Changes In Streamflow Research Articles

Human influence on water availability variations in the upper Ewaso Ng’iro river basin, Kenya

Study regionUpper Ewaso Ng’iro catchment is located in Eastern Africa – a water-scarce region. Study focusStreamflow is in decline in the catchment despite the pattern of increasing amounts of rainfall in the East African region. This study explored historical human-water interplays to understand the coevolution of hydrological systems and societal development and to identify the dominant features contributing to water deficits and water-related conflicts in the lowland zone of the catchment. A socio-hydrological conceptual framework was used to mimic and explain the two-way coupling between human and hydrological systems. New hydrological insights for the regionThe results show that the dominant features that have likely contributed to changes in lowland streamflow and water-related conflicts include: population growth, land use/cover changes, and economic dynamics. The dry season flows have been extremely reduced with a 100% reduction observed in the month of February especially between 2010 and 2020 while the wet season flows have increased by about 10% and 15% in the upland and midland zones respectively. The decreasing trends in the streamflow against increasing amounts of rainfall emphasize the direct role humans have on catchment water availability. East Africa being a water-scarce region, there is a need to shift the focus from ‘the sustainable natural flows perspective’ to ‘the amount of water retained during wet seasons perspective’, improving land conservation activities to increase water infiltration during wet seasons, and the need to bring all stakeholders together for collective understanding.

Impacts of climate change on streamflow in the McKenzie Creek watershed in the Great Lakes region

Introduction: This study explored streamflow dynamics of the McKenzie Creek watershed in Southern Ontario, Canada under a changing climate. The Creek is located in the southern portion of the Grand River watershed in the Great Lakes region and is an important water and ecosystem service provider for the Six Nations of the Grand River reserve, the largest (by population) Indigenous community in Canada and the fourth largest in North America.Methods: The Coupled Groundwater and Surface-Water Flow Model (GSFLOW) was used to simulate streamflow from 1951 to 2020 using observed gridded meteorological data from Natural Resources Canada (NRCANmet) and in situ data from Environment and Climate Change Canada (ECCC). Downscaled data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) for two Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathways (RCP) climate warming scenarios, RCP 4.5 and RCP 8.5 were used to run GSFLOW for the historic (1951–2020) and projected (2021–2099) period.Results: Results suggested that streamflow in the McKenzie Creek will be significantly impacted by climate change in winter months when streamflow is projected to increase due to higher temperatures causing early melting of snowpack and increasing winter precipitation. Consequently, spring streamflow is expected to decrease and little or no change in streamflow in the summer and autumn. These changes in streamflow dynamics may lead to more flooding incidents in the winter, while at the same time, the region may face reduced water availability or dry conditions in late spring and summer due to warm temperatures.Discussion: This study provides important information about streamflow and hydrologic dynamics of this watershed that will help managers and planners to better manage water resources and be prepared to deal with climate change and its impacts on water availability and security not only for the Six Nations area but also for Southern Ontario which houses one-third of Canada’s population.

A surface and ground‐water integrated investigation of streamflow drying up in semi‐arid regions

AbstractGroundwater exploitations in semi‐arid regions result in significant declines of groundwater levels and streamflow and even trigger seasonal streamflow drying up, which may bring adverse impacts on riparian ecosystems. It is critical to explore future streamflow changes caused by variable climate scenarios and groundwater exploitation intensities; however, a comprehensive assessment of the streamflow drying up frequency remains lacking. Furthermore, the thresholds of controlling factors beyond which streamflow drying up occurs have rarely been explored. Here we develop an integrated surface water‐groundwater model for the Taoer River Basin (TRB), a typical semi‐arid watershed in northeast China. We apply the model to multiple future scenarios to reveal the frequency of potential streamflow drying up and explore the thresholds of controlling factors of streamflow drying up based on a decision tree method. We find that the groundwater level in the lower region of the TRB (LTRB) is hard to recover to the riverbed elevation and the streamflow drying up frequency is still high unless the groundwater exploitation is reduced by 50%. However, this leads to a competition between the ecological and economic objectives. Our results also show that the streamflow drying up frequency in the LTRB is very sensitive to the upstream water inflow that can be regulated through the reservoir operation or inter‐basin water diversion, indicating the importance of a basin scale strategic management. The findings of this paper can help policy makers sustainably exploit water resources in semi‐arid regions.

