Future Streamflow Projections Research Articles

Comparative assessment of empirical random forest family's model in simulating future streamflow in different basin of Sarawak, Malaysia

In Sarawak, a region highly vulnerable to climate change, the translation of climate-induced changes in rainfall to river flow is non-linear, presenting a challenge for water resource managers. This research investigates the impact of climate change on hydrological processes in Sarawak, Malaysia, with a specific focus on assessing future spatiotemporal variations in streamflow. The families of Random Forest (RF) empirical models based on data mining techniques were compared and utilized to develop a continuous hydrologic model. Then, by incorporating rainfall and evapotranspiration future projection prepared based on RF past performance statistical downscaling, the top performing RF empirical hydrological model was used for future streamflow projection. The results showed that despite an expected increase in rainfall, the RFR (Random Forest by Randomization) empirical hydrological model demonstrated a potential decrease in river runoff due to heightened evapotranspiration demands associated with rising temperatures. The examination of climate-induced alterations in both rainfall and evapotranspiration patterns revealed a consistent decrease in river discharges during the early to middle period across Sarawak, followed by a shift towards an increasing trend by the end of the 21st century. The central region along the Rajang basin exhibited a prevailing decrease in river discharge, with contrasting patterns in the last part of the century. The northern region displayed diverse trends, with some basins experiencing decreases in river runoff despite augmented rainfall, emphasizing the heterogeneity in response. By employing empirical models, and projecting future scenarios, the study contributes to a better understanding of climate change impacts on hydrology in the region, essential for effective water resource management and environmental conservation.

Stress Testing California's Hydroclimatic Whiplash: Potential Challenges, Trade‐Offs and Adaptations in Water Management and Hydropower Generation

AbstractInter‐annual precipitation in California is highly variable, and future projections indicate an increase in the intensity and frequency of hydroclimatic “whiplash.” Understanding the implications of these shocks on California's water system and its degree of resiliency is critical from a planning perspective. Therefore, we quantify the resilience of reservoir services provided by water and hydropower systems in four basins in the western Sierra Nevada. Using downscaled runoff from 10 climate model outputs, we generated 200 synthetic hydrologic whiplash sequences of alternating dry and wet years to represent a wide range of extremes and transitional conditions used as inputs to a water system simulation model. Sequences were derived from upper (wet) and lower (dry) quintiles of future streamflow projections (2030–2060). Results show that carryover storage was negatively affected in all basins, particularly in those with lower storage capacity. All basins experienced negative impacts on hydropower generation, with losses ranging from 5% to nearly 90%. Reservoir sizes and inflexible operating rules are a particular challenge for flood control, as in extremely wet years spillage averaged nearly the annual basins' total discharge. The reliability of environmental flows and agricultural deliveries varied depending on the basin, intensity, and duration of whiplash sequences. Overall, wet years temporarily rebound negative drought effects, and greater storage capacity results in higher reliability and resiliency, and lesser volatility in services. We highlight potential policy changes to improve flexibility, increase resilience, and better equip managers to face challenges posed by whiplash while meeting human and environmental needs.

Assessment of Climate Change Impact on Hydropower Generation: A Case Study for Três Marias Power Plant in Brazil

Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning.

The role of internal climate variability on future streamflow projections

Uncertainty about the future impacts of climate change represents a significant barrier to implementing adaptation measures. This work explores the impact of internal climate variability on streamflow projections for 133 catchments across the eastern and northeastern United States. Using data from a single model initial-condition large ensemble (SMILE) at high spatial and temporal resolution, this work assesses the magnitude of anthropogenic climate change and internal climate variability on projected future streamflow. The impact of catchment size is studied by grouping catchments into three different size classes (less than500 km2, between 500 and 1000 km2, and greater than 1000 km2). Results show that in a warmer climate, low to middle quantiles of future streamflow will systematically decrease, while the upper quantiles will increase. Increases are largest for more extreme streamflow indices. Using three different approaches, the role of internal variability is studied to estimate the time of emergence (TOE). In this case, results show that the climate change signal of extreme floods and droughts emerges later than that of median flow quantiles, even though the changes for floods as droughts is more significant. There is a clear relationship between catchment size and TOE, with small catchments seeing an earlier TOE for floods, and a later one for droughts. These results provide insight into adaptation times for small to large watersheds.

