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Nonstationarity in hydrology has aroused great concern among people, and various influence factors have been proved to challenge the assumptions of the traditional hydrological analysis. For the selection of a reasonable flood frequency model and rationality of hydraulic engineering design, it is of great significance to study the variation characteristics of rainfall and floods and their relationship under the nonstationary condition. Taking the Yue River Basin, a mountain basin in northwest China, as an example, the nonparametric Mann-Kendall test and moving t -test methods were used to test the temporal trends and mutation of hydrological variables during the recent 53 years. The recession curve was derived from the historical flood series by the genetic algorithm, and the flood hydrographs were separated by the constant-slope base flow separation method. The results showed that the annual rainfall and runoff in the Yue River Basin during the recent 53 years exhibited insignificant increasing and decreasing trends, respectively. The abrupt change point of annual runoff series occurred in 1985. The average proportion of underground runoff changed from 21% before 1985 to 17% after 1985, and the relationship between rainfall and floods became weakened after 1985, indicating that the stationarity of flood was challenged. [ABSTRACT FROM AUTHOR]

Quantifying the effects of climate change and catchment structure changes (like anthropic activities) on runoff variations effectively and accurately is always a challenge for hydrological community. In this study, three widely used methods: climate elasticity method, Budyko curve decomposition approach, and hydrologic simulation method, were applied in the Tao River basin, a typical first-order tributary of the Yellow River basin. The results indicated that the annual runoff of the Tao River basin dropped significantly, especially after 1986, with the changing rate of −13.66 mm/10a during the research period (1956–2014). According to the attribution results, in the moderate influenced period (1969–1986), the climate change was less influential than watershed structure changes to runoff variations, while after 1986, about 45% of the total decline in mean annual runoff were caused by the decreasing P and the significant rising air temperature, which enhances the watershed evaporation, and about 55% was probably attributed to watershed structure changes. There were systematic deviations between results from two Budyko-based conceptual approaches and hydrologic simulation method. The Budyko-based decomposition method tends to attribute more runoff variations to changes in watershed structure, while the hydrologic modeling approach tends to emphasize the influence of climate change on runoff changes at catchment scale. The difference in effect quantification is attributed to the errors in the mean annual runoff naturalization during the postchange period. The impacts of watershed structure changes induced by climate change (like vegetation condition changes) on runoff variations may fall into the human impact category in attribution calculation. [ABSTRACT FROM AUTHOR]

The source regions of the Yangtze River (SRYZ) and the Yellow River (SRYR) are sensitive areas of global climate change. Hence, determining the variation characteristics of the runoff and the main influencing factors in this region would be of great significance. In this study, different methods were used to quantify the contributions of climate change and other environmental factors to the runoff variation in the two regions, and the similarities and differences in the driving mechanisms of runoff change in the two regions were explored further. First, the change characteristics of precipitation, potential evapotranspiration, and runoff were analyzed through the observational data of the basin. Then, considering the non-linearity and non-stationarity of the runoff series, a heuristic segmentation algorithm method was used to divide the entire study period into natural and impacted periods. Finally, the effects of climate change and other environmental factors on runoff variation in two regions were evaluated comprehensively using three methods, including the improved double mass curve (IDMC), the slope change ratio of cumulative quantity (SCRCQ), and the Budyko-based elasticity (BBE). Results indicated that the annual precipitation and potential evapotranspiration increased during the study period in the two regions. However, the runoff increased in the SRYZ and decreased in the SRYR. The intra-annual distribution of the runoff in the SRYZ was unimodal during the natural period and bimodal in the SRYR. The mutation test indicated that the change points of annual runoff series in the SRYZ and SRYR occurred in 2004 and 1989, respectively. The attribution analysis methods yielded similar results that climate change had the greatest effect on the runoff variation in the SRYZ, with a contribution of 59.6%~104.6%, and precipitation contributed 65.3%~109.6% of the increase in runoff. In contrast, the runoff variation in the SRYR was mainly controlled by other environmental factors such as permafrost degradation, land desertification, and human water consumption, which contributed 83.7%~96.5% of the decrease in the runoff. The results are meaningful for improving the efficiency of water resources utilization in the SRYZ and SRYR. [ABSTRACT FROM AUTHOR]

