Changes In Streamflow Research Articles

The Effect of Climate Change on the Hydropower Potential in the Kunhar River Watershed, Pakistan

Climate change plays a vital role in the hydrology of any river basin, which may have multidimensional consequences. There is a need to conduct climate change impact assessment studies with updated models and scenarios. This study aimed to assess the impact of climate change on the streamflow and hydropower in Pakistan’s Kunhar River basin. Three general circulation models (GCMs), under two Shared Socioeconomic Pathway scenarios (SSPs 2–45 and 5–85), the Soil and Water Assessment Tool, and the flow duration curve were used to project the change in climatic parameters, streamflow, and hydropower potential, respectively. The findings indicated that in the 2080s, the precipitation, maximum, and minimum temperatures are projected to increase by 10%, 2.0 °C, and 3.0 °C under the SSP 2–45 scenario and are projected to increase by 8%, 3.7 °C, and 4.4 °C under the SSP 5–85 scenario, respectively. The annual streamflow may increase by 15 to 11%, and the seasonal fluctuations are more likely to be dominant compared with the annual fluctuations. The hydropower potential will probably increase by 24 to 16% under the SSP 2–45 and 5–85 scenarios in the 2080s. However, seasonal changes in streamflow and hydropower may impact the hydropower plant operation in the basin. The Kunhar River’s hydrology may change from snow-fed to a rainfall–runoff river.

Using a two-step downscaling method to assess the impact of climate change on total nitrogen load in a small basin

Climate data with high spatial and temporal resolution were an important basis for predicting regional climate and water environment changes. The current downscaling targets for GCM data were mainly medium-resolution (MR) reanalysis data, which were still coarse for small basins. We designed a two-step downscaling method in this study, aiming to perform 100 × resolution enhancements of GCM monthly temperature and precipitation data (i.e., target spatial resolution of 1 km × 1 km). We obtained a predictable set of MR climate data by adding a first-step downscaling process, which was then combined with high-resolution (HR) topographic data as auxiliary data in the second-step downscaling process to supplement the spatial and temporal details. The results showed that the performances of two-step downscaling method were better than the one-step downscaling method using only HR topographic data as auxiliary data. Then the two-step downscaling method was applied to downscale the future climate data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) during 2015–2099 for the target small basin. We fed the HR climate prediction data into a Bayesian water quality model, which had been successfully applied in this basin, to simulate the responses of hydrology and water quality to climate change. The results demonstrated that the downscaled climate data with high spatial resolution could be successfully used in the water quality model for spatially differentiated simulations of climate change, streamflow change and total nitrogen load change in a small basin.

Assessment of changes in monthly streamflow using innovative polygon trend analysis in the South Indian Rivers

Assessment of changes in monthly streamflow using innovative polygon trend analysis in the South Indian Rivers

Quantification of agricultural best management practices impacts on sediment and phosphorous export in a small catchment in southeastern Sweden

Agricultural activities contribute to water pollution through sediments and nutrient export, negatively affecting water quality and aquatic ecosystems. However, implementing best management practices (BMPs) could help control sediments and nutrient losses from agricultural catchments. This study used the Soil Water Assessment Tool (SWAT) model to assess the effectiveness of four BMPs in reducing sediment and phosphorus export in a small agricultural catchment (33 km2) in southeastern Sweden. The SWAT model was first evaluated for its ability to simulate streamflow, sediment load, and total phosphorous load from 2005 to 2020. Then, the calibrated parameters were used to simulate the agricultural BMP scenarios by modifying relevant parameters. The model performed satisfactorily during calibration and validation for streamflow (NSE = 0.80/0.84), sediment load (NSE = 0.67/0.69), and total phosphorous load (NSE = 0.61/0.62), indicating its suitability for this study. The results demonstrate varying effects of BMP implementation on sediment and phosphorus (soluble and total) export, with no significant change in streamflow. Filter strips were highly effective in reducing sediment (−32%), soluble phosphorus (−67%), and total phosphorous (−66%) exports, followed by sedimentation ponds with −35%, −36%, and −50% reductions, respectively. Grassed waterways and no-tillage were less impactful on pollutant reduction, with grassed waterways showing a slight increase (+4%) in soluble phosphorus and no-tillage having a minimal effect on sediment (−1.3%) and total phosphorus (−0.2%) export. These findings contribute to the ongoing efforts to mitigate sediment and nutrient pollution in Swedish agricultural areas, thereby supporting the conservation and restoration of aquatic ecosystems, and enhancing sustainable agricultural practices.

