scholarly journals Multivariate and spatially calibrated hydrological model for assessing climate change impacts on hydrological processes in West Africa

2020 ◽  
Author(s):  
Moctar Dembélé ◽  
Sander Zwart ◽  
Natalie Ceperley ◽  
Grégoire Mariéthoz ◽  
Bettina Schaefli
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
West Africa ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Data Availability ◽  
Hydrological Processes ◽  
Climate Vulnerability

<p>Robust hydrological models are critical for the assessment of climate change impacts on hydrological processes. This study analysis the future evolution of the spatiotemporal dynamics of multiple hydrological processes (i.e. streamflow, soil moisture, evaporation and terrestrial water storage) with the fully distributed mesoscale hydrologic Model (mHM), which is constrained with a novel multivariate calibration approach based on the spatial patterns of satellite remote sensing data (Dembélé et al., 2020). The experiment is done in the large and transboundary Volta River Basin (VRB) in West Africa, which is a hotspot of climate vulnerability. Climate change and land use changes lead to recurrent floods and drought that impact agriculture and affect the lives of the inhabitants.</p><p>Based on data availability on the Earth System Grid Federation (ESGF) platform, nine Global Circulation Models (i.e. CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR and NorESM1-M) available from the CORDEX-Africa initiative and dynamically downscaled with the latest version of the Rossby Centre's regional atmospheric model (RCA4) are selected for this study. Daily datasets of meteorological variables (i.e. precipitation and air temperature) for the medium and high emission scenarios (RCP4.5 and RCP8.5) are bias-corrected and used to force the mHM model for the reference period 1991-2020, and the near- and long-term future periods 2021-2050 and 2051-2080.</p><p>The results show contrasting trends among the hydrological processes as well as among the GCMs. The findings reveal uncertainties in the spatial patterns of hydrological processes (e.g. soil moisture and evaporation), which ultimately have implications for flood and drought predictions. This study highlights the importance of plausible spatial patterns for the assessment of climate change impacts on hydrological processes, and thereby provide valuable information with the potential to reduce the climate vulnerability of the local population.</p><p> </p><p>Reference</p><p>Dembélé, M., Hrachowitz, M., Savenije, H., Mariéthoz, G., & Schaefli, B. (2020). Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets. Water Resources Research.</p>

scholarly journals Do changes in climate or vegetation regulate evapotranspiration and streamflow trends in water-limited basins?

2014 ◽  
Vol 11 (10) ◽  
pp. 11183-11202
Author(s):  
Q. Liu ◽  
Z. Yang ◽  
L. Liang ◽  
W. Nan
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Vegetation Type ◽  
Climate Change Impacts ◽  
Hydrological Processes ◽  
Rooting Depth ◽  
Climate Change Scenarios ◽  
The Impact ◽  
Streamflow Trends

Abstract. Interactions between climate change, vegetation, and soil regulate hydrological processes. In this study, it was assumed that vegetation type and extent remained fixed and unchanged throughout the study period, while the effective rooting depth (Ze) changed under climate change scenarios. Budyko's hydrological model was used to explore the impact of climate change and vegetation on evapotranspiration (E) and streamflow (Q) on the static vegetation rooting depth and the dynamic vegetation rooting depth. Results showed that both precipitation (P) and potential evapotranspiration (Ep) exhibited negative trends, which resulted in decreasing trends for dynamic Ze scenarios. Combined with climatic change, decreasing trends in Ze altered the partitioning of P into E and Q. For dynamic scenarios, total E and Q were predicted to be −1.73 and 28.22%, respectively, greater than static scenarios. Although climate change regulated changes in E and Q, the response of Ze to climate change had a greater overall contribution to changes in hydrological processes. Results from this study suggest that with the exception of vegetation type and extent, Ze scenarios were able to alter water balances, which in itself should help to regulate climate change impacts on water resources.

scholarly journals Suitability of 17 rainfall and temperature gridded datasets for largescale hydrological modelling in West Africa

2020 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Nick van de Giesen ◽  
Grégoire Mariéthoz
Keyword(s):  
Soil Moisture ◽  
West Africa ◽  
Spatial Patterns ◽  
Temporal Dynamics ◽  
Water Storage ◽  
Hydrological Modelling ◽  
Hydrological Processes ◽  
Terrestrial Water Storage ◽  
Meteorological Forcing ◽  
Terrestrial Water