Application of hydrological model to simulate streamflow contribution on water balance in Himalaya river basin, Nepal

Hydrological models are widely used and often regarded as reliable tools for accurately estimating various components of the water balance. In a remote Himalayan catchment, such as Tamakoshi basin, where limited hydrometric dataset is available, such models often provide essential insights that are crucial to water researchers and planners. In this regard, we employed the semi-distributed HBV-light (version 4.0.0.25) hydrological model for glacierized Tamakoshi river basin and attempted to quantify various water balance components. For our model tests, using the daily streamflow records, we selected two distinct periods, i.e., 2004–2008 as a calibration period whilst 2011–2012 for model validation. Based on our findings, the model was able to reasonably predict the streamflow (validation efficiency: Nash-Sutcliffe Efficiency of 0.82 and percent bias −21%). At our site, HBV-light model predicted that the change in streamflow was mostly governed by monsoonal rain (62%) followed by baseflow (20%), glacier melt (13%) and snowmelt (5%). As expected, the streamflow peaked during the month of August where monsoon-induced rain and melting of glaciers significantly contributed to river flow. As a result, monsoon period showcased largest fluctuation in water storage while negligible change was observed during post-monsoon season. Nonetheless, our findings revealed that the baseflow contribution to streamflow was maximum during the month of October and lowest during February. Our findings indicated that the water balance of the Tamakoshi basin is largely influenced by monsoonal rain during June–September window as well as baseflow and glacier melt during the dry season. Runoff components contribution to streamflow was increasing but water storage changes was decreasing in recent decade (2011–2020). We believe our findings are crucial for future initiatives involving water resources, water-induced disaster management, and studies of climate change may benefit from the findings of this study, especially in a region with limited hydrometric data availability.

Evaluating climate change effects on hydrological functionality and water‐related ecosystem services

AbstractThe paper investigates the effects of climate change on riverine ecosystems in the Upper Arno River basin, adopting a hybrid methodology relating the changes in streamflow to its related water ecosystem services. Twenty hydrological models were run using the Soil and Water Assessment Tool (SWAT), under their respective climate model and emission scenario. The streamflow variable obtained was analysed according to the method of Indicators of Hydrologic Alteration (IHA). The development of spatial maps of IHA allowed us to represent the spatial of the results according to the three river subbasins studied, with a focus on the presence of hotspots. Using IHA allowed to capture of the streamflow quantity and dynamics, through groups of magnitude, timing and rates, where the potential effect could be then linked to the concept of water ecosystem services. Finally, the results obtained were presented as a proxy that can be integrated into the existing Driver, Pressure, State, Impact, Response (DPSIR) management framework to eventually move towards an ecosystem‐based management methodology. The main results, induced from the Mann–Kendall trend analysis of the IHA, suggested that provisioning and regulating services are to be affected by climate change where the quantity of flows shows a decreasing trend under several emission scenarios, in addition to an intensification of the extreme events. This shift is accompanied by a change in the dynamics of flow, which holds the potential of affecting habitat suitability. With the emergence of studies directed towards ecosystem‐based management, the study refers to well‐established hydrological modelling to evaluate climate change as a driver and provide screening results to ensure a proactive response planning that converges with the idea of ecosystems.