Climate Change Impacts on Streamflow in the Krishna River Basin, India: Uncertainty and Multi-Site Analysis

In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.

Assessing the Physical Realism of Deep Learning Hydrologic Model Projections Under Climate Change

AbstractThis study examines whether deep learning models can produce reliable future projections of streamflow under warming. We train a regional long short‐term memory network (LSTM) to daily streamflow in 15 watersheds in California and develop three process models (HYMOD, SAC‐SMA, and VIC) as benchmarks. We force all models with scenarios of warming and assess their hydrologic response, including shifts in the hydrograph and total runoff ratio. All process models show a shift to more winter runoff, reduced summer runoff, and a decline in the runoff ratio due to increased evapotranspiration. The LSTM predicts similar hydrograph shifts but in some watersheds predicts an unrealistic increase in the runoff ratio. We then test two alternative versions of the LSTM in which process model outputs are used as either additional training targets (i.e., multi‐output LSTM) or input features. Results indicate that the multi‐output LSTM does not correct the unrealistic streamflow projections under warming. The hybrid LSTM using estimates of evapotranspiration from SAC‐SMA as an additional input feature produces more realistic streamflow projections, but this does not hold for VIC or HYMOD. This suggests that the hybrid method depends on the fidelity of the process model. Finally, we test climate change responses under an LSTM trained to over 500 watersheds across the United States and find more realistic streamflow projections under warming. Ultimately, this work suggests that hybrid modeling may support the use of LSTMs for hydrologic projections under climate change, but so may training LSTMs to a large, diverse set of watersheds.

Different Hydroclimate Modelling Approaches Can Lead to a Large Range of Streamflow Projections under Climate Change: Implications for Water Resources Management

The paper compares future streamflow projections for 133 catchments in the Murray–Darling Basin simulated by a hydrological model with future rainfall inputs generated from different methods informed by climate change signals from different global climate models and dynamically downscaled datasets. The results show a large range in future projections of hydrological metrics, mainly because of the uncertainty in rainfall projections within and across the different climate projection datasets. Dynamical downscaling provides simulations at higher spatial resolutions, but projections from different datasets can be very different. The large number of approaches help provide a robust understanding of future hydroclimate conditions, but they can also be confusing. For water resources management, it may be prudent to communicate just a couple of future scenarios for impact assessments with stakeholders and policymakers, particularly when practically all of the projections indicate a drier future in the Basin. The median projection for 2046–2075 relative to 1981–2010 for a high global warming scenario is a 20% decline in streamflow across the Basin. More detailed assessments of the impact and adaptation options could then use all of the available datasets to represent the full modelled range of plausible futures.

Optimal Implementation of Climate Change Adaptation Measures to Ensure Long-term Sustainability on Large Irrigation Systems

Observed and projected consequences of climate change on streamflow generated in the Pyrenees threatens the long-term sustainability of water resources systems downstream, especially those with high irrigation demands. To tackle this challenge, the participation of stakeholders in defining potential adaptation strategies is crucial to building awareness and capacity for the community, providing agreed solutions, and reducing conflict. However, there is also a need for a top-down approach to incorporate other, large-scale, or innovative adaptation strategies. This article describes a bottom-up-meets-top-down approach to estimate the optimal implementation intensity of adaptation strategies under different climate scenarios on a complex water resources system. Future streamflow projections were used in a water allocation model combined with a Markov Chain Monte Carlo sampling process to obtain optimal combinations of measures to meet different sustainability objectives. The methodology was applied to the Gállego-Cinca River system in NE Spain, which relies on water from the Pyrenees. A stakeholder workshop identified storage development and irrigation modernisation as the preferred adaptation options. However, the modelling results show that more storage in the basin, especially on-farm reservoirs, is not enough to maintain current sustainability levels. This will enable the adoption of demand management measures that optimise water use despite not being among stakeholder preferences.