In order to systematically analyze the impacts of climate change and human activities on runoff, this paper takes the Zhanghe River Basin, which is greatly affected by human activities, as the research object, constructs an attribution analysis model of runoff changes based on historical data and the SWAT (Soil and Water Assessment Tool) model. The results show that the runoff of the watershed has significantly decreased in the past 60 years, in which the contribution rate of climate change is 36.2% and that of human activities is 63.8%. Among the climate change factors, precipitation is the main contributing factor and canal diversion is the main contributing factor among human activities. In addition, with the decrease in precipitation during the flood season and the increase in the crop planting area in the catchment, the distribution of canal water diversion has also changed, and the water consumption of summer crops has gradually become the main factor affecting canal water diversion. [ABSTRACT FROM AUTHOR]

This paper looks at regional water security in eastern China in the context of global climate change. The response of runoff to climate change in the Qinhuai River Basin, a typical river in eastern China, was quantitatively investigated by using the Soil and Water Assessment Tool (SWAT) model and the ensemble projection of multiple general circulation models (GCMs) under three different shared socioeconomic pathways (SSPs) emission scenarios. The results show that the calibrated SWAT model is applicable to the Qinhuai River Basin and can accurately characterize the runoff process at daily and monthly scales with the Nash–Sutcliffe efficiency coefficients (NSE), correlation coefficients (R), and the Kling–Gupta efficiency (KGE) in calibration and validation periods being above 0.75 and relative errors (RE) are ±3.5%. In comparison to the baseline of 1980–2015, the mean annual precipitation in the future period (2025–2060) under the three emission scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 will probably increase by 5.64%, 2.60%, and 6.68% respectively. Correspondingly, the multiple-year average of daily maximum and minimum air temperatures are projected to rise by 1.6–2.1 °C and 1.4–2.0 °C, respectively, in 2025–2060. As a result of climate change, the average annual runoff will increase by 16.24%, 8.84%, and 17.96%, respectively, in the period of 2025–2060 under the three SSPs scenarios. The increase in runoff in the future will provide sufficient water supply to support socioeconomic development. However, increases in both rainfall and runoff also imply an increased risk of flooding due to climate change. Therefore, the impact of climate change on flooding in the Qinhuai River Basin should be fully considered in the planning of flood control and the basin's development. [ABSTRACT FROM AUTHOR]

The long-term overexploitation of groundwater has caused sharp decreases in groundwater table depth and water storage in the agricultural areas of the North China Plain, which has led to obvious changes in the runoff process of the hydrological cycle, affecting the mechanism of runoff generation. Evaluating the impact of groundwater overdraft on runoff generation using hydrological models is the focus of the current work. Herein, a hydrological modeling framework is proposed based on the Variable Infiltration Capacity (VIC) model. The optimal parameters of the VIC model were determined by the synergetic calibration method, combining runoff, evaporation, and water storage levels. Meanwhile, a sliding calibration scheme was employed to explore the implied relationships among runoff coefficient, groundwater exploitation, and model parameters, particularly for the thickness of the second soil layer (i.e., parameter d2), both for the whole period and the sliding window periods. Overall, the VIC model showed good applicability in the southern Haihe river plain, as demonstrated by the low absolute value of the relative error (RE) between the simulated and observed data for runoff and evaporation, with all REs < 8%, as well as large correlation coefficients (CC, all > 0.8). In addition, the CCs between the simulated and the observed data for water storage were all above 0.7. The calibrated optimal parameter d2 increased as the sliding window period increased, and the average d2 gradually increased from 0.372 m to 0.415 m, for which we also found high correlations with both the groundwater table and water storage levels. Additionally, increases in the parameter d2 led to decreases in the runoff coefficient. From 2003 to 2016, the parameter d2 increased from 0.36 m to 0.42 m, and the runoff coefficient decreased by about 0.02. [ABSTRACT FROM AUTHOR]

