Comment on hess-2021-597

Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods

Climate change impact assessment on hydropower generation using multi-model climate ensemble

Understanding the interaction between terrestrial microclimate, hydrology and \necology is a key to determining the effect of land-use and climate change on \nhydrological systems. Traditionally, the hydrological impacts of climate change have \nbeen based on driving hydrological models with the output of regional climate \nmodels. These climate models often operate at spatial and temporal scales that are \nmuch larger than the scales required to analyse the effects on the hydrological \nsystem. This is in part because of computational limitations and in part because of \nthe physics of the regional models do not justify much higher resolution. \nFurthermore there is an inherent contradiction in this approach since these climate \nmodels include their own hydrological model component. Similarly in analysing the \nhydrological effects of land-use change the feedback to the meteorological system is \noften neglected.\n\nTo address these issues a dynamically coupled hydrological and meteorological model \nsystem has been developed for evaluating interactions at hydrological (catchment) \nscales. Such a coupled system provides a unique framework for investigation of land \nsurface - atmosphere interactions at hydrological scales. A comprehensive \nhydrological modelling system describing the terrestrial component of the \nhydrological cycle MIKE SHE has been modified to allow coupling to a local scale \nmeteorological model. The coupling exploits the European Open Modelling Interface \nand Environment (OpenMI) Open MI to link the two models systems. As simulations can \nbe run with and without coupling to the meteorological model, it is possible to \nevaluate the impact of feedbacks between the two systems on hydrological \npredictions. The uncoupled system has been validated against remote sensing and \neddy correlation measurements at the field and landscape scale describing the \nhydrological and energy fluxes on the land-surface. The coupled system is then \nvalidated against field data describing both the atmosphere and hydrological system. \nFinally, a sensitivity analysis is carried out to examine the sensitivity of \nhydrological predictions to atmospheric feedbacks to identify where feedback will \nsignificantly affect the simulated impact.

Climatic changes have altered hydrological and climatic parameters worldwide, and climate projections suggest that such alterations will continue. In order to maintain the sustainable development and acquire the knowledge of water availability, climatic projection must be coupled with hydrological models. In this study, Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models output were integrated with a calibrated hydrological model, Soil and Water Assessment Tools (SWAT) to evaluate the potential effect of climate change on green and blue water over Upper Narmada river Basin (UNB). Therefore, top three representative climate models (MIROC5, CNRM-CM5 and MPI-ESM-LR) from 24 CMIP5 climate models were selected for hydrological modelling. Selected representative climate model outputs were bias corrected by distribution mapping to remove systematic bias correction. Multi-site model calibration approaches indicated Nash Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) as 0.77 and 0.76 for calibration (1978–1995), and 0.73 and 0.70 for validation (1996–2005), respectively. Calibrated model was run for baseline period (1970–2000) and three futuristic period P1 (2011–2040), P2 (2041–2070) and P3 (2071–2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios. Results indicated annual precipitation decreasing under RCP4.5 and RCP8.5 scenarios changes in green and blue water varying from 16.22 to −14.10% (CNRM,P3) under RCP4.5 and from 38.25 to −22.57% under RCP8.5 with reference to baseline scenario. This study established the sensitivity of UNB to future climatic changes employing projections from CMIP5 climate models and exhibited an approach that applied multiple climate model outputs to estimate potential change over the river basin.