Quantitative assessment of hydrological response to vegetation change in the upper reaches of Luanhe River with the modified Budyko framework

For assessing the influence of vegetation variation in upper reaches of Luanhe River (URLR) on streamflow, we first computed the equation between underlying surface parameter (ω) and the Normalized Difference Vegetation Index (NDVI). Then we introduced the vegetation information into the Budyko equation and established a modified Budyko equation. Finally, the impact of vegetation change on streamflow in URLR were estimated using the modified Budyko equation. Results showed that: (1) The NDVI in the URLR presented an increasing trend from 1982 to 2016, which is opposite to the runoff depth. The sudden change of NDVI occurred in 1998, and a simple linear model between ω and NDVI was obtained (p < 0.01). (2) The contribution rate of precipitation, potential evapotranspiration, NDVI, and anthropogenic factor on streamflow change during the changing period (1999–2016) are 44.99, 11.26, 29.45, and 17.30%, respectively. Although precipitation is still the main driver of runoff impact in the ULRB, with the increasing vegetation in the ULRB, vegetation has become the second driver of runoff impact followed by human activities and potential evapotranspiration. The mechanism of the effect of vegetation change on the water cycle needs to be further investigated. The results of this study can provide a theoretical basis for water use and conservation in the URLR.

Understanding the Climate Change and Land Use Impact on Streamflow in the Present and Future under CMIP6 Climate Scenarios for the Parvara Mula Basin, India

Understanding the likely impacts of climate change (CC) and Land Use Land Cover (LULC) on water resources (WR) is critical for a water basin’s mitigation. The present study intends to quantify the impact of (CC) and (LULC) on the streamflow (SF) of the Parvara Mula Basin (PMB) using SWAT. The SWAT model was calibrated and validated using the SWAT Calibration Uncertainty Program (SWAT-CUP) for the two time periods (2003–2007 and 2013–2016) and (2008–2010 and 2017–2018), respectively. To evaluate the model’s performance, statistical matrices such as R2, NSE, PBIAS, and RSR were computed for both the calibrated and validated periods. For both these periods, the calibrated and validated results of the model were found to be very good. In this study, three bias-corrected CMIP6 GCMs (ACCESS-CM2, BCC-CSM2-MR, and CanESM5) under three scenarios (ssp245, ssp370, and ssp585) have been adopted by assuming no change in the existing LULC (2018). The results obtained from the SWAT simulation at the end of the century show that there will be an increase in streamflow (SF) by 44.75% to 53.72%, 45.80% to 77.31%, and 48.51% to 83.12% according to ACCESS-CM2, BCC-CSM2-MR, and CanESM5, respectively. A mean ensemble model was created to determine the net change in streamflow under different scenarios for different future time projections. The results obtained from the mean ensembled model also reveal an increase in the SF for the near future (2020–2040), mid future (2041–2070), and far future (2071–2100) to be 64.19%, 47.33%, and 70.59%, respectively. Finally, based on the obtained results, it was concluded that the CanESM5 model produces better results than the ACCESS-CM2 and BCC-CSM2-MR models. As a result, the streamflow evaluated with this model can be used for the PMB’s future water management strategies. Thus, this study’s findings may be helpful in developing water management strategies and preventing the pessimistic effect of CC in the PMB.

Persistent chemostatic behaviour of stream solutes in a northern hardwood forest under climatic and atmospheric deposition changes

AbstractConcentration – discharge (C – Q) relationships in streamflow can serve a variety of purposes, including elucidating catchment water sources, quantifying chemical weathering, and estimating solute fluxes. Four main C – Q relationships have been identified: dilution (decreasing C with increasing Q), flushing (increasing C with increasing Q), chemostatic (little or no change in C with increasing Q) and chemostochastic (great variation in C relative to that of Q) behaviour. We used a 40‐year data record for 10 headwater forested catchments in central Ontario, Canada, to examine C – Q relationships for a range of geogenic, exogenic and bioactive solutes. The thin overburden cover of this landscape and general absence of deep groundwater contributions to streamflow suggest that streamflow solute concentrations and their relationship with discharge would be sensitive to pronounced changes in climate and atmospheric deposition chemistry over the monitoring period. We explored the temporal and spatial stability of C – Q relationships in response to these changes at annual and seasonal timescales for relatively wet and dry conditions and for periods with relatively high and low atmospheric deposition. Chemostasis dominated C – Q relationships at the annual and seasonal scales for geogenic (e.g., SiO2) and exogenic (e.g., Cl−) solutes, although bioactive solutes (e.g., NO3−, NH4+) exhibited non‐chemostatic behaviour for some catchments, time periods and wetness conditions. C – Q relationships derived at the annual timescale often masked pronounced seasonal shifts in bioactive solute concentration response to changes in streamflow and provide only partial insight into the production and delivery of such solutes in this forest landscape. Relatively subtle changes in overburden thickness across this landscape appeared to explain some of the deviations from widespread chemostasis. Nevertheless, this general chemostatic behaviour was largely temporally stable across catchments and solutes, highlighting its resilience in the face of such non‐stationary influences as climate conditions and atmospheric deposition.