Climate change and local human activities are regarded as the two main factors influencing runoff. Using observed runoff, there is a statistically significant decreasing trend for annual and monthly runoff detected by the Mann-Kendall's test, in the upper reaches of Luanhe River basin (URLRB), 1954–2000. With the break point analysis, the whole time series are divided into two sub periods: "natural period (1954–1970) and "impact period" (1971–2000). "Natural runoff" from 1954 to 2000, is reconstructed by the variable infiltration capacity (VIC) model, in which the model parameters are calibrated in "natural period" representing the natural watershed characteristics without the impact of local human activities. By comparing the difference between observed runoff and "natural runoff" in "impact period", the contributions of climate change and local human activities are quantitatively separated. The results indicate that climate change and local human activities account for 49% and 51%, respectively, on the annual runoff decrease in the URLRB. That means the effects of climate change on runoff are roughly the same as the effects of local human activities. Climate change results in decrease in monthly runoff; and local human activities mainly affect flood season runoff. The results could be a reference for water resources projection and management in the URLRB and other catchments in northern China. [ABSTRACT FROM AUTHOR]

Climate variability and human activities are two main contributing attributions for runoff changes in the Yellow River, China. In the loess hilly-gully regions of the middle Yellow River, water shortage has been a serious problem, and this results in large-scale constructions of soil and water conservation (SWC) measures in the past decades in order to retain water for agricultural irrigation and industrial production. This disturbed the natural runoff characteristics. In this paper, we focused on a typical loess hilly-gully region (Wudinghe and Luhe River basins) and investigated the effects of SWC measures and climate variability on runoff during the period of 1961-2013, while the SWC measures were the main representative of human activities in this region. The nonparametric Mann-Kendall test was used to analyze the changes of annual precipitation, air temperature, potential evapotranspiration (PET), and runoff. The analysis revealed the decrease in precipitation, significant rise in temperature, and remarkable runoff reduction with a rate of more than 0.4 mm per year. It was found that runoff capacity in this region also decreased. Using the change point detection methods, the abrupt change point of annual runoff series was found at 1970, and thus, the study period was divided into the baseline period (1961-1970) and changed period (1971-2013). A conceptual framework based on four statistical runoff methods was used for attribution analysis of runoff decline in the Wudinghe and Luhe River basins (−37.3 and −56.4%, respectively). Results showed that runoff reduction can be explained by 85.2-90.3% (83.3-85.7%) with the SWC measures in the Wudinghe (Luhe) River basin while the remaining proportions were caused by climate variability. The findings suggested that the large-scale SWC measures demonstrated a dominant influence on runoff decline, and the change of precipitation extreme was also a promoting factor of the upward trending of SWC measures' contribution to runoff decline. This study enhances our understanding of runoff changes caused by SWC measures and climate variability in the typical semiarid region of Loess Plateau, China. [ABSTRACT FROM AUTHOR]

In the last 40 years, the Beijing section of the Yongdinghe River evolved from a perennial and wandering river reach to a channellized and fragmentation dry river channel. Such changes threatened the local safety, eco-environment health, and economy development. In order to clarify the channel change patterns and its mechanisms, the spatial and temporal changes of the river channel and the causes are analysed. The results show that the river channel was greatly reshaped by the intensive land using, with a total of 66.95 km2 degraded area including the overflow land, water area, and agricultural land. The overflow land was mainly changed to dry riverbed, agricultural land, water conservancy construction land, planted woodland, and bare land. The water area was mainly replaced by dry riverbed, channellized watercourse, water conservancy construction land, and agricultural land. Agricultural land was mainly changed to residential land, dry riverbed, and bare land. The abrupt river runoff and sediment change after construction of the Guanting Reservoir (the largest reservoir in the Yongdinghe River) indicates a potential trigger for the river channel changes. The river channel change processes are finally concluded: before 1980, the large number of the water conservancy projects' construction including the reservoirs, check dams, and sluice in the Guanting Gorge (upstream of the study river reach) changed the river connectivity and the flow continuity of the upstream river channel. Together with the water use, the soil and water conservation activities, and the variable precipitation, almost all the runoff and sediment to the study river reach were intercepted; then the perennial dry up river and the increased land requirement impelled people to occupy the downstream river space. [ABSTRACT FROM AUTHOR]