<p>Snow cover is an essential climate variable directly affecting the earth energy balance. Proper description and assimilation of snow cover information into hydrological, land surface, meteorological and climate models are critical to address the impact of snow on various phenomena, to predict local snow water resources and to warn about snow-related natural hazards. This induces a challenging problem of bridging information from micro-structural scales of the snowpack up to the grid resolution in models. </p><p>These questions are included in the actions of the Nordic Snow Network, NordSnowNet, http://nordsnownet.fmi.fi/, a recent project launched in 2019 under the Nordic Arctic Co-operation Programme of the Nordic Council of Ministers, which is administered by Nordregio and contributes to enhancing knowledge about the Arctic region (https://nordregioprojects.org/arctic-programme/nordic-snow-network-nordsnownet/). The point of view of the network will be focused on snow in Nordic and related Arctic areas: making existing Nordic-Arctic research and snow data from observations and models (forecasts and assimilated observations by weather and hydrology models, projections by the climate models) visible for the researcher, data user and education communities. It supports snow-related research and development of applications by the exchange of information and data, arranging workshops, training and supporting also informal Nordic researcher contacts and meetings. For example, citizen observations will be collected on color snow and ice, to estimate the appearance of dirty snow due to deposition of atmospheric aerosols and algae existing on snow and ice, in co-operation with the Ministry of Foreign Affairs of Finland’s project IBA-FIN-BCDUST (2019-2020, PI Meinander FMI, https://en.ilmatieteenlaitos.fi/iba-project). The color of snow and ice has effect on the snow and ice albedo, which in turn influences snow and ice melt.</p><p>The project is managed by the Finnish Meteorological Institute (FMI) by Ali Nadir Arslan, and the steering committee with representatives from each partner country, including Finland (Outi Meinander, FMI), Denmark (Kristian Pagh Nielsen, Danish Meteorological Institute), Estonia (Marko Kaasik, University of Tartu), Greenland (Kirsty Langley, Asiaq Greenland), Iceland (Pavla Dagsson-Waldhauserova, Agricultural Institute of Iceland), Sweden (Patrick Samuelsson, Swedish Meteorological Institute), and Norway (Mariken Homleid, Norwegian Meteorological Institute). The partners are Nordic meteorological institutes and collaborative universities and research institutes. The first field work activity will be in the Arctic Space Centre in Sodankylä, Finland, in early April 2020. The network is open and welcomes collaborators to join by contacting the corresponding country representative.</p><p>We gratefully acknowledge the Nordic Council of Ministers Arctic Co-operation Programme and Nordregio for their support to the NordSnowNet.</p>

<div><span>Snow as a major part of the cryosphere is an important component of Earth’s hydrological cycle and energy balance. Understanding the microstructural, macrophysical, thermal and optical properties of the snowpack is essential for integration into numerical models and there is a great need for accurate snow data at different spatial and temporal resolutions to address the challenges of changing snow conditions.</span></div><div><span>Physical snow properties are currently determined by traditional ground-based measurements as well as remote sensing, over a range of temporal and spatial scales, following considerable developments in instrument technology over recent years. </span></div><div><span>Data assimilation (DA)</span><span> methods are widely used</span> <span>to combine data from different observations</span><span> with numerical model using uncertainties </span><span>of observed and modeled variables  to produce an optimal estimate. DA provides a reliable improvement of the initial states of the numerical model and a benefit for hydrological and snow model forecasts. </span></div><div> </div><div><span>European efforts to harmonize approaches for validation, and methodologies of snow measurement practices, instrumentation, algorithms and data assimilation techniques were coordinated by the European Cooperation in Science and Technology (COST) Action ES1404 “HarmoSnow”, entitled, “A European network for a harmonized monitoring of snow for the benefit of climate change scenarios, hydrology and numerical weather prediction” (2014-2018) .</span></div><div><span>One of the key objectives of the action was “Advance the application of snow DA in numerical weather prediction (NWP) and hydrological models, and show its benefit for weather and hydrological forecasting as well as other applications.” </span></div><div><span>One key result from COST HarmoSnow is a better knowledge about the diversity of usage of snow observations in DA, forcing, monitoring, validation, or verification within NWP, hydrology, snow and climate models. The main parts of this knowledge are retrieved from a COST HarmoSnow survey exploring the common practices on the use of snow observations in different modeling environments. We will show results from the survey and their implications towards standardized and improved usage of snow observations in various data assimilation applications.</span></div>

The future bloom and risk of blossom frosts for Malus domestica were projected using regional climate realizations and phenological ( = impact) models. As climate impact projections are susceptible to uncertainties of climate and impact models and model concatenation, the significant horizon of the climate impact signal was analyzed by applying 7 impact models, including two new developments, on 13 climate realizations of the IPCC emission scenario A1B. Advancement of phenophases and a decrease in blossom frost risk for Lower Saxony (Germany) for early and late ripeners was determined by six out of seven phenological models. Single model/single grid point time series of bloom showed significant trends by 2021–2050 compared to 1971–2000, whereas the joint signal of all climate and impact models did not stabilize until 2043. Regarding blossom frost risk, joint projection variability exceeded the projected signal. Thus, blossom frost risk cannot be stated to be lower by the end of the 21st century despite a negative trend. As a consequence it is however unlikely to increase. Uncertainty of temperature, blooming date and blossom frost risk projection reached a minimum at 2078–2087. The projected phenophases advanced by 5.5 d K−1, showing partial compensation of delayed fulfillment of the winter chill requirement and faster completion of the following forcing phase in spring. Finally, phenological model performance was improved by considering the length of day.

