190 research outputs found

    Spatio-temporal assessment of WRF, TRMM and in situ precipitation data in a tropical mountain environment (Cordillera Blanca, Peru)

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    The estimation of precipitation over the broad range of scales of interest for climatologists, meteorologists and hydrologists is challenging at high altitudes of tropical regions, where the spatial variability of precipitation is important while in situ measurements remain scarce largely due to operational constraints. Three different types of rainfall products - ground based (kriging interpolation), satellite derived (TRMM3B42), and atmospheric model outputs (WRF - Weather Research and Forecasting) - are compared for 1 hydrological year in order to retrieve rainfall patterns at timescales ranging from sub-daily to annual over a watershed of approximately 10 000 km(2) in Peru. An ensemble of three different spatial resolutions is considered for the comparison (27, 9 and 3 km), as long as well as a range of timescales (annual totals, daily rainfall patterns, diurnal cycle). WRF simulations largely overestimate the annual totals, especially at low spatial resolution, while reproducing correctly the diurnal cycle and locating the spots of heavy rainfall more realistically than either the ground-based KED or the Tropical Rainfall Measuring Mission (TRMM) products. The main weakness of kriged products is the production of annual rainfall maxima over the summit rather than on the slopes, mainly due to a lack of in situ data above 3800 ma.s.l. This study also confirms that one limitation of TRMM is its poor performance over ice-covered areas because ice on the ground behaves in a similar way as rain or ice drops in the atmosphere in terms of scattering the microwave energy. While all three products are able to correctly represent the spatial rainfall patterns at the annual scale, it not surprisingly turns out that none of them meets the challenge of representing both accumulated quantities of precipitation and frequency of occurrence at the short timescales (sub-daily and daily) required for glacio-hydrological studies in this region. It is concluded that new methods should be used to merge various rainfall products so as to make the most of their respective strengths

    Representing past and future hydro-climatic variability over multi-decadal periods in poorly-gauged regions: the case of Ecuador

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    Cette thèse évalue des méthodes pour représenter la variabilité spatio-temporelle hydro-climatique passée et future dans les régions peu jaugées. Elle propose une procédure complète et reproductible appliquée à l'Équateur et s'appuyant sur des données hydro-climatiques observées et simulées en vue de représenter la variabilité passée et de projeter l'impact potentiel des changements climatiques sur les écoulements à la fin du 21ème siècle. Un état de l'art a permis d'identifier plusieurs techniques qui ont été intégrées dans une chaîne méthodologique pour obtenir des séries spatio-temporelles continues de température, de précipitation et de débit sur les périodes multi-décennales passées et futures. Trois chapitres centraux sont consacrés à cet objectif selon les thèmes suivants : (1) régionalisation de la température et des précipitations à partir de mesures in situ en comparant des techniques déterministes et géostatistiques avec une prise en compte de corrections orographiques; (2) reconstruction du débit dans différents bassins versants à l'aide de modèles hydrologiques conceptuels utilisés selon une approche multimodèle et multiparamétrique; et (3) projections hydro-climatiques basées sur des simulations de modèles climatiques sous contrainte d'un scénario marqué d'émission de gaz à effet de serre. La régionalisation du climat a révélé l'importance de caler les paramètres de spatialisation et d'évaluer les champs interpolés par rapport à des stations ponctuelles indépendantes et via des analyses de sensibilité hydrologique. La reconstruction des débits a été possible grâce aux simulations combinées de trois modèles hydrologiques évalués dans des conditions climatiques contrastées, et forcés par les variables climatiques régionalisées. Des simulations de changements hydro-climatiques à moyen terme (2040-2070) et à long terme (2070-2100) ont ensuite été analysées avec des intervalles de confiance de 95 %, en utilisant des scénarios de neuf modèles climatiques et en transférant les paramètres hydrologiques calibrés pour la reconstruction des débits. L'analyse de la variabilité hydro-climatique montre une légère augmentation des températures sur la période 1985-2015, tandis que la variabilité des précipitations est liée aux principaux modes des phases El Niño et La Niña à l'échelle inter-annuelle et au déplacement de la zone de convergence inter-tropicale (ZCIT) à l'échelle saisonnière. Une augmentation générale de la température (+4,4 °C) et des précipitations (+17 %) est attendue d'ici à la fin du 21ème siècle, ce qui pourrait entraîner une augmentation de +5 % à +71 % du débit annuel moyen selon les bassins versants. Ces résultats sont discutés en termes d'importance pour la gestion de l'eau, avant de suggérer de futures recherches hydrologiques telles que la régionalisation du débit des cours d'eau, une meilleure quantification des incertitudes et une évaluation de la capacité à satisfaire les futurs besoins en eau.This thesis investigates methods to represent the past and future hydro-climatic variability in space and over time in poorly-gauged regions. It proposes a complete and reproducible procedure applied to the continental Ecuador to deal with observed and simulated hydro-climatic data in order to represent past variability and project the potential impact of climate change on water resources by the end of the 21st century. Up-to-date techniques were identified in a literature review and were integrated in a chain protocol to obtain continuous space-time series of air temperature, precipitation and streamflow over past and future multi-decadal periods. Three central chapters are dedicated to this objective according to the following topics: (1) regionalization of air temperature and precipitation from in situ measurements by comparing deterministic and geostatistical techniques including orographic corrections; (2) streamflow reconstruction in various catchments using conceptual hydrological models in a multi-model, multi-parameter approach; and (3) hydro-climate projections using climate model simulations under a high range emission scenario. Climate regionalization revealed the importance of calibrating parameters and of assessing interpolated fields against independent gauges and via hydrological sensitivity analyses. Streamflow reconstruction was possible with the regionalized climate inputs and the combined simulations of three hydrological models evaluated in contrasting climate conditions. Future medium term (2040-2070) and long term (2070-2100) hydro-climatic changes were analysed with confidence intervals of 95% using scenarios from nine climate models and transferring the model parameters calibrated for streamflow reconstruction. Analysis of hydro-climatic variability over the period 1985-2015 showed a slight increase in temperature, while precipitation variability was linked to the main modes of El Niño and La Niña phases at inter-annual scale and to the displacement of the inter-tropical convergence zone (ITCZ) at seasonal scale. Under climate change, a general increase in temperature (+4.4 °C) and precipitation (+17%) is expected by the end of the 21st century, which could lead to between +5% and 71% increase in mean annual streamflow depending on the catchments. These results are discussed in terms of significance for water management before suggesting future hydrological research such as regionalizing streamflow, better quantifying uncertainties and assessing the capacity to meet future water requirements

