Precipitation-Runoff Modeling System Research Articles

Seasonal and Monthly Climate Variability in South Korea’s River Basins: Insights from a Multi-Model Ensemble Approach

This study conducts a comprehensive analysis of the impacts of climate change on South Korea’s climate and hydrology, utilizing a Multi-Model Ensemble (MME) approach with thirteen Climate Model Intercomparison Project Phase 5 (CMIP5) models under two Representative Concentration Pathways, RCP4.5 and RCP8.5. We observed an average temperature increase of up to 3.5 °C under RCP8.5 and around 2.0 °C under RCP4.5. Precipitation patterns showed an overall increase, particularly during the summer months, with increases up to 20% under RCP8.5 and 15% under RCP4.5, characterized by more intense and frequent rainfall events. Evapotranspiration rates are projected to rise by approximately 5–10% under RCP8.5 and 3–7% under RCP4.5. Runoff is expected to increase significantly, particularly in the summer and autumn months, with increases up to 25% under RCP8.5 and 18% under RCP4.5. This research focuses on employing the Precipitation Runoff Modeling System (PRMS) to project future streamflow across South Korea, with an emphasis on both monthly and seasonal scales to understand the varying impacts of climate change on different river basins. These climatic changes have profound implications for agriculture, urban water management, and ecosystem sustainability, stressing the need for dynamic and region-specific adaptation measures. This study emphasizes the critical role of localized factors, such as topography, land use, and basin-specific characteristics, in influencing the hydrological cycle under changing climatic conditions.

Benchmarking high-resolution hydrologic model performance of long-term retrospective streamflow simulations in the contiguous United States

Abstract. Because use of high-resolution hydrologic models is becoming more widespread and estimates are made over large domains, there is a pressing need for systematic evaluation of their performance. Most evaluation efforts to date have focused on smaller basins that have been relatively undisturbed by human activity, but there is also a need to benchmark model performance more comprehensively, including basins impacted by human activities. This study benchmarks the long-term performance of two process-oriented, high-resolution, continental-scale hydrologic models that have been developed to assess water availability and risks in the United States (US): the National Water Model v2.1 application of WRF-Hydro (NWMv2.1) and the National Hydrologic Model v1.0 application of the Precipitation–Runoff Modeling System (NHMv1.0). The evaluation is performed on 5390 streamflow gages from 1983 to 2016 (∼ 33 years) at a daily time step, including both natural and human-impacted catchments, representing one of the most comprehensive evaluations over the contiguous US. Using the Kling–Gupta efficiency as the main evaluation metric, the models are compared against a climatological benchmark that accounts for seasonality. Overall, the model applications show similar performance, with better performance in minimally disturbed basins than in those impacted by human activities. Relative regional differences are also similar: the best performance is found in the Northeast, followed by the Southeast, and generally worse performance is found in the Central and West areas. For both models, about 80 % of the sites exceed the seasonal climatological benchmark. Basins that do not exceed the climatological benchmark are further scrutinized to provide model diagnostics for each application. Using the underperforming subset, both models tend to overestimate streamflow volumes in the West, which could be attributed to not accounting for human activities, such as active management. Both models underestimate flow variability, especially the highest flows; this was more pronounced for NHMv1.0. Low flows tended to be overestimated by NWMv2.1, whereas there were both over and underestimations for NHMv1.0, but they were less severe. Although this study focused on model diagnostics for underperforming sites based on the seasonal climatological benchmark, metrics for all sites for both model applications are openly available online.

Stochastic Watershed Model Ensembles for Long‐Range Planning: Verification and Validation

AbstractDeterministic watershed models (DWMs) are used in nearly all hydrologic planning, design, and management activities, yet they cannot generate streamflow ensembles needed for hydrologic risk management (HRM). The stochastic component of DWMs is often ignored in practice, leading to a systematic bias in extreme events. Since traditional stochastic streamflow models used in HRM struggle to account for anthropogenic change, there is a need to convert DWMs into stochastic watershed models (SWMs) to generate ensembles for use in HRM. A DWM can be converted to an SWM using a post‐processing (pp) approach to add error to the DWM predictions. Many pp methods advanced in the area of flood forecasting are useful in HRM and for correcting extreme event biases. Selecting a suitable error model for pp is challenging due to nonnormality, skewness, heteroscedasticity, and autocorrelation. We develop a parsimonious pp method based on an autoregressive (AR) model of the logarithm of the ratio of the observations and simulations, which leads to AR model residuals that are approximately symmetric and independent. We document the value of pp for improving flood and low flow frequency analysis and we reintroduce the concepts of verification and validation of stochastic streamflow ensembles to ensure that the SWM can reproduce both statistics it was and was not designed to reproduce, respectively. These concepts are illustrated on a Massachusetts basin using the USGS Precipitation Runoff Modeling System, with an additional analysis indicating the approach may be applicable to 1,225 other sites across the United States.