Can direct CMIP6 model simulations reproduce mean annual historical streamflow change?

Direct streamflow outputs from CMIP6 models are important for studying future water supply under climate change. However, the hydrological community generally ignores such data as its ability to predict streamflow change is largely unquantified and presumably considered biased. Here, we examined the ability of the 15 CMIP6 models to directly reproduce the observed mean annual streamflow change (ΔQ) between 1999–2014 and 1982–1998 for 115 large basins across the globe. The ΔQ and error of ΔQ in CMIP6 models (ΔQ_error) were then attributed to nine independent climate and vegetation variables using a stepwise regression model. Compared to the observations, almost all of the CMIP6 models overestimate ΔQ. In variable attribution, the observed ΔQ in the 115 basins can be well attributed (R2 = 0.59) to changes in precipitation (66 %), temperature (15 %), and leaf area index (-18 %). However, these variables could not explain ΔQ obtained from CMIP6 models (R2 = 0.13). In error attribution, errors in precipitation change contributes the most (69 ± 12 %) to ΔQ_error in CMIP6 models, followed by location-related variables, changes in vegetation-related variables, and changes in temperature-related variables. Although the current performance of CMIP6 model outputs varies in simulating observed streamflow change, they may become more reliable with improved model structures for better representations of climate and land surface processes.

Economically optimal hydropower development with uncertain climate change

Climate change affects the planning, construction, operation and management of hydropower plants in various ways. Evaluating hydropower generation with nonstationary climate changes is important for planning electric power infrastructure development. For a new hydropower plant with climate change, specifying too large a reservoir may result in excessive investment if the future becomes drier, while too small a reservoir may cause insufficient utilization of hydropower resources. Whether to build a new hydropower station at once or build it dynamically in stages over time is challenged by uncertainty in future conditions. This study proposes a planning method for developing a flexible hydropower construction schedule to adapt to long-term uncertain hydrology with climate changes. Projected uncertain nonstationary hydrology scenarios were developed first, and Bayes’ theorem is used to update the probability of each scenario based on observed hydrologic conditions over time. A profit maximization model integrates stochastic dynamic programming and linear programming for a cascade hydropower system to evaluate hydropower plant expansion and how to build it dynamically with uncertain nonstationary climate conditions. In this, stochastic dynamic programming evaluates and prescribes inter-stage reservoir upgrades and linear programming prescribes inner-stage hydroelectric power generation. The case study of Jinsha cascade hydropower system in China’s Yunnan province illustrates the method. The intensity and trend of climate change effects on streamflow directly affect the optimized results. If streamflow decreases in the future, the best strategy is to build a small reservoir initially, without future changes. If streamflow increases in the future, it is better to build a small reservoir first, and gradually increase capacity according to observed changes in streamflow and economies of scale in capacity construction. If there is no clear trend in streamflow change, it is also better to build a small reservoir at first, then decide whether to expand the reservoir according to future observed changes in streamflow. This method with uncertain nonstationary climate change and upgrading adaptively with observed hydrology usually produced better results than optimization for a deterministic nonstationary hydrology.

