[1] Water table dynamics, a basic hydrologic process, was traditionally not considered in global-scale land surface models (LSMs). In this study, a representation of water table dynamics is integrated into a global LSM to address the shortcomings of existing global-scale modeling studies and the appropriateness of specifying certain parameters as globally constant. Evaluation of model simulation using globally varying parameters against river discharge observations in selected large rivers shows improvements when the water table dynamics is included in the model. The mechanisms by which the water table dynamics affects land surface hydrologic simulation are then investigated by analyzing the sensitivity simulation of groundwater (GW) capillary flux. The result indicates that global mean evapotranspiration (ET) increases by ~9% when GW capillary flux is considered. The semiarid regions with marked dry season have the largest increase (~25%), while the humid and high-latitude regions with sufficient moisture but limited radiation energy have the smallest increase. Increase in ET is more pronounced in dry season when GW recharge becomes negative (upward moisture supply from the aquifer), but its magnitude depends on the water table depth (WTD). On the other hand, a deeper WTD caused by the GW capillary flux is found to decrease runoff throughout the year in regions with a large increase in ET in dry season only. Based on our modeling result, about 50% of global land area (especially in humid and high-latitude regions) is simulated to have the mean WTD shallower than 5 m, which emphasizes the significance of representing water table dynamics in global-scale LSMs.

[1] Land water storage plays a fundamental role in the West African water cycle and has an important impact on climate and on the natural resources of this region. However, measurements of land water storage are scarce at regional and global scales and especially in poorly instrumented endorheic regions, such as most of the Sahel, where little useful information can be derived from river flow measurements and basin water budgets. The Gravity Recovery and Climate Experiment (GRACE) satellite mission provides an accurate measurement of the terrestrial gravity field variations from which land water storage variations can be derived. However, their retrieval is not straightforward, and different methods are employed, which results in different water storage GRACE products. On the other hand, water storage can be estimated by land surface modeling forced with observed or satellite-based boundary conditions, but such estimates can be highly model dependent. In this study, land water storage by six GRACE products and soil moisture estimations by nine land surface models (run within the framework of the African Monsoon Multidisciplinary Analysis Land Surface Intercomparison Project (ALMIP)) are evaluated over West Africa, with a particular focus on the Sahelian area. The water storage spatial distribution, including zonal transects, its seasonal cycle, and its and interannual variability, are analyzed for the years 2003–2007. Despite the nonnegligible differences among the various GRACE products and among the different models, a generally good agreement between satellite and model estimates is found over the West Africa study region. In particular, GRACE data are shown to reproduce well the water storage interannual variability over the Sahel for the 5 year study period. The comparison between satellite estimates and ALMIP results leads to the identification of processes needing improvement in the land surface models. In particular, our results point out the importance of correctly simulating slow water reservoirs as well as evapotranspiration during the dry season for accurate soil moisture modeling over West Africa.

We thank the reviewers, editor and representatives from the various organisational units of the CMIP panel for their constructive comments on the LS3MIP documentation paper. Although the LS3MIP protocol as described in this paper can be regarded as a reference document guiding the implementation of the experiment, many questions on technical and scientific aspects still arise as the experiment is being set up and critically evaluated. We therefore welcome the editors suggestion to provide a version number to the experimental description, as future redesigns or reconsiderations may be likely.

We explore the possibility of using remotely sensed soil moisture data and in situ discharge observations to calibrate a large-extent hydrological model. The model used is PCR-GLOBWB-MOD, which is a physically based and fully coupled groundwater-land surface model operating at a daily basis and having a resolution of 30 arc sec (about 1 km at the equator). As a test bed, we use the combined Rhine-Meuse basin (total area: about 200,000 km2), where there are 4250 point-scale observed groundwater head time series that are used to verify the model results. Calibration is performed by simulating 3045 model runs with varying parameter values affecting groundwater head dynamics. The simulation results of all runs are evaluated against the remotely sensed soil moisture time series of SWI (Soil Water Index) and field discharge data. The former is derived from European Remote Sensing scatterometers and provides estimates of the first meter profile soil moisture content at 30 arc min resolution (50 km at the equator). From the evaluation of these runs, we then introduce a stepwise calibration approach that considers stream discharge first, then soil moisture, and finally verify the resulting simulation to groundwater head observations. Our results indicate that the remotely sensed soil moisture data can be used for the calibration of upper soil hydraulic conductivities determining simulated groundwater recharge of the model. However, discharge data should be included to obtain full calibration of the coupled model, specifically to constrain aquifer transmissivities and runoff-infiltration partitioning processes. The stepwise approach introduced in this study, using both discharge and soil moisture data, can calibrate both discharge and soil moisture, as well as predicting groundwater head dynamics with acceptable accuracy. As our approach to parameterize and calibrate the model uses globally available data sets only, it opens up the possibility to set up large-extent coupled groundwater-land surface models in other basins or even globally. Key Points Soil moisture data can be used to calibrate upper soil conductivities Yet, discharge data should be included to fully calibrate the coupled model The combined calibration approach reproduces groundwater head dynamics well ©2013. American Geophysical Union. All Rights Reserved.