In a recent WRR paper, Yan et al. (2018, https://doi.org/10.1002/2017WR021290) have derived a simple, elegant, and useful way of reanalyzing quality‐controlled SNOTEL data sets to produce quantitative estimates of water reaching the land surface during rain, snowmelt, and rain‐on‐snow events, and use these data to generate “next‐generation” intensity duration, and frequency curves for these quantities at SNOTEL measurement locations. These new data sets may prove useful as direct inputs to design processes in some cases, but I argue in this commentary that they will probably have much more important application to the validation and refinement of off‐line hydrology models and land surface schemes embedded in climate models, which can then be used to extend these data in space and time to create more comprehensive products to guide infrastructure design. If well validated, such tools can be used to extend backward in time to make detailed hindcasts of the historical record with more complete spatial and temporal coverage (which also facilitates more accurate estimation of extremes with longer return intervals) and forward in time to project future conditions that are needed to design long‐lived infrastructure in what will likely be a highly nonstationary environment. In addition, there is a need to extend these data sets to include lower elevation areas in mountain environments, and other areas of the United States and Canada with cold winter climates.

Effective precipitation duration for runoff peaks based on catchment modelling

Recently, the conceptual data-driven approach (CDDA) was proposed to correct residuals of ensemble hydrological models (HMs) using data-driven models (DDMs), followed by the stochastic CDDA (SCDDA) that used HM simulations as input to DDMs within a stochastic framework - both approaches improved ensemble HMs' simulations. Here, a new SCDDA is introduced where CDDA uncertainty is estimated (instead of DDM uncertainty in the original SCDDA). Using nine HM-DDM combinations for daily streamflow simulation in three Swiss catchments, the new SCDDA improved CDDA's mean continuous ranked probability score up to 15% and performed similarly without a snow-routine in a snowy catchment, suggesting that SCDDA may account for missing processes in HMs. The stochastic framework can convert unreliable ensemble models into more reliable (stochastic) models at the cost of simulation sharpness. The coverage probability plot is proposed as a diagnostic tool, predicting SCDDA's out-of-sample reliability using validation set data (CDDA simulations and observations).

<p>Production of a large ensemble of climate simulations suitable for impact assessments is an attempt to enhance our knowledge about the associated uncertainties in future projections. However, the actual quantification of the change in the climate and its impact relies on the ensemble of models selected, particularly given the wide availability of climatic simulations from various initiatives, i.e. CMIP5, CORDEX.</p><p>Here, we hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of climate model ensembles. We apply eight selection methods (based on democracy, diversity of GCM, diversity of RCM, maximum information minimum redundancy, best performing hindcasted climate depiction, best performing hydrological model, simple climate model averaging and reliable ensemble average) to subset an ensemble available from 16 combinations of Euro-CORDEX GCM-RCM by comparing observed to simulated streamflow shift of the Danube from a reference period (1960–1989) to an evaluation period (1990–2014). Simulations are carried out with the well-performing Upper Danube COSERO hydrological model, spanning a calibration and evaluation period of more than 100 years. Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts where methods that maintain the diversity and information content of the full ensemble are favourable. In addition, the method followed allows the assessment which individual climate models perform best, where only three of 16 models were able to correctly reproduce the direction of streamflow change in each season.</p><p>Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test can further be applied in multi-basin experiments to obtain a better understanding of uncertainty propagation and uncertainty reduction in hydrological impact studies.</p>

Recent advances in global hydrological modeling yield many global runoff datasets that are extensively used in global hydrological analyses. Here, we provide a comprehensive evaluation of simulated runoff from 21 global models, including 12 climate models from CMIP6, six global hydrological models from the Inter-Sectoral Impact Model Inter-Comparison Project (ISMIP2a) and three land surface models from the Global Land Data Assimilation System (GLDAS), against observed streamflow in 840 unimpaired catchments globally. Our results show that (i) no model performs consistently better in estimating runoff from all aspects, and all models tend to perform better in more humid regions and non-cold areas; (ii) the interannual runoff variability is well represented in ISIMIP2a and GLDAS models, and no model performs satisfactorily in capturing the annual runoff trend; (iii) the runoff intra-annual cycle is reasonably captured by all models yet an overestimation of intra-annual variability and an early bias in peak flow timing are commonly found; and (iv) model uncertainty leads to a larger uncertainty in runoff estimates than that induced by forcing uncertainty in ISIMIP2a, and model uncertainty in GLDAS is larger than that in ISIMIP2a. Finally, we confirm that the multi-model ensemble is an effective way to reduce uncertainty in individual models except for CMIP6 regarding mean annual magnitude and annual runoff trend. Overall, our findings suggest that assessments/projections of runoff changes based on these global outputs contain great uncertainties and should be interpreted with caution, and call for more advanced, observation-guided ensemble techniques for better large-scale hydrological applications.