Thermal tolerance exceedances of an endangered unionid mussel in the Rio Grande Basin, with implications for river management

Abstract Freshwater mussels are globally imperilled, which is attributed to their sensitivity to changes in streamflow and temperature. The Black River in south‐eastern New Mexico, USA, harbours a stronghold population of the federally endangered unionid Popenaias popeii (Texas hornshell). Decreasing discharge and elevated water temperatures are considered the primary factors responsible for the species’ decline. However, the impact of these factors has not been quantified directly, which may hinder conservation efforts. The upper thermal tolerances (LT05 and LT50) of Black River P. popeii larvae (glochidia) and newly transformed juveniles were evaluated. Individuals were acclimated to 27°C and then immersed at five experimental temperatures (28, 30, 32, 34 and 36°C) for 24 h (glochidia) or 96 h (juveniles). The thermal tolerances of P. popeii and its presumed host fishes were then overlain onto in situ water temperature and discharge data to determine thermal exceedances in the past 15 years. Temperature was hindcast back to 2007 using machine learning (random forest model) and coupled with discharge. For glochidia, LT05 was exceeded frequently (41%) and LT50 was exceeded occasionally (13%) during spring and summer periods, whereas juvenile thresholds were never exceeded. Upper continuous duration above threshold (UCAT) analysis revealed periods of catastrophic high temperature, which were used to determine discharge bottlenecks. This study demonstrates how laboratory‐derived physiological thresholds can be used in conjunction with environmental data to evaluate the hydrological needs of aquatic organisms, which is useful in efforts to maintain flow regimes that protect native ecosystems.

Estimating future streamflow under climate and land use change conditions in northeastern Hokkaido, Japan

Study regionAbashiri River, northeastern Hokkaido, Japan. Study focusIn response to the growing concern over likely future climate and land use land cover (LULC) changes, a better understanding of the consequent streamflow changes is needed in northeastern Hokkaido, Japan. The present study focuses on the Abashiri River Basin (ARB), northeastern Hokkaido, and estimates the future local streamflow and its change in quantity, seasonality, flow regime, and spatial distribution under various possible impacts of climate and LULC conditions. The estimation is produced using the Soil and Water Assessment Tool (SWAT), a hydrological model suitable for managemental analysis. Future conditions are applied as alterations to the initial model inputs. New hydrological insights for the regionPossible impact of climate change leads to significant changes in streamflow quantity and flow pattern around the snowmelt season, while negative and insignificant change in streamflow is expected in response to possible LULC change. The results highlight a concerning future for local agricultural water use. Consequently, practical recommendations for local water resources management are advised, focusing on precautionary measures and development insights. Compared to existing research in this region, the present study provides a comprehensive estimation of the possible future streamflow changes on a finer spatiotemporal scale. Further application in the future to help the local water resources management is expected.

Attributing historical streamflow changes in the Jhelum River basin to climate change

Amid a heated debate on what are possible and what are plausible climate futures, ascertaining evident changes that are attributable to historical climate change can provide a clear understanding of how warmer climates will shape our future habitability. Hence, we detect changes in the streamflow simulated using three different datasets for the historical period (1901–2019) and analyze whether these changes can be attributed to observed climate change. For this, we first calibrate and validate the Soil and Water Integrated Model and then force it with factual (observed) and counterfactual (baseline) climates presented in the Inter-Sectoral Impact Model Intercomparison Project Phase 3a protocol. We assessed the differences in simulated streamflow driven by the factual and counterfactual climates by comparing their trend changes ascertained using the Modified Mann–Kendall test on monthly, seasonal, and annual timescales. In contrast to no trend for counterfactual climate, our results suggest that mean annual streamflow under factual climate features statistically significant decreasing trends, which are − 5.6, − 3.9, and − 1.9 m3s−1 for the 20CRv3-w5e5, 20CRv3, and GSWP3-w5e5 datasets, respectively. Such trends, which are more pronounced after the 1960s, for summer, and for high flows can be attributed to the weakening of the monsoonal precipitation regime in the factual climate. Further, discharge volumes in the recent factual climate dropped compared to the early twentieth-century climate, especially prominently during summer and mainly for high flows whereas earlier shifts found in the center of volume timings are due to early shifts in the nival regime. These findings clearly suggest a critical role of monsoonal precipitation in disrupting the hydrological regime of the Jhelum River basin in the future.

