Reply on RC2

There is an urgent demand for assessments of climate change impact on the hydrological cycle at high spatialresolution. In particular, the impacts on shallow groundwater levels, which can lead to both flooding and drought, havemajor implications for agriculture, adaptation and urban planning. Predicting such hydrological impacts is typicallyperformed using physically-based hydrological models. However, such models are computationally expensive, especially athigh spatial resolutions. This study is based on the Danish national groundwater model, setup as a distributed, integrated surface-subsurface model at500 m horizontal resolution. Recently, a version at a higher resolution of 100 m was created; amongst others, to betterrepresent the uppermost groundwater table and to meet end user demands for water management and climate adaptation. Theincrease in resolution of the hydrological model, however, also increases computational bottleneck. To evaluate climatechange impact, a large ensemble of climate models was run with the 500 m hydrological model, while performing the sameensemble run with the 100 m resolution nation-wide model was deemed infeasible. The desired outputs at 100 m resolutionwere 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 ofhydrogeological settings, were run at 100 m resolution with forcings from a reduced ensemble of climate models. RandomForest algorithms were established using the simulated climate change impacts (future – present) on water table depth at 100m 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 theshallow groundwater table at 100 m resolution. These downscaled maps were successfully validated against results from avalidation submodel at 100 m resolution excluded from training the algorithms, and compared to the impact signals from the500 m hydrological model across Denmark. The suggested downscaling algorithm also opens for the spatial downscaling of other model outputs. It has the potential forfurther applications where, for example, computational limitations inhibit running distributed hydrological models at fineresolutions.

<p>Climate models project increased extreme precipitation for the coming decades, which may lead to higher soil erosion in many locations worldwide. The impact of climate change on soil erosion is most often assessed by applying a soil erosion model forced by bias-corrected climate model output. A literature review among more than 100 papers showed that many studies use different soil erosion models, bias-correction methods and climate model ensembles. In this study, we assessed how these differences affect the outcome of climate change impact assessments on soil erosion. The study was performed in two contrasting Mediterranean catchments (SE Spain), where climate change is projected to lead to a decrease in annual precipitation sum and an increase in extreme precipitation, based on the RCP8.5 emission scenario. First, we assessed the impact of soil erosion model selection using the three most widely used model concepts, i.e. a model forced by precipitation (RUSLE), a model forced by runoff (MUSLE), and a model forced by precipitation and runoff (MMF). Depending on the model, soil erosion in the study area is projected to decrease (RUSLE) or increase (MUSLE and MMF). The differences between the model projections are inherently a result of their model conceptualization, such as a decrease of soil loss due to decreased annual precipitation sum (RUSLE) and an increase of soil loss due to increased extreme precipitation and, consequently, increased runoff (MUSLE). An intermediate result is obtained with MMF, where a projected decrease in detachment by raindrop impact is counteracted by a projected increase in detachment by runoff. Second, we evaluated the implications of three bias‐correction methods, i.e. delta change, quantile mapping and scaled distribution mapping. Scaled distribution mapping best reproduces the raw climate change signal, in particular for extreme precipitation. Depending on the bias‐correction method, soil erosion is projected to decrease (delta change) or increase (quantile mapping and scaled distribution mapping). Finally, we assessed the effect of climate model ensembles on soil erosion projections. We showed that individual climate models may project opposite changes with respect to the ensemble average, hence, climate model ensembles are essential in soil erosion impact assessments to account for climate model uncertainty. We conclude that in climate change impact assessments it is important to select a soil erosion model that is forced by both precipitation and runoff, which under climate change may have a contrasting effect on soil erosion. Furthermore, the impact of climate change on soil erosion can only accurately be assessed with a bias‐correction method that best reproduces the projected climate change signal, in combination with a representative ensemble of climate models.</p>