Climate change threats to the global functional diversity of freshwater fish.

Climate change impacts on freshwater ecosystems and freshwater biodiversity show strong spatial variability, highlighting the importance of a global perspective. While previous studies on biodiversity mostly focused on species richness, functional diversity, which is a better predictor of ecosystem functioning, has received much less attention. This study aims to comprehensively assess climate change threats to the functional diversity of freshwater fish across the world, considering three complementary metrics-functional richness, evenness and divergence. We built on existing spatially explicit projections of geographical ranges for 11,425 riverine fish species as affected by changes in streamflow and water temperature extremes at four warming levels (1.5°C, 2.0°C, 3.2°C and 4.5°C). To estimate functional diversity, we considered the following four continuous, morphological and physiological traits: relative head length, relative body depth, trophic level and relative growth rate. Together, these traits cover five ecological functions. We treated missing trait values in two different ways: we either removed species with missing trait values or imputed them. Depending on the warming level, 6%-25% of the locations globally face a complete loss of functional diversity when assuming no dispersal (6%-17% when assuming maximal dispersal), with hotspots in the Amazon and Paraná River basins. The three facets of functional diversity do not always follow the same pattern. Sometimes, functional richness is not yet affected despite species loss, while functional evenness and divergence are already reducing. Other times, functional richness reduces, while functional evenness and/or divergence increase instead. The contrasting patterns of the three facets of functional diversity show their complementarity among each other and their added value compared to species richness. With increasing climate change, impacts on freshwater communities accelerate, making early mitigation critically important.

Modeling wildfire effects on streamflow in the Cascade Mountains, Oregon, USA

Increasing occurrence of large and severe wildfires represents a growing threat to forested watersheds and the many ecosystem services they provide. Past research has shown that wildfires can cause substantial increases in peak flows and annual water yields, leading to potential water quality concerns and land and water management challenges. However, responses have been variable, and there have been few studies at large basin scales, leading to uncertainties about post-fire hydrologic responses. To address these uncertainties, we projected the effect of three large wildfires (>70,000 ha) on streamflow in two important forested watersheds in the Cascade Range of Oregon, US. We modeled the streamflow response using the Soil and Water Assessment Tool (SWAT) model, calibrated on data from prior to the wildfires. We modified model parameters to represent the impacts of the wildfires based on burn severity maps. Burned and unburned scenarios were compared using random forest models to identify drivers of increased annual water yields and peak flows. Post-fire annual water yield changes were controlled by burn severity, annual precipitation, area burned, aridity, and vegetation type, while post-fire peak flow changes were controlled by burn severity, area burned, aridity, soil type, and geologic province. We also found that post-fire increases in annual water yields, peak flows, and low flows were greatest at the headwater scale but were more muted at the downstream basin scale. Our work provides valuable insights into the range of potential post-fire streamflow changes at the headwater and larger basin scale, which is becoming increasingly critical for effective forest and water management decisions.

Effects of Climate Change on Streamflow in the Godavari Basin Simulated Using a Conceptual Model including CMIP6 Dataset