Abstract. This study evaluates the ability of different gridded rainfall datasets to plausibly represent the spatiotemporal patterns of multiple hydrological processes (i.e. streamflow, actual evaporation, soil moisture and terrestrial water storage) for large-scale hydrological modelling in the predominantly semi-arid Volta River Basin (VRB) in West Africa. Seventeen precipitation products based on satellite data (TAMSAT, CHIRPS, ARC, RFE, MSWEP, GSMaP, PERSIANN-CDR, CMORPH-CRT, TRMM 3B42, TRMM 3B42RT) and on reanalysis (JRA-55, EWEMBI, WFDEI-GPCC, WFDEI-CRU, MERRA-2, PGF and ERA5) are compared as input for the fully distributed mesoscale Hydrologic Model (mHM). To assess the model sensitivity to meteorological forcing during rainfall partitioning into evaporation and runoff, six different temperature reanalysis datasets are used in combination with the precipitation datasets, which results in evaluating 102 combinations of rainfall-temperature input data. The model is recalibrated for each of the 102 input combinations, and the model responses are evaluated by using in-situ streamflow data and satellite remote sensing datasets from GLEAM evaporation, ESA CCI soil moisture, and GRACE terrestrial water storage. A bias-insensitive metric is used to assess the impact of meteorological forcing on the simulation of the spatial patterns of hydrological processes. The results of the process-based evaluation show that the rainfall datasets have contrasting performances across the four climatic zones present in the VRB, suggesting that, in general, basin-wide hydrological model performance might be misleading and invalid for a smaller spatial domain. No single rainfall or temperature dataset consistently ranks first in reproducing the spatiotemporal variability of all hydrological processes. A dataset that is best in reproducing the temporal dynamics is not necessarily the best for the spatial patterns. In addition, the results suggest that there is more uncertainty in representing the spatial patterns of hydrological processes than their temporal dynamics. Finally, some region-tailored datasets outperform the global datasets, thereby stressing the necessity and importance of regional evaluation studies for satellite and reanalysis meteorological datasets.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals The Long-term Projection of Surface Runoff in the Regions above Danjiangkou in Hanjiang River Basin based on Water-energy Balance

2018 ◽  
Vol 246 ◽  
pp. 01099
Author(s):  
Jun Yin ◽  
Zhe Yuan ◽  
Run Wang
Keyword(s):  
Climate Change ◽  
Energy Balance ◽  
River Basin ◽  
Surface Runoff ◽  
Hydrological Model ◽  
Climatic Factors ◽  
Climate Change Impacts ◽  
Basin Scale ◽  
Hanjiang River ◽  
Distributed Hydrological Models

The projection of surface runoff in the context of climate change is important to the rational utilization and distribution of water resources. This study did a case study in regions above Danjiangkou in Hanjiang River Basin. A basin scale hydrological model was built based on macroscale processes of surface runoff and water-energy balance. This model can describe the quantity relationship among climatic factors, underlying surface and surface runoff. Driven by hypothetical climatic scenarios and climate change dataset coming from CMIP5, the climate change impacts on surface runoff in the regions above Danjiangkou in Hanjiang River Basin can be addressed. The results showed that: (1) Compared with other distributed hydrological models, the hydrological model in this study has fewer parameters and simpler calculation methods. The model was good at simulating annual surface runoff. (2) The surface runoff was less sensitivity to climate change in the regions above Danjiangkou in Hanjiang River Basin. A 1°C increase in temperature might results in a surface runoff decrease of 2~5% and a 10% precipitation increase might result in a streamflow increase of 14~17%. (3) The temperature across the Fu River Basin were projected to increase by 1.4~2.3°C in 1961 to 1990 compared with that in 1961 to 1990. But the uncertainty existed among the projection results of precipitation. The surface runoff was excepted to decrease by 1.3~23.9% without considering the climate change projected by NorESM1-M and MIROC-ESM-CHEM, which was much different from other GCMs.

scholarly journals Performance evaluation of global hydrological models in six large Pan-Arctic watersheds