Simulating the Hydrologic Response to Climate Change in Three New Zealand Headwater Basins Using CMIP6 Datasets

Abstract Air temperature and precipitation outputs from 10 CMIP6 GCMs were input to the Precipitation-Runoff Modeling System hydrologic model to evaluate water and energy responses in three headwater basins to projected climate change over the twenty-first century. The headwater basins (398–801 km2) are located within the Mataura River basin in the South Island of New Zealand. CMIP6 datasets included two emission scenarios [shared socioeconomic pathways (SSPs) SSP2-4.5 and SSP5-8.5]. Half of the 10 GCMs selected in the study have equilibrium climate sensitivity (ECS) values above 4.5°C, which has been considered the upper end of equilibrium climate sensitivity. Modeling results included increased annual air temperature, evapotranspiration, and precipitation by the end of the twenty-first century for both SSP emissions scenarios, both high- and low-ECS GCMs, and all three headwater basins. Monthly precipitation and evapotranspiration totals also increased for all or most months. Monthly streamflow changes generally corresponded with monthly precipitation changes. Snowpack decreased significantly in depth and seasonal duration in all basins. However, streamflow increased for all SSP and ECS groups and basins because increased precipitation was consistently greater than increased evapotranspiration losses. Sources of uncertainty include the GCMs, climate sensitivity, downscaling, bias adjustment, emission scenarios, and the hydrologic model. Simulated hydrologic responses based on climate data from GCMs with ECS values of greater than 4.5°C could be less plausible since previous studies have suggested true ECS ranges from 1.5° to 4.5°C.

Insight into Hurricane Maria Peak Daily Streamflows from the Development and Application of the Precipitation-Runoff Modeling System (PRMS): Including Río Grande de Arecibo, Puerto Rico, 1981–2017

The Precipitation-Runoff Modeling System (PRMS) was used to develop a simulation of watershed hydrology on the island of Puerto Rico for the period 1981–2017, concentrating on the Río Grande de Arecibo, a river with some of the highest streamflows on the island. This development is part of the U.S. Geological Survey’s (USGS) National Hydrologic Model (NHM) infrastructure which supports coordinated, comprehensive, and consistent hydrologic modeling at the watershed scale for the coterminous United States (CONUS). A goal of the NHM program is to expand the domain outside of CONUS, leading to a PRMS application in Puerto Rico. This model was used to simulate the effects of Hurricane Maria on daily streamflow and provide information at locations where streamgages were damaged by the hurricane. Comparisons with streamflow estimates made by indirect methods in the field, up to ten times higher than simulated values, lends insight into the uncertainties in both the indirect methods and model simulated values and helps to identify potential error in the daily streamflow estimates. The PRMS can be applied to look at the effects of changes in climate and land use, water management, industrial and public water usage, and many other factors that affect hydrology on the island of Puerto Rico. The model is also designed as a support tool for the USGS National Water Census which provides comprehensive reporting of national information on withdrawal, conveyance, consumptive use, and return flow by water-use category.

HydroBench: Jupyter supported reproducible hydrological model benchmarking and diagnostic tool

Evaluating whether hydrological models are right for the right reasons demands reproducible model benchmarking and diagnostics that evaluate not just statistical predictive model performance but also internal processes. Such model benchmarking and diagnostic efforts will benefit from standardized methods and ready-to-use toolkits. Using the Jupyter platform, this work presents HydroBench, a model-agnostic benchmarking tool consisting of three sets of metrics: 1) common statistical predictive measures, 2) hydrological signature-based process metrics, including a new time-linked flow duration curve and 3) information-theoretic diagnostics that measure the flow of information among model variables. As a test case, HydroBench was applied to compare two model products (calibrated and uncalibrated) of the National Hydrologic Model - Precipitation Runoff Modeling System (NHM-PRMS) at the Cedar River watershed, WA, United States. Although the uncalibrated model has the highest predictive performance, particularly for high flows, the signature-based diagnostics showed that the model overestimates low flows and poorly represents the recession processes. Elucidating why low flows may have been overestimated, the information-theoretic diagnostics indicated a higher flow of information from precipitation to snowmelt to streamflow in the uncalibrated model compared to the calibrated model, where information flowed more directly from precipitation to streamflow. This test case demonstrated the capability of HydroBench in process diagnostics and model predictive and functional performance evaluations, along with their tradeoffs. Having such a model benchmarking tool not only provides modelers with a comprehensive model evaluation system but also provides an open-source tool that can further be developed by the hydrological community.

