| publications-1601 |
PEER REVIEWED ARTICLE |
2017 |
Haileyesus Belay Lakew , Semu Ayalew Moges , Dereje Hailu Asfaw |
Hydrological Evaluation of Satellite and Reanalysis Precipitation Products in the Upper Blue Nile Basin: A Case Study of Gilgel Abbay |
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10.3390/hydrology4030039 |
AI & Machine Learning |
Water Distribution Networks |
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The aim of this study is to assess the performance of various global precipitation products for water resources application in the Upper Blue Nile basin, Ethiopia. Three precipitation products of gauge-adjusted (corrected) CMORPH, (TRMM) TMPA 3B42v7 and ECMWF reanalysis products are evaluated. A Coupled Routing and Excess Storage (CREST) distributed hydrological model is calibrated and used for the evaluation. The model is calibrated for 2000–2005 and validated for 2006–2011 periods using daily observed rainfall and discharge datasets. The results indicate the precipitation products consistently provide a better performance of runoff estimation when they are independently calibrated than simulation modes of the products. We conclude as long as each product is calibrated independently, global precipitation products can provide enough information for water resource management in data-scarce regions of upper Blue Nile Basin. Further analysis is underway to understand the response characteristics of the precipitation products at larger spatio-temporal scales. |
603608 |
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| publications-1602 |
PEER REVIEWED ARTICLE |
2018 |
Fuxing Wang , Jan Polcher , Philippe Peylin , Vladislav Bastrikov |
Assimilation of river discharge in a land surface model to improve estimates of the continental water cycles |
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10.5194/hess-2017-731 |
AI & Machine Learning |
Water Distribution Networks |
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Abstract. The river discharge plays an important role in earth’s water cycles, but it is difficult to estimate due to un-gauged rivers, human activities, and measurement errors. One approach is based on the observed flux and a simple annual water balance model (ignoring human processes) for ungauged rivers, but it only provides annual mean values which is insufficient for oceanic modellings. Another way is by forcing a land surface model (LSM) with atmospheric conditions. It provides daily values but with uncertainties associated to models. We use data assimilation techniques by merging the modelled river discharges by ORCHIDEE (without human processes currently) LSM and the observations from Global Runoff Data Center (GRDC) to obtain optimized discharges over the entire basin. The model systematic errors and human impacts (e.g., dam operation, irrigation, etc.) are taken into account by an optimization parameter x (with annual variation), which is applied to correct model intermediate variables runoff and drainage over each sub-watershed. The method is illustrated over Iberian Peninsula with 27 GRDC stations over the period 1979–1989. ORCHDIEE represents a realistic discharge over north of Iberian Peninsula with small model systematic errors, while the model overestimates discharges by 30 %–150 % over south and northeast region where the blue water footprint is large. The bias (absolute value) has been significantly reduced to less than 30 % after assimilation, and the assimilation result is not sensitive to assimilation strategies. This method also corrects the discharge bias for the basins without observations assimilated by extrapolating the correction from adjacent basins. The correction increases the inter-annual variability of river discharge because of the fluctuation of water usage. The E (P-E) of GLEAM (Global Land Evaporation Amsterdam Model, v3.1a) is lower (higher) than the bias corrected value, which could be due to the different P forcing and probably the missing processes in the GLEAM model. |
603608 |
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| publications-1603 |
PEER REVIEWED ARTICLE |
2017 |
Emmanouil Flaounas , Vassiliki Kotroni , Konstantinos Lagouvardos , Suzanne L. Gray , Jean-François Rysman , Chantal Claud |
Heavy rainfall in Mediterranean cyclones. Part I: contribution of deep convection and warm conveyor belt |
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10.1007/s00382-017-3783-x |
Data Management & Analytics |
Water Distribution Networks |
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No abstract available |
603608 |
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| publications-1604 |
PEER REVIEWED ARTICLE |
2018 |
Abhinna K. Behera , Emmanuel D. Rivière , Virginie Marécal , Jean-François Rysman , Chantal Claud , Geneviève Sèze , Nadir Amarouche , Mélanie G |
Modeling the TTL at continental scale for a wet season: an evaluation of the BRAMS mesoscale model using TRO-Pico campaign, and measurements from air- and space-borne sensors |
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10.1002/2017jd027969 |
IoT & Sensors |
Water Distribution Networks |