Hydrological reaction to climate change anticipates water cycle alterations. To ensure long-term water availability and accessibility, it is essential to develop sustainable water management strategies and better hydrological models that can simulate peak flow. These efforts will aid in water resource planning, management, and climate change mitigation. This study develops and compares Sacramento, Australian Water Balance Model (AWBM), TANK, and SIMHYD conceptual models to simulate daily streamflow at Rajegaon station of the Pranhita subbasin in the Godavari basin of India. The study uses daily Indian Meteorological Department (IMD) gridded rainfall and temperature datasets. For 1987–2019, 70% of the models were calibrated and 30% validated. Pearson correlation (CC), Nash Sutcliffe efficiency (NSE), Root mean square error (RMSE), and coefficient of determination (CD) between the observed and simulated streamflow to evaluate model efficacy. The best conceptual (Sacramento) model selected to forecast future streamflow for the SSP126, SSP245, SSP370, and SSP585 scenarios for the near (2021–2040), middle (2041–2070), and far future (2071–2100) using EC-Earth3 data was resampled and bias-corrected using distribution mapping. In the far future, the SSP585 scenario had the most significant relative rainfall change (55.02%) and absolute rise in the annual mean temperature (3.29 °C). In the middle and far future, the 95th percentile of monthly streamflow in the wettest July is anticipated to rise 40.09% to 127.06% and 73.90% to 215.13%. SSP370 and SSP585 scenarios predicted the largest streamflow increases in all three time periods. In the near, middle, and far future, the SSP585 scenario projects yearly relative streamflow changes of 72.49%, 93.80%, and 150.76%. Overall, the findings emphasize the importance of considering the potential impacts of future scenarios on water resources to develop effective and sustainable water management practices.

Quantifying the streamflow change and influencing factors with a spatio-temporal coupling analysis framework

AbstractStreamflow change and its influencing factors are synchronous and correlated in temporal and spatial scales. The aim of this study is to develop a spatio-temporal coupling analysis framework for quantifying streamflow change and its influencing factors was established. Specifically, the Mann–Kendall test, Pettitt's test, hierarchical cluster analysis, and Ripley's L-function were jointly used to study the spatial heterogeneity of the temporal evolution of streamflow; and the Soil and Water Assessment Tool (SWAT) model was employed to quantify the impacts of climate and human activities on streamflow change. The preliminary application in the Dawen River Basin (China) case has shown that (1) the natural streamflow change in the basin during 1953–2013 is mainly affected by climate change–human activities, followed by climate change and human activities, accounting for a total area of 52.04, 24.90, and 23.06%, respectively; and (2) the vast majority of sub-basins with relatively large natural streamflow change are mainly driven by climate change (i.e., precipitation). In general, the proposed framework can effectively reflect the spatio-temporal patterns of streamflow change and its influencing factors, which can provide a theoretical basis for water resources management in the context of global change.

Characterization factors for the impact of climate change on freshwater fish species

Human activities increasingly threaten the highly biodiverse freshwater ecosystems. Life Cycle Assessment (LCA) is a useful tool to quantify the impacts of products and services on freshwater biodiversity. Current methodologies in LCA address the impact of climate change on freshwater fish diversity by changes in average river discharge only. Given the ectothermic nature of fish, previous studies have highlighted the importance of including water temperature changes as a driver of species loss. Moreover, the impact of climate extremes might be more important than changes in average conditions. In this study, we derived new characterization factors for 207 individual greenhouse gas (GHG) emissions that quantify the impact of an emission change on freshwater fish, accounting for climate-driven changes in both streamflow and water temperature extremes. We combined a novel dataset of fish range climate threats with a newly developed species-area relationship to quantify global freshwater fish extinction risks at various global warming levels. The global characterization factors range from 0.00 to 4.56·10−10 Potentially Disappeared Fraction (PDF)·yr·kg−1. Our results imply that freshwater fish diversity impacts per kg of GHG emission have been underestimated in previous LCA methods that excluded the impact of water temperature and climate extremes, as the newly developed effect factor is higher by 172%. Future contributions should focus on increasing taxonomic coverage (e.g., by including lentic fish species and macro-invertebrates) and developing complementary models to reflect other aspects of biodiversity in LCA.