Abstract. The annual cycle of temperature and precipitation changes as projected by climate models is of fundamental interest in climate impact studies. Its estimation, however, is impaired by natural variability. Using a simple form of the delta change method, we show that on regional scales relevant for hydrological impact models, the projected changes in the annual cycle are prone to sampling artefacts. For precipitation at station locations, these artefacts may have amplitudes that are comparable to the climate change signal itself. Therefore, the annual cycle of the climate change signal should be filtered when generating climate change scenarios. We test a spectral smoothing method to remove the artificial fluctuations. Comparison against moving monthly averages shows that sampling artefacts in the climate change signal can successfully be removed by spectral smoothing. The method is tested at Swiss climate stations and applied to regional climate model output of the ENSEMBLES project. The spectral method performs well, except in cases with a strong annual cycle and large relative precipitation changes.

We investigate the ability of hydrological multimodel ensemble predictions to enhance the skill of streamflow forecasts at short‐ to medium‐range timescales. To generate the multimodel ensembles, we implement a new statistical postprocessor, namely, quantile regression‐Bayesian model averaging (QR‐BMA). Quantile regression‐Bayesian model averaging uses quantile regression to bias correct the ensemble streamflow forecasts from the individual models and Bayesian model averaging to optimally combine their probability density functions. Additionally, we use an information‐theoretic measure, namely, conditional mutual information, to quantify the skill enhancements from the multimodel forecasts. We generate ensemble streamflow forecasts at lead times from 1 to 7 days using three hydrological models: (i) Antecedent Precipitation Index‐Continuous, (ii) Hydrology Laboratory‐Research Distributed Hydrologic Model, and (iii) Weather Research and Forecasting Hydrological modeling system. As forcing to the hydrological models, we use weather ensemble forecasts from the National Centers for Environmental Prediction 11‐member Global Ensemble Forecast System Reforecast version 2. The forecasting experiments are performed for four nested basins of the North Branch Susquehanna River, USA. We find that after bias correcting the streamflow forecasts from each model, their skill performance becomes comparable. We find that the multimodel ensemble forecasts have higher skill than the best single‐model forecasts. Furthermore, the skill enhancements obtained by the multimodel ensemble forecasts are found to be dominated by model diversity, rather than by increased ensemble size alone. This result, obtained using conditional mutual information, indicates that each hydrological model contributes additional information to enhance forecast skill. Overall, our results highlight benefits of hydrological multimodel forecasting for improving streamflow predictions.

<strong class="journal-contentHeaderColor">Abstract.</strong> There is an urgent demand for assessments of climate change impact on the hydrological cycle at high spatial<br />resolution. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, have<br />major implications for agriculture, adaptation and urban planning. Predicting such hydrological impacts is typically<br />performed using physically-based hydrological models. However, such models are computationally expensive, especially at<br />high spatial resolutions. This study is based on the Danish national groundwater model, setup as a distributed, integrated surface-subsurface model at<br />500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created; amongst others, to better<br />represent the uppermost groundwater table and to meet end user demands for water management and climate adaptation. The<br />increase in resolution of the hydrological model, however, also increases computational bottleneck. To evaluate climate<br />change impact, a large ensemble of climate models was run with the 500 m hydrological model, while performing the same<br />ensemble run with the 100 m resolution nation-wide model was deemed infeasible. The desired outputs at 100 m resolution<br />were produced by developing a novel, hybrid downscaling method based on machine learning. Hydrological models for five subcatchments, covering around 9 % of Denmark and selected to represent a range of<br />hydrogeological settings, were run at 100 m resolution with forcings from a reduced ensemble of climate models. Random<br />Forest algorithms were established using the simulated climate change impacts (future &ndash; present) on water table depth at 100<br />m resolution from those submodels as training data. The trained downscaling algorithms then were applied to create nation-wide maps of climate change-induced impacts on the<br />shallow groundwater table at 100 m resolution. These downscaled maps were successfully validated against results from a<br />validation submodel at 100 m resolution excluded from training the algorithms, and compared to the impact signals from the<br />500 m hydrological model across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential for<br />further applications where, for example, computational limitations inhibit running distributed hydrological models at fine<br />resolutions.