    Assessing the impacts of land-use change on the hydrology of the tropical Andes

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    Land-use and land-cover change (LUCC) has been identified as a major driver of change to the hydrological cycle. However, it is still a scientific challenge to quantify these effects. Land surface models are increasingly being used for such hydrological assessment because of their state-of-the-art representation of physical processes and versatility. A physically-based model has the advantage to map the modeller’s knowledge about the hydrological impacts of land-use and land-cover change into physically meaningful parameters. This PhD thesis explores the use of a land surface model (Joint UK Land-Environment Simulator, JULES) in combination with high temporal resolution in-situ data on streamflow, precipitation, and several weather variables, collected by a grassroots hydrological monitoring initiative (called iMHEA) in the tropical Andes. I find that the in-situ data can improve the hydrological simulation substantially, mainly by reducing uncertainty inherent in using large-scale precipitation data. The commonly used soil parameters based on pedotransfer functions lead to an underestimation of the flow. Therefore, I modified the soil parameterisation with experimental data for a more accurate representation of subsurface flow generation. Subsequently, I assessed the potential impacts of watershed interventions (grazing, afforestation, cultivation) using the calibrated soil parameters. A reduction in water yield and water regulation ability under these land use scenarios was identified, which is in line with observed impacts and relevant for water resources managers. In a next step, I implemented an open source land use change model, the lulcc R package, to analyse the regional land cover changes in the Andean region, and to generate predictive land use maps that can be used to drive the JULES model. For this purpose, the JULES model has been implemented at a regional scale using multiple sources of global data. The use of the JULES model allows the effects of LUCC to be assessed using knowledge about physical processes. My results show a further 3.7% of deforestation occurring in the region, which changes the flow by ±17% consequently.Open Acces

    Evaluation of mesoscale convective systems in South America using multiple satellite products and an object‐based approach