Abstract: Recently, runoff in many river basins in China has been decreasing. Therefore, the role that climate change and human activities are playing in this decrease is currently of interest. In this study, we evaluated an assessment method that was designed to quantitatively separate the effects of climate change and human activities on runoff in river basins. Specifically, we calibrated the SIMHYD rainfall runoff model using naturally recorded hydro-meteorologic data pertaining to the Sanchuanhe River basin and then determined the effects of climate change and human activities on runoff by comparing the estimated natural runoff that occurred during the period in which humans disturbed the basin to the runoff that occurred during the period prior to disturbance by humans. The results of this study revealed that the SIMHYD rainfall runoff model performs well for estimating monthly discharge. In addition, we found that absolute runoff reductions have increased in response to human activities and climate change, with average reductions of 70.1% and 29.9% in total runoff being caused by human activities and climate change, respectively. Taken together, the results of this study indicate that human activities are the primary cause of runoff reduction in the Sanchuanhe River basin. [ABSTRACT FROM AUTHOR]

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture. [ABSTRACT FROM AUTHOR]

In this study, 6 widely used precipitation products APHRODITE, CPC_UNI_PRCP, CN05.1, PERSIANN-CDR, Princeton Global Forcing (PGF), and TRMM 3B42 V7 (TMPA), were evaluated against gauge observations (CMA data) from 1998 to 2014, and applied to streamflow simulation over the Upper Yellow River basin (UYRB), using 4 hydrological models (DWBM, RCCC-WBM, GR4J, and VIC). The relative membership degree (u), as the comprehensive evaluation index in the hydrological evaluation, was calculated by the optimum fuzzy model. The results showed that the spatial pattern of precipitation from the CMA dataset and the other 6 precipitation products were very consistent with each other. The satellite-derived rainfall products (SDFE), like PSERSIANN-CDR and TMPA, depicted considerably finer and more detailed spatial heterogeneity. The SDFE and reanalysis (RA) products could estimate the monthly precipitation very well at both gauge and basin-average scales. The runoff simulation results indicated that the APHRODITE and TMPA were superior to the other 4 precipitation datasets, obtaining much higher scores, with average u values of 0.88 and 0.77. The precipitation estimation products tended to show better performance in streamflow simulation at the downstream hydrometric stations. In terms of performance of hydrological models, the RCCC–WBM model showed the best potential for monthly streamflow simulation, followed by the DWBM. It indicated that the monthly models were more flexible than daily conceptual or distributed models in hydrological evaluation of SDFE or RA products, and that the difference in precipitation estimates from various precipitation datasets were more influential in the GR4J and VIC models. [ABSTRACT FROM AUTHOR]

Concentration–discharge (C-Q) relationships are a convenient and increasingly popular tool for interpreting the episodic hydrochemical response to the varying discharge in small basins, providing insights into solute transport and streamflow generation. While most studies are focused on total runoff, this study quantified C-Q relationships in four runoff components during precipitation events at the Hydrohill experimental catchment in Chuzhou, China. This unique artificial catchment is carefully engineered, allowing observations of the interacting runoff components that collectively determine total flow issuing from the catchment. The four runoff components, or flow paths, include surface runoff (SR), shallow interflow at 0–30 cm depth (SSR30), deeper interflow at 30–60 cm depth (SSR60), and groundwater flow at 60–100 cm depth (SSR100). Water samples were collected during three consecutive precipitation events to study how the concentrations of primary solutes vary with flow. Analysis of C-Q relationships reveals that concentrations of Na+, Ca2+, Mg2+, SO42−, and HCO3− in the four runoff components had a negative relationship with discharge, while the concentration of K+ and Cl− were negatively correlated with discharge in SR and SSR30 but positively correlated in SSR60 and SSR100. Further insights were gained from principal component analysis. Three eigenvectors explained 92% of the variability in hydrochemistry in surface runoff, while two eigenvectors explained most of the variability in the hydrochemistry of subsurface flows observed at various depths in the soil profile (73% for SSR30, 79% for SSR60, and 76% for SSR100). PC1 (the first Principal Component) can be interpreted as a salinity factor, deriving from carbonate minerals such as dolomites and limestone minerals. Results indicated that leaching and dilution processes, water–soil interaction, and macropore flows in soils are the primary factors controlling the C-Q relationships. Our work sheds light on the coupled processes and streamflow generation mechanisms that control water quality at the catchment scale. [ABSTRACT FROM AUTHOR]

