ORCID Profile
0000-0002-4914-8845
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Publisher: Copernicus GmbH
Date: 17-01-2020
Publisher: American Meteorological Society
Date: 03-2016
Abstract: Ongoing (2014–16) drought in the state of California has played a major role in the depletion of groundwater. Within California’s Central Valley, home to one of the world’s most productive agricultural regions, drought and increased groundwater depletion occurs almost hand in hand, but this relationship appears to have changed over the last decade. Data derived from 497 wells have revealed a continued depletion of groundwater lasting a full year after drought, a phenomenon that was not observed in earlier records before the twenty-first century. Possible causes include 1) lengthening of drought associated with lification in the 4–6-yr drought and El Niño frequency since the late 1990s and 2) intensification of drought and increased pumping that enhances depletion. Altogether, the implication is that current groundwater storage in the Central Valley will likely continue to diminish even further in 2016, regardless of the drought status.
Publisher: Copernicus GmbH
Date: 29-09-2021
Publisher: Copernicus GmbH
Date: 17-01-2020
Publisher: Copernicus GmbH
Date: 18-02-2022
Abstract: Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Copernicus GmbH
Date: 02-09-2021
DOI: 10.5194/HESS-25-4773-2021
Abstract: Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.
Publisher: Elsevier BV
Date: 07-2022
Publisher: Copernicus GmbH
Date: 28-06-2018
Abstract: Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of in idual variables. However, univariate correction neglects the relationships that exist between climate variables. In this study glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2100. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable’s distribution in the same way, the marginal distributions of the in idual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach lead to more precipitation below air temperatures of 0 °C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias correction forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates in the future. For the historical period the fraction of precipitation above and below 0 °C, the simulated snow water equivalents, glacier volumes, and the streamflow regime resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow concluding definitively that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies.
Publisher: Copernicus GmbH
Date: 07-10-2019
Publisher: American Meteorological Society
Date: 08-2018
Abstract: Variables simulated by climate models are usually evaluated independently. Yet, climate change impacts often stem from the combined effect of these variables, making the evaluation of intervariable relationships essential. These relationships can be evaluated in a statistical framework (e.g., using correlation coefficients), but this does not test whether complex processes driven by nonlinear relationships are correctly represented. To overcome this limitation, we propose to evaluate climate model simulations in a more process-oriented framework using hydrological modeling. Our modeling chain consists of 12 regional climate models (RCMs) from the Coordinated Downscaling Experiment–European Domain (EURO-CORDEX) forced by five general circulation models (GCMs), eight Swiss catchments, 10 optimized parameter sets for the hydrological model Hydrologiska Byråns Vattenbalansavdelning (HBV), and one bias correction method [quantile mapping (QM)]. We used seven discharge metrics to explore the representation of different hydrological processes under current climate. Specific combinations of biases in GCM–RCM simulations can lead to significant biases in simulated discharge (e.g., excessive precipitation in the winter months combined with a cold temperature bias). Other biases, such as exaggerated snow accumulation, do not necessarily impact temperature over the historical period to the point where discharge is affected. Our results confirm the importance of bias correction when all catchments, GCM–RCMs, and discharge metrics were considered, QM improved discharge simulations in the vast majority of all cases. Additionally, we present a ranking of climate models according to their hydrological performance. Ranking GCM–RCMs is most meaningful prior to bias correction since QM reduces differences between GCM–RCM-driven hydrological simulations. Overall, this work introduces a multivariate assessment method of GCM–RCMs, which enables a more process-oriented evaluation of their simulations.
