The derived maps of flash flood susceptibility suggest that over half of the province, in terms of either area or the number of catchments, are prone to flash floods, in particular the north, northeast and south. These empirical findings can help to develop plans for disaster prevention and control, as well as improving public knowledge of flash flood hazards.

Study region: Yunnan Province, China. Study focus: Yunnan Province (YP) is affected by frequent droughts that severely affect local agriculture and the ecological environment. Therefore, the identification of droughts and an analysis of their driving factors are of significant importance for mitigating local drought losses and guiding agricultural practices. This study identified the spatiotemporal distribution and dynamic changes in drought events over YP (period 1961–2018) using the severity-area-duration method. The impact of El Niño-Southern Oscillation (ENSO) on seasonal droughts and their lag period were also quantified by employing the sliding correlation coefficient and cross-wavelet analysis method. New hydrological insights for the region: 74 drought events were identified during 1961–2018 in YP, which were mainly short-duration that occurred in the 1980 s and 2000 s, and most drought centers are located over the northern and eastern parts of YP. We found that a significant correlation and different lag periods exist between Oceanic Niño Index (ONI) and seasonal precipitation in YP. Spring droughts mainly occurred in El Niño years during the 1980 s and the 1990 s, whilst winter droughts mainly occurred in La Niña years during the 1990 s and the 2000 s with a lag period of up to 12 months.

Flash drought typically refers to the rapid intensification process that would only persist for a certain amount of time. In spite of short duration, flash drought may cause destructive impacts on agricultural and ecological sectors due to the sustained drought severity during or after the rapid intensification period. Understanding the persistence of flash drought, its regional patterns, and to what extent a transition from rapid intensification to conventional drought occurs is beneficial for drought monitoring and drought management. Employing surface and root-zone soil moisture reanalysis datasets, the notion of accumulated water deficits over varying time scales (can be detected by the moving averages of daily soil moisture series from 1 to 300 d) was introduced to explore how the signal of flash drought fades away over China during the period of 1950–2021. Results show that the flash drought signal gradually attenuates under the increased time scales of water deficits. With significant spatial differences, more than 50% of flash drought on average would be lost at a 10 d time scale, and the attenuation ratio may reach 90% when the time scale increases to 100 d. Under the effects of accumulated water deficits, the majority of flash drought events may evolve into conventional drought before dissipating completely. Soil moisture memory has a finite effect on the attenuation of flash drought signal. Flash drought signal dissipates slowly in areas with strong soil moisture memory. As time scale increases, both flash drought signal and the memory of soil moisture decrease, and their correlation also weakens.

The Yangtze River Basin in China was hit by an unprecedented extreme drought in 2022. Such a record-breaking event is jointly driven by a few outlier factors and shows abnormal phenomena. The exceptionality of this drought event cannot be fully described by any individual indicator. Therefore, we performed this comprehensive study to highlight the exceptionality of 2022 Yangtze River Basin drought. We evaluated three drought characteristics: onset period (the time interval of moisture condition from normal to extreme), intensity, and affected area and their compound features. Additionally, historical climatology (1950–2022) and four past severe events are used as references. Lastly, we investigated the driving mechanisms of this event from synoptic perspective. Our results indicate the 2022 drought ranked as the most severe event in history. The recurrence interval of the 2022 Yangtze River drought is estimated to be 120–400 years by considering individual and combined drought characteristics. Behind the scenes, the synoptic environment triggered the abnormally high temperatures and abrupt alteration of precipitation, which is the main driver of the event. The 2022 Yangtze River drought is an archetype of an event characterized by both rapid intensification over time and space, which is indicative for drought monitoring and early warning in a warming climate.