Evaluation of cloud seeding on the securement of additional water resources in the Boryeong Dam Basin, Korea

This study evaluates the effect of cloud seeding on the increase of runoff or securing of additional water resources. Unlike previous studies, this study concentrates more on the change of various components of the hydrologic processes. Both the event-based analysis and the annual analysis are conducted to evaluate the change of these components. The Boryeong Dam Basin in Korea is considered as a study basin. The WRF (Weather Research and Forecasting) model is used in the cloud seeding simulation, while the PRMS (Precipitation Runoff Modeling System) model is used in the rainfall–runoff analysis. The target years of this study are 2018 and 2019. As results, the event-based analysis shows that the infiltration, effective rainfall, interflow, and direct runoff are increased relatively more among the eight rainfall-runoff components considered in this study. In particular, the increase of interflow is noticeable. More than 60 % of the increased rainfall is found to contribute to the runoff increase. In the annual analysis, this ratio is found to become even higher. The ratio is estimated to be more than 90 %, which is mostly due to the contribution of baseflow. As important factors controlling the effectiveness of cloud seeding, the effects of antecedent soil moisture and threshold daily rainfall depth on the increase of total runoff are also evaluated. The results show that wet soil condition is more favorable to the successful cloud seeding experiment to secure additional water resources. Also, it is confirmed that the cloud seeding experiment can be more effective when the daily rainfall is more than 20 mm. Finally, the results of this study imply the efficiency of cloud seeding and its economic values are potentially high.

Assessment of Termination Criteria at Each Drought Response Stage on Climate Change in a Multi-Purpose Dam

In this study, the termination criteria at each drought response stage were shown to increase storage volume while maintaining the stability of the water supply service during drought in multi-purpose dams. For 52 of the Representative Concentration Pathway (RCP) 4.5/8.5 scenarios of the IPCC (Intergovernmental Panel on Climate Change) fifth assessment report (AR5), dam inflows were calculated through the precipitation-runoff modeling system (PRMS), and time reliability and supply reliability were estimated. Moreover, given the application of termination criteria at each drought response stage, the volume of additional water supply was calculated, while the number of days for additional supply availability of water for residential and industrial use and water for residential, industrial, and agricultural use and river maintenance was quantified as well. The CMCC-CMS(RCP4.5) GCM exhibited the largest volume of additional water supply at 74.15 million m3, which corresponds to 52.0 days of water for residential and industrial use and to 47.7 days of water for residential, industrial, and agricultural use and river maintenance. The analysis revealed that the volume and the number of days of additional water supply also increased for other GCMs due to the application of termination criteria at each drought response stage.

Development and evaluation of temperature-induced variable source area runoff generation model

In permafrost regions, temperature and precipitation play decisive roles in hydrological processes. Soil freeze–thaw cycles in the active layer cause the runoff generation process to show multiple mechanisms and seasonal alternating patterns. In this study, based on the precipitation-runoff modeling system implemented in the Java modeling framework Object Modeling System (PRMS-OMS), we developed a runoff generation model with temperature-induced variable source area (TVSA) for permafrost regions by introducing an active layer parameterization scheme, an active layer freeze–thaw module, a glacier module, and the sub-permafrost groundwater module. The TVSA model was calibrated and validated using field observations in Fenghuoshan (FHS), a typical permafrost watershed, and Tuotuohe (TTH), a typical permafrost-glacier catchment, on the Qinghai-Tibet Plateau (QTP). During the calibration period, parameters related to the thermal conditions of the active layer and runoff processes were calibrated using the observed freeze and thaw depth data and the discharge data. For the validation period, the model successfully reproduced the freeze–thaw processes (average root mean squared error = 0.205 m) and discharges (FHS, Nash–Sutcliffe efficiency coefficient (NSE) = 0.99; TTH, NSE = 0.85). The TVSA model is a powerful tool for the system identification of variable source runoff generation processes and the associated physical mechanism under temperature control in permafrost basins; additionally, the model accurately reproduces hydrological processes and the associated response to climate change.