Almost no hydrological model takes into account that changes in evapotranspiration are affected by how the vegetation responds to changing CO2 and climate. This severely limits their ability to quantify the impact of climate change on evapotranspiration and thus water resources. We developed a simple approach for mimicking, in hydrological models, the impact of active vegetation on potential evapotranspiration (PET) under climate change. This approach can be applied for climate change impact studies by hydrological models that compute PET as a function of net radiation and temperature only, i.e., using the Priestley-Taylor (PT) equation. Our approach is based on the work of Milly and Dunne (2016) (MD), which compared the change of non-water-stressed actual evapotranspiration (NWSAET) as computed by an ensemble of global climate models (GCM) with various methods for computing PET change. MD proposed to estimate the impact of climate change on PET as a function of only the change in net energy input at the land surface, i.e., to neglect changes in other climate variables. The new mimicking approach (PT-MA) for application in hydrological models retains the impact of temperature on daily to interannual as well as spatial PET variations but removes the impact of the long-term temperature trend on PET such that long-term changes in future PET are driven by changes in net radiation only. We implemented the PT-MA approach in the global hydrological model WaterGAP 2.2d and computed daily time series of PET between 1901 and 2099 using the bias-adjusted output of four GCMs for RCP8.5. With PT-MA, increases of GCM-derived NWSAET between the end of the 20th and the end of the 21st century are simulated well by WaterGAP, while severely overestimated with the standard PT. Application of the mimicking approach in WaterGAP results in smaller future decreases or larger future increases in renewable water resources (RWR) as compared to neglecting active vegetation, except in a small number of grid cells where increased inflow from upstream due to increased upstream runoff leads to enhanced evapotranspiration from surface water bodies or irrigated fields. On about 20 % of the global land area, the mimicking approach leads to an increase of RWR that is more than 20 % higher than when neglecting the active vegetation, while on more than 10 % of the global land area, the projected RWR decrease is lowered by more than 20 %. We recommend applying the mimicking approach to assess climate change hazards by any hydrological model that does not include stomatal conductance when calculating PET.

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analyzed and the result of a research experiment is presented using model weighting with the participation of 6 climate model experts and 6 hydrological model experts. For the experiment, seven climate models are a-priori selected from a larger Euro-CORDEX ensemble of climate models and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual elicitations of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological impact modellers in general are more open for assigning weights to different models in a multi model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models, and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only reestablish a uniform weight between climate models.

The rapid development of free and open-access hydrological models and coupling framework tools continues to present more opportunities for coupled model development for improved assessment of floodplain hydrology. In this study, we set up an Upper Zambezi hydrological model and a fully spatially hydrological-hydrodynamic coupled model for the Barotse Floodplain using GLOFRIM (GLObally applicable computational FRamework for Integrated hydrological–hydrodynamic Modelling). The hydrological and hydrodynamic models used are WFLOW and LISFLOOD-FP, respectively. The simulated flows generated by the wflow model for the upstream gauge stations before the Barotse Floodplain were quite similar and closely matched the observed flow as indicated by the evaluation statistics; Chavuma, nse = 0.738; kge = 0.738; pbias = 2.561 and RSR = 0.511; Watopa, nse = 0.684; kge = 0.816; pbias = 10.577 and RSR = 0.557; and Lukulu, nse = 0.736; kge = 0.795; pbias = 10.437 and RSR = 0.509. However, even though the wflow hydrological model was able to simulate the upstream hydrology very well, the results at the floodplain outlet gauge stations did not quite match the observed monthly flows at Senanga gauge station as indicated by the evaluation statistics: nse = 0.132; kge = 0.509; pbias = 37.740 and RSR = 0.9233. This is mainly because the representation of both floodplain channel hydrodynamics and vertical hydrological processes is necessary to correctly capture floodplain dynamics. Thus, the need for an approach that saves as a basis for developing fully spatially distributed coupled hydrodynamic and hydraulic models’ assessments for groundwater dependent tropical floodplains such as the Barotse floodplain, in closing the gap between hydrology and hydrodynamics in floodplain assessments. A fully coupled model has the potential to be used in implementing adaptive wetland management strategies for water resources allocation, environmental flow (eflows), flood control, land use and climate change impact assessments. Doi: 10.28991/HEF-2022-03-02-09 Full Text: PDF

Hydrological models are commonly used to quantify the hydrological impacts of climate change using general circulation model (GCM) simulations as input. However, application of the model results with respect to future changes in streamflow scenarios remains limited by the large uncertainties stemming from various sources. Therefore, this study aimed to explore uncertainties involved in climate change impact assessment in Hulu Langat Basin, Malaysia, and define the contribution of uncertainty sources to the final uncertainty level. Hydrological model parameters, GCMs, and emission scenario uncertainties were considered the main uncertainty contributors in local-scale impact studies. The equidistant quantile matching method is used to bias-correct simulations of 19 GCMs under two emission scenarios of RCP4.5 and RCP8.5. The Soil and Water Assessment Tool (SWAT) hydrological model is next run by the bias-corrected GCM data to generate a wide spectrum of future streamflow scenarios. Projected monthly streamflow pattern under RCP8.5 showed a different temporal pattern from the observed one. Hydrological model parameter uncertainty was proven to be a larger uncertainty contributor than emission scenario during baseline climate. GCM and emission scenario uncertainties escalated as progressed in time and GCM uncertainty showed larger increments. The monthly pattern of effect of each uncertainty source varied when comparing the two periods of 2030s and 2080s. Therefore, for a superior management of water resources, a study of climate change impacts and uncertainty sources on a smaller scale than the decadal or annual scales can be more informative to the decision makers.