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    In this study, an object-based verification method was used to reveal the existence of systematic errors in three satellite precipitation products: Tropical Rainfall Measurement Mission (TRMM), Climate Prediction Center Morphing Technique (CMORPH), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN). Mesoscale convective systems (MCSs) for the austral summer 2002–2003 in the La Plata river basin, southeastern South America, were analyzed with the Contiguous Rain Area (CRA) method. Errors in storms intensity, volume, and spatial location were evaluated. A macroscale hydrological model was used to assess the impact of spatially shifted precipitation on streamflows simulations. PERSIANN underestimated the observed average rainfall rate and maximum rainfall consistent with the detection of storm areas systematically larger than observed. CMORPH overestimated the average rainfall rate while the maximum rainfall was slightly underestimated. TRMM average rainfall rate and rainfall volume correlated extremely well with ground observations whereas the maximum rainfall was systematically overestimated suggesting deficiencies in the bias correction procedure to filter noisy measurements. The preferential direction of error displacement in satellite-estimated MCSs was in the east-west direction for CMORPH and TRMM. Discrepancies in the fine structure of the storms dominated the error decomposition of all satellite products. Errors in the spatial location of the systems influenced the magnitude of simulated peaks but did not have a significant impact on the timing indicating that the system's response to precipitation was mitigating the effect of the errors.Fil: Demaria, E. M. C.. University Of Arizona; Estados UnidosFil: Rodriguez, D. A.. Centro de Previsao de Tempo e Estudos Climaticos. Instituto Nacional de Pesquisas Espaciais; BrasilFil: Ebert, E. E.. Centre for Australian Weather and Climate Research; AustraliaFil: Salio, Paola Veronica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Su, F.. University of Washington; Estados UnidosFil: Valdes, J. B.. University Of Arizona; Estados Unido

    Recovery of rapid water mass changes (RWMC) by Kalman filtering of GRACE observations

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    We demonstrate a new approach to recover water mass changes from GRACE satellite data at a daily temporal resolution. Such a product can be beneficial in monitoring extreme weather events that last a few days and are missing by conventional monthly GRACE data. The determination of the distribution of these water mass sources over networks of juxtaposed triangular tiles was made using Kalman Filtering (KF) of daily GRACE geopotential difference observations that were reduced for isolating the continental hydrology contribution of the measured gravity field. Geopotential differences were obtained from the along-track K-Band Range Rate (KBRR) measurements according to the method of energy integral. The recovery approach was validated by inverting synthetic GRACE geopotential differences simulated using GLDAS/WGHM global hydrology model outputs. Series of daily regional and global KF solutions were estimated from real GRACE KBRR data for the period 2003–2012. They provide a realistic description of hydrological fluxes at monthly time scales, which are consistent with classical spherical harmonics and mascons solutions provided by the GRACE official centers but also give an intra-month/daily continuity of these variations

    Development of tools for water management in the Hatra watershed (Northwestern Iraq) using satellite technologies

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    “All around the world the demand for water is increasing, especially in arid and semi-arid regions, including Iraq which subject to continuous desertification that is worsening, more importantly the Jezira region in northwestern Iraq. Thus, it’s crucial to have a better strategy for water management. One of these strategies is to promote groundwater recharge for restoring the aquifer depletion. The successful groundwater recharge is limited by some potential data such as the annual water budge and precipitation measurements. The atomospheric and hydrological observations are limited by sparse population which tends to be less in arid and semi-arid regions. Therefore, an alternative to the ground measurement of rainfall is needed. Satellite-based measurements limit the restriction of ground station. However, the satellite products have significant uncertainty. Therefore, seven precipitation estimates have tested against rain gauges in Orange County and Los Angeles County, California. In order to establish a water management strategy in Jezira region, annual water budget should be known, which could be measure through observational discharge station. Unfortunately, only few months of discharge was measured manually in the north Jezira, which Hatra subwatershed. Computer model was used to recover the streamflow measurement. The Soil and Water Assessment Tool (SWAT) was great candidate to overcome the problem. The observational data of stream discharge was used to calibrate the model. In conclusion, water management is possible in ungauged arid and semi-arid regions by using remote sensing data and computer modeling”--Abstract, page iv

    MSWEP : 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data

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    Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3hourly temporal and 0.25 degrees ffi spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0% of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km(2)) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byrans Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9% of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org