Attributing runoff change to different drivers is vital in order to better understand how and why runoff varies, and to further support decision makers on water resources planning and management. Most previous works attributed runoff change in the arid or semi-arid areas to climate variability and human activity on an annual scale. However, attribution results may differ greatly according to different climatic zones, decades, temporal scales, and different contributors. This study aims to quantitatively attribute runoff change in a humid subtropical basin (the Qingliu River basin, East China) to climate variability, land-use change, and human activity on multiple scales over different periods by using the Soil and Water Assessment Tool (SWAT) model. The results show that runoff increased during 1960–2012 with an abrupt change occurring in 1984. Annual runoff in the post-change period (1985–2012) increased by 16.05% (38.05 mm) relative to the pre-change period (1960–1984), most of which occurred in the winter and early spring (March). On the annual scale, climate variability, human activity, and land-use change (mainly for forest cover decrease) contributed 95.36%, 4.64%, and 12.23% to runoff increase during 1985–2012, respectively. On the seasonal scale, human activity dominated runoff change (accounting for 72.11%) in the dry season during 1985–2012, while climate variability contributed the most to runoff change in the wet season. On the monthly scale, human activity was the dominant contributor to runoff variation in all of the months except for January, May, July, and August during 1985–2012. Impacts of climate variability and human activity on runoff during 2001–2012 both became stronger than those during 1985–2000, but counteracted each other. The findings should help understandings of runoff behavior in the Qingliu River and provide scientific support for local water resources management. [ABSTRACT FROM AUTHOR]

This study applied a GR4J model in the Xiangjiang and Qujiang River basins for rainfall-runoff simulation. Four recurrent neural networks (RNNs)—the Elman recurrent neural network (ERNN), echo state network (ESN), nonlinear autoregressive exogenous inputs neural network (NARX), and long short-term memory (LSTM) network—were applied in predicting discharges. The performances of models were compared and assessed, and the best two RNNs were selected and integrated with the lumped hydrological model GR4J to forecast the discharges; meanwhile, uncertainties of the simulated discharges were estimated. The generalized likelihood uncertainty estimation method was applied to quantify the uncertainties. The results show that the LSTM and NARX better captured the time-series dynamics than the other RNNs. The hybrid models improved the prediction of high, median, and low flows, particularly in reducing the bias of underestimation of high flows in the Xiangjiang River basin. The hybrid models reduced the uncertainty intervals by more than 50% for median and low flows, and increased the cover ratios for observations. The integration of a hydrological model with a recurrent neural network considering long-term dependencies is recommended in discharge forecasting. [ABSTRACT FROM AUTHOR]

Abstract In this study, we introduce a data stream method for dynamic runoff simulation, which allows capturing the evolving relationship between runoff and its impact factors (e.g., temperature, rainfall). The basic idea is to view continuously arriving data of runoff and its impact factors as a data stream, and dynamically learn its relationship. To validate the effectiveness of the proposed method, we compare its performance with that of three data driven models (ANN, SVR, Random Forest) and six representative hydrological models (SWAT, AWBM, SimHyd, SMAR, Sacramento, and Tank) in simulating monthly runoff. The proposed method performs well with the best NSE of 0.88, being superior to comparable models. Furthermore, the data stream model also shows its advantage in the flexibility of combing various impact factors of runoff into the model. The findings demonstrate that the data stream method provides a promising way to dynamically simulate runoff in a changing environment. Graphical abstract Image Highlights • An instance-based data stream method is introduced for dynamic runoff simulation. • The proposed method outperforms comparable data-driven and hydrological models. • It provides a useful tool for runoff simulation in a changing environment. • It is flexible to integrate various impact factors of runoff into runoff simulation. [ABSTRACT FROM AUTHOR]