Trend analysis of hydro-climate variables in the Jemma sub-basin of Upper Blue Nile (Abbay) Basin, Ethiopia

The change in climate elements such as rainfall and temperature are determinant factors of hydrological components (e.g., streamflow, water yield, evapotranspiration). Thus, understanding the trends of hydro-climate variables are imperative for planning water resources management measures. This study examines the trends of precipitation and temperature (1980–2014) as well as streamflow (1990–2008) changes in the Jemma sub-basin of the Upper Blue Nile (Abbay) Basin. A homogeneity test was performed for pre-processing data to inspect the statistical nature of data homogeneity viz., Pettitt’s, SNHT, Buishand range test, and Von Neumann test (VNT). The Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test, Sen’s slope estimator, and Innovative trend analysis (ITA) were applied to compute the existence of trend and the magnitude of change at an annual timescale. In a nutshell, the study portrays a change in the trends of hydro-climate variables when using different statistical tests. The result showed that the mean annual temperature in many stations depicted upward trends. There is a significant increasing trend (p < 0.05) by 0.029 °C per year in the mean annual temperature of all climatic stations. Based on Sen’s slope estimator, the annual precipitation and streamflow were increased by 1.781 mm/year and 0.085 m3/s, respectively. But no significant trends were detected in precipitation and streamflow when using Sen’s slope estimator test. The results of this study are worthwhile for evaluating the trends of hydro-climatic variables in other areas of Ethiopia in particular and elsewhere in the world in general, which are fundamental for planning water resource management measures.

Rainfall and Streamflow Trends in the Upper &lt;em&gt;Maduru Oya&lt;/em&gt; Sub-watershed in Sri Lanka: A Comparative Study Using Different Statistical Methods

Analysis of historical data on rainfall and streamflow provides useful information for water resources management in a river basin. The objective of this study was to assess the variations and trend analysis of rainfall and streamflow data in the upper Maduru Oya sub watershed in Sri Lanka during a 30 year period (1989 October to 2019 September) using daily rainfall data from two gauging locations (Padiyathalawa and Bibile) and daily average streamflow data from one gauging location (Padiyathalawa). Descriptive statistics were used to assess the variations. The Mann Kendal Trend Test (MK test) and Innovative Trend Analysis method (ITA) were used to analyze the trend, and Sen’s Slope Estimator was used to assess the magnitude of the trend. Based on the MK test, most of the rainfall trends obtained are in an increasing direction, and streamflow trends are in a decreasing direction. Rainfall trends found in Padiyathalawa are not statistically significant. Annual, First Inter Monsoon, March, and May time scales of rainfall showed significant upward trends at Bibile (p&lt;0.1) while streamflow in January, April, and September showed significant negative trends (p&lt;0.1) at Padiyathalawa. The results displayed by all three tests were in very good agreement for rainfall. ITA results showed similar trend types of 77%, 95% and 41% at Padiyathalawa and Bibile rainfall and streamflow, respectively. A significant positive (ρ&lt;0.01) strong and lower correlation between the trend value of the ITA and the Z value of the MK test were found for rainfall and streamflow, respectively. Changes in rainfall are not sufficient to explain the observed trends in streamflow. Therefore, future research should be directed at the causal aspects of streamflow change in this watershed.

Impacts of Climate Change on Hydrological Regimes in the Congo River Basin

Surface water resources are essential for a wide range of human activities, such as municipal water supply, fishing, navigation, irrigation, and hydropower. Their regime is also linked to environmental sustainability, water-related risks, human health, and various ecosystem services. Global warming is expected to modify surface water availability, quality, and distribution and therefore affect water use productivity as well as the incidence of water-related risks. Thus, it is important for communities to plan and adapt to the potential impacts of climate change. The Congo River Basin, home to 75 million people, is subject to recurrent flood and drought events, which are expected to worsen as a result of climate change. This study aims to assess future modifications of the hydrological regime of the Congo River and the socio–economic impacts of these projected changes for three future periods: 2011–2041, 2041–2070, and 2071–2100. A Soil and Water Assessment Tool (SWAT) model of the Congo River Basin was developed, calibrated, and validated using daily rainfall observations combined with daily time series of precipitation, temperatures, relative humidity, solar radiation, and wind speed derived from the WFDEI (Watch Forced Era Interim) reanalysis data set. The outputs of ten Regional Climate Models (RCMs) from the Coordinated Downscaling Experiment (CORDEX-AFRICA) were statistically downscaled to obtain future climate time series, considering two Representative Concentration Pathways: RCP8.5 and RCP4.5. The calibrated model was used to assess changes in streamflow in all reaches of the Congo River. Results suggest relative changes ranging from −31.8% to +9.2% under RCP4.5 and from −42.5% to +55.5% under RCP 8.5. Larger relative changes occur in the most upstream reaches of the network. Results also point to an overall decrease in discharge in the center and southern parts of the basin and increases in the northwestern and southeastern parts of the basin under both emission scenarios, with RCP8.5 leading to the most severe changes. River discharge is likely to decrease significantly, with potential consequences for agriculture, hydropower production, and water availability for human and ecological systems.

