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Trend analysis of climate variables such as streamflow, precipitation, and temperature provides useful information for understanding the hydrological changes associated with climate change. In this study, a nonparametric Mann-Kendall test was employed to evaluate annual, seasonal, and monthly trends of precipitation and streamflow for the Neka basin in the north of Iran over a 44-year period (1972 to 2015). In addition, the Inverse Distance Weight (IDW) method was used for annual seasonal, monthly, and daily precipitation trends in order to investigate the spatial correlation between precipitation and streamflow trends in the study area. Results showed a downward trend in annual and winter precipitation (Z < −1.96) and an upward trend in annual maximum daily precipitation. Annual and monthly mean flows for most of the months in the Neka basin decreased by 14% significantly, but the annual maximum daily flow increased by 118%. Results for the trend analysis of streamflow and climatic variables showed that there are statistically significant relationships between precipitation and streamflow (p value < 0.05). Correlation coefficients for Kendall, Spearman’s rank and linear regression are 0.43, 0.61, and 0.67, respectively. The spatial presentation of the detected precipitation and streamflow trends showed a downward trend for the mean annual precipitation observed in the upstream part of the study area which is consistent with the streamflow trend. Also, there is a good correlation between monthly and seasonal precipitation and streamflow for all sub-basins (Sefidchah, Gelvard, Abelu). In general, from a hydro-climatic point of view, the results showed that the study area is moving towards a situation with more severe drought events.

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

Global warming has significantly altered the hydrological cycle and increased the frequency and intensity of extreme hydrological events. Therefore, quantifying trends in the variability and extremes of streamflow is crucial for deepening our understanding of changes in the hydrological cycle. However, comprehensive nationwide studies of extreme streamflow trends across China remain scarce. In this study, we explored trends in the variability and extremes of seasonal streamflow from 1961 to 2018 over China using gridded monthly streamflow data. The results revealed a significant increase in the streamflow variability and extremes in approximately half of the country (35.9%–52.9% of the grid points), with particularly strong increases in the Northwest River Basin (over 52% of the grid points). Additionally, we found a strong positive correlation between streamflow variability and extremely high streamflows across China (r > 0.41, p < 0.05). Furthermore, at the seasonal scale, the largest increases in trends of streamflow extremes were observed in summer (mean slope = 0.094 mm mo−1 yr−1), whereas autumn exhibits milder decreases (mean slope = −0.0009 mm mo−1 yr−1) for a large number (>25.8%) of grid points. These findings address a critical knowledge gap by providing a comprehensive nationwide assessment of seasonal streamflow variability and extreme trends in China and offering new perspectives for the development of sustainable strategies for watershed and water resource management.

Changes in climate are driving an intensification of the hydrologic cycle and leading to alterations of natural streamflow regimes. Human disturbances such as dams, land-cover change, and water diversions are thought to obscure climate signals in hydrologic systems. As a result, most studies of changing hydroclimatic conditions are limited to areas with natural streamflow. Here, we compare trends in observed streamflow from natural and human-modified watersheds in the United States and Canada for the 1981–2015 water years to evaluate whether comparable responses to climate change are present in both systems. We find that patterns and magnitudes of trends in median daily streamflow, daily streamflow variability, and daily extremes in human-modified watersheds are similar to those from nearby natural watersheds. Streamflow in both systems show negative trends throughout the southern and western United States and positive trends throughout the northeastern United States, the northern Great Plains, and southern prairies of Canada. The trends in both natural and human-modified watersheds are linked to local trends in precipitation and reference evapotranspiration, demonstrating that water management and land-cover change have not substantially altered the effects of climate change on human-modified watersheds compared with nearby natural watersheds.

Recently, there has been an increase in the number of natural disasters caused by extreme events, which are enhanced by climate change and anthropogenic interference. Therefore, understanding the hydrological behavior in areas with high vulnerability to floods and water scarcity is essential to capably manage water resources. In this context the study aimed to analyze the streamflow trend in the Piranga river basin, as well as to evaluate the determining factors in the streamflow variation regime in the watershed. For this reason, historical series of seven stream gauging stations were analyzed, adopting the base period of studies from 1975 to 2018. In order to identify the trend in maximum, average and minimum streamflow data, the Mann–Kendall, Pettitt and Spearman correlation tests were used. To understand the possible causes of streamflow trends, precipitation data, land use and occupation, and water demand were analyzed. It was observed that all stations showed some significant trend of streamflow reduction, especially in the dry season, having reduced from 10 to 35% comparing to the historical series average. On an annual scale, significant trends of reduction in average and minimum streamflow were detected. The change in streamflow behavior was not related to the distribution of precipitation over the years in the watershed. The cause of streamflow reduction may be related to the increase in water demand and with changes in land use and occupation, mainly characterized by the increase in planted forest, forest formation and urban areas and the reduction of areas destined to agriculture. The methodology proposed in this study can be adapted to other watersheds in the world, aiming to assist in the planning of water resources.

Climate change has remarkable global impacts on hydrological systems, prompting the need to attribute past changes for better future risk estimation and adaptation planning. This study evaluates the differences in simulated discharge from hydrological models when driven by a set of factual and counterfactual climate data, obtained using the Inter-Sectoral Impact Model Intercomparison Project's recommended data and detrending method, for quantification of climate change impact attribution. The results reveal that climate change has substantially amplified streamflow trends in the Upper Yangtze and Upper Yellow basins from 1961 to 2019, aligning with precipitation patterns. Notably, decreasing trends of river flows under counterfactual climate have been reversed, resulting in significant increases. Climate change contributes to 13%, 15% and 8% increases of long-term mean annual discharge, Q10, and Q90 in the Upper Yangtze at Pingshan, and 11%, 10%, 10% in the Upper Yellow at Tangnaihai. The impact are more pronounced at headwater stations, particularly in the Upper Yangtze, where they are twice as high as at the Pingshan outlet. Climate change has a greater impact on Q10 than on Q90 in the Upper Yangtze, while the difference is smaller in the Upper Yellow. The impact of climate change on these flows has accelerated in the recent 30 years compared to the previous 29 years. The attribution of detected differences to climate change is more obvious for the Upper Yangtze than for the Upper Yellow.

