scholarly journals Streamflows over a West African Basin from the ALMIP2 Model Ensemble

2017 ◽  
Vol 18 (7) ◽  
pp. 1831-1845 ◽  
Author(s):  
Augusto Getirana ◽  
Aaron Boone ◽  
Christophe Peugeot ◽  

Abstract Comparing streamflow simulations against observations has become a straightforward way to evaluate a land surface model’s (LSM) ability in simulating water budget within a catchment. Using a mesoscale river routing scheme (RRS), this study evaluates simulated streamflows over the upper Ouémé River basin resulting from 14 LSMs within the framework of phase 2 of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project (ALMIP2). The ALMIP2 RRS (ARTS) has been used to route LSM outputs. ARTS is based on the nonlinear Muskingum–Cunge method and a simple deep water infiltration formulation representing water-table recharge as previously observed in that region. Simulations are performed for the 2005–08 period during which ground observations are largely available. Experiments are designed using different ground-based rainfall datasets derived from two interpolation methods: the Thiessen technique and a combined kriging–Lagrangian methodology. LSM-based total runoff (TR) averages vary from 0.07 to 1.97 mm day−1, while optimal TR was estimated as ~0.65 mm day−1. This highly affected the RRS parameterization and streamflow simulations. Optimal Nash–Sutcliffe coefficients for LSM-averaged streamflows varied from 0.66 to 0.92, depending on the gauge station. However, individual LSM performances show a wider range. A more detailed rainfall distribution provided by the kriging–Lagrangian methodology resulted in overall better streamflow simulations. The early runoff generation related to reduced infiltration rates during early rainfall events features as one of the main reasons for poor LSM performances.

2014 ◽  
Vol 15 (6) ◽  
pp. 2331-2346 ◽  
Author(s):  
Augusto C. V. Getirana ◽  
Aaron Boone ◽  
Christophe Peugeot

Abstract Within the framework of the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project phase 2 (ALMIP-2), this study evaluates the water balance simulated by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) over the upper Ouémé River basin, in Benin, using a mesoscale river routing scheme (RRS). The RRS is based on the nonlinear Muskingum–Cunge method coupled with two linear reservoirs that simulate the time delay of both surface runoff and base flow that are produced by land surface models. On the basis of the evidence of a deep water-table recharge in that region, a reservoir representing the deep-water infiltration (DWI) is introduced. The hydrological processes of the basin are simulated for the 2005–08 AMMA field campaign period during which rainfall and streamflow data were intensively collected over the study area. Optimal RRS parameter sets were determined for three optimization experiments that were performed using daily streamflow at five gauges within the basin. Results demonstrate that the RRS simulates streamflow at all gauges with relative errors varying from −20% to 3% and Nash–Sutcliffe coefficients varying from 0.62 to 0.90. DWI varies from 24% to 67% of the base flow as a function of the subbasin. The relatively simple reservoir DWI approach is quite robust, and further improvements would likely necessitate more complex solutions (e.g., considering seasonality and soil type in ISBA); thus, such modifications are recommended for future studies. Although the evaluation shows that the simulated streamflows are generally satisfactory, further field investigations are necessary to confirm some of the model assumptions.


2018 ◽  
Vol 19 (11) ◽  
pp. 1835-1852 ◽  
Author(s):  
Grey S. Nearing ◽  
Benjamin L. Ruddell ◽  
Martyn P. Clark ◽  
Bart Nijssen ◽  
Christa Peters-Lidard

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.


2009 ◽  
Vol 90 (12) ◽  
pp. 1865-1880 ◽  
Author(s):  
Aaron Boone ◽  
Patricia de Rosnay ◽  
Gianpaolo Balsamo ◽  
Anton Beljaars ◽  
Franck Chopin ◽  
...  