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

Climate change impact assessments form the basis for the development of suitable climate change adaptation strategies. For this purpose, ensembles consisting of stepwise coupled models are generally used [emission scenario → global circulation model → downscaling approach (DA) → bias correction → impact model (hydrological model)], in which every item is affected by considerable uncertainty. The aim of the current study is (1) to analyse the uncertainty related to the choice of the DA as well as the hydrological model and its parameterization and (2) to evaluate the vulnerability of the studied catchment, a subcatchment of the highly anthropogenically impacted Spree River catchment, to hydrological change. Four different DAs are used to drive four different model configurations of two conceptually different hydrological models (Water Balance Simulation Model developed at ETH Zürich and HBV‐light). In total, 452 simulations are carried out. The results show that all simulations compute an increase in air temperature and potential evapotranspiration. For precipitation, runoff and actual evapotranspiration, opposing trends are computed depending on the DA used to drive the hydrological models. Overall, the largest source of uncertainty can be attributed to the choice of the DA, especially regarding whether it is statistical or dynamical. The choice of the hydrological model and its parameterization is of less importance when long‐term mean annual changes are compared. The large bandwidth at the end of the modelling chain may exacerbate the formulation of suitable climate change adaption strategies on the regional scale. Copyright © 2013 John Wiley & Sons, Ltd.

<strong class="journal-contentHeaderColor">Abstract.</strong> Hydrological modelling is at the core of most studies related to water, especially for anticipating disasters, managing water resources, and planning adaptation strategies. Consequently, teaching hydrological modeling is an important, but difficult, matter. Teaching hydrological modeling requires appropriate software and teaching material (exercises, projects); however, although many hydrological modeling tools exist today, only few are adapted to teaching purposes. In this article, we present the airGRteaching package, which is an open-source R package relying on the GR rainfall-runoff models. In this package, thanks to a graphical user interface and a limited number of functions, numerous hydrological modelling exercises representing a wide range of hydrological applications are proposed. To ease its use by students and teachers, the package contains several vignettes describing complete projects that can be proposed to investigate various topics such as streamflow reconstruction, hydrological forecasting, and assessment of climate change impact.

&amp;lt;p&amp;gt;Climate change (CC) already have widespread and significant impacts in Europe, which is expected to increase in the future. Groundwater plays a vital role for the land phase of the freshwater cycle and have the capability of buffering or enhancing the impact from extreme climate events causing droughts or floods, depending on the subsurface properties and the status of the system (dry/wet) prior to the climate event. Understanding and taking the hydrogeology into account is therefore essential in the assessment of climate change impacts.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The Geological Survey Organisations (GSOs) in Europe compile the necessary data and knowledge of the groundwater systems across Europe. The overall vision of the project &amp;amp;#8220;Tools for Assessment of ClimaTe change ImpacT on Groundwater and Adaptation Strategies &amp;amp;#8211; TACTIC&amp;amp;#8221; is to enhance the utilisation of these data and knowledge of the subsurface system in CC impact assessments, and the identification and analyses of potential adaptation strategies. To reach this vision, the objective of TACTIC is to contribute to the development of coherent and transparent assessments of CC impacts on groundwater and surface water, supporting improved EU policy making, and providing decision support for stakeholders and decision makers. To accomplish this, an infra-structure among European Geological Survey Organisations are developed in TACTIC to foster advancement and harmonisation of CC assessments, made up by: 1) The TACTIC Toolbox, consisting of relevant tools and methods for CC impact assessments, 2) TACTIC guidelines that will guide GSOs and other relevant stakeholders on the selection of appropriate tools and their use for producing comparable results, 3) The European Geological Data Infrastructure (EGDI) where data, reports and open-access papers will be stored &amp;amp;#160;and made freely available &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The project is centred around 40 pilot studies covering a variety of CC challenges as well as different hydrogeological settings and different management systems found in Europe. The pilot activities are coordinated centrally in the project, to ensure that assessments, to the extent possible, are harmonised and can be compared across pilots. Synthesizing the experiences and results from the pilots will enable the development of a guideline and future roadmap, with the aim of 1) encouraging more GSOs to contribute in CC impact assessments 2) providing guidance to make the learning curve less steep and 3)ensuring that new assessments are comparable with assessments conducted in TACTIC.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;TACTIC is part of the Horizon 2020 ERA-NET on Applied Geoscience (GeoERA) and together with the three other GeoERA groundwater projects, TACTIC will provide new and important data for further development of the European Geological Data Infrastructure (EGDI) with publicly available data enabling the development of EU-wide decision support systems for sustainable management of subsurface resources in a changing climate.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 731166.&amp;lt;/p&amp;gt;