Intermittent records of satellite soil moisture data are major obstacles that constrain their hydrometeorological applications. Based on the European Space Agency Climate Change Initiative (ESA CCI) soil moisture combined product, two machine learning models were employed to reconstruct soil moisture in China during 1979–2019 in both temporal and spatial domains, and latent errors for reconstructed series, as well as their performances for tracing climate extremes, were analyzed. The results showed that with the homogeneity of available data over space, the spatial approach performed well in reproducing the spatial heterogeneity of soil moisture (with medians of the correlation coefficient (CC) above 0.8 and root mean square errors (RMSEs) ranging from 0.02 to 0.03 m3∙m−3). The temporal approach (CC values of 0.7 and RMSEs ranging between 0.02 and 0.03 m3∙m−3) was superior in capturing the seasonality features and the timely and accurate mapping of short-term soil moisture dynamics impacted by rainstorms. However, both approaches failed to identify the location and severity of droughts accurately. The findings highlight the benefits of combining the strengths of both temporal and spatial gap-filling approaches for improving the estimation of missing values and hydrometeorological applications.

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications.

The Laohahe basin is one of the typical semi-arid regions in Northeast China. Quantitatively estimating the contributions of human intervention and climatic variability on runoff changes in this region will help enhance the understanding of local hydrological mechanisms and provide an effective reference for water resources planning and management in other semi-arid regions of the world. The non-parametric Mann–Kendall test was used to analyze the temporal trends of annual precipitation, potential evapotranspiration (PET), and runoff in the whole Laohahe basin and its three sub-basins from 1964 to 2015. The annual runoff showed a decreasing trend in each sub-basin. The change-points of annual runoff detected by Pettitt’s test and residual analysis based on double mass curves (RA-DMC) are 1979 and 1998, and the baseline period and change period of each basin are 1964–1979 and 1980–2015. The RA-DMC method and the VIC model were used to quantitatively evaluate the contributions of human intervention and climatic variability to runoff change, which vary in time and space over the past 52 years. The contributions of human intervention to runoff reduction during 1980–2015 was more than 80%. On a multi-decadal temporal scale, human intervention had a stronger impact on runoff during 1980–1989, 2000–2009, and 2010–2015. The influence of human intervention on runoff reduction is gradually increasing in the 21st century. Besides, human intervention has a greater impact in dry years than in wet years. The increase of cropland area leads to a significant increase in irrigation area, which further leads to an increase in the demand for agricultural water, which is also the main reason for the sharp reduction of runoff in the Laohahe basin.

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

Climate change directly impacts the hydrological cycle via increasing temperatures and seasonal precipitation shifts, which are variable at local scales. The water resources of the Upper Yangtze River Basin (UYRB) account for almost 40% and 15% of all water resources used in the Yangtze Basin and China, respectively. Future climate change and the possible responses of surface runoff in this region are urgent issues for China’s water security and sustainable socioeconomic development. This study evaluated the potential impacts of future climate change on the hydrological regimes (high flow (Q5), low flow (Q95), and mean annual runoff (MAR)) of the UYRB using global climate models (GCMs) and a variable infiltration capacity (VIC) model. We used the eight bias-corrected GCM outputs from Phase 5 of the Coupled Model Intercomparison Project (CMIP5) to examine the effects of climate change under two future representative concentration pathways (RCP4.5 and RCP8.5). The direct variance method was adopted to analyze the contributions of precipitation and temperature to future Q5, Q95, and MAR. The results showed that the equidistant cumulative distribution function (EDCDF) can considerably reduce biases in the temperature and precipitation fields of CMIP5 models and that the EDCDF captured the extreme values and spatial pattern of the climate fields. Relative to the baseline period (1961–1990), precipitation is projected to slightly increase in the future, while temperature is projected to considerably increase. Furthermore, Q5, Q95, and MAR are projected to decrease. The projected decreases in the median value of Q95 were 21.08% to 24.88% and 16.05% to 26.70% under RCP4.5 and RCP8.5, respectively; these decreases were larger than those of MAR and Q5. Temperature increases accounted for more than 99% of the projected changes, whereas precipitation had limited projected effects on Q95 and MAR. These results indicate the drought risk over the UYRB will increase considerably in the future.

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR.