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE’s coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

Two along-track (level 2) satellite precipitation retrievals by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Dual Frequency Precipitation Radar Ku-band (DPR-Ku) and two multisatellite precipitation products, global satellite mapping of precipitation (GSMaP) and Integrated Multisatellite Retrievals for GPM (IMERG), are intercompared for different cloud types during warm season over the western North Pacific region. It is found that the biases of the precipitation measurements are systematically associated with cloud types. The best agreements of passive microwave (PMW) products and infrared-based (IR) products with satellite radar-based estimates are found for a relatively weak precipitation range for mid-low clouds (except over land) and high clouds, while similar agreement is found for heavier precipitation range for deep convection regardless of surface type. Precipitation from mid-low clouds over land is considerably underestimated by PMW and IR products over almost the entire intensity range. The IR-based precipitation estimates for deep convective clouds considerably overestimate the intensity for both weak precipitation and cases where precipitation was not detected by the DPR-Ku algorithm. The findings reveal the characteristics of the biases of the products depend on the associated cloud types, which suggests consideration of the cloud type information to improve satellite-based precipitation estimates.

This study presents monthly estimates of groundwater anomalies in a large river basin dominated by extensive floodplains, the Negro River Basin, based on the synergistic analysis using multisatellite observations and hydrological models. For the period 2003-2004, changes in water stored in the aquifer is isolated from the total water storage measured by GRACE by removing contributions of both the surface reservoir, derived from satellite imagery and radar altimetry, and the root zone reservoir simulated by WGHM and LaD hydrological models. The groundwater anomalies show a realistic spatial pattern compared with the hydrogeological map of the basin, and similar temporal variations to local in situ groundwater observations and altimetry-derived level height measurements. Results highlight the potential of combining multiple satellite techniques with hydrological modeling to estimate the evolution of groundwater storage.

This study examines how the interannual variability of rainfall impacts the land water storage in the Amazon basin during the 2003-2010 time span at monthly time-scale using respectively, Tropical Rainfall Measuring Mission (TRMM) and Gravity Recovery And Climate Experiment (GRACE) satellite observations. Monthly estimates of GRACE-based terrestrial water storage (TWS) are compared to (1) TRMM rainfall, (2) in situ discharges at the outlet of the major sub-basins of the Amazon over 2003-2010 to characterize the redistribution of precipitation on land water. The time-variations of land water storage derived from GRACE are consistent with those of rainfall and discharges at basin and sub-basin scales even at interannual time-scales (correlation generally greater than 0.7). The study of the relationship between these two quantities reveals large differences in terms of rainfall amount, water storage, time delays, resulting of the water transport among the sub-basins of the Amazon. The analysis of GRACE data has enabled identification of the signature of the recent extreme climatic events (droughts of 2005 and 2010, flood of 2009) on the land water storage, in terms of spatial patterns and intensity. These results are in good agreement with what was observed on independent datasets (water levels and discharges, vegetation activity, forest fires, and drought index), highlighting the interest of gravimetry from space missions for the characterization of the interannual variability of the TWS. GRACE data offer the unique opportunity to monitor the hydrological cycle in ungauged basins where reliable observations of rainfall and discharges are missing.