Differentiating the Effects of Streamflow and Topographic Changes on the Water Level of Dongting Lake, China, Using the LSTM Network and Scenario Analysis

Dongting Lake is the second largest freshwater lake in China and an internationally critical habitat for migratory birds. However, after 2004, the multiyear mean water levels of West Dongting Lake (WDL), South Dongting Lake (SDL), and East Dongting Lake (EDL) in the high-water stage decreased by 1.05 m, 1.15 m, and 1.32 m, respectively. Different areas of Dongting Lake experienced various degrees of shrinkage. It is necessary to study the dominant driving factors and their contributions to the falling water level. In this study, the water level changes in Dongting Lake were analyzed, and a long short-term memory neural network model was constructed to simulate the water level of Dongting Lake. Moreover, the contribution of changes in streamflow and topographic conditions to the water level changes in different areas of Dongting Lake was estimated with scenario analysis. The research results show that the changes in the streamflow were the main driving factors for the water level decline of WDL, SDL, and EDL in the high-water stage, and their contributions were 0.74 m, 0.97 m, and 1.16 m, respectively. The topographic changes had a great falling effect on the water level of Dongting Lake, and the falling effect on the water levels from October to June of the following year was the strongest in EDL (0.81 m), followed by WDL (0.49 m), and the weakest in SDL (0.3 m). These results can provide a scientific reference for the management of the water resources of Dongting Lake.

Detection and attribution of changes in streamflow and snowpack in Arctic river basins

Detection and attribution of changes in streamflow and snowpack in Arctic river basins

Forecasting and meta-features estimation of wastewater and climate change impacts in coastal region using manifold learning

South Asia's coastlines are the most densely inhabited and economically active ecosystems have already begun to shift due to climate change. Over the past century, climate change has contributed to a gradual and considerable rise in sea level, which has eroded shorelines and increased storm-related coastal flooding. The differences in estuary water quality over time, both seasonally and annually, have been efficiently controlled by changes in stream flow. Assessment requires digitized analytical platforms to lower the risk of catastrophes associated with climate change in coastal towns. To predict future changes in an area's vulnerability and waste planning decisions, a prospective investigation requires qualitative and quantitative scenarios. The paper concentrates on the development of a forecasting platform to evaluate the climate change and waste water impacts on the south coastal region of India. Due to the enhancement of Digitization, a multi-model ensemble combined with manifold learning is implemented on the multi-case models influencing the uncertainty probability rate of 23% and can be ignored with desired precaution on the coastal environmental. Because Manifold Learning Analysis results cannot be utilized directly in wastewater management studies because of their inherent biases, a statistical bias correction and meta-feature estimation have been implemented. Within the climate-hydrology modeling chain, the results demonstrate a wide range of expected changes in water resources in some places. Experimental statistics reveal that the forecasted rate of 91.45% will be the better choice to reduce the uncertainty of climatic change and wastewater management.

Hydrological model-based streamflow reconstruction for Indian sub-continental river basins, 1951–2021

Streamflow is a vital component of the global water cycle. Long-term streamflow observations are required for water resources planning and management, hydroclimatic extremes analysis, and ecological assessment. However, long-term streamflow observations for the Indian-Subcontinental (ISC) river basins are lacking. Using meteorological observations, state-of-the-art hydrological model, and river routing model, we developed hydrological model-simulated monthly streamflow from 1951–2021 for the ISC river basins. We used high-resolution vector-based routing model (mizuRoute) to generate streamflow at 9579 stream reaches in the sub-continental river basins. The model-simulated streamflow showed good performance against the observed flow with coefficient of determination (R2) and Nash-Sutcliffe efficiency (NSE) above 0.70 for more than 60% of the gauge stations. The dataset was used to examine the variability in low, average, and high flow across the streams. Long-term changes in streamflow showed a significant decline in flow in the Ganga basin while an increase in the semi-arid western India and Indus basin. Long-term streamflow can be used for planning water management and climate change adaptation in the Indian sub-continent.

Drought and Flood Extremes on the Amazon River and in Northeast Brazil, 1790–1900

Abstract Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century. Significance Statement Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.

Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections.