Projected changes in extreme streamflow and inland flooding in the mid-21st century over Northeastern United States using ensemble WRF-Hydro simulations

Study regionNortheastern United States (NEUS). Study focusWe investigate the potential impacts of climate change on precipitation, streamflow, and inland flooding in the NEUS during the mid-21st century. Dynamically downscaled climate projections from three global climate models for historical (1995–2004) and future (2045–2054) periods under business-as-usual scenarios were used to force the hydrologic model WRF-Hydro at 200-meter resolution and create ensemble hydrologic simulations. Additionally, an extreme value model was developed to project the risks associated with low-frequency hydrologic events. New hydrological insights for the regionResults from four major watersheds indicate a significantly wetter regime in winter months and potential drier conditions during late spring to early summer. Discharges in fall are projected to decrease in the northern watersheds and increase toward the south. Extreme flow and water depths resulting from extreme inland flooding are projected to increase by 5–20% and > 100%, respectively. The extent of the total flooded area is likely to be 20% greater by the mid-century. These increased risks can be attributed to (i) an approximate 25% increase in decadal mean and > 40% increase in decadal extreme precipitation intensity, (ii) up to 30% lower snow availability and 5–25% higher evapotranspiration throughout the year, and (iii) a projected 5% increase in soil moisture in all seasons except summer. Furthermore, rapid snow melting in winter will likely cause an earlier peak flow in the rivers.

Drivers of future streamflow changes in watersheds across the Northeastern United States

AbstractAccurate projections of streamflow, which have implications for flooding, water resources, hydropower, and ecosystems, are critical to climate change adaptation and require an understanding of streamflow sensitivity to climate drivers. The northeastern United States has experienced a dramatic increase in extreme precipitation over the past 25 years; however, the effects of these changes, as well as changes in other drivers of streamflow, remain unclear. Here, we use a random forest model forced with a regional climate model to examine historical and future streamflow dynamics of four watersheds across the Northeast. We find that streamflow in the cold season (November–May) is primarily driven by 3‐day rainfall and antecedent wetness (Antecedent Precipitation Index) in three rainfall‐dominant watersheds, and 30‐day rainfall, antecedent wetness, and 30‐day snowmelt in the fourth, more snowmelt‐dominated watershed. In the warm season (June–October), streamflow is driven by antecedent wetness and rainfall in all watersheds. By the end of the century (2070–2099), cold season streamflow depends on the importance placed on snow in the machine learning model, with changes ranging from −7% (with snow) to +40% (without snow) in a single watershed. Simulated future warm season streamflow increases in two watersheds (56% and 193%) due to increased precipitation and antecedent soil wetness, but decreases in the other two watersheds (−6% and −27%) due to reduced precipitation.

Quantifying the impact of climate variability and human activities on streamflow variation in Taoer River Basin, China.

