scholarly journals Land Surface Modeling 2.0 for agricultural climate change impact assessments

2021 ◽  
Author(s):  
James Franke
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Change Impact ◽  
Surface Modeling ◽  
Land Surface Modeling ◽  
Change Impact

scholarly journals Variations in the Simulation of Climate Change Impact Indices due to Different Land Surface Schemes over the Mediterranean, Middle East and Northern Africa

Atmosphere ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 26 ◽  
Author(s):  
Katiana Constantinidou ◽  
George Zittis ◽  
Panos Hadjinicolaou
Keyword(s):  
Climate Change ◽  
Middle East ◽  
Land Surface ◽  
Climate Change Impact ◽  
Climate Model ◽  
Eastern Mediterranean ◽  
Regional Climate ◽  
Climatic Conditions ◽  
Relative Dispersion ◽  
Change Impact

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions.

scholarly journals On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments

2020 ◽  
Vol 24 (1) ◽  
pp. 397-416 ◽  
Author(s):  
Thanh Duc Dang ◽  
A. F. M. Kamal Chowdhury ◽  
Stefano Galelli
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Change Impact ◽  
Large Scale ◽  
Water Reservoir ◽  
Hydrological Models ◽  
Water Reservoirs ◽  
Reservoir Storage ◽  
Global Circulation Models ◽  
Change Impact

Abstract. During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human–water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated with potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a multi-objective evolutionary algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing model that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005), a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated with the use of such a model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five global circulation models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human–water interactions in large-scale hydrological models.

scholarly journals On the representation of water reservoir storage and operations in large-scale hydrological models: implications on model parameterization and climate change impact assessments

2019 ◽  
Author(s):  
Thanh Duc Dang ◽  
AFM Kamal Chowdhury ◽  
Stefano Galelli
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Change Impact ◽  
Large Scale ◽  
Water Reservoir ◽  
Hydrological Models ◽  
Water Reservoirs ◽  
Reservoir Storage ◽  
Global Circulation Models ◽  
Change Impact

Abstract. During the past decades, the increased impact of anthropogenic interventions on river basins has prompted hydrologists to develop various approaches for representing human-water interactions in large-scale hydrological and land surface models. The simulation of water reservoir storage and operations has received particular attention, owing to the ubiquitous presence of dams. Yet, little is known about (1) the effect of the representation of water reservoirs on the parameterization of hydrological models, and, therefore, (2) the risks associated to potential flaws in the calibration process. To fill in this gap, we contribute a computational framework based on the Variable Infiltration Capacity (VIC) model and a Multi-Objective Evolutionary Algorithm, which we use to calibrate VIC's parameters. An important feature of our framework is a novel variant of VIC's routing module that allows us to simulate the storage dynamics of water reservoirs. Using the upper Mekong river basin as a case study, we calibrate two instances of VIC – with and without reservoirs. We show that both model instances have the same accuracy in reproducing daily discharges (over the period 1996–2005); a result attained by the model without reservoirs by adopting a parameterization that compensates for the absence of these infrastructures. The first implication of this flawed parameter estimation stands in a poor representation of key hydrological processes, such as surface runoff, infiltration, and baseflow. To further demonstrate the risks associated to the use of such model, we carry out a climate change impact assessment (for the period 2050–2060), for which we use precipitation and temperature data retrieved from five Global Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 4.5 and 8.5). Results show that the two model instances (with and without reservoirs) provide different projections of the minimum, maximum, and average monthly discharges. These results are consistent across both RCPs. Overall, our study reinforces the message about the correct representation of human-water interactions in large-scale hydrological models.

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