Changes in Headwater Streamflow from Impacts of Climate Change in the Tibetan Plateau

The Tibetan Plateau (TP) is the headwater of the Yangtze, Yellow, and the transboundary Yarlung Zangbo, Lancang, and Nujiang Rivers, providing essential and pristine freshwater to around 1.6 billion people in Southeast and South Asia. However, the temperature rise TP has experienced is almost three times that of the global warming rate. The rising temperature has resulted in glacier retreat, snow cover reduction, permafrost layer thawing, and so forth. Here we show, based on the longest observed streamflow data available for the region so far, that changing climatic conditions in the TP already had significant impacts on the streamflow in the headwater basins in the area. Our analysis indicated that the annual average temperature in the headwater basins of these five major rivers has been rising on a trend averaging 0.38 °C·decade−1 since 1998, almost triple the rate before 1998, and the change of streamflow has been predominantly impacted by precipitation in these headwater basins. As a result, streamflow in the Yangtze, Yarlung Zangbo, Lancang, and Nujiang River headwater areas is on a decreasing trend with a reduction of flow ranging from 3.0 ×109–5.9 ×109 m3·decade−1 (−9.12% to −16.89% per decade) since 1998. The increased precipitation in the Tangnahai (TNH) and Lanzhou (LZ) Basins contributed to the increase of their streamflows at 8.04% and 14.29% per decade, respectively. Although the increased streamflow in the headwater basins of the Yellow River may ease some of the water resources concerns, the decreasing trend of streamflow in the headwater areas of the southeastern TP region since 1998 could lead to a water crisis in transboundary river basins for billions of people in Southeast and South Asia.

Application of artificial neural network (ANN) for investigation of the impact of past and future land use–land cover change on streamflow in the Upper Gilgel Abay watershed, Abay Basin, Ethiopia

Changes in land area features, such as vegetation type and soil conditions, have an impact on carbon sources and sinks and support food production; this is critical in addressing global sustainability challenges such as climate change, biodiversity loss, and food security. The study's major goal was to determine how LULC changes in the past and future might affect streamflow in the Upper Gilgel Abay watershed. The modeling was conducted using the MOLUSCE Quantum GIS plugins cellular automata simulation method and streamflow modeled using SWAT. Landsat 5 TM (1995), Landsat 7 ETM + (2007), and Landsat 8 imaging (2018) satellites were used to collect the images, which were then categorized using ERDAS 2014 software, and the kappa coefficient becomes 84.04%, 82.37%, and 85.54% for 1995, 2007, and 2018 LULC, respectively. SWAT model better performed the simulation which is R2 of 0.77 for calibration and 0.68 for validation and ENS becomes 0.71 and 0.62 for calibration and validation, respectively. The output change in streamflow due to past and future LULC maps shows an increase in LULC in cultivated areas and resulted in 39%, 46.81%, and 52.45% in each of the years 1995, 2007, and 2018, respectively. The three LULC modifications in the land cover maps from 1995, 2007, and 2018 had simulated mean monthly peak discharges of 62.20 m3/s, 66.51 m3/s, and 72.10 m3/s, respectively. The projected LULC 2027 also shows a similar increase in the study area, and dominantly cultivated land illustrates the highest change at around 53.77% but the highest change occurs on grassland during (2018–2027) land use at around 12.29%. And the highest streamflow was found around a monthly average of 1400 m3/s. The study primarily provides insight into how LULC fluctuation affects streamflow, and it is crucial for water planners and natural resource professionals whose focus is on the Upper Gilgel Abay basin.

Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects

Study regionHaihe River Basin (HRB) in North China Plain. Study focusChanges in streamflow due to climate change and human activities are highly uncertain. An improved Soil and Water Assessment Tool (SWAT) model equipped with a dynamic CO2 input method was used to quantify the environmental change impacts on streamflow in the HRB. Streamflow changes were analyzed based on 22 bias-corrected CMIP6 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 during two 30-year periods of the middle (2041–2070) and end (2071–2100) of the 21st century relative to the historical period (1971–2000). New hydrological insights for the regionLong-term simulations of annual streamflow in the HRB demonstrated substantial discrepancies between the dynamic and constant CO2 input methods under the SSP1-2.6 and SSP5-8.5 scenarios, which highlighted the importance of using the dynamic CO2 input method for streamflow simulations. Spatio-temporal analysis using the improved SWAT model revealed that streamflow generally increased under four emission scenarios compared to the historical period. In the HRB, annual streamflow in the downstream plains was higher than in the upstream mountains, and this was more evident under the highest emission scenario of SSP5-8.5. Monthly streamflow showed considerable intra-annual variability that was generally greater from July to November. The strongest increment in streamflow was projected in the SSP3-7.0 and SSP5-8.5 scenarios, indicating that flood risk would possibly increase in the late 21st century.

Attribution assessment of hydrological trends and extremes to climate change for Northern high latitude catchments in Norway

The northern high latitudes have experienced the strongest warming in the world and substantial changes in streamflow and hydrological extremes. However, there have been limited attribution studies of changes in streamflow and hydrological extremes in this region. This study provides the first trend detection and attribution assessment on 33 hydrological variables for 50 Norwegian catchments in the period 1961–2019, using observed and simulated runoff data from four hydrological models driven by factual (observed) and counterfactual forcing data. Significant increasing trends are detected in observed annual, spring and winter runoff in most catchments and significant trends towards earlier spring floods are found in 40% of catchments. The four hydrological models show similarly good performance in terms of daily discharge in both calibration and validation periods, and they can reproduce 62% of the observed significant trends considering both trend direction and significance. The counterfactual forcing data were generated by the ATTRICI model, which removed all warming trends and most significant trends in precipitation in the factual time series. Ninety-four percent of the simulated significant trends driven by the factual forcing data are insignificant under counterfactual conditions, with trend slopes approaching zero. Thus, based on the model performance in trend reproduction and the difference of significant trends under factual and counterfactual conditions, we conclude that about 58% of the observed significant trends in Norwegian catchments can be attributed mainly to climate change. The comparisons of the historical extreme events under factual and counterfactual conditions show that more than 65% of floods and droughts in the 2010s could have been magnified by climate change.

Combining Landsat TIR‐imagery data and ERA5 reanalysis information with different calibration strategies to improve simulations of streamflow and river temperature in the Canadian Subarctic

AbstractArctic and Subarctic environments are among the most vulnerable regions to climate change. Increases in liquid precipitation and changes in snowmelt onset are cited as the main drivers of change in streamflow and water temperature patterns in some of the largest rivers of the Canadian Arctic. However, in spite of this evidence, there is still a lack of research on water temperature, particularly in the eastern Canadian Arctic. In this paper, we use the CEQUEAU hydrological‐water temperature model to derive consistent long‐term daily flow and stream temperature time series in Aux Mélèzes River, a non‐regulated basin (41 297 km2) in the eastern Canadian subarctic. The model was forced using reanalysis data from the fifth‐generation ECMWF atmospheric reanalyses (ERA5) from 1979 to 2020. We used water temperature derived from thermal infrared (TIR) images as reference data to calibrate CEQUEAU's water temperature model, with calibration performed using single‐site, multi‐site, and upscaling factors approaches. Our results indicate that the CEQUEAU model can simulate streamflow patterns in the river and shows excellent spatiotemporal performance with Kling‐Gupta Efficiency (KGE) metric >0.8. Using the best‐performing flow simulation as one of the inputs allowed us to produce synthetic daily water temperature time series throughout the basin, with the multi‐site calibration approach being the most accurate with root mean square errors (RMSE) <2.0°C. The validation of the water temperature simulations with a three‐year in situ data logger dataset yielded an RMSE = 1.38°C for the summer temperatures, highlighting the robustness of the calibrated parameters and the chosen calibration strategy. This research demonstrates the reliability of TIR imagery and ERA5 as sources of model calibration data in data‐sparse environments and underlines the CEQUEAU model as an assessment tool, opening the door to its use to assess climate change impact on the arctic regions of Canada.