The snow simulations from the recently developed multivariate land snow data assimilation system (SNODAS) for the Northern Hemisphere are assessed with regard to uncertainties in atmospheric forcing, model structure, data assimilation technique, and satellite remote sensing product. The SNODAS consists of the Data Assimilation Research Testbed (DART) and the Community Land Model version 4 (CLM4). A series of experiments are conducted to estimate each of the above uncertainty sources. The experiments include several open‐loop model cases and data assimilation cases that assimilate the snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE). The atmospheric forcing uncertainty in terms of precipitation and radiation is found to be the largest among the various uncertainty sources examined, especially over the Tibetan Plateau (TP) and most of the mid‐ and high‐latitudes. Model structure and choice of data assimilation technique are also two big sources of uncertainty in SNODAS. The uncertainty of model structure is represented by two different parameterizations of SCF. The density‐based SCF scheme (as used in CLM4) generally results in better snow simulations than does the stochastic SCF scheme (as in CLM4.5) within the data assimilation framework. The choice of TWS products retrieved from GRACE has relatively the least impact on the snow data assimilation.

The measurement of spatially distributed water surface elevation (WSE) at the global scale, which is planned by “Surface Water and Ocean Topography (SWOT) mission”, will bring lots of fresh knowledge on global-scale terrestrial water circulation especially when observed WSE is merged into global hydrodynamics modes by data assimilation. In this paper, we discussed the predictability of WSE by a global hydrodynamics model, which is required as a dynamic core for global-scale data assimilation using SWOT. From the comparison against WSE observed by the altimeter on Envisat, we found that the up-to-date global hydrodynamics model is able to reproduce the seasonal variations of WSE in the Amazon River, though predicted WSE still has errors due to the uncertainties in topographic parameters and sub-grid-scale dynamics of the model. Reduction of these uncertainties is required along with the development of the algorithm itself for achieving global-scale data assimilation of WSE.

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

Increasing Earth’s surface air temperature yields an intensification of its hydrological cycle1. As a consequence, the risk of river floods will increase regionally within the next two decades due to the atmospheric warming caused by past anthropogenic greenhouse gas emissions2–4. The direct economic losses5,6 caused by these floods can yield regionally heterogeneous losses and gains by propagation within the global trade and supply network7. Here we show that, in the absence of large-scale structural adaptation, the total economic losses due to fluvial floods will increase in the next 20 years globally by 17% despite partial compensation through market adjustment within the global trade network. China will suffer the strongest direct losses, with an increase of 82%. The United States is mostly affected indirectly through its trade relations. By contrast to the United States, recent intensification of the trade relations with China leaves the European Union better prepared for the import of production losses in the future.Economic losses due to river floods are expected to increase globally in the next 20 years. Direct local losses and indirect losses propagated through a global supply network are derived.

In this study, we developed a global river water temperature model that explicitly represents water and heat budgets. The model considers fluvial dynamics such as floodplain inundation and as well simulates seasonal freeze‐thaw cycles for temperate and arctic rivers, and it was found that proposed physically based parameterizations appropriately reproduced in situ observed seasonal variations in 12 global rivers. Theoretically, floodplain inundation has two major effects on water temperature variability: (1) “broadening” of water surfaces, which increases heat exchange with the atmosphere and friction with river bed, and (2) “shallowing” mean water depth, which increases the absorption rate of shortwave radiation per unit volume. In contrast to the broadening which affects both warming and cooling processes, the shallowing causes warming only and has dominant impacts mainly during the ice melting season in Arctic regions and the rainy season in the tropics. Furthermore, it was revealed that lateral mixing between channel and floodplain has a significant impact on determination of river water temperature. Although water temperature plays an important role in water quality and riverine material circulation linking terrestrial and coastal environments, it has not been considered in most Earth system models. The improvements in global‐scale river water temperature modeling demonstrated in this study will benefit for future Earth systems research.

In the companion paper to this one (Part I), the Interactions between Soil, Biosphere, and Atmosphere-Total Runoff Integrating Pathways (ISBA-TRIP) continental hydrological system of the Centre National de Recherches Meteorologiques is evaluated by using river discharge measurements and terrestrial water storage (TWS) variations derived from three independent datasets of the Gravity Recovery and Climate Experiment (GRACE). One of the conclusions is that the river reservoir simulated by TRIP at the global scale seems to be one of the main sources of TWS and/or discharge errors. Here, the authors study these uncertainties in river routing processes, such as flow velocity and groundwater storage. For this purpose, a simple groundwater reservoir depending on a time delay factor and a variable streamflow velocity calculated via Manning's formula are added to TRIP following the approach of Arora and Boer. The previous and the new TRIP are then compared, and two studies of the sensitivity to the groundwater time delay factor and to the flow velocity are performed. Using the same experiment design as in Part I, the authors show that the effect of this flow velocity and of the groundwater time delay factor on the ISBA-TRIP simulation is potentially significant. Nevertheless, over tropical and temperate basins, a competition between the two processes implies a slight difference between the previous and the new TRIP compared to both the GRACE and the discharge signals. The global results underline that simulating a realistic streamflow velocity is a key process for global-scale application.