• An integrated framework was proposed for analyzing life-cycle patterns of hydrological droughts. • Humid basins had faster hydrological drought onset and recovery patterns than semi-arid basins in China. • Hydrological droughts in different climatic basins require locally tailored early warning and tracking. The hydrological drought life-cycle begins at the onset and includes intensification and recovery stages. Previous studies have mainly considered each stage separately, so a comprehensive life-cycle pattern analysis is lacking. Moreover, differences in hydrological drought life-cycle patterns across different climatic basins still need in-depth research. This study proposed an integrated framework for analyzing the life-cycle patterns of hydrological droughts and used it to compare semi-arid and humid basins of China. The empirical analysis involved long-term (≥30 years) monthly runoff and precipitation data from six basins—three in semi-arid and three in humid zones of China. We identified the onset thresholds of hydrological drought events from the response relationship of hydrological drought to meteorological drought, considering both duration and severity thresholds. Then, using the "time–speed" process relationship, the intensification and recovery stages of hydrological drought events were quantified. Finally, the life-cycle patterns of hydrological drought in humid and semi-arid basins of China were compared. The results indicated that: (1) the proposed integrated framework, utilizing life-cycle analysis techniques to investigate the onset thresholds, intensification, and recovery of hydrological drought, provided valuable insights into their differences across different climatic basins; (2) compared to the semi-arid basins of northern China, the humid basins exhibited lower onset thresholds, slower intensification, and quicker recovery. This indicated that, in the humid basins of southern China, the onset thresholds for hydrological drought are reached rapidly after meteorological drought, followed by more gradual intensification and faster recovery stages. This underscored the importance of recognizing the distinctive life-cycle patterns of hydrological drought in different climatic zones so that tailored strategies for effective drought prevention and mitigation can be devised. [ABSTRACT FROM AUTHOR]

• An enhancement strategy is proposed to calibrate snow parameters and capture snowmelt runoff. • The improvement in runoff simulation accuracy primarily stems from the precise capture of snowmelt runoff. • The joint reconstruction of parameter schemes and physical processes enhances the modeling capability. Hydrological models stand as a pivotal instrument for runoff simulation, while encountering notable uncertainties in output due to intricate terrain conditions and limited ground-based observations, especially in high-elevation basins. Leveraging satellite-based images presents a promising avenue for deciphering the hydrological model's state variables. In pursuit of refining runoff simulation, this study developed a two-step enhancement strategy, which involved (1) calibrating snow-related parameters in the hydrological model using remotely sensed snow cover area (SCA) and snow water equivalent (SWE) and (2) capturing snowmelt runoff through the hydrological model and image-based products. Coupled with the Variable Infiltration Capacity (VIC) model, we adopted this strategy as a case study in the Dadu River Basin, China. The results indicated (1) the daily Nash-Sutcliffe Efficiency (NSE) of runoff simulation reached 0.84 by the enhancement strategy, markedly surpassing the strategy reliant on soil parameters with a single calibrated reference (daily NSE of 0.66), and exhibited comparability to the strategy incorporating snow parameters but calibrated solely based on observed discharge (daily NSE of 0.83). (2) the enhancement strategy demonstrated hydrological consistency with snowmelt information derived from imagery. Specifically, multi-year average contributions of model's and image-based snowmelt calculations were 31.8% and 33.4%, respectively. Additionally, simulated dates of snow accumulation and ablation appeared an average deviation of approximately one week compared to the imagery results. This study elucidates a potential methodological approach for offering valuable insights into hydrological processes within analogous high-elevation basins globally. [ABSTRACT FROM AUTHOR]