Hydrologic Effects of Fire in a Sub-Alpine Watershed: AgES Outperforms Previous PRMS Simulations

HighlightsAgES and PRMS accurately simulated the dramatic change in streamflow response to precipitation after fire.Measured annual streamflow increased by 170% in 4-year postfire period with similar precipitation.Postfire AgES parameters were drastically reduced for snow and rain interception, canopy density, and ET.Decreased soil depression storage and field capacity also contributed to increased streamflow in AgES.AgES shows promise for simulating hydrologic impacts of sub-alpine forest resource management and fire response.Abstract. Forest fires alter hydrologic responses to precipitation, and streamflow changes can be challenging to simulate. Streamflow data before and after wildfire in a 14 km2 mixed-conifer, sub-alpine watershed in south-central New Mexico were used to calibrate a watershed model (Agricultural Ecosystems Services, AgES) using four years of prefire and postfire conditions. Streamflow results and parameter values from AgES and a prior Precipitation Runoff Modeling System (PRMS) modeling study in the same watershed were compared. Both AgES and PRMS simulated the dramatic change in streamflow response to precipitation after a fire, including a smaller precipitation threshold to induce streamflow. Still, AgES had substantially less bias and better performance when comparing monthly metrics. Only AgES was assessed at the daily scale. AgES simulated daily streamflow accurately throughout the study period, with slight overestimation in the prefire period (Oct 2007 to Oct 2011, 1.2% bias, 0.90 Nash-Sutcliffe efficiency [NSE]) and in the postfire period (Oct 2013 to Oct 2017, 4.4% bias, 0.54 NSE). Although each 4-year model period had nearly identical cumulative precipitation, annual streamflow increased by 170% in the postfire period compared to the prefire period. Appropriate soil and vegetation parameters were modified by the stepwise calibration process to represent the effects of fire in AgES: interception storage was decreased for rain (-99.8%) and snow (-26%), two factors influencing ET were decreased, soil depression storage was reduced (-97%), and soil field capacity was reduced (-50%). AgES demonstrated an improved streamflow simulation compared with PRMS, calibrated parameter shifts that were more consistent to interpret, and particular skill in simulating daily streamflow response to fire. Knowledge gained from this study will guide future simulations of hydrologic responses to fires in the western USA using AgES. Keywords: Agricultural Ecosystems Services model, Forest, Landscape change, Precipitation-Runoff Modeling System, Streamflow change, Surface hydrology, Wildfire.

Assessment of the potential changes in low flow projections estimated by Coupled Model Intercomparison Project Phase 5 climate models at monthly and seasonal scales

AbstractThis study assesses changes in low flow seasonality in response to climate change using Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. The precipitation‐runoff modelling system (PRMS) model was applied to project future streamflow using 13 global climate models (GCMs) under two emissions scenarios (representative concentration pathway [RCP] 4.5 and RCP8.5). Then, daily projected streamflows for each month in the reference and future periods were used to assess the monthly variation in low flows in Korean river basins. The results showed that high seasonal variation exists in low flows during the wet and dry seasons, including increasing low flows during the dry season and decreasing low flows during the wet season. The low flows are projected to decrease during the wet season by −6.48% in the 2025 period and by −2.59% in the 2085 period under RCP4.5. Under RCP8.5, the low flow is projected to decline in the wet season by −1.72% in 2025 and −4.35% in 2085. However, during the dry season, the low flow is projected to decrease slightly in the 2025 period (−0.49%) but increase significantly in the 2085 period (12.87%) under RCP4.5. Additionally, the low flow is projected to increase in the near future under RCP8.5 (1.87%). The results reveal that under RCP4.5, most Korean river basins are projected to shift from winter low flows to summer low flows. However, under RCP8.5, except for the Han River basin, which is projected to shift from winter low flows to summer low flows, other basins are expected to retain winter low flows. The findings of this study reveal that more consideration of future low flow conditions is needed, and a robust need exists for the preparation of an adaptation plan to ensure a sustainable water supply to prevent severe water shortages during low flows.

Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts

Abstract. Multi-year droughts in Mediterranean climates may shift the water balance, that is, the partitioning rule of precipitation across runoff, evapotranspiration, and sub-surface storage. Mechanisms causing these shifts remain largely unknown and are not well represented in hydrologic models. Focusing on measurements from the headwaters of California's Feather River, we found that also in these mixed rain–snow Mediterranean basins a lower fraction of precipitation was partitioned to runoff during multi-year droughts compared to non-drought years. This shift in the precipitation–runoff relationship was larger in the surface-runoff-dominated than subsurface-flow-dominated headwaters (−39 % vs. −18 % decline of runoff, respectively, for a representative precipitation amount). The predictive skill of the Precipitation Runoff Modeling System (PRMS) hydrologic model in these basins decreased during droughts, with evapotranspiration (ET) being the only water-balance component besides runoff for which the drop in predictive skill during drought vs. non-drought years was statistically significant. In particular, the model underestimated the response time required by ET to adjust to interannual climate variability, which we define as climate elasticity of ET. Differences between simulated and data-driven estimates of ET were well correlated with accompanying data-driven estimates of changes in sub-surface storage (ΔS, r=0.78). This correlation points to shifts in precipitation–runoff relationships being evidence of a hysteretic response of the water budget to climate elasticity of ET during and after multi-year droughts. This hysteresis is caused by carryover storage offsetting precipitation deficit during the initial drought period, followed by vegetation mortality when storage is depleted and subsequent post-drought vegetation expansion. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including the climate elasticity of ET and better accounting for actual subsurface water storage in not only soil, but also deeper regolith that stores water accessible to roots. This can be done by explicitly parametrizing carryover storage and feedback mechanisms capturing vegetation response to atmospheric demand for moisture.

Hydrologic impacts of climate change in relation to Ontario’s source water protection planning program

Water management activities are currently predicated on the assumption of a stationary climate, despite the reality of climate change. Hydrologic impacts of climate change for three sub-watersheds north of Toronto for 2041–70 were investigated using the precipitation-runoff modeling system to model six global climate model projections from each of representative concentration pathway (RCP) 2.6, RCP 4.5, and RCP 8.5. Annual groundwater recharge, evapotranspiration, and the 7Q20 low streamflow statistic were projected to change from 1976–2005 conditions by −2.2% to +20.5%, +0.9% to +14.4%, and −25.5% to +9.8%, respectively. Seasonal shifts included an earlier date of peak streamflow for the majority of simulations and a +14.0% to +103.9% increase in winter recharge. A steady-state MODFLOW model was employed as a preliminary assessment into the effects of climate change on source water protection outputs. The results of this research further the understanding of climate change impacts on human and ecological systems in southern Ontario.

Future shift in winter streamflow modulated by the internal variability of climate in southern Ontario

Abstract. Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties mainly due to the different scenarios of greenhouse gas emissions, global climate models (GCMs) or the choice of the hydrological model. The internal variability of climate, defined as the chaotic variability of atmospheric circulation due to natural internal processes within the climate system, is also a source of uncertainties to consider. Uncertainties of internal variability can be assessed using hydrological models fed by downscaled data of a global climate model large ensemble (GCM-LE), but GCM outputs have too coarse of a scale to be used in hydrological modeling. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a 50-member ensemble downscaled from the Canadian Earth System Model version 2 Large Ensemble (CanESM2-LE), was developed to simulate local climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under an RCP8.5 scenario were used as input in the Precipitation Runoff Modeling System (PRMS) to simulate streamflow at a near-future horizon (2026–2055) for four watersheds in southern Ontario. To investigate the role of the internal variability of climate in the modulation of streamflow, the 50 members were first grouped in classes of similar projected change in January–February streamflow and temperature and precipitation between 1961–1990 and 2026–2055. Then, the regional change in geopotential height (Z500) from CanESM2-LE was calculated for each class. Model simulations showed an average January–February increase in streamflow of 18 % (±8.7) in Big Creek, 30.5 % (±10.8) in Grand River, 29.8 % (±10.4) in Thames River and 31.2 % (±13.3) in Credit River. A total of 14 % of all ensemble members projected positive Z500 anomalies in North America's eastern coast enhancing rain, snowmelt and streamflow volume in January–February. For these members the increase of streamflow is expected to be as high as 31.6 % (±8.1) in Big Creek, 48.3 % (±11.1) in Grand River, 47 % (±9.6) in Thames River and 53.7 % (±15) in Credit River. Conversely, 14 % of the ensemble projected negative Z500 anomalies in North America's eastern coast and were associated with a much lower increase in streamflow: 8.3 % (±7.8) in Big Creek, 18.8 % (±5.8) in Grand River, 17.8 % (±6.4) in Thames River and 18.6 % (±6.5) in Credit River. These results provide important information to researchers, managers, policymakers and society about the expected ranges of increase in winter streamflow in a highly populated region of Canada, and they will help to explain how the internal variability of climate is expected to modulate the future streamflow in this region.