Hydrologically induced deformation of Earth's surface can be measured with high precision geodetic techniques, which in turn can be used to study the underlying hydrologic process. For geodetic study of other Earth processes such as tectonic and volcanic deformation, or coastal subsidence and its relation to relative sea level rise and flood risk, hydrological loading may be a source of systematic error, requiring accurate correction. Accurate estimation of the hydrologic loading deformation may require consideration of local as well as regional loading effects. We present a new hybrid approach to this problem, providing a mathematical basis for combining local (near field) and regional to global (far field) loading data with different accuracies and spatial resolutions. We use a high‐resolution hydrological model (WGHM) for the near field and GRACE data for the far field. The near field is defined as a spherical cap and its contribution is calculated using numerical evaluation of Green's functions. The far field covers the entire Earth, excluding only the near‐field cap. The far‐field contribution is calculated using a modified spherical harmonic approach. We test our method with a large GPS data set from central North America. Our new hybrid approach improves fits to GPS‐measured vertical displacements, with 25% and 35% average improvement relative to GRACE‐only or WGHM‐only spherical harmonic solutions. Our hybrid approach can be applied to a wide variety of environmental surface loading problems.

Gravity Recovery and Climate Experiment (GRACE) derived groundwater storage (GWS) data are compared with in-situ groundwater levels from five groundwater basins in Jordan, using newly gridded GRACE GRCTellus land data. It is shown that (1) the time series for GRACE-derived GWS data and in-situ groundwater-level measurements can be correlated, with R2 from 0.55 to 0.74, (2) the correlation can be widely ascribed to the seasonal and trend component, since the detrended and deseasonalized time series show no significant correlation for most cases, implying that anomalous signals that deviate from the trend or seasonal behaviour are overlaid by noise, (3) estimates for water losses in Jordan based on the trend of GRACE data from 2003 to 2013 could be up to four times higher than previously assumed using estimated recharge and abstraction rates, and (4) a significant time-lagged cross correlation of the monthly changes in GRACE-derived groundwater storage and precipitation data was found, suggesting that the conventional method for deriving GWS from GRACE data probably does not account for the typical conditions in the study basins. Furthermore, a new method for deriving plausible specific yields from GRACE data and groundwater levels is demonstrated.RésuméLes données des réserves d’eau souterraine issues du système GRACE (Gravity Recovery and Climate Experiment) sont comparées avec les données piézométriques mesurées in-situ dans cinq bassins hydrogéologiques en Jordanie, en utilisant une nouvelle grille de données GRSTellusland du système GRACE. Il a été montré que (1) les séries temporelles des données de la réserve en eau souterraine issues du système GRACE et les mesures piézométriques in-situ peuvent être corrélées, avec un coefficient R2 compris entre 0.55 et 0.74, (2) la corrélation peut être largement attribuée à la composante tendancielle et saisonnière, puisque les séries temporelles défaites de la tendance et désaisonnalisées ne montrent pas de corrélation significative dans la plupart des cas, ce qui indique que les signaux d’anomalies qui s’écartent de la tendance ou du comportement saisonnier sont couverts par le bruit de fond, (3) les estimations pour les pertes d’eau en Jordanie basées sur la tendance des données de GRACE de 2003 à 2013 pourraient être plus de quatre fois supérieures aux estimations antérieures de recharge et de taux de prélèvement, et (4) une importante corrélation croisée décalée dans le temps des variations mensuelles du stock d’eau souterraine dérivé des données de GRACE et des données de précipitations a été mise en évidence, suggérant que la méthode conventionnelle pour évaluer les réserves en eau souterraine à partir des données de GRACE ne tient probablement pas