scholarly journals A GLOBAL INTEGRATED WATER RESOURCES MODEL BASED ON A BUCKET TYPE LAND SURFACE MODEL

2006 ◽  
Vol 50 ◽  
pp. 529-534
Author(s):  
Naota Hanasaki ◽  
Shinjiro Kanae ◽  
Taikan Oki
Keyword(s):  
Water Resources ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Based

scholarly journals Land-use change may exacerbate climate change impacts on water resources in the Ganges basin

2018 ◽  
Vol 22 (2) ◽  
pp. 1411-1435 ◽  
Author(s):  
Gina Tsarouchi ◽  
Wouter Buytaert
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Water Resources ◽  
Land Use Change ◽  
Land Surface ◽  
Land Surface Model ◽  
Coupled Model ◽  
Surface Model ◽  
Climate Change Scenarios ◽  
Ganges River

Abstract. Quantifying how land-use change and climate change affect water resources is a challenge in hydrological science. This work aims to quantify how future projections of land-use and climate change might affect the hydrological response of the Upper Ganges river basin in northern India, which experiences monsoon flooding almost every year. Three different sets of modelling experiments were run using the Joint UK Land Environment Simulator (JULES) land surface model (LSM) and covering the period 2000–2035: in the first set, only climate change is taken into account, and JULES was driven by the CMIP5 (Coupled Model Intercomparison Project Phase 5) outputs of 21 models, under two representative concentration pathways (RCP4.5 and RCP8.5), whilst land use was held fixed at the year 2010. In the second set, only land-use change is taken into account, and JULES was driven by a time series of 15 future land-use pathways, based on Landsat satellite imagery and the Markov chain simulation, whilst the meteorological boundary conditions were held fixed at years 2000–2005. In the third set, both climate change and land-use change were taken into consideration, as the CMIP5 model outputs were used in conjunction with the 15 future land-use pathways to force JULES. Variations in hydrological variables (stream flow, evapotranspiration and soil moisture) are calculated during the simulation period. Significant changes in the near-future (years 2030–2035) hydrologic fluxes arise under future land-cover and climate change scenarios pointing towards a severe increase in high extremes of flow: the multi-model mean of the 95th percentile of streamflow (Q5) is projected to increase by 63 % under the combined land-use and climate change high emissions scenario (RCP8.5). The changes in all examined hydrological components are greater in the combined land-use and climate change experiment. Results are further presented in a water resources context, aiming to address potential implications of climate change and land-use change from a water demand perspective. We conclude that future water demands in the Upper Ganges region for winter months may not be met.

scholarly journals ERA-Interim/Land: a global land water resources dataset

2013 ◽  
Vol 10 (12) ◽  
pp. 14705-14745 ◽  
Author(s):  
G. Balsamo ◽  
C. Albergel ◽  
A. Beljaars ◽  
S. Boussetta ◽  
H. Cloke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Resources ◽  
Land Surface ◽  
Climate Models ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Surface Model ◽  
Meteorological Forcing ◽  
Global Land ◽  
Land Water

Abstract. The ERA-Interim/Land is a global land-surface dataset covering the period 1979–2010 and describing the evolution of the soil (moisture and temperature) and snowpack. ERA-Interim/Land is the result of a single 32 yr simulation with the latest ECMWF land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on GPCP v2.1. ERA-Interim/Land preserves closure of the water balance and includes a number of parameterisations improvements in the land surface scheme with respect to the original ERA-Interim dataset, which makes it suitable for climate studies involving land water resources. The quality of ERA-Interim/Land, assessed by comparing with ground-based and remote sensing observations is discussed. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of sites measurements. ERA-Interim/Land provides a global integrated and coherent water resources estimate that is used also for the initialization of numerical weather prediction and climate models.

Afforesting the UK: Potential Hydrological Impacts

2021 ◽  
Author(s):  
Marcus Buechel ◽  
Simon Dadson ◽  
Louise Slater
Keyword(s):  
Water Resources ◽  
Land Cover ◽  
Land Surface ◽  
Drainage Basin ◽  
Land Surface Model ◽  
Flood Management ◽  
Surface Model ◽  
Catchment Scale ◽  
Strong Negative Correlation ◽  
The Uk

<p>Ambitious targets to expand forested land area have increased over the last decade as governments, businesses, and individuals seek to use woodland as carbon sinks. Currently, it is unknown how proposed afforestation rates will influence catchment water resources and hydrological processes. Both the temporal and spatial scale of proposed afforestation are unprecedented on contemporary timescales and we lack the systematic and quantified understanding of its impact on streamflow at catchment scales. Furthermore, the efficacy of afforestation as a form of natural flood management has yet to be tested across multiple catchments (> 30 km<sup>2</sup>).</p><p> </p><p>The UK Government has pledged to use afforestation as a major component of its approach to reach net zero carbon emissions by 2050. In this project, we investigate the influence of afforestation upon streamflow dynamics in twelve catchments across the British Isles. We aim to determine how woodland planting extent and location influences catchment streamflow response and sensitivity, and which catchment attributes account for these changes. To do this, we use physics-based land surface model JULES (Joint UK Land Environment Simulator) at a 1 km resolution to understand the potential hydrological changes to theoretical afforestation scenarios.</p><p> </p><p>Land cover afforestation scenarios were created according to proximity to existing land cover, drainage basin structure and afforestation rate (up to 288 potential land cover scenarios per catchment). The period of 2000-2010, a flood-rich period, was used to simulate and compare how each afforestation scenario would influence catchment flow exceedance levels and streamflow regime using the CHESS-met dataset.</p><p> </p><p>Results show increasing afforestation has a clear impact upon streamflow dynamics. A strong negative correlation between increasing afforestation and median and low flows exists but is weaker for higher flows. Some afforestation scenarios could increase the highest flows in the period. Quantile regression on the results of our simulations shows a median change of -1.0 ± 0.21 mm yr<sup>-1</sup> (-0.26 ± 0.10%) for the median flow exceedance per percentage point of broadleaf woodland planted across all catchments. Planting according to Shreve order, or contributing area, led to statistically significant differences in streamflow dynamics. Climatic catchment attributes correlated strongly with catchment median flow sensitivity to afforestation.</p><p> </p><p>These results help us to understand how afforestation may influence catchment response to external climatic forcing.  We hope it provides evidence to policymakers wishing to understand the implications of afforestation on water resources and the foundation to understand its future catchment-scale impacts on streamflow.</p>