This study evaluated the suitability of the latest retrospective Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 (IMERG) Final Run product with a relatively long period (beginning from June 2000) for drought monitoring over mainland China. First, the accuracy of IMERG was evaluated by using observed precipitation data from 807 meteorological stations at multiple temporal (daily, monthly, and yearly) and spatial (pointed and regional) scales. Second, the IMERG-based standardized precipitation index (SPI) was validated and analyzed through statistical indicators. Third, a light–extreme–light drought-event process was adopted as the case study to dissect the latent performance of IMERG-based SPI in capturing the spatiotemporal variation of drought events. Our results demonstrated a sufficient consistency and small error of the IMERG precipitation data against the gauge observations with the regional mean correlation coefficient (CC) at the daily (0.7), monthly (0.93), and annual (0.86) scales for mainland China. The IMERG possessed a strong capacity for estimating intra-annual precipitation changes; especially, it performed well at the monthly scale. There was a strong agreement between the IMERG-based SPI values and gauge-based SPI values for drought monitoring in most regions in China (with CCs above 0.8). In contrast, there was a comparatively poorer capability and notably higher heterogeneity in the Xinjiang and Qinghai-Tibet Plateau regions with more widely varying statistical metrics. The IMERG featured the advantage of satisfactory spatiotemporal accuracy in terms of depicting the onset and extinction of representative drought disasters for specific consecutive months. Furthermore, the IMERG has obvious drought monitoring abilities, which was also complemented when compared with the Precipitation Estimation from the Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The outcomes of this study demonstrate that the retrospective IMERG can provide a more competent data source and potential opportunity for better drought monitoring utility across mainland China, particularly for eastern China.

As the successor of Tropical Rainfall Measuring Mission, Global Precipitation Measurement (GPM) has released a range of satellite-based precipitation products (SPPs). This study conducts a comparative analysis on the quality of the integrated multisatellite retrievals for GPM (IMERG) and global satellite mapping of precipitation (GSMaP) SPPs in the Yellow River source region (YRSR). This research includes the eight latest GPM-era SPPs, namely, IMERG “Early,” “Late,” and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F) and GSMaP gauge-adjusted product (GSMaP-Gauge), microwave-infrared reanalyzed product (GSMaP-MVK), near-real-time product (GSMaP-NRT), near-real-time product with gauge-based adjustment (GSMaP-Gauge-NRT), and real-time product (GSMaP-NOW). In addition, the IMERG SPPs were compared with GSMaP SPPs at multiple spatiotemporal scales. Results indicate that among the three IMERG SPPs, IMERG-F exhibited the lowest systematic errors and the best quality, followed by IMERG-E and IMERG-L. IMERG-E and IMERG-L underestimated the occurrences of light-rain events but overestimated the moderate and heavy rain events. For GSMaP SPPs, GSMaP-Gauge presented the best performance in terms of various statistical metrics, followed by GSMaP-Gauge-NRT. GSMaP-MVK and GSMaP-NRT remarkably overestimated total precipitation, and GSMaP-NOW showed an evident underestimation. By comparing the performances of IMERG and GSMaP SPPs, GSMaP-Gauge-NRT provided the best precipitation estimates among all real-time and near-real-time SPPs. For post-real-time SPPs, GSMaP-Gauge presented the highest capability at the daily scale, and IMERG-F slightly outperformed the other SPPs at the monthly scale. This study is one of the earliest studies focusing on the quality of the latest IMERG and GSMaP SPPs. The findings of this study provide SPP developers with valuable information on the quality of the latest GPM-era SPPs in YRSR and help SPP researchers to refine the precipitation retrieving algorithms to improve the applicability of SPPs.

Global climate change not only affects the processes within the water cycle but also leads to the frequent occurrences of local and regional extreme drought events. In China, spatial and temporal characterizations of drought events and their future changing trends are of great importance in water resources planning and management. In this study, we employed self-calibrating Palmer drought severity index (SC-PDSI), cluster algorithm, and severity-area-duration (SAD) methods to identify drought events and analyze the spatial and temporal distributions of various drought characteristics in China using observed data and CMIP5 model outputs. Results showed that during the historical period (1961−2000), the drought event of September 1965 was the most severe, affecting 47.07% of the entire land area of China, and shorter duration drought centers (lasting less than 6 months) were distributed all over the country. In the future (2021−2060), under both representative concentration pathway (RCP) 4.5 and RCP 8.5 scenarios, drought is projected to occur less frequently, but the duration of the most severe drought event is expected to be longer than that in the historical period. Furthermore, drought centers with shorter duration are expected to occur throughout China, but the long-duration drought centers (lasting more than 24 months) are expected to mostly occur in the west of the arid region and in the northeast of the semi-arid region.