compte des conditions spécifiques des bassins étudiés. De plus, une nouvelle méthode pour dériver des productivités spécifiques plausibles à partir des données de GRACE et des niveaux piézométriques est démontrée.ResumenSe comparan los datos derivados del almacenamiento de agua subterránea (GWS) del Gravity Recovery and Climate Experiment (GRACE) con los niveles de agua subterránea medidos in situ en cinco cuencas de aguas subterráneas en Jordania, utilizando los datos de GRACE GRCTellus reticulados recientemente. Se muestra que (1) la serie temporal de datos de GWS derivados de GRACE y las mediciones in situ del nivel del agua subterránea se pueden correlacionar con R2 a partir 0.55 a 0.74; (2) la correlación puede ser ampliamente atribuida a la componente estacional y a la tendencia , ya que la serie de tiempo desestacionalizada y sin tendencia no muestra ninguna correlación significativa en la mayoría de los casos, lo que implica que las señales anómalas que se apartan de la tendencia o comportamiento estacional, se superponen con el ruido, (3) las pérdidas de agua estimadas en Jordania sobre la base de la tendencia de los datos de GRACE 2003–2013 podría ser hasta cuatro veces mayor de lo que se supone usando las tasas de recarga y de extracción estimados, y (4) la correlación cruzada encontró que un tiempo significativo de retardo entre la variaciones mensuales de almacenamiento de agua subterránea derivada de GRACE y datos de precipitación, lo que sugiere que el método convencional para derivar GWS de los datos de GRACE, probablemente, no tiene en cuenta las condiciones típicas en las cuencas de estudio. Por otra parte, se muestra un nuevo método para derivar los posibles rendimientos específicos a partir de los datos de GRACE y de los niveles del agua subterránea.摘要利用新划分网格的GRACE GRCTellus土地数据,对GRACE导出的地下水储存数据和约旦5个地下水盆地现场的地下水位进行了对比。结果显示,(1)GRACE导出的地下水储存数据和现场地下水位测量结果的时间序列可以相互关联,R2的范围为0.55 到 0.74,(2)关联性广泛归因于季节性和趋势因素,因为大多数情况下去趋势化的和消除季节的时间序列没有显示出多大的关联性,意味着偏离趋势和季节性行为的异常信号被噪音覆盖,(3)根据2003年到2013年GRACE数据趋势得到的约旦水损耗估算结果可能是先前采用估算的补给和抽取率假设的结果的4倍,(4)发现GRACE导出的地下水储存量和降水数据月度变化有重要的时间延迟相互关联性,表明从GRACE数据导出地下水储存量的常规方法可能解释不了研究区的典型情况。此外,本文还论述了从GRACE数据和地下水位导出似乎可信的单位出水量的一种新方法。ResumoDados de armazenamento de água subterrânea (AAS) derivados do satélite GRACE (Gravity Recovery and Climate Experiment) foram comparados com níveis de água subterrânea in-loco de cinco bacias subterrâneas na Jordânia. Utilizaram-se os novos dados em grade do GRACE (GRCTellus). Mostra-se que (1) as séries temporais para os dados de AAS derivados do GRACE e as medições in-loco de níveis de água subterrânea podem ser correlacionadas, com 0.55 ≤ R2 ≤ 0.74, (2) a correlação pode ser largamente atribuída às componentes de tendência e sazonalidade, uma vez que as séries temporais após remoção de tendência e sazonalidade não apresentam correlação significativa na maioria dos casos, indicando que sinais anômalos que desviam da tendência ou do comportamento sazonal são sobrepostos por ruído, (3) estimativas para perda de água na Jordânia baseadas em tendências de dados GRACE de 2003 a 2013 podem ser até quatro vezes maiores que as previamente assumidas utilizando a recarga estimada e taxas de abstração, e (4) uma significante correlação cruzada defasada no tempo entre mudanças mensais no armazenamento de água subterrânea derivada do GRACE e dados de precipitação foi encontrada, sugerindo que o método convencional para derivar AAS a partir de dados GRACE provavelmente não considera condições típicas da área de estudo. Além disso, um novo método para derivar rendimentos específicos plausíveis a partir de dados GRACE e níveis das águas subterrâneas é demonstrado.

Global river models are an essential tool for both earth system studies and water resources assessments. As advanced physical processes have been implemented in global river models, increasing computational cost has become problematic for executing ensemble or long-term simulations. To improve computational efficiency, we here propose the use of a local inertial flow equation combined with a vector-based river network map. A local inertial equation, a simplified formulation of the shallow water equations, was introduced to replace a diffusion wave equation. A vector-based river network map