Winter hydrometeorological extreme events modulated by large-scale atmospheric circulation in southern Ontario

Abstract. Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 ∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.

Multi-model Hydroclimate Projections for the Alabama-Coosa-Tallapoosa River Basin in the Southeastern United States

This study uses a high-resolution, process-based modeling framework to assess the impacts of changing climate on water resources for the Alabama-Coosa-Tallapoosa River Basin in the southeastern United States. A 33-member ensemble of hydrologic projections was generated using 3 distributed hydrologic models (Precipitation-Runoff Modeling System, Variable Infiltration Capacity, and Distributed Hydrology Soil Vegetation Model) of different complexity. These hydrologic models were driven by dynamically downscaled and bias-corrected future climate simulations from 11 Coupled Model Intercomparison Project Phase 5 global climate models under Representative Concentration Pathway 8.5 emission scenario, with 40 years each in baseline (1966–2005) and future (2011–2050) periods. The hydroclimate response, in general, projects an increase in mean seasonal precipitation, runoff, and streamflow. The high and low flows are projected to increase and decrease, respectively, in general, suggesting increased likelihood of extreme rainfall events and intensification of the hydrologic cycle. The uncertainty associated with the ensemble hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice of climate model is more critical than the choice of hydrologic model for the studied region. This study provides in-depth insights of hydroclimate response and associated uncertainties to support informed decisions by water resource managers.

Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement

Abstract. This paper studies how to improve the accuracy of hydrologic models using machine-learning models as post-processors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving-window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics.

Calibration of the US Geological Survey National Hydrologic Model in Ungauged Basins Using Statistical At-Site Streamflow Simulations

In the absence of measured streamflow, statistically simulated daily streamflow can be used to support the ability of physical models to represent hydrologic processes at ungauged locations. This study determined the feasibility of using statistical simulations in place of measured streamflow to calibrate physical models in ungauged basins. Daily streamflow was simulated at each of the 1,410 gauged watersheds using a cross-validated implementation of pooled ordinary kriging (POK). In this manner, the streamflow at each gauge was simulated as if no at-site streamflow information were available. The National Hydrologic Model application of the Precipitation-Runoff Modeling System was then calibrated through two separate procedures: (1) with measured streamflow, and (2) with statistically simulated streamflow in lieu of measured streamflow. Calibrating with statistically simulated streamflow produced performance within 23% of the performance of applications with knowledge of at-site measurements. Furthermore, statistically generated streamflow produced accurate timing information, which, when combined with alternative data sets (e.g., evapotranspiration, recharge, and so forth), can be used to improve representation of hydrologic processes at ungauged locations.

Atmospheric circulation amplifies shift of winter streamflow in southern Ontario

Flooding is a major concern for Canadian society as it is the costliest natural disaster type in Canada. Southern Ontario, which houses one-third of the Canadian population, is located in an area of high vulnerability for floods. The most significant floods in the region have historically occurred during the months of March and April due to snowmelt coupled with extreme rain events. However, during the last three decades, there has been a shift of flooding events to earlier months. The aim of this study was to understand the impacts of atmospheric circulation on the temporal shift of streamflow and high flow events observed in southern Ontario over 1957–2013 period. Predominant weather regimes over North America, corresponding to recurrent meteorological situations, were identified using a discretization of daily geopotential height at 500HpA level (Z500). A regime-normalized hypothetical temperature and precipitation dataset was constructed to quantify the contribution of atmospheric circulation on streamflow response. The hypothetical dataset was used as input in the Precipitation Runoff Modeling System (PRMS), a rainfall-runoff semi-distributed hydrological model, and applied to four watersheds in southern Ontario. The results showed an increase in the temporal frequency of the regime identified here as High Pressure (HP) close to eight occurrences per decade. Regime HP, characterized by a northern position of the polar vortex, is correlated with a positive phase of the NAO and is associated with warm and wet conditions over southern Ontario during winter. The temporal increase in HP contributed more than 40% of the increase in streamflow in winter and 30–45% decrease in streamflow in April. This atmospheric situation also contributed to increase the number of high flows by 25–50% in January. These results are important to improve the seasonal forecasting of high flows and to assess the uncertainty in the temporal evolution of streamflow in the Great Lakes region.