Future Runoff Projections Research Articles

Projections of future streamflow for Australia informed by CMIP6 and previous generations of global climate models

Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts.

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

Quantifying the contribution of SWAT modeling and CMIP6 inputting to streamflow prediction uncertainty under climate change

Assessing the impacts of climate change on hydrologic systems is essential for establishing adaptive strategies for water resources management, flood risk control, mitigation, and ecological protection. Such assessments are typically performed by obtaining future levels of climate factors such as precipitation and temperature from climate models and coupling them with hydrological models to simulate future hydrological processes. Different algorithm choices for the hydrological cycle module lead to non-negligible uncertainties in the simulation results. Based on a case study of a small watershed located in the upper Huaihe River, China, we develop a SWAT model-based analyzing workflow that integrates two evapotranspiration calculation methods (Haegeraves and Penman-Monteith), two river routing models (Variable Storage and Muskingum), and four calibrated parameters sets. This workflow can identify the uncertainties from algorithms of the internal modules of the hydrological model, model parameterization, and climate model (i.e., General Circulation Models, GCMs, and climate scenarios in Shared Socioeconomic Pathways, SSPs) in the future runoff projections, and quantify their relative contributions. The results show that (1) the monthly runoff will increase in June and August and have little change in other months in 2021–2060, and increase in all months in 2061–2100; (2) annual mean runoff (Qm) and annual maximum runoff (Qp) will increase by 1.8% and 2.6% in the 2040s, and 14.7% and 18.6% in the 2080s; (3) the results of Bayesian model averaging (BMA) analysis indicates the probability of increase in Qm and Qp in all emission scenarios save the SSP 1–2.6 in the 2080s, and especially the probability of increase in Qp will be up to 97%; (4) for Qm, the evapotranspiration calculation method is the main source of uncertainty, accounting for more than half of the total uncertainty; and for Qp, GCM, SSP, and their interaction are the main sources of uncertainty with the contribution of about 70%. In contrast, the uncertainty arising from the parameterization of the hydrological model is small and can be ignored.

Future runoff projections based on land change using integrated Markov-Cellular Automata model and Soil Water Assessment Tool in Lam Pachi Basin, Thailand

Future runoff projections based on land change using integrated Markov-Cellular Automata model and Soil Water Assessment Tool in Lam Pachi Basin, Thailand

Uncertainty Impacts of Climate Change and Downscaling Methods on Future Runoff Projections in the Biliu River Basin

This paper assesses the uncertainties in the projected future runoff resulting from climate change and downscaling methods in the Biliu River basin (Liaoning province, Northeast China). One widely used hydrological model SWAT, 11 Global Climate Models (GCMs), two statistical downscaling methods, four dynamical downscaling datasets, and two Representative Concentration Pathways (RCP4.5 and RCP8.5) are applied to construct 22 scenarios to project runoff. Hydrology variables in historical and future periods are compared to investigate their variations, and the uncertainties associated with climate change and downscaling methods are also analyzed. The results show that future temperatures will increase under all scenarios and will increase more under RCP8.5 than RCP4.5, while future precipitation will increase under 16 scenarios. Future runoff tends to decrease under 13 out of the 22 scenarios. We also found that the mean runoff changes ranging from −38.38% to 33.98%. Future monthly runoff increases in May, June, September, and October and decreases in all the other months. Different downscaling methods have little impact on the lower envelope of runoff, and they mainly impact the upper envelope of the runoff. The impact of climate change can be regarded as the main source of the runoff uncertainty during the flood period (from May to September), while the impact of downscaling methods can be regarded as the main source during the non-flood season (from October to April). This study separated the uncertainty impact of different factors, and the results could provide very important information for water resource management.

Relevance and Scale Dependence of Hydrological Changes in Glacierized Catchments: Insights from Historical Data Series in the Eastern Italian Alps

Glaciers have an important hydrological buffering effect, but their current rapid reduction raises concerns about future water availability and management. This work presents a hydrological sensitivity analysis to different climatic and glacier cover conditions, carried out over four catchments with area between 8 and 1050 km2, and with glacierization between 2% and 70%, in the Italian Alps. The analysis is based on past observations, and exploits a unique dataset of glacier change and hydro-meteorological data. The working approach is aimed at avoiding uncertainties typical of future runoff projections in glacierized catchments. The results highlight a transition from glacial to nival hydrological regime, with the highest impacts in August runoff over smaller catchments. The buffering effect of current glaciers has largely decreased if compared to the Little Ice Age, up to 75% for larger catchments, but it is still important during warm and dry summers like 2003. We confirm a non-linear relationship between glacier contribution in late summer and catchment area/percent glacierization. The peak in runoff attributable to glacier melt, expected in the next 2–3 decades on highly glacierized alpine catchments, has already passed in the study area.