For winter wheat ( L.) that grows during the rainless season, the contribution of groundwater to the root zone (CGWR) is an important water source for growth. Accurately estimating the CGWR is important for making decisions on irrigation and discharge for winter wheat fields and preventing water pollution. Because winter wheat slows and even stops root growth over winter, so the fixed root density distribution function that is suitable for soybean [ (L.) Merr.] may not suit winter wheat calculations. Therefore, when estimating the CGWR of winter wheat with the numerical model HYDRUS-1D, the root density distribution function should first be determined from two types: fixed or piecewise root density distribution functions. Based on field observations and local weather data for 2004–2005 and 2005–2006, HYDRUS-1D was evaluated with different root density distribution functions by comparing simulated and measured root zone soil water contents. The evaluated model with the most suitable distribution function was used to estimate the daily CGWR for six winter wheat hydrological growth seasons. For all seasons, winter wheat growth was assumed to be at its optimal state. The main results were: (i) a piecewise root density distribution function was the most suitable for winter wheat; (ii) simulated seasonal CGWRs were 154, 128, and 136 mm in the dry, normal, and wet seasons, respectively; and (iii) the CGWR for winter wheat transpiration was about 58, 47, and 69% of the total in dry, normal, and wet seasons, respectively. Overall, we concluded that accurate description of the root density distribution was helpful to estimate the CGWR.

Understanding the patterns and mechanisms of precipitation variations is important for assessing flood and drought risks and for ensuring sustainable water use. Here, we analyzed the characteristics of annual precipitation changes in eight subregions of China using the Mann−Kendall test based on daily precipitation data from 774 rain gauge stations during 1961−2017. Then, we used the monthly precipitation concentration index (PCI) and daily concentration index (CI) to analyze precipitation concentrations. The results are as follows: (1) PCI and CI in northern China generally decreased with time, indicating a uniform precipitation distribution. Notably, the annual precipitation increased significantly in Xinjiang and the Qinghai-Tibet Plateau, which could alleviate future drought. (2) PCI increased and CI decreased in the plain regions of the Yangtze River and Southeast China, exhibiting high CI values with marked increases of annual precipitation. Such heavy rainfall events combined with high rainfall concentrations could increase the flood risk. (3) A significant PCI increase and CI decrease occurred in Southwest China, where annual precipitation decreased significantly. Regular rainfall decreased notably, which could increase the likelihood of drought hazards. (4) Overall, both indices showed negative trends at most stations; precipitation distribution was generally more uniform over China. These findings improve our understanding of extreme rainfall evolution and water resource distribution over China. Furthermore, PCI and CI can serve as warning tools for disaster control and water resource management.