Publisher: American Geophysical Union (AGU)
Date: 12-2015
DOI: 10.1002/2015WR017498
Publisher: Copernicus GmbH
Date: 11-03-2019
DOI: 10.5194/HESS-23-1339-2019
Abstract: Abstract. Alpine catchments show a high sensitivity to climate variation as they include the elevation range of the snow line. Therefore, the correct representation of climate variables and their interdependence is crucial when describing or predicting hydrological processes. When using climate model simulations in hydrological impact studies, forcing meteorological data are usually downscaled and bias corrected, most often by univariate approaches such as quantile mapping of in idual variables, neglecting the relationships that exist between climate variables. In this study we test the hypothesis that the explicit consideration of the relation between air temperature and precipitation will affect hydrological impact modelling in a snow-dominated mountain environment. Glacio-hydrological simulations were performed for two partly glacierized alpine catchments using a recently developed multivariate bias correction method to post-process EURO-CORDEX regional climate model outputs between 1976 and 2099. These simulations were compared to those obtained by using the common univariate quantile mapping for bias correction. As both methods correct each climate variable's distribution in the same way, the marginal distributions of the in idual variables show no differences. Yet, regarding the interdependence of precipitation and air temperature, clear differences are notable in the studied catchments. Simultaneous correction based on the multivariate approach led to more precipitation below air temperatures of 0 ∘C and therefore more simulated snowfall than with the data of the univariate approach. This difference translated to considerable consequences for the hydrological responses of the catchments. The multivariate bias-correction-forced simulations showed distinctly different results for projected snow cover characteristics, snowmelt-driven streamflow components, and expected glacier disappearance dates. In all aspects – the fraction of precipitation above and below 0 ∘C, the simulated snow water equivalents, glacier volumes, and the streamflow regime – simulations resulting from the multivariate-corrected data corresponded better with reference data than the results of univariate bias correction. Differences in simulated total streamflow due to the different bias correction approaches may be considered negligible given the generally large spread of the projections, but systematic differences in the seasonally delayed streamflow components from snowmelt in particular will matter from a planning perspective. While this study does not allow conclusive evidence that multivariate bias correction approaches are generally preferable, it clearly demonstrates that incorporating or ignoring inter-variable relationships between air temperature and precipitation data can impact the conclusions drawn in hydrological climate change impact studies in snow-dominated environments.
Publisher: Springer Science and Business Media LLC
Date: 05-03-2020
DOI: 10.1007/S10113-020-01615-8
Abstract: Tunisia has a long history of coping with water scarcity, and the quantification of climate change impacts on runoff is important for future water management. A major requirement for such studies is an estimation of potential evapotranspiration (PET), which is challenging as many regions often lack the observational data needed for physically based PET equations. In this study, different PET estimation approaches were used to study the impact of PET estimation on discharge projections for catchments in Northern Tunisia. Discharge was simulated for five catchments using three rainfall-runoff models (RRMs): HBV, GR4 and IHACRES. A general differential split s le test (GDSST) was used for an RRM robustness evaluation based on subperiods with contrasting climatic conditions for the 1970–2000 period. Three cases with varying PET were considered: (1) daily calculated PET, (2) long-term daily mean PET with the same values for calibration and validation periods (calculated over the calibration period) and (3) long-term daily mean PET varying between calibration and validation periods (calculated over the calibration and validation period separately). Over the historical period, the comparison between cases 1 and 3 showed little impact of reduced PET information on the RRM performance and robustness. The comparison of cases 2 and 3 indicated a limited impact of varying PET between calibration and validation on the RRM results. The impact of varying levels of PET information on hydrological projections was also analysed over two future 30-year periods: mid-term period (2040–2070) and long-term period (2070–2100), with two representative concentration pathway scenarios (RCPs 4.5 and 8.5), by comparing cases 1 and 2. The projected discharge with constant PET (case 2) was generally lower than the projected discharge with variable PET (case 1) but the difference in volume change did not exceed 9% for both the time period and the RCP scenario considered. While PET slightly increased under the different climate change scenarios, actual evapotranspiration (AET) was found to decrease. These opposite trends of PET and AET can be attributed to the projected decrease in precipitation. Overall, our results demonstrate that discharge, in semi-arid regions like Northern Tunisia, is not sensitive to PET estimates since AET is mainly controlled by the availability of soil moisture. This finding is useful for performing studies of climate change impact on hydrological cycles in arid regions, as our study shows that simple PET estimation is a valid approach for such studies.