This study is an attempt to quantify the impact of climate change on the hydrology of Armur watershed in Godavari river basin, India. A GIS-based semi-distributed hydrological model, soil and water assessment tool (SWAT) has been employed to estimate the water balance components on the basis of unique combinations of slope, soil and land cover classes for the base line (1961–1990) and future climate scenarios (2071–2100). Sensitivity analysis of the model has been performed to identify the most critical parameters of the watershed. Average monthly calibration (1987–1994) and validation (1995–2000) have been performed using the observed discharge data. Coefficient of determination (R^{2}), Nash–Sutcliffe efficiency (ENS) and root mean square error (RMSE) were used to evaluate the model performance. Calibrated SWAT setup has been used to evaluate the changes in water balance components of future projection over the study area. HadRM3, a regional climatic data, have been used as input of the hydrological model for climate change impact studies. In results, it was found that changes in average annual temperature (+3.25 °C), average annual rainfall (+28 %), evapotranspiration (28 %) and water yield (49 %) increased for GHG scenarios with respect to the base line scenario.

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.

Abstract. Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

The planning of river basin management should utilize a high-resolution, process-based hydrological model to tackle issues such as diffuse pollution, drought, flood forecasting, and the impact of climate change. The studies available to date only encompass five meso-scale and one large-scale river basins in Ukraine. The objective of this study is to calibrate the Soil and Water Assessment Tool (SWAT) for all Ukrainian river basins, including upstream transboundary parts. The model could potentially assist in land management and assessing the impact of agriculture on water resources; hence, considerable attention is paid to agricultural practices and crop rotations. The Soil and Water Assessment Tool (SWAT) is a process-based semi-distributed hydrological model developed by the United States Department of Agriculture's Agricultural Research Service (USDA-ARS) in collaboration with numerous institutions. SWAT is widely used for simulating the impact of land management practices on water resources, including water quantity and quality, as well as assessing the overall environmental impact of land use and climate changes. The watershed, encompassing transboundary areas, covers an area of 873,600 km2, with Ukraine accounting for 68.7% of it. The inputs for the model consist of topography, river network, merged national soil maps with the properties for each soil polygon and underlying horizons, land cover, and agricultural practices such as crop rotations, fertilization, and operation schedules. In calibrating the model, we arranged daily discharge data from 56 gauges, snow cover from 61 locations, and crop yields of primary crops. The modeling period spans 41 years from 1980 to 2020. The modeling results are evaluated based on three criteria: the Nash-Sutcliffe coefficient (NS), the coefficient of determination (R2), and the percent bias (PBIAS). The model is available via a user-friendly web platform that features an interactive map of Ukrainian subbasins. Users can inspect the model inputs for each subbasin and monitor the daily dynamics of key outputs: river discharge, water flow components, evapotranspiration, soil water, and snow cover. The results can be downloaded as an image or a CSV file for further research. The hydrological model of Ukraine has the potential to address a wide range of issues related to water and agriculture: water supply, flood forecasting, soil water availability, water quality, the impact of climate change, and so on. The model will be expanded in the future to include sediment and nutrient transport.

The goal was to analyze hydrological modeling as strategic tool to assess the impact of land use and climate change on hydrological resources in agricultural systems. The documentary research technique was applied, through the process of collecting and systematizing information. A bibliometric study was carried out to know the scientific production between 2013 and 2023 registered in SCOPUS. Bibliometric network maps were built and indicators were generated. Results showed that hydrological modeling has increased in the last decade both internationally and in Mexico. In the international context the following lines of research have been developed: i) surface runoff at the basin level (37%), ii) hydrological models for comprehensive water management (22%) and iii) impact of climate change on the hydrological balance (20%). In Mexico, they have mainly been developed on: i) vulnerability and risk to floods (25%); ii) surface runoff (18%) and iii) impact of climate change on the hydrological balance (17%). Our findings show that the use of the SWAT model stands out in most of the research topics worldwide and in Mexico. This is because it is the most robust model according to the review. There are evidence on the need to delve deeper into the impact of the hydrological balance in agricultural production systems and the effect they will have under climate change conditions.