Tropical Rainfall Measuring Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), have provided hydrologists with important precipitation data sources for hydrological applications in sparsely gauged or ungauged basins. This study proposes a framework for statistical and hydrological assessment of the TRMM- and GPM-era satellite-based precipitation products (SPPs) in both near- and post-real-time versions at sub-daily temporal scales in a poorly gauged watershed in Myanmar. It evaluates six of the latest GPM-era SPPs: Integrated Multi-satellite Retrievals for GPM (IMERG) “Early”, “Late”, and “Final” run SPPs (IMERG-E, IMERG-L, and IMERG-F, respectively), and Global Satellite Mapping of Precipitation (GSMaP) near-real-time (GSMaP-NRT), standard version (GSMaP-MVK), and standard version with gauge-adjustment (GSMaP-GAUGE) SPPs, and two TRMM Multi-satellite Precipitation Analysis SPPs (3B42RT and 3B42V7). Statistical assessment at grid and basin scales shows that 3B42RT generally presents higher quality, followed by IMERG-F and 3B42V7. IMERG-E, IMERG-L, GSMaP-NRT, GSMaP-MVK, and GSMaP-GAUGE largely underestimate total precipitation, and the three GSMaP SPPs have the lowest accuracy. Given that 3B42RT demonstrates the best quality among the evaluated four near-real-time SPPs, 3B42RT obtains satisfactory hydrological performance in 3-hourly flood simulation, with a Nash–Sutcliffe model efficiency coefficient (NSE) of 0.868, and it is comparable with the rain-gauge-based precipitation data (NSE = 0.895). In terms of post-real-time SPPs, IMERG-F and 3B42V7 demonstrate acceptable hydrological utility, and IMERG-F (NSE = 0.840) slightly outperforms 3B42V7 (NSE = 0.828). This study found that IMERG-F demonstrates comparable or even slightly better accuracy in statistical and hydrological evaluations in comparison with its predecessor, 3B42V7, indicating that GPM-era IMERG-F is the reliable replacement for TRMM-era 3B42V7 in the study area. The GPM scientific community still needs to further refine precipitation retrieving algorithms and improve the accuracy of SPPs, particularly IMERG-E, IMERG-L, and GSMaP SPPs, because ungauged basins urgently require accurate and timely precipitation data for flood control and disaster mitigation.

Focusing on the shortages of moisture estimation and time scale in the self-calibrating Palmer drought severity index (scPDSI), this study proposed a new Palmer variant by introducing the Variable Infiltration Capacity (VIC) model in hydrologic accounting module, and modifying the standardization process to make the index capable for monitoring droughts at short time scales. The performance of the newly generated index was evaluated over the Yellow River Basin (YRB) during 1961⁻2012. For time scale verification, the standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) at a 3-month time scale were employed. Results show that the new Palmer variant is highly correlated with SPI and SPEI, combined with a more stable behavior in drought frequency than original scPDSI. For drought trend detection, this new index is more inclined to reflect comprehensive moisture conditions and reveals a different spatial pattern from SPI and SPEI in winter. Besides, two remote sensing products of soil moisture and vegetation were also employed for comparison. Given their general consistent behaviors in monitoring the spatiotemporal evolution of the 2000 drought, it is suggested that the new Palmer variant is a good indicator for monitoring soil moisture variation and the dynamics of vegetation growth.

A case study on the evolution of hydrological drought in nonstationary environments is conducted over the Laohahe catchment in northern China. Using hydrometeorological observations during 1964–2009, meteorological and hydrological droughts are firstly analyzed with the threshold level method. Then, a comprehensive analysis on the changes within the catchment is conducted on the basis of hydrological variables and socioeconomic indices, and the whole period is divided into two parts: the undisturbed period (1964–1979) and the disturbed period (1980–2009). A separating framework is further introduced to distinguish droughts induced by different causes, that is, the naturalized drought and human-induced drought. Results showed that human activities are more inclined to play a negative role in aggravating droughts. Drought duration and deficit volume in naturalized conditions are amplified two to four times and three to eight times, respectively, when human activities are involved. For the two dry decades 1980s and 2000s, human activities have caused several consecutive drought events with rather long durations (up to 29 months). These results reflect the considerable impacts of human activities on hydrological drought, which could provide some theoretical support for local drought mitigation and water resources management.

Quantitative evaluation of future climate change impacts on hydrological drought characteristics is one of important measures for implementing sustainable water resources management and effective disaster mitigation in drought-prone regions under the changing environment. In this study, a modeling system for projecting the potential future climate change impacts on hydrological droughts in the Weihe River basin (WRB) in North China is presented. This system consists of a large-scale hydrological model driven by climate outputs from three climate models (CMs) for future streamflow projections, a probabilistic model for univariate drought assessment, and a copula-based bivariate model for joint drought frequency analysis under historical and future climates. With the observed historical climate data as the inputs, the Variable Infiltration Capacity hydrological model projects an overall runoff reduction in the WRB under the Intergovernmental Panel on Climate Change A1B scenario. The univariate drought assessment found that although fewer hydrological drought events would occur under A1B scenario, drought duration and severity tend to increase remarkably. Moreover, the bivariate drought assessment reveals that future droughts in the same return period as the baseline droughts would become more serious. With these trends in the future, the hydrological drought situation in the WRB would be further deteriorated.