Land use, climate, and water change in the Vietnamese Mekong Delta (VMD) using earth observation and hydrological modeling

Study regionThe Vietnamese Mekong Delta (VMD) is located in Vietnam Study focusThe Vietnamese Mekong Delta (VMD) region has one of the leading productions of rice in the world and it stands at the intersection of extreme anthropogenic activity and climate change. To this end, the major focus of this study is to explore the changes in land use, climate, water resources, and their inter-relationship, which are intended to showcase the ability of publicly available earth observations and models in improving understanding of the past changes and future scenarios and contribute to improved decision making. We analyzed the change of agricultural crops (single, double, and triple) and climatic parameters (precipitation, and land surface temperature, and evapotranspiration). Consequently, we used Soil and Water Assessment Tool Model (SWAT) and selected six GCMs for extreme climate to investigate the change of streamflow. New hydrological insightsOur results indicated that double rice crop and aquaculture are the top two land use categories in the VMD, the areas of triple rice crop have increased significantly, especially for the An Giang and Dong Thap provinces. However, by examining the climate, water, and land data analytics, we see challenges in the expansion of triple rice crop over VMD. The spatio-temporal changes in climate variables and future streamflow projections provide strong evidence to water resources managers and decision-makers in the VMD.

Implication of bias correction on climate change impact projection of surface water resources in the Gidabo sub-basin, Southern Ethiopia

AbstractClimate change impact studies that evaluated the biases of climate models' simulations showed the presence of large systematic errors in their outputs. However, many studies continue to arbitrarily select bias correction methods for error reduction. This work evaluated the implications of bias correction methods on the projections of climate change impact on streamflow of the Gidabo sub-basin, Ethiopia. Climate outputs from four global climate model and regional climate model (GCM–RCM) combinations for the representative concentration pathway (RCP4.5) scenario were used. Five bias correction methods were used to reduce the systematic errors of the simulated rainfall data. The future changes in rainfall pattern, evapotranspiration, and streamflow were analyzed by using their relative percentage difference between the projected and the baseline period. The distribution mapping method provided better results in mean and extreme rainfall cases. This is also reflected in streamflow projections, as the daily interquartile range value indicates the lowest variability of the projected streamflow. The wet season streamflow will likely decrease in the future, whereas the short rainy season streamflow will increase. Our findings show that climate models and bias correction methods considerably limit the magnitude of future projections of streamflow. However, similar research should be conducted in other catchments to extend the conclusions of this study.

Climate Change Impacts on Inflows into Lake Eppalock Reservoir from Upper Campaspe Catchment

Climate change has significant effects on societies and ecosystems. Due to the strong link between climate and the hydrological cycle, water resources is one of the most affected fields by climate change. It is of great importance to investigate climate change effects on streamflows by producing future streamflow projections under different scenarios to create adaptation measures and mitigate potential impacts of climate change. The Upper Campaspe Catchment (UCC), located at North Central Victoria in Australia, is a significant catchment as it provides a large portion of total inflow to the Lake Eppalock Reservoir, which supplies irrigation to the Campaspe Irrigation district and urban water to Bendigo, Heathcote, and Ballarat cities. In this study, climate change effects on monthly streamflows in the UCC was investigated using high resolution future climate data from CSIRO and MIROC climate models in calibrated IHACRES hydrological model. The IHACRES model was found to be very successful to simulate monthly streamflow in UCC. Remarkable streamflow reductions were projected based on the climate input from both models (CSIRO and MIROC). According to the most optimistic scenario (with the highest projected streamflows) by the MIROC-RCP4.5 model in near future (2035–2064), the Upper Campaspe River will completely dry out from January to May. The worst scenario (with the lowest streamflow projection) by the CSIRO-RCP8.5 model in the far future (2075–2104) showed that streamflows will be produced only for three months (July, August, and September) throughout the year. Findings from this study indicated that climate change will have significant adverse impacts on reservoir inflow, operation, water supply, and allocation in the study area.

Influence of multisite calibration on streamflow estimation based on the hydrological model with CMADS inputs

Abstract Due to the spatial heterogeneity, the hydrological model calibration results only at the total outlet of the basin may not represent the whole basin. To more accurately simulate the historical streamflow process within the Qujiang River Basin, we set up three calibration strategies (single-site, S1; multisite simultaneous, S2; and multisite sequential, S3) for four hydrological stations based on the SWAT (Soil and Water Assessment Tool) model driven by CMADS (China Meteorological Assimilation Driving Datasets for the SWAT model). In addition, the implications of these calibration issues are extended to future streamflow projections using multimodel ensemble data in CMIP6 (Coupled Model Intercomparison Project Phase 6). In the model calibration phase, the SWAT model achieved very satisfactory results in the study area. Compared with S1 and S2, S3 can effectively improve the accuracy of streamflow simulation of stations within the basin and reduce the simulation deviation. Especially at the daily scale, the average NSE values of the four stations with S3 increased by 0.26 and 0.07, and the overall deviation decreased by 0.25 and 6.43%, respectively. Parameter sensitivity analysis also shows that spatial heterogeneity can be more adequately considered when using S3 to calibrate the model. As for the results of future streamflow projection, when using the S3, the average annual streamflow of four stations in the three climate scenarios from 2021 to 2050 is about 44.21, 130.00, 321.55 and 713.24 m3/s, respectively. Correspondingly, the use of S1 and S2 would bring certain risks to future water resource management.

Changing River Network Synchrony Modulates Projected Increases in High Flows

AbstractProjections of change in high‐flow extremes with global warming vary widely among, and within, large midlatitude river basins. The spatial variability of these changes is attributable to multiple causes. One possible and little‐studied cause of changes in high‐flow extremes is a change in the synchrony of mainstem and tributary streamflow during high‐flow extremes at the mainstem‐tributary confluence. We examined reconstructed and simulated naturalized daily streamflow at confluences on the Columbia River in western North America, quantifying changes in synchrony in future streamflow projections and estimating the impact of these changes on high‐flow extremes. In the Columbia River basin, projected flow regimes across colder tributaries initially diverge with warming as they respond to climate change at different rates, leading to a general decrease in synchrony, and lower high‐flow extremes, relative to a scenario with no changes in synchrony. Where future warming is sufficiently large to cause most subbasins upstream from a confluence to transition toward a rain‐dominated, warm regime, the decreasing trend in synchrony reverses itself. At one confluence with a major tributary (the Willamette River), where the mainstem and tributary flow regimes are initially very different, warming increases synchrony and, therefore, high‐flow magnitudes. These results may be generalizable to the class of large rivers with large contributions to flood risk from the snow (i.e., cold) regime, but that also receive considerable discharge from tributaries that drain warmer basins.

Application of dynamic contributing area for modelling the hydrologic response of the Assiniboine River Basin to a changing climate

The Prairie landscape consists of numerous pothole depressions which produce complex fill-and-spill runoff generation processes that result in intermittent hydrologic connectivity and dynamic contributing areas (DCA). We investigated the effect of including DCA in the modified version of the Soil and Water Assessment Tool (SWAT) model and its implication on future streamflow projection for the pothole dominated Assiniboine River Basin (ARB). The fill-and-spill processes that lead to DCA were captured using a physically-based approach, with a volumetric threshold to reduce the computational demand. Despite the challenges in accurately simulating prairie pothole hydrology, both in terms of timing and volume of runoff, the modified approach improved streamflow modelling performance, and reduced model uncertainty. Further, we evaluated the effects of representing DCA on projecting future streamflow by using eight statistically downscaled CMIP5 GCMs, forced with the RCP4.5 and RCP8.5 scenarios. End of century projections indicate increases in annual precipitation and temperature across the ARB, with decreasing summer precipitation relative to the 1976–2005 baseline period. Compared to the standard SWAT setup that does not allow for DCA, the modified model was found to be more responsive to climatic change with relatively larger projected increases in seasonal and annual flows at the majority of evaluated stations. This advance in DCA modelling will facilitate longer-term large basin-scale simulations that are more representative for the Prairie region.

Hydropower generation and ecological operation under climate change: a case study of the downstream cascade of Lancang River hydropower plants

Climate change has intensified the conflict between economic and ecological water demands, and brought challenges to the ecological operation of hydropower plants. This study examined the effect of climate change on hydropower generation and ecological operation of hydropower plants, and investigated the response of the interactions between power generation and ecological water demand to climate change. The hydropower operation model and the future streamflow projections modeled by climate projections of several global climate models are used. The downstream cascade of Lancang River hydropower plants was used as a case study. Results show that the overall streamflow and hydrological variability were predicted to increase under climate change, and the ecological flow destruction rate was also predicted to increase. The benefit of hydropower generation and its ecological effects varied more among different operational schemes than among different climate change scenarios, indicating that future conflicts between hydropower generation and ecological water demand are largely inevitable. The increase in hydrological variability caused by climate change can exacerbate the conflict between hydropower generation and ecological water demand, causing larger costs of ecological degradation when retaining the current hydropower benefits.

Using Convolutional Neural Networks for Streamflow Projection in California

In this study, a novel temporal convolutional neural network model is developed for long-term streamflow projection in California within the CAMELS watershed regions. The ensemble performance of the model is first compared with other machine learning models for streamflow prediction. The model is further assessed through comparison with reduced models and using different hyperparameters, with results suggesting that this model correctly ascertains the physical relationship between input variables and streamflow. The stability of the model and its behavior in the extrapolated regime is assessed through an idealized extreme test with quadruple precipitation and 5 C higher temperature. Future streamflow projections are then developed using daily high-resolution LOCA statistically downscaled input products. To understand the importance of the nonlinear machine learning approach, we estimate the degree of nonlinearity in the streamflow response among input variables. Our work shows the ability and potential for TCNNs to perform future hydrology projections.

Investigating the Role of Hydrological Model Parameter Uncertainties in Future Streamflow Projections

AbstractCalibrated hydrological models forced with the climate data from various climate models have been widely employed for future streamflow projection. But a major cause of concern in such an a...

Characterizing Hydrological Drought and Water Scarcity Changes in the Future: A Case Study in the Jinghe River Basin of China

The assessment of future climate changes on drought and water scarcity is extremely important for water resources management. A modeling system is developed to study the potential status of hydrological drought and water scarcity in the future, and this modeling system is applied to the Jinghe River Basin (JRB) of China. Driven by high-resolution climate projections from the Regional Climate Modeling System (RegCM), the Variable Infiltration Capacity model is employed to produce future streamflow projections (2020–2099) under two Representative Concentration Pathway (RCP) scenarios. The copula-based method is applied to identify the correlation between drought variables (i.e., duration and severity), and to further quantify their joint risks. Based on a variety of hypothetical water use scenarios in the future, the water scarcity conditions including extreme cases are estimated through the Water Exploitation Index Plus (WEI+) indicator. The results indicate that the joint risks of drought variables at different return periods would decrease. In detail, the severity of future drought events would become less serious under different RCP scenarios when compared with that in the historical period. However, considering the increase in water consumption in the future, the water scarcity in JRB may not be alleviated in the future, and thus drought assessment alone may underestimate the severity of future water shortage. The results obtained from the modeling system can help policy makers to develop reasonable future water-saving planning schemes, as well as drought mitigation measures.

Estimating Hydrologic Vulnerabilities to Climate Change Using Simulated Historical Data: A Proof-of-Concept for a Rapid Assessment Algorithm in the Colorado River Basin

Study FocusFuture climate impacts on streamflow are of critical concern due to its importance for water and energy supplies. However, rigorous studies of such impacts involve complicated modeling chains that can be time-consuming and costly to implement. We examine an alternative approach by developing a method to predict the 21st century peak March-May (MAM) streamflow response to warming from historical data alone, specifically, past observations of the Snow-to-Precipitation ratio divided by basin Area (SPA). Study RegionUsing the Colorado River Basin (CRB) as a proving ground, we test this empirical relationship over 23 basins ranging in size from 9,728 to 639,806 km2 within the CRB. Data was derived from hydrology model simulations forced by a suite of climate models, run under two emissions scenarios. New Hydrological InsightsOur proof-of-concept study revealed useful predictive capability (r2 = 0.58) and additionally identified three response types: rain- (basin SPA < 1.2 × 10-3 km-2), snow-rain- (1.4 × 10-3 to 3 × 10-3 km-2), and snow-dominant (>3 × 10-3 km-2). The most dramatic changes occurred in snow-dominant basins where streamflow increased up to 320% in MAM, but decreased by up to 60% in the critical high demand months of July-September (JAS). By quantifying the relationship between historical data and future streamflow projections, our findings suggest this empirical relationship is useful to quickly and cheaply determine where and when large changes in hydrology will occur from future warming.

Streamflow change on the Qinghai-Tibet Plateau and its impacts

The Qinghai-Tibet Plateau (QTP), also often called the Third Pole, is considered the Water Tower of Asia because it is the source of many major Asian rivers. The environmental change on the QTP can affect the climate system over the surrounding area, and the changes in glacier and river streamflow on the QTP will lead to cascading impacts in downstream area where billions of people live. This paper reviews the hydrological observations and streamflow changes of the major Asian rivers originating from the QTP. From the 1950s to the beginning of the 21st century, streamflow on the QTP overall shows large interannual variations but no significant trends. The monthly mean streamflows during the flooding seasons are the largest in the 1960s for the outlet stations on the QTP. Annual streamflow in the source region of the Yellow River decreased while that in the source region of the Yangtze River increased slightly. No significant trends of annual streamflow have been reported for the other river source regions. The mean streamflows during peak season are relatively large in the 2000s at the river source region (upper reaches) of most rivers on the QTP. An increasing trend of streamflow in spring has been found in the upper reaches of the Yellow River, the Lancang River, the Tuotuo River (of the Yangtze River), and the Lhasa River (of the Yarlung Zangbo River). The largest month of streamflow often appears in July for most stations, but in August at the Lhasa and Nuxia stations which are located in the Yarlung Zangbo River. Streamflow changes on the QTP could be mainly attributed to changes in snow and ice, as little influence from direct human activities were found. However, the examination of the streamflow changes largely relies on the hydrological observations. So far, due to data unavailability, we are still unclear about the long-term change in the streamflow on the QTP, especially the changes in recent years. The changes in ice and snow pack on the QTP could have significant impact on the downstream water resources and ecosystem. As more water resources have been generated from ice/snow melting, from a long-term perspective, water resources would be reduced along with shrinking and disappearing glaciers. Hydrological projections under future climate change suggest that streamflow in most river source regions would increase along with precipitation and increases in ice/snow melting, and hydrological extremes such as flooding would occur more frequently. Large uncertainties across Generic Circulation Models (GCMs) and hydrological models have been found in future projections of streamflow on the QTP. Reduction of ice/snow melting would aggravate the water stress conditions for both the ecosystem and human society on the QTP and its downstream areas. Sparse hydrometeorological observations in the past, particularly in the remote region of the QTP, are a major limiting factor to studies on streamflow change and its impacts. Further efforts are urgently needed to combine the advanced observation and modeling technologies to improve the observation and simulation capabilities of the water cycle over the QTP, and to provide scientific and technological support for coping with the accelerated ice/snow melting, increasing hydrological extremes and their impacts over the QTP.