• Reveal spatial and upstream–downstream patterns of runoff change drivers in eastern Tibetan Plateau for the first time. • Climate change dominant runoff trend and change in eastern Qinghai-Tibetan Plateau. • Quantitatively explained climatic and underlying surface conditions change contributed to runoff change. • Both hydrological model and Budyko framework were employed for robust results. Quantitatively identifying the impact of climatic and underlying surface condition changes on runoff is crucial for the efficient utilization of water resources and understanding hydroclimatic variability processes. This study aims to employ both Grid-RCCC-WBM model and Fu's equation based on Budyko hypothesis to quantitatively analyze the spatial patterns of runoff changes, driving forces, and upstream–downstream relationships in ten typical basins across the eastern Qinghai-Tibet Plateau (QTP) for the first time. Breaks for Additive Season and Trend method was used to detect breakpoints in runoff series and both hydrological model and Budyko equation categorized driving forces of runoff change into change in climatic (including precipitation and potential evaporation) and underlying surface conditions. The results indicated (i) significant abrupt changes in the runoff time series around 1998, with runoff increasing in all basins except for the source region of the Yellow River. (ii) significant upstream–downstream differences in the trend and magnitude of runoff changes between breakpoints in the Yangtze and Lancang Rivers over the past 20 years, and (iii) significant runoff response to climate variability after the breakpoints in the source region of the Yangtze and Yellow river. Our findings revealed that, contrary to the backdrop of decreasing precipitation, the upstream basins maintained increasing runoff relying on permafrost and glacier meltwater, while the downstream basins exhibited decreasing trend. The differences in runoff changes after the detected breakpoints were dominated by changes in underlying surface conditions, with the highest contribution rates observed in the Dajin (494 %), Lanzhou (398%), and Tangnaihai (197%) basins. This study involved both spatial pattern and upstream–downstream relationship of runoff change that can be widely applied to other large-scale regions and especially holds important implications for the scientific and rational utilization of water resources in the QTP. [ABSTRACT FROM AUTHOR]

• Understanding future change with the aspects of runoff and hydroclimatic conditions. • Source region of Yangtze River will be warmer and wetter in the future. • Runoff will increase by around 15% with highly spatial heterogeneity. Exploring the future hydroclimatic conditions of source region of Yangtze River (SRYaR), an alpine affected by climate change significantly, is essential for basin water resources management and development ss global climate change intensifies and the process of climate warming and humidification in Northwest China. This study proposed a practical framework for assessing water resource response to the context of climate changes in alpine catchments from the respective of both runoff and hydroclimatic conditions. Utilizing Grid-RCCC-WBM driven by corrected climatic forcing from the global climate models, this study estimate the prospective overall warmer and wetter pattern in the source region of Yangtze River. The key results indicated that: (1) Under all future scenarios, both temperature and precipitation within the catchment exhibit a significant upward trend. Projections from multi-model ensembles (MME) suggest that during the mid-term period (2041–2060, MT), temperatures are expected to rise by [0.74 °C, 3.08 °C] compared to the baseline period (1995–2014), with precipitation changes ranging from [4.8%, 21.4%]. (2) Future runoff within the catchment exhibits a consistent increase, with a linear trend rate of 1.1 mm/decade. runoff changes in MT compared to the baseline period vary from [−5.1%, 33.7%]. Runoff decreases in the northern part of the catchment, while notable increases occur in the southeastern and western regions. (3) In the future, the ratio of catchment evaporation capacity to precipitation decreases in comparison to the baseline period with an augmentation in soil moisture, enhancing its capacity for water retention and reducing the conversion of precipitation to evaporation, resulting a wetting trend of the catchment. (4) The future snowpack in the catchment continues to decrease, with a significant reduction in both the proportion of snowfall relative to total precipitation and the proportion of snowmelt runoff relative to total runoff, the risk of water resources crisis in the watershed is escalating. [ABSTRACT FROM AUTHOR]