Satellite precipitation products (SPPs) are critical data sources for hydrological prediction and extreme event monitoring, especially for ungauged basins. This study conducted a comprehensive hydrological evaluation of six mainstream SPPs (i.e., TMPA 3B42RT, CMORPH-RT, PERSIANN-RT, TMPA 3B42V7, CMORPH-CRT, and PERSIANN-CDR) over humid Xixian basin in central eastern China for a period of 14 years (2000–2013). The evaluation specifically focused on the performance of the six SSPs in capturing precipitation and streamflow extremes. Results show that the two post-real-time research products of TMPA 3B42V7 and CMORPH-CRT exhibit much better performance than that of their corresponding real-time SPPs for precipitation estimation at daily and monthly time scales. By contrast, the newly released post-real-time research product PERSIANN-CDR insignificantly improves precipitation estimates compared with the real-time PERSIANN-RT does at daily time scale. The daily streamflow simulation of TMPA 3B42V7 fits best with the observed streamflow series among those of the six SPPs. The three month-to-month gauge-adjusted post-real-time research products can simulate acceptable monthly runoff series. TMPA 3B42V7 and CMORPH-CRT present good performance in capturing precipitation and streamflow extremes, although they still exhibit non-ignorable deviation and occurrence time inconsistency problems compared with gauge-based results. Caution should be observed when using the current TMPA, CMORPH, and PERSIANN products for monitoring and predicting extreme precipitation and flood at such medium-sized basin. This work will be valuable for the utilization of SPPs in extreme precipitation monitoring, streamflow forecasting, and water resource management in other regions with similar climate and topography characteristics.

The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments.

The spatial distribution of precipitation is an important aspect of water-related research. The use of different interpolation schemes in the same catchment may cause large differences and deviations from the actual spatial distribution of rainfall. Our study analyzes different methods of spatial rainfall interpolation at annual, daily, and hourly time scales to provide a comprehensive evaluation. An improved regression-based scheme is proposed using principal component regression with residual correction (PCRR) and is compared with inverse distance weighting (IDW) and multiple linear regression (MLR) interpolation methods. In this study, the meso-scale catchment of the Fuhe River in southeastern China was selected as a typical region. Furthermore, a hydrological model HEC-HMS was used to calculate streamflow and to evaluate the impact of rainfall interpolation methods on the results of the hydrological model. Results show that the PCRR method performed better than the other methods tested in the study and can effectively eliminate the interpolation anomalies caused by terrain differences between observation points and surrounding areas. Simulated streamflow showed different characteristics based on the mean, maximum, minimum, and peak flows. The results simulated by PCRR exhibited the lowest streamflow error and highest correlation with measured values at the daily time scale. The application of the PCRR method is found to be promising because it considers multicollinearity among variables.

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG.

In this study, efforts are focused on the comparison and validation of standard Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products—Version-7 3B42RT estimates before and after assimilation by using a Kalman filter with independent rain gauge networks located within the Jinghe basin of China. Generally, the direct comparison of TMPA precipitation estimates to 200 collocated rain gauges from 2006 to 2008 demonstrate that the spatial and temporal rainfall characteristics over the region are well captured by the assimilation estimates. Especially, results also show that using Kalman filter to assimilate TRMM-based multi-satellite real-time precipitation estimates tends to perform well over regions, where gauge network is rather sparse. Last, this study highlights that accurate detection and estimation of precipitation in the summer season by Kalman filter, particularly for nonlinear convective precipitation events, is still a challenging task for the future development of assimilation technique for improving the satellite-based precipitation accuracy.