Publisher: Copernicus GmbH
Date: 29-07-2020
DOI: 10.5194/HESS-24-3815-2020
Abstract: Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of in idual catchments, and, importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability. Here, we collaborated with Groupe E, a hydroelectricity company operating several reservoirs in the Swiss pre-Alps, and we co-produced hydroclimatic projections tailored to support the upcoming negotiations of their water concession renewal. We started by identifying the vulnerabilities of their activities to climate change together, we then selected streamflow and electricity demand indices to characterize the associated risks and opportunities. We provided Groupe E with figures showing the projected impacts, which were refined over several meetings. The selected indices enabled us to assess a variety of impacts induced by changes in (i) the seasonal water volume distribution, (ii) low flows, (iii) high flows, and (iv) electricity demand. This enabled us to identify key opportunities (e.g., the future increase in reservoir inflow in winter, when electricity prices have historically been high) and risks (e.g., the expected increase in consecutive days of low flows in summer and fall which is likely to make it more difficult to meet residual flow requirements). We highlight that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. This stakeholder-centered approach, which relies on identifying stakeholder's needs and using them to inform the production and visualization of impact projections, is transferable to other climate impact studies, in the field of water resources and beyond.
Publisher: Wiley
Date: 29-12-2019
Publisher: Copernicus GmbH
Date: 29-09-2021
Abstract: Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from General Circulation Models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in ETo forecasts. However, time-dependent errors, resulting from GCM’s misrepresentations of climate trends, have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian Joint Probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 % in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modelling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasts, and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
Publisher: Copernicus GmbH
Date: 04-02-2021
DOI: 10.5194/HESS-2021-69
Abstract: Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subjective to systematic errors, calibration is necessary for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the potential predictability of ETo may not be fully explored by this approach, which ignores the non-linear interactions of input variables in constructing ETo forecasts. We hypothesize that reducing errors in in idual inputs as a precursor to ETo forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies, including (i) calibration directly applied to raw ETo forecasts constructed with raw forecasts of input variables, and (ii) bias-correcting input variables first, and then calibrating the ETo forecasts constructed with bias-corrected input variables. We calibrate ETo forecasts based on weather forecasts of the Australian Community Climate and Earth System Simulator G2 version (ACCESS-G2). Calibrated ETo forecasts with bias-corrected input variables (strategy ii) demonstrate lower bias, higher correlation coefficient, and higher skills than the calibration based on raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could enhance ETo forecast calibration and produce more skillful ETo forecasts. This calibration strategy is expected to enhance future NWP-based ETo forecasting.
Publisher: Copernicus GmbH
Date: 04-02-2021
Publisher: American Geophysical Union (AGU)
Date: 05-02-2014
DOI: 10.1002/2013GL059042
Publisher: Copernicus GmbH
Date: 07-10-2019
Abstract: Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, climate change impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of in idual catchments, and more importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability, and thereby provide limited insights into climate change adaptation. Here, we collaborated with Groupe E, a hydropower company operating several reservoirs in the Swiss pre-Alps and worked with them to produce hydroclimatic projections tailored to support their upcoming water concession negotiations. We started by identifying the vulnerabilities of their activities to climate change and then together chose streamflow and energy indices to characterize the associated risks. We provided Groupe E with figures showing the projected climate change impacts, which were refined over several meetings. The selected indices enabled us to simultaneously assess a variety of impacts induced by changes on i) the seasonal water volume distribution, ii) low flows, iii) high flows, and iv) energy demand. We were hence able to identify key opportunities (e.g., the future increase of reservoir inflow in winter, when electricity prices are historically high) and risks (e.g., the expected increase of consecutive days of low flows in summer and fall, which is likely to make it more difficult to meet residual flow requirements). This study highlights that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. We also illustrate the importance of identifying stakeholder needs and using them to inform the production of climate impact projections. Our user-centered approach is transferable to other impact modeling studies, in the field of water resources and beyond.
Publisher: American Society of Civil Engineers (ASCE)
Date: 09-2023
No related grants have been discovered for Kirsti Hakala Assendelft.