Most existing hydrologic models and machine learning models failed to perform well on runoff prediction in data scarce regions. As an alternative to this, the Long Short-Term Memory (LSTM)-prototypical network fusion model based on few-shot learning is proposed, where the strong learning ability of LSTM and the low data dependence of prototypical network are combined. The proposed model was calibrated and implemented on monthly runoff prediction in the Lancang River basin (LRB) and the source region of the Yellow River basin (SRYRB). Compared with eight state-of-the-art data driven models (LSTM, SVR, ANN, ARMA, Random Forest, SimpleRNN, GRU, and BiLSTM), the proposed model outperformed especially when less training data were used. Results in the LRB indicate NSE of the proposed model achieved 0.802 and 0.832 when the proportion of training data (K) was 20% and 45%, improved by 0.527 and 0.222 relative to the mean NSE of other models, respectively. In the SRYRB, NSE reached 0.830 and improved by 0.354 when K was 40%. The findings imply that the new few-shot learning model provides a promising tool for runoff prediction in the two investigated basins and possibly other data-scarce basins where precipitation dominates runoff change, which will benefit regional water resources management and water security. • LSTM-prototypical network fusion model is proposed for monthly runoff prediction. • NSE ranges from 0.802 to 0.832 when training data proportion increases from 20% to 45% in LRB. • NSE is improved by 0.212–0.527 relative to the mean of 8 comparative data driven models. • The model has lower data dependance, suitable for data scarce regions where rainfall dominates runoff change. [ABSTRACT FROM AUTHOR]

• Synthetic samples are added to LSTM by previously undiscussed physical mechanisms. • Using extreme events to improve flood peaks and avoid negative streamflow. • Proposed PHY-LSTM outperforms conventional one both in local and regional models. • Physics-based monotonic relationships are upheld in the PHY-LSTM. Deep learning methods have recently shown a broad application prospect in rainfall-runoff modeling. However, the lack of physical mechanism becomes a major limitation in using machine learning methods that rely on the available labeled observations. To address this issue, the study proposes that synthetic samples are added to train the deep learning network by using three previously undiscussed physical mechanisms as follows: (1) extreme heavy rainfalls when the soil water is saturated, (2) long-duration rainless events when soil water is exhausted, and (3) the monotonic relationship between rainfall and runoff. A physics-guided Long Short-Term Memory (LSTM) network, shortly named PHY-LSTM, is then formulated. PHY-LSTM network is trained on 531 basins of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset, indicating that the performance is significantly improved compared to conventional LSTM. Specifically, the mean Nash-Sutcliffe Efficiency (NSE) improves from 0.52 to 0.61 from the daily simulations during the testing period in local models. It is demonstrated that synthetic samples can effectively improve the simulation of flood peaks and reduce the number of negative streamflow, and strong monotonicity is still maintained even if a slight disturbance is involved in the training dataset. The proposed PHY-LSTM shows that physical mechanisms are very useful to improve efficiencies of the data-driven rainfall-runoff model. [ABSTRACT FROM AUTHOR]

The contributions of different driving factors to runoff change have been extensively quantified in previous studies. However, how the contributions change over time is commonly unknown. In this study, the authors propose a framework for inter-annual attribution analysis of runoff change by using a Soil and Water Assessment Tool (SWAT) model with integrated continuous land use. Following the framework, contributions of driving factors (i.e., climate variability, land use change and other human activity factors) to runoff variation in each year during 1989-2012 were quantified for the Qingliu River catchment, China. The results indicate that runoff increased (p>0.05) during 1960-2012 with an abrupt change occurring in 1984. Land use changes year by year with two largest transitions occurring from 1995-1996 and from 2001 to 2002. The contributions of different driving factors change over time. Climate variability dominates runoff change in most years over 1989-2012 except for 2005 and 2007, during which human activity is the main contributor. Land use change causes runoff increase and exhibits relatively small contribution to runoff change. The other human activity factors show stronger impact on runoff after 2004. The results highlight the importance of analysing attribution of runoff changes in a dynamic manner. The findings should benefit decision-makers on taking and adjusting adaptive practices and strategies for water resources management in a changing environment. [ABSTRACT FROM AUTHOR]