    The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset

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    We introduce the first catchment dataset for large sample studies in Chile. This dataset includes 516 catchments; it covers particularly wide latitude (17.8 to 55.0∘ S) and elevation (0 to 6993 m a.s.l.) ranges, and it relies on multiple data sources (including ground data, remote-sensed products and reanalyses) to characterise the hydroclimatic conditions and landscape of a region where in situ measurements are scarce. For each catchment, the dataset provides boundaries, daily streamflow records and basin-averaged daily time series of precipitation (from one national and three global datasets), maximum, minimum and mean temperatures, potential evapotranspiration (PET; from two datasets), and snow water equivalent. We calculated hydro-climatological indices using these time series, and leveraged diverse data sources to extract topographic, geological and land cover features. Relying on publicly available reservoirs and water rights data for the country, we estimated the degree of anthropic intervention within the catchments. To facilitate the use of this dataset and promote common standards in large sample studies, we computed most catchment attributes introduced by Addor et al. (2017) in their Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) dataset, and added several others. We used the dataset presented here (named CAMELS-CL) to characterise regional variations in hydroclimatic conditions over Chile and to explore how basin behaviour is influenced by catchment attributes and water extractions. Further, CAMELS-CL enabled us to analyse biases and uncertainties in basin-wide precipitation and PET. The characterisation of catchment water balances revealed large discrepancies between precipitation products in arid regions and a systematic precipitation underestimation in headwater mountain catchments (high elevations and steep slopes) over humid regions. We evaluated PET products based on ground data and found a fairly good performance of both products in humid regions (r>0.91) and lower correlation (r<0.76) in hyper-arid regions. Further, the satellite-based PET showed a consistent overestimation of observation-based PET. Finally, we explored local anomalies in catchment response by analysing the relationship between hydrological signatures and an attribute characterising the level of anthropic interventions. We showed that larger anthropic interventions are correlated with lower than normal annual flows, runoff ratios, elasticity of runoff with respect to precipitation, and flashiness of runoff, especially in arid catchments. CAMELS-CL provides unprecedented information on catchments in a region largely underrepresented in large sample studies. This effort is part of an international initiative to create multi-national large sample datasets freely available for the community. CAMELS-CL can be visualised from http://camels.cr2.cl and downloaded from https://doi.pangaea.de/10.1594/PANGAEA.894885

    The hydrology of the Peruvian Amazon river and its sensitivity to climate change

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    This PhD thesis explores the utility of a land surface model (Joint UK Land-Environment Simulator, JULES) for large-scale hydrological modelling of the Peruvian Amazon - a humid tropical mountain basin where process understanding is poor and data are scarce. A sparse rain gauge network necessitates the use of large-scale data from satellite and global climate model reanalysis to complement ground observations, commanding a closer look at (1) the uncertainties (2) merging techniques to utilise multiple observations in the model forcing. A main outcome of the research is establishing the model’s sensitivity to precipitation error, and at the same time, demonstrating an increasing reliability of global remote sensing products as model forcing, specifically, with data from the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis version 7 algorithm. Furthermore, satellite-rain gauge data assimilation techniques such as mean-bias correction, double smoothing residual blending, and Bayesian combination, are shown to reduce the mean errors in the satellite-based product. Secondly, with regional calibration and an offline runoff routing scheme, JULES is shown to be reasonably skillful at reproducing the observed streamflow dynamic and extremes. Representing the subgrid heterogeneity of soil moisture using the probability distributed model (PDM) was key to improving surface runoff generation. However, evapotranspirative fluxes in the lower basin remain poorly reproduced without an adequate floodplain system representation. Finally, under the Intergovernmental Panel for Climate Change’s RCP4.5 future climate scenario, which projects a warming and wetting up to the year 2035, the Peruvian Amazon basin is shown to respond nonlinearly to the increase in wet season precipitation with more than 40% increase in the peak flows compared to the baseline scenario. There is limited confidence in the projections due to climate projections uncertainty and the assumptions of model stationarity.Open Acces

    Effect of baseline meteorological data selection on hydrological modelling of climate change scenarios

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    This study evaluates how differences in hydrological model parameterisation resulting from the choice of gridded global precipitation data sets and reference evapotranspiration (ETo) equations affects simulated climate change impacts, using the north western Himalayan Beas river catchment as a case study. Six combinations of baseline precipitation data (the Tropical Rainfall Measuring Mission (TRMM) and the Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE)) and Reference Evapotranspiration equations of differing complexity and data requirements (Penman-Monteith, Hargreaves –Samani and Priestley – Taylor) were used in the calibration of the HySim model. Although the six validated hydrological models had similar historical model performance (Nash–Sutcliffe model efficiency coefficient (NSE) from 0.64-0.70), impact response surfaces derived using a scenario neutral approach demonstrated significant deviations in the models’ responses to changes in future annual precipitation and temperature. For example, the change in Q10 varies between -6.5 % to -11.5% in the driest and coolest climate change simulation and +79% to +118% in the wettest and hottest climate change simulation among the six models. The results demonstrate that the baseline meteorological data choices made in model construction significantly condition the magnitude of simulated hydrological impacts of climate change, with important implications for impact study design.NER
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