Climate variability and human activity are the two driving forces that alter the hydrological cycle and spatiotemporal distribution of water resources. Using the Taoer River Basin (TRB) as an example, this study analyzed the impacts of climate variability and human activities on streamflow discharge in various periods and the resulting hydrological alterations. First, historical streamflow data were divided into four periods (baseline period and altered periods 1, 2, and 3). Based on the proposed basic identification framework, four assessment methods (the hydrological sensitivity method, distributed hydrological model, linear regression model, and runoff restoring computation) are used and provided relatively consistent estimates of streamflow attribution. Climate variability is the driving factor for streamflow changes, and the relative contributions in altered periods 1, 2, and 3 are 81% (+ 50.34mm), 68% (+ 13.37mm), and 53% (-19.23mm), respectively. In addition, climate variability and reservoir construction have different impacts on the hydrological regime at different periods, and reservoir regulation's effect on the hydrological regime depends on climatic conditions. Combined with this case study, we further discuss the necessity of breakpoint selection and period subdivision in the attribution of streamflow changes, and analyze the applicability of different methods with current ideas for improvement. This study not only has practical significance for water resource planning and adaptive policy formulation in the TRB but also provides a useful reference for similar studies.

Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data

Abstract. The alteration in river flow patterns, particularly those that originate in the Himalaya, has been caused by the increased temperature and rainfall variability brought on by climate change. Due to the impending intensification of extreme climate events, as predicted by the Intergovernmental Panel on Climate Change (IPCC) in its Sixth Assessment Report, it is more essential than ever to predict changes in streamflow for future periods. Despite the fact that some research has utilised machine-learning- and deep-learning-based models to predict streamflow patterns in response to climate change, very few studies have been undertaken for a mountainous catchment, with the number of studies for the western Himalaya being minimal. This study investigates the capability of five different machine learning (ML) models and one deep learning (DL) model, namely the Gaussian linear regression model (GLM), Gaussian generalised additive model (GAM), multivariate adaptive regression splines (MARSs), artificial neural network (ANN), random forest (RF), and 1D convolutional neural network (1D-CNN), in streamflow prediction over the Sutlej River basin in the western Himalaya during the periods 2041–2070 (2050s) and 2071–2100 (2080s). Bias-corrected data downscaled at a grid resolution of 0.25∘ × 0.25∘ from six general circulation models (GCMs) of the Coupled Model Intercomparison Project Phase 6 GCM framework under two greenhouse gas (GHG) trajectories (SSP245 and SSP585) were used for this purpose. Four different rainfall scenarios (R0, R1, R2, and R3) were applied to the models trained with daily data (1979–2009) at Kasol (the outlet of the basin) in order to better understand how catchment size and the geo-hydromorphological aspects of the basin affect runoff. The predictive power of each model was assessed using six statistical measures, i.e. the coefficient of determination (R2), the ratio of the root mean square error to the standard deviation of the measured data (RSR), the mean absolute error (MAE), the Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), and the percent bias (PBIAS). The RF model with rainfall scenario R3, which outperformed other models during the training (R2 = 0.90; RSR = 0.32; KGE = 0.87; NSE = 0.87; PBIAS = 0.03) and testing (R2 = 0.78; RSR = 0.47; KGE = 0.82; NSE = 0.71; PBIAS = −0.31) period, therefore was chosen to simulate streamflow in the Sutlej River in the 2050s and 2080s under the SSP245 and SSP585 scenarios. Bias correction was further applied to the projected daily streamflow in order to generate a reliable times series of the discharge. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between 2050s and 2080s by 0.79 % to 1.43 % for SSP585 and by 0.87 % to 1.10 % for SSP245. In addition, streamflow will increase during the monsoon (9.70 % to 11.41 % and 11.64 % to 12.70 %) in the 2050s and 2080s under both emission scenarios, but it will decrease during the pre-monsoon (−10.36 % to −6.12 % and −10.0 % to −9.13 %), post-monsoon (−1.23 % to −0.22 % and −5.59 % to −2.83 %), and during the winter (−21.87 % to −21.52 % and −21.87 % to −21.11 %). This variability in streamflow is highly correlated with the pattern of precipitation and temperature predicted by CMIP6 GCMs for future emission scenarios and with physical processes operating within the catchment. Predicted declines in the Sutlej River streamflow over the pre-monsoon (April to June) and winter (December to March) seasons might have a significant impact on agriculture downstream of the river, which is already having problems due to water restrictions at this time of year. The present study will therefore assist in strategy planning to ensure the sustainable use of water resources downstream by acquiring knowledge of the nature and causes of unpredictable streamflow patterns.