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AbstractIn order to better understand the water vapor (WV) intrusion into the tropical stratosphere, a mesoscale simulation of the tropical tropopause layer using the BRAMS (Brazilian version of Regional Atmospheric Modeling System (RAMS)) model is evaluated for a wet season. This simulation with a horizontal grid point resolution of 20 km × 20 km cannot resolve the stratospheric overshooting convection (SOC). Its ability to reproduce other key parameters playing a role in the stratospheric WV abundance is investigated using the balloon‐borne TRO‐Pico campaign measurements, the upper‐air soundings over Brazil, and the satellite observations by Aura Microwave Limb Sounder, Microwave Humidity Sounder, and Geostationary Operational Environmental Satellite 12. The BRAMS exhibits a good ability in simulating temperature, cold‐point, WV variability around the tropopause. However, the simulation is typically observed to be warmer by ∼2.0°C and wetter by ∼0.4 ppmv at the hygropause, which can be partly affiliated with the grid boundary nudging of the model by European Centre for Medium‐Range Weather Forecasts operational analyses. The modeled cloud tops show a good correlation (maximum cross‐correlation of ∼0.7) with Geostationary Operational Environmental Satellite 12. Furthermore, the overshooting cells detected by Microwave Humidity Sounder are observed at the locations, where 75% of the modeled cloud tops are higher than 11 km. Finally, the modeled inertia‐gravity wave periodicity and wavelength are comparable with those deduced from the radio sounding measurements during TRO‐Pico campaign. The good behavior of BRAMS confirms the SOC contribution in the WV abundance, and variability is of lesser importance than the large‐scale processes. This simulation can be used as a reference run for upscaling the impact of SOC at a continental scale for future studies. |
603608 |
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| publications-1605 |
PEER REVIEWED ARTICLE |
2018 |
Rogier Westerhoff , Paul White , Zara Rawlinson |
Incorporation of Satellite Data and Uncertainty in a Nationwide Groundwater Recharge Model in New Zealand |
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10.3390/rs10010058 |
Data Management & Analytics |
Water Distribution Networks |
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A nationwide model of groundwater recharge for New Zealand (NGRM), as described in this paper, demonstrated the benefits of satellite data and global models to improve the spatial definition of recharge and the estimation of recharge uncertainty. NGRM was inspired by the global-scale WaterGAP model but with the key development of rainfall recharge calculation on scales relevant to national- and catchment-scale studies (i.e., a 1 km × 1 km cell size and a monthly timestep in the period 2000–2014) provided by satellite data (i.e., MODIS-derived evapotranspiration, AET and vegetation) in combination with national datasets of rainfall, elevation, soil and geology. The resulting nationwide model calculates groundwater recharge estimates, including their uncertainty, consistent across the country, which makes the model unique compared to all other New Zealand estimates targeted towards groundwater recharge. At the national scale, NGRM estimated an average recharge of 2500 m 3 /s, or 298 mm/year, with a model uncertainty of 17%. Those results were similar to the WaterGAP model, but the improved input data resulted in better spatial characteristics of recharge estimates. Multiple uncertainty analyses led to these main conclusions: the NGRM model could give valuable initial estimates in data-sparse areas, since it compared well to most ground-observed lysimeter data and local recharge models; and the nationwide input data of rainfall and geology caused the largest uncertainty in the model equation, which revealed that the satellite data could improve spatial characteristics without significantly increasing the uncertainty. Clearly the increasing volume and availability of large-scale satellite data is creating more opportunities for the application of national-scale models at the catchment, and smaller, scales. This should result in improved utility of these models including provision of initial estimates in data-sparse areas. Topics for future collaborative research associated with the NGRM model include: improvement of rainfall-runoff models, establishment of snowmelt and river recharge modules, further improvement of estimates of rainfall and AET, and satellite-derived AET in irrigated areas. Importantly, the quantification of uncertainty, which should be associated with all future models, should give further impetus to field measurements of rainfall recharge for the purpose of model calibration. |
603608 |
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| publications-1606 |
PEER REVIEWED ARTICLE |
2016 |
Rogier Westerhoff , Paul White , Zara Rawlinson |
Application of global models and satellite data forsmaller-scale groundwater recharge studies |
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10.5194/hess-2016-410 |
IoT & Sensors |
Water Distribution Networks |