Implementation of vegetation and soil carbonyl sulfide exchanges in the ORCHIDEE land surface model to better constrain ecosystem gross primary production

2021 ◽  
Author(s):  
Camille Abadie ◽  
Fabienne Maignan ◽  
Marine Remaud ◽  
Linda M. J. Kooijmans ◽  
Kukka-Maaria Kohonen ◽  
...  
Keyword(s):  
Land Surface ◽  
Photosynthetic Activity ◽  
Mechanistic Model ◽  
Carbonyl Sulfide ◽  
Land Surface Model ◽  
Field Measurements ◽  
Environmental Drivers ◽  
Surface Model ◽  
High Temporal Resolution ◽  
Model Based

<p>Better constraining the ecosystem gross photosynthetic CO<sub>2</sub> uptake (GPP) is necessary to reduce the uncertainties on continental vegetation response to climate change. As GPP cannot be directly measured at the ecosystem scale, different proxies of vegetation CO<sub>2</sub> uptake have emerged. These proxies are essential for land surface modelers to estimate GPP at large scale. Carbonyl sulfide (COS) shows many similarities with CO<sub>2</sub>, following the same diffusional pathways through the leaves. However, unlike CO<sub>2</sub> that is also emitted by plants during respiration, COS is essentially only taken up by leaves and not re-emitted back to the atmosphere. Therefore, COS is a promising proxy of photosynthetic activity. In previous studies, fixed values of the leaf relative uptake (LRU) ratio of COS to CO<sub>2</sub> fluxes normalized by their respective concentration have typically been used to infer GPP for the different biomes. However, it has been shown that LRU ratio changes with varying Photosynthetically Active Radiation (PAR), which limits its accuracy to constrain photosynthetic activity. Therefore, we redefined the COS-based GPP estimation approach to better capture GPP response to changing environmental conditions, by implementing a mechanistic model of COS exchange by continental vegetation in the ORCHIDEE land surface model. We compared the modelled fluxes against field measurements at two sites and studied the model behavior and environmental drivers. Then, we ran global simulations and computed the annual COS vegetation uptake that was found in the middle range values of previous reported budgets (-490 to -1335 Gg S yr<sup>-1</sup>), with -756 Gg S yr<sup>-1</sup>. The simulated fluxes were transported, and COS concentrations were evaluated against measurements from the NOAA atmospheric stations. Our results show that the mechanistic approach is more appropriate when studying photosynthetic activity at high temporal resolution, but similar results in concentrations are obtained between the mechanistic and LRU approaches at the global scale. Accurate evaluation of the continental vegetation COS uptake is necessary as it is the main COS sink. However, COS can also be absorbed or emitted by soils, a flux that complicates the use of eddy covariance COS flux measurements or atmospheric COS measurements to derive information on GPP estimates. Therefore, the soil COS exchange should also be represented in land surface models. We implemented two soil COS exchange models in ORCHIDEE, a mechanistic model (based on Ogée et al. 2016) and a second model based on an empirical relationship with soil respiration (following Berry et al., 2013). We evaluated the two models at several sites against field measurements. We also performed global simulations to evaluate the spatial distribution of soil COS fluxes and their seasonal variations. Finally, we estimated the contributions of the combined impact of soil COS exchange and leaf COS uptake (both from the ORCHIDEE model) to the global COS budget and on the COS atmospheric concentration latitudinal gradient.</p>

Application of hyper-resolution hydrological modeling for water resources decision making

2020 ◽  
Author(s):  
Eric Wood ◽  
Noemi Vergopolan ◽  
Peirong Lin ◽  
Ming Pan
Keyword(s):  
Decision Making ◽  
Water Resources ◽  
Satellite Data ◽  
Land Surface ◽  
Hydrological Modeling ◽  
Land Surface Model ◽  
Management Decision ◽  
Surface Model ◽  
The Impact

<p>Managing water resources and basin reclamation requires hydrological data across a set of scales.  Unfortunately, in many areas the in-situ data is sparse, or not made available to water managers.  With NASA, ESA and Chinese satellites, their data can potentially be merged with in-situ gauge data.  Doing so results in a number of research challenges: 1. Satellite data based on microwave sensors (e.g. L-band sensors from SMAP or SMOS) results in coarse resolution (~35-50 km) making the data difficult for management; (ii) Satellite data from instruments like LandSat (~90m) suffers from cloud contamination.  New satellites improve resolution but still suffer cloud contamination; (iii) Precipitation (along with radiation) falls between these two spectrums, and its fast dynamics can impact water management decision making; (iv) Topographic and soil characteristics, which govern the runoff from the land to rivers; and (v) river flows that are a water source for drought and a site for reservoirs.</p><p>In this talk I will present a new land surface model (HydroBlocks) that we run at a 30m resolution at regional to continental scales.  The water is transmitted to hyper-resolution streams for which we have extracted ~2,900,000 reaches.  Visualization of the models will offer the listener the impact of moving to these scales; and the data needed for water resources management of river basins.</p>

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

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