Conjunctive management of surface and groundwater resources under projected future climate change scenarios

This study introduces a mixed integer linear fractional programming (MILFP) method to optimize conjunctive use of future surface water and groundwater resources under projected climate change scenarios. The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. Future inflows to the reservoirs were estimated from the future runoffs projected through hydroclimate modeling considering the Variable Infiltration Capacity model, and 11 sets of downscaled Coupled Model Intercomparison Project phase 5 global climate model projections. Bayesian model averaging was adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. Optimized conjunctive management solutions were investigated for a water supply network in northern Louisiana which includes the Sparta aquifer. Runoff projections under climate change scenarios indicate that runoff will likely decrease in winter and increase in other seasons. Results from the developed conjunctive management model with MILFP indicate that the future reservoir water, even at 2.5% low inflow cumulative probability level, could counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraints.

Hydro-Meteorological Drought Projections into the 21-st Century for Selected Polish Catchments

The nature of drought conditions is estimated using a range of indices describing different aspects of drought events. Three drought indices are evaluated, namely the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Runoff Index (SRI), using observed hydroclimatic data and applying them to hydro-meteorological projections into the 21st century. The first two indices are evaluated using only meteorological variables and from this point of view, are better suited to meteorological drought projections than the third index, SRI, which is based on catchment discharge and represents hydrological drought. We assess information contained in those indices and their suitability to catchment scale climate projection drought assessment in ten selected Polish catchments, representing different hydro-climatic conditions, which are used as a case study. Projections of climatic variables (precipitation and temperature) are obtained from the EURO-CORDEX initiative derived from seven climate models at a grid resolution of 12.5 km for the time period 1971–2100. Future runoff projections for the catchments are obtained using a conceptual rainfall-runoff model (HBV). The results of analyses of indices based on observations in the reference period show consistent estimates for most of the catchments. Hydro-meteorological climate model projections for three periods, including the reference period 1971–2000, and two 30-year periods, near-future 2021–2050 and far-future 2071–2100, are used to estimate changes of future drought conditions in the catchments studied. The results show a substantial variation of temporal drought patterns over the catchments and their dependence on projected precipitation and temperature variables and the type of indices applied. Of the three indices studied, only SPEI projections indicate drier conditions in the catchments in the far-future period. The other two indices, SPI and SRI, indicate wetter climates in the future.

A Comparison Study of Runoff Projections for Yongdam Dam Watershed Using SWAT

In this study, reliable future runoff projections based on RCPs for Yongdam dam watershed was performed using SWAT model, which was validated by k-fold cross validation method, and investigated the factors that cause the differences with respect to runoff projections between this study and previous studies. As a result, annual average runoff compared to baseline runoff would increase 17.7% and 26.1% in 2040s and 2080s respectively under RCP8.5 scenario, and 21.9% and 44.6% in 2040s and 2080s respectively under RCP4.5 scenario. Comparing the results to previous studies, minimum and maximum differences between runoff projections over different studies were 10.3% and 53.2%, even though runoff was projected by the same rainfall-runoff model. SWAT model has 27 parameters and physically based complex structure, so it tends to make different results by the model users` setting. In the future, it is necessary to reduce the cause of difference to generate standard runoff scenarios.

Implication of calibration period on modelling climate change impact on future runoff

Abstract. This paper explores the consideration and implication of calibration period on the modelled climate change impact on future runoff. The results show that modelled runoff and hydrologic responses can be influenced by the choice of historical data period used to calibrate and develop the hydrological model. Modelling approaches that do not take this into account may therefore underestimate the range and uncertainty in future runoff projections. Nevertheless, the uncertainty associated with the choice of hydrological models and consideration of calibration dataset for modelling climate change impact on runoff is likely to be small compared to the uncertainty in the future rainfall projections.

Comparison of future runoff projections using Budyko framework and global hydrologic model: conceptual simplicity vs process complexity

Comparison of future runoff projections using Budyko framework and global hydrologic model: conceptual simplicity vs process complexity

Stream Flow Response to Skilled and Non-linear Bias Corrected GCM Precipitation Change in the Wami River Sub-basin, Tanzania