2020 ◽  
Vol 163 (3) ◽  
pp. 1329-1351 ◽  
Author(s):  
Anne Gädeke ◽  
Valentina Krysanova ◽  
Aashutosh Aryal ◽  
Jinfeng Chang ◽  
Manolis Grillakis ◽  
...  
Keyword(s):  
Climate Change ◽  
Performance Evaluation ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Explanatory Power ◽  
Model Performance ◽  
Climate Change Impacts ◽  
Hydrological Processes ◽  
Climate Data ◽  
Seasonal Discharge

AbstractGlobal Water Models (GWMs), which include Global Hydrological, Land Surface, and Dynamic Global Vegetation Models, present valuable tools for quantifying climate change impacts on hydrological processes in the data scarce high latitudes. Here we performed a systematic model performance evaluation in six major Pan-Arctic watersheds for different hydrological indicators (monthly and seasonal discharge, extremes, trends (or lack of), and snow water equivalent (SWE)) via a novel Aggregated Performance Index (API) that is based on commonly used statistical evaluation metrics. The machine learning Boruta feature selection algorithm was used to evaluate the explanatory power of the API attributes. Our results show that the majority of the nine GWMs included in the study exhibit considerable difficulties in realistically representing Pan-Arctic hydrological processes. Average APIdischarge (monthly and seasonal discharge) over nine GWMs is > 50% only in the Kolyma basin (55%), as low as 30% in the Yukon basin and averaged over all watersheds APIdischarge is 43%. WATERGAP2 and MATSIRO present the highest (APIdischarge > 55%) while ORCHIDEE and JULES-W1 the lowest (APIdischarge ≤ 25%) performing GWMs over all watersheds. For the high and low flows, average APIextreme is 35% and 26%, respectively, and over six GWMs APISWE is 57%. The Boruta algorithm suggests that using different observation-based climate data sets does not influence the total score of the APIs in all watersheds. Ultimately, only satisfactory to good performing GWMs that effectively represent cold-region hydrological processes (including snow-related processes, permafrost) should be included in multi-model climate change impact assessments in Pan-Arctic watersheds.

scholarly journals Robust features of future climate change impacts on sorghum yields in West Africa

2014 ◽  
Vol 9 (10) ◽  
pp. 104006 ◽  
Author(s):  
B Sultan ◽  
K Guan ◽  
M Kouressy ◽  
M Biasutti ◽  
C Piani ◽  
...  
Keyword(s):  
Climate Change ◽  
West Africa ◽  
Climate Change Impacts ◽  
Future Climate ◽  
Future Climate Change

Targeting attention on local vulnerabilities using an integrated index approach: the example of the climate vulnerability index

2005 ◽  
Vol 51 (5) ◽  
pp. 69-78 ◽  
Author(s):  
C. Sullivan ◽  
J. Meigh
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Vulnerability Index ◽  
Climate Impacts ◽  
Human Populations ◽  
Mass Migration ◽  
Indicator Approach ◽  
Index Approach ◽  
Climate Vulnerability ◽  
Climate Vulnerability Index

It is known that climate impacts can have significant effects on the environment, societies and economies. For human populations, climate change impacts can be devastating, giving rise to economic disruption and mass migration as agricultural systems fail, either through drought or floods. Such events impact significantly, not only where they happen, but also in the neighbouring areas. Vulnerability to the impacts of climate change needs to be assessed, so that adaptation strategies can be developed and populations can be protected. In this paper, we address the issue of vulnerability assessment through the use of an indicator approach, the climate vulnerability index (CVI). We show how this can overcome some of the difficulties of incommensurability associated with the combination of different types of data, and how the approach can be applied at a variety of scales. Through the development of nested index values, more reliable and robust coverage of large areas can be achieved, and we provide an indication of how this could be done. While further work is required to improve the methodology through wider application and component refinement, it seems likely that this approach will have useful application in the assessment of climate vulnerability. Through its application at sub-national and community scales, the CVI can help to identify those human populations most at risk from climate change impacts, and as a result, resources can be targeted towards those most in need.

Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts

2015 ◽  
Vol 16 (2) ◽  
pp. 762-780 ◽  
Author(s):  
Pablo A. Mendoza ◽  
Martyn P. Clark ◽  
Naoki Mizukami ◽  
Andrew J. Newman ◽  
Michael Barlage ◽  
...  
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Climate Models ◽  
Climate Change Impacts ◽  
Hydrologic Model ◽  
Global Climate Models ◽  
Change Scenario ◽  
Hydrologic Models ◽  
Climate System Model

Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

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