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Abstract. Large-scale models and satellite data are increasingly used to characterise groundwater and its recharge at the global scale. Although these models have the potential to fill in data gaps and solve trans-boundary issues, they are often neglected in smaller-scale studies, since data are often coarse or uncertain. Large-scale models and satellite data could play a more important role in smaller-scale (i.e., national or regional) studies, if they could be adjusted to fit that scale. In New Zealand, large-scale models and satellite data are not used for groundwater recharge estimation at the national scale, since regional councils (i.e., the water managers) have varying water policy and models are calibrated at the local scale. Also, some regions have many localised ground observations (but poor record coverage), whereas others are data-sparse. Therefore, estimation of recharge is inconsistent at the national scale. This paper presents an approach to apply large-scale, global, models and satellite data to estimate rainfall recharge at the national to regional scale across New Zealand. We present a model, NGRM, that is largely inspired by the global-scale WaterGAP recharge model, but is improved and adjusted using national data. The NGRM model uses MODIS-derived ET and vegetation satellite data, and the available nation-wide datasets on rainfall, elevation, soil and geology. A valuable addition to the recharge estimation is the model uncertainty estimate, based on variance, covariance and sensitivity of all input data components in the model environment. This research shows that, with minor model adjustments and use of improved input data, large-scale models and satellite data can be used to derive rainfall recharge estimates, including their uncertainty, at the smaller scale, i.e., national and regional scale of New Zealand. The estimated New Zealand recharge of the NGRM model compare well to most local and regional lysimeter data and recharge models. The NGRM is therefore assumed to be capable to fill in gaps in data-sparse areas and to create more consistency between datasets from different regions, i.e., to solve trans-boundary issues. This research also shows that smaller-scale recharge studies in New Zealand should include larger boundaries than only a (sub-)aquifer, and preferably the whole catchment. This research points out the need for improved collaboration on the international to national to regional levels to further merge large-scale (global) models to smaller (i.e., national or regional) scales. Future research topics should, collaboratively, focus on: improvement of rainfall-runoff and snowmelt methods; inclusion of river recharge; further improvement of input data (rainfall, evapotranspiration, soil and geology); and the impact of recharge uncertainty in mountainous and irrigated areas. |
603608 |
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| publications-1607 |
PEER REVIEWED ARTICLE |
2018 |
A. Gruber , W. T. Crow , W. A. Dorigo |
Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain |
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10.1002/2017wr021277 |
Simulation & Modeling |
Water Distribution Networks |
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AbstractGrowth in the availability of near‐real‐time soil moisture observations from ground‐based networks has spurred interest in the assimilation of these observations into land surface models via a two‐dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model‐based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two‐dimensional (2‐D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground‐based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2‐D system is compared to that obtained from the 1‐D assimilation of remote sensing retrievals to assess the value of ground‐based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite‐based surface soil moisture retrievals. |
603608 |
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| publications-1608 |
PEER REVIEWED ARTICLE |
2017 |
Dejene Sahlu , Semu A. Moges , Efthymios I. Nikolopoulos , Emmanouil N. Anagnostou , Dereje Hailu |
Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa |
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10.1155/2017/4957960 |
AI & Machine Learning |
Water Distribution Networks |
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The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons. |
603608 |
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| publications-1609 |
PEER REVIEWED ARTICLE |
2017 |
Viviana Maggioni , Efthymios I. Nikolopoulos , Emmanouil N. Anagnostou , Marco Borga |
Modeling Satellite Precipitation Errors Over Mountainous Terrain: The Influence of Gauge Density, Seasonality, and Temporal Resolution |
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10.1109/tgrs.2017.2688998 |
IoT & Sensors |
Uncategorized |
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No abstract available |
603608 |
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| publications-1610 |
PEER REVIEWED ARTICLE |
2018 |
Albert I. J. M. van Dijk , Jaap Schellekens , Marta Yebra , Hylke E. Beck , Luigi J. Renzullo , Albrecht Weerts , Gennadii Donchyts |
Global 5-km resolution estimates of secondary evaporation including irrigation through satellite data assimilation |
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10.5194/hess-2017-757 |
Simulation & Modeling |
Irrigation Systems |
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Abstract. A portion of globally generated surface and groundwater resources evaporates from wetlands, water bodies and irrigated areas. This secondary evaporation of blue water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05° or c. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance, and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of surface water extent, evaporation flux adjustment based on LST, and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 mm/d to +2 mm/d). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1 • 1012 m3 y−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58% from terrestrial water bodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation. |
603608 |
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