2013 ◽  
Vol 14 (5) ◽  
pp. 1605-1619 ◽  
Author(s):  
Martin G. De Kauwe ◽  
Christopher M. Taylor ◽  
Philip P. Harris ◽  
Graham P. Weedon ◽  
Richard. J. Ellis

Abstract Land–atmosphere feedbacks play an important role in the weather and climate of many semiarid regions. These feedbacks are strongly controlled by how the surface responds to precipitation events, which regulate the return of heat and moisture to the atmosphere. Characteristics of the surface can result in both differing amplitudes and rates of warming following rain. Spectral analysis is used to quantify these surface responses to rainfall events using land surface temperature (LST) derived from Earth observations (EOs). The authors analyzed two mesoscale regions in the Sahel and identified distinct differences in the strength of the short-term (<5 days) spectral variance, notably, a shift toward lower-frequency variability in forest pixels relative to nonforest areas and an increase in amplitude with decreasing vegetation cover. Consistent with these spectral signatures, areas of forest and, to a lesser extent, grassland regions were found to warm up more slowly than sparsely vegetated or barren pixels. The authors applied the same spectral analysis method to simulated LST data from the Joint UK Land Environment Simulator (JULES) land surface model. A reasonable level of agreement was found with the EO spectral analysis for two contrasting land surface regions. However, JULES shows a significant underestimate in the magnitude of the observed response to rain compared to EOs. A sensitivity analysis of the JULES model highlights an unrealistically high level of soil water availability as a key deficiency, which dampens the models response to rainfall events.


2013 ◽  
Vol 10 (8) ◽  
pp. 11093-11128 ◽  
Author(s):  
N. C. MacKellar ◽  
S. J. Dadson ◽  
M. New ◽  
P. Wolski

Abstract. Land surface models (LSMs) are advanced tools which can be used to estimate energy, water and biogeochemical exchanges at regional scales. The inclusion of a river flow routing module in an LSM allows for the simulation of river discharge from a catchment and offers an approach to evaluate the response of the system to variations in climate and land-use, which can provide useful information for regional water resource management. This study offers insight into some of the pragmatic considerations of applying an LSM over a regional domain in Southern Africa. The objectives are to identify key parameter sensitivities and investigate differences between two runoff production schemes in physically contrasted catchments. The Joint UK Land Environment Simulator (JULES) LSM was configured for a domain covering Southern Africa at a 0.5° resolution. The model was forced with meteorological input from the WATCH Forcing Data for the period 1981–2001 and sensitivity to various model configurations and parameter settings were tested. Both the PDM and TOPMODEL sub-grid scale runoff generation schemes were tested for parameter sensitivities, with the evaluation focussing on simulated river discharge in sub-catchments of the Orange, Okavango and Zambezi rivers. It was found that three catchments respond differently to the model configurations and there is no single runoff parameterization scheme or parameter values that yield optimal results across all catchments. The PDM scheme performs well in the upper Orange catchment, but poorly in the Okavango and Zambezi, whereas TOPMODEL grossly underestimates discharge in the upper Orange and shows marked improvement over PDM for the Okavango and Zambezi. A major shortcoming of PDM is that it does not realistically represent subsurface runoff in the deep, porous soils typical of the Okavango and Zambezi headwaters. The dry-season discharge in these catchments is therefore not replicated by PDM. TOPMODEL, however, simulates a more realistic seasonal cycle of subsurface runoff and hence improved dry-season flow.


2019 ◽  
Vol 20 (2) ◽  
pp. 339-354
Author(s):  
Mehnaz Rashid ◽  
Rong-You Chien ◽  
Agnès Ducharne ◽  
Hyungjun Kim ◽  
Pat J.-F. Yeh ◽  
...  

AbstractA comprehensive estimation of water budget components, particularly groundwater storage (GWS) and fluxes, is crucial. In this study, we evaluate the terrestrial water budget of the Donga basin (Benin, West Africa), as simulated by three land surface models (LSMs) used in the African Monsoon Multidisciplinary Analysis Land Surface Model Intercomparison Project, phase 2 (ALMIP2): CLM4, Catchment LSM (CLSM), and Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). All three models include an unconfined groundwater component and are driven by the same ALMIP2 atmospheric forcing from 2005 to 2008. Results show that all three models simulate substantially shallower water table depth (WTD) with smaller seasonal variations, approximately 1–1.5 m compared to the observed values that range between 4 and 9.6 m, while the seasonal variations of GWS are overestimated by all the models. These seemingly contradictory simulation results can be explained by the overly high specific yield prescribed in all models. All models achieve similar GWS simulations but with different fractions of precipitation partitioning into surface runoff, base flow, and evapotranspiration (ET), suggesting high uncertainty and errors in the terrestrial and groundwater budgets among models. The poor performances of models can be attributed to bias in the hydrological partitioning (base flow vs surface runoff) and sparse subsurface data. This analysis confirms the importance of subsurface hydrological processes in the current generation of LSMs and calls for substantial improvement in both surface water budget (which controls groundwater recharge) and the groundwater system (hydrodynamic parameters, vertical geometry).


2009 ◽  
Vol 35 (1) ◽  
pp. 127-142 ◽  
Author(s):  
Aaron Anthony Boone ◽  
Isabelle Poccard-Leclercq ◽  
Yongkang Xue ◽  
Jinming Feng ◽  
Patricia de Rosnay

2011 ◽  
Vol 12 (1) ◽  
pp. 45-64 ◽  
Author(s):  
Enrique Rosero ◽  
Lindsey E. Gulden ◽  
Zong-Liang Yang ◽  
Luis G. De Goncalves ◽  
Guo-Yue Niu ◽  
...  

Abstract The ability of two versions of the Noah land surface model (LSM) to simulate the water cycle of the Little Washita River experimental watershed is evaluated. One version that uses the standard hydrological parameterizations of Noah 2.7 (STD) is compared another version that replaces STD’s subsurface hydrology with a simple aquifer model and topography-related surface and subsurface runoff parameterizations (GW). Simulations on a distributed grid at fine resolution are compared to the long-term distribution of observed daily-mean runoff, the spatial statistics of observed soil moisture, and locally observed latent heat flux. The evaluation targets the typical behavior of ensembles of models that use realistic, near-optimal sets of parameters important to runoff. STD and GW overestimate the ratio of runoff to evapotranspiration. In the subset of STD and GW runs that best reproduce the timing and the volume of streamflow, the surface-to-subsurface runoff ratio is overestimated and simulated streamflow is much flashier than observations. Both models’ soil columns wet and dry too quickly, implying that there are structural shortcomings in the formulation of STD that cannot be overcome by adding GW’s increased complexity to the model. In its current formulation, GW extremely underestimates baseflow’s contribution to total runoff and requires a shallow water table to function realistically. In the catchment (depth to water table >10 m), GW functions as a simple bucket model. Because model parameters are likely scale and site dependent, the need for even “physically based” models to be extensively calibrated for all domains on which they are applied is underscored.


2019 ◽  
Author(s):  
Salma Tafasca ◽  
Agnès Ducharne ◽  
Christian Valentin

Abstract. Soil physical properties play an important role for estimating soil water and energy fluxes. Many hydrological and land surface models (LSMs) use soil texture maps to infer these properties. Here, we investigate the impact of soil texture on soil water fluxes and storage at global scale using the ORCHIDEE LSM, forced by several complex or globally-uniform soil texture maps. The model shows a realistic sensitivity of runoff processes and soil moisture to soil texture, and reveals that medium textures give the highest evapotranspiration and lowest total runoff rates. The three tested complex soil texture maps being rather similar by construction, especially when upscaled at the 0.5° resolution used here, they result in similar water budgets at all scales, compared to the uncertainties of observation-based products and meteorological forcing datasets. A useful outcome is that the choice of the input soil texture map is not crucial for large-scale modelling. The added-value of more detailed soil information (horizontal and vertical resolution, soil composition) deserves further studies.


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