In this study, the trend of monthly mean and annual streamflow values of 16 streamflow gauge stations in the Tigris basin, which holds 13% water potential for Turkey, is determined. The monotonic trends are calculated using non-parametric Mann−Kendall (MK) test. To remove serial correlation from time series, a modified ‘pre-whitening’ method is used. The trend slopes are determined by Sen’s slope method. Moreover, innovative trend analysis (ITA) method is also used to determine trends of low, medium and high streamflow values. As a result of the study, trend indicators, namely, Z values of MK tests and D values of the ITA method are compared and it has been observed that the trend directions by MK and ITA are generally similar. Nevertheless, only the negative D values calculated by ITA are mostly higher than the negative Z values calculated by MK. The ITA results of the annual mean streamflow show 80% of the stations show a strong decrease in trend for high values. Most of the significant trends found by MK are obtained in a decreasing direction, and the linear slopes are mostly determined in the range of ±2% per year. According to the spatial analysis, the significant decreasing trends are generally located in the middle region of the basin.

This study investigates the attribution of climate change to trends in river discharge during six decades from 1955 until 2014 in 12 selected river catchments across six Central Asian countries located upstream of the main rivers. For this purpose, the semi-distributed eco-hydrological model SWIM (Soil and Water Integrated Model) was firstly calibrated and validated for all study catchments. Attributing climate change to streamflow simulation trends was forced by factual (reanalysis) and counterfactual climate data (assuming the absence of anthropogenic influence) proposed in the framework of the ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) or ESM without anthropogenic forcing that were firstly tested and then compared. The trend analysis was performed for three variables: mean annual discharge and high flow (Q5) and low flow (Q95) indices. The results show that trends in the annual and seasonal discharge could be attributed to climate change for some of the studied catchments. In the three northern catchments (Derkul, Shagan, and Tobol), there are positive trends, and in two catchments (Sarysu and Kafirnigan), there are negative streamflow trends under the factual climate, which could be attributed to climate change. Also, our analysis shows that the average level of discharge in Murghab has increased during the historical study period due to climate change, despite the overall decreasing trend during this period. In addition, the study reveals a clear signal of shifting spring streamflow peaks in all catchments across the study area.

East of the Continental Divide in the cold interior of Western Canada, the Mackenzie and Nelson River basins have some of the world's most extreme and variable climates, and the warming climate is changing the landscape, vegetation, cryosphere, and hydrology. Available data consist of streamflow records from a large number (395) of natural (unmanaged) gauged basins, where flow may be perennial or temporary, collected either year-round or during only the warm season, for a different series of years between 1910 and 2012. An annual warm-season time window where observations were available across all stations was used to classify (1) streamflow regime and (2) seasonal trend patterns. Streamflow trends were compared to changes in satellite Normalized Difference Indices. Clustering using dynamic time warping, which overcomes differences in streamflow timing due to latitude or elevation, identified 12 regime types. Streamflow regime types exhibit a strong connection to location; there is a strong distinction between mountains and plains and associated with ecozones. Clustering of seasonal trends resulted in six trend patterns that also follow a distinct spatial organization. The trend patterns include one with decreasing streamflow, four with different patterns of increasing streamflow, and one without structure. The spatial patterns of trends in mean, minimum, and maximum of Normalized Difference Indices of water and snow (NDWI and NDSI) were similar to each other but different from Normalized Difference Index of vegetation (NDVI) trends. Regime types, trend patterns, and satellite indices trends each showed spatially coherent patterns separating the Canadian Rockies and other mountain ranges in the west from the poorly defined drainage basins in the east and north. Three specific areas of change were identified: (i) in the mountains and cold taiga-covered subarctic, streamflow and greenness were increasing while wetness and snowcover were decreasing, (ii) in the forested Boreal Plains, particularly in the mountainous west, streamflows and greenness were decreasing but wetness and snowcover were not changing, and (iii) in the semi-arid to sub-humid agricultural Prairies, three patterns of increasing streamflow and an increase in the wetness index were observed. The largest changes in streamflow occurred in the eastern Canadian Prairies.

Abstract Knowledge of the historical changes in streamflow is essential for operating and planning water structures. Hence, the monthly mean streamflow trends of 20 streamflow gauge stations in Turkey’s Mediterranean basins, which are sensitive to climate change, are examined using the Mann–Kendall test, and trend slopes are calculated using Sen’s slope method. Furthermore, the innovative trend significance test (ITST) and recently proposed innovative polygon trend analysis (IPTA) method are used to analyse the historical changes in streamflow values at the stations. The trend analysis results of a total of 240-time series between 1977 and 2015 are evaluated and compared with three different trend methods. As a result of the study, the MK method determined significant trends in only 24% (3 increasing, 55 decreasing), ITST in 83% (41 increasing, 158 decreasing), and IPTA in 82% (38 increasing, 158 decreasing) of the 240-time series. The IPTA and ITST methods are more sensitive to determining significant monthly streamflow trends than the MK method. In addition, there is a more significant decrease in the streamflow values of the stations located east of the basin, and the trend slope value reaches − 26%/decade. Therefore, the results of the streamflow trend in the Mediterranean basins will benefit decision-makers in planning the efficient use of water resources in the region.