The reliability of stream flow projection under changing climate cannot be guaranteed if the General Circulation Model (GCM) used for the projection of future climate does not predict well its past climate. In this study stream flows in the Wami River sub-basin were simulated under changing climate by the skilled and non-linear bias corrected GCM using a physically based and semi distributed rainfall runoff model, SWAT. The SWAT model was setup using the terrain, land use, soil, precipitation and temperature data. The baseline water uses were used to naturalise stream flows and the SWAT model was calibrated and validated using the historical stream flows. In addressing future runoff projections the domestic, livestock, irrigation and industrial water demands in the subbasin were projected to the year 2039 using the current irrigation area growth rates, Tanzania vision 2025 and development plans for the Wami River sub-basin. The GCMs were incorporated in the hydrological model so as to factor in the effects of climate change. Precipitation was selected as the changing climatic variable for projection because runoff is very sensitive to precipitation as compared to other climatic variables like temperature. A total of twenty four GCMs from CMIP3 database representing twentieth century precipitation were interpolated into forty five sub-catchments in the subOriginal Research Article British Journal of Environment & Climate Change, 4(4): 389-408, 2014 390 basin and evaluated for their skills. The HADCM3 model was selected due to its highest skill score in predicting past climate. Then the HADCM3 precipitation signal of scenario A2, was corrected by Non-linear Bias Correction (NBC) in the forty five sub-catchments in the sub-basin and used to simulate future stream flow. The results of stream flow simulated using skilled and non-linear corrected HADCM3 precipitation signal shows that stream flow is projected to increase for the near term climatology (2010 – 2039).

Sources of uncertainty in modeling the glaciohydrological response of a Karakoram watershed to climate change

[1] In the headwater catchments of the main Asian rivers, glaciohydrological models are a useful tool to anticipate impacts of climatic changes. However, the reliability of their projections strongly depends on the quality and quantity of data that are available for parameter estimation, model calibration and validation, as well as on the accuracy of climate change projections. In this study the physically oriented, glaciohydrological model TOPKAPI-ETH is used to simulate future changes in snow, glacier, and runoff from the Hunza River Basin in northern Pakistan. Three key sources of model uncertainty in future runoff projections are compared: model parameters, climate projections, and natural climate variability. A novel approach, applicable also to ungauged catchments, is used to determine which model parameters and model components significantly affect the overall model uncertainty. We show that the model is capable of reproducing streamflow and glacier mass balances, but that all analyzed sources of uncertainty significantly affect the reliability of future projections, and that their effect is variable in time and in space. The effect of parametric uncertainty often exceeds the impact of climate uncertainty and natural climate variability, especially in heavily glacierized subcatchments. The results of the uncertainty analysis allow detailed recommendations on network design and the timing and location of field measurements, which could efficiently help to reduce model uncertainty in the future.

Extracting information from an ensemble of GCMs to reliably assess future global runoff change

Future runoff projections derived from different global climate models (GCMs) show large differences. Therefore, within this study the, information from multiple GCMs has been combined to better assess hydrological changes. For projections of precipitation and temperature the Reliability ensemble averaging (REA) method was introduced to calculate weighted average ensemble change with an accompanying uncertainty range. In this study the original REA method is compared with three other methods that calculate runoff change by selecting or weighting GCMs on their inter-model similarity for the current and future climate. All methods are applied to distributed runoff fields calculated with the hydrological model PCR-GLOBWB forced with meteorological data from an ensemble of 12 GCMs. Differences between weighted and non-weighted average runoff changes for 2100 are small. Within a validation experiment, where GCM ensemble mean change is derived between the time-slices 1961–1975 and 1976–1990, the non-weighted ensemble change resembled observed change best. Yet, both the weighted and non-weighted average change were too conservative. This underscores the importance of considering an uncertainty range alongside the ensemble average change. In this study the uncertainty range (or 95% confidence interval) is defined by four times the root mean square difference around the ensemble mean change. The uncertainty range derived with the non-weighted method is relatively wide, upper and lower uncertainty bounds show large biases from observed change. However, the uncertainty range was reliably be reduced by using only a selection of GCMs which show high inter-model similarity for the current and future climate.

Influence of Rainfall Scenario Construction Methods on Runoff Projections

Abstract The future rainfall series used to drive hydrological models in most climate change impact studies is informed by global climate models (GCMs). This paper compares future runoff projections in ∼11 000 0.25° grid cells across Australia from a daily rainfall–runoff model driven with future daily rainfall series obtained using three simple scaling methods, informed by 14 GCMs. In the constant scaling and daily scaling methods, the historical daily rainfall series is scaled by the relative difference between GCM simulations for the future and historical climates. The constant scaling method scales all the daily rainfall by the same factor, and the daily scaling method takes into account changes in the daily rainfall distribution by scaling the different daily rainfall amounts differently. In the daily translation method, the GCM future daily rainfall series is translated to a 0.25° gridcell rainfall series using the relationship established between the historical GCM-scale rainfall and 0.25° gridcell rainfall data. The daily scaling and daily translation methods generally give higher extreme and annual runoff than the constant scaling method because they take into account the increase in extreme daily rainfall (which generates significant runoff) simulated by the large majority of the GCMs. However, the difference between the mean annual runoff simulated with future daily rainfall series obtained using the constant versus daily scaling methods is generally less than 5%, which is relatively smaller than the range of runoff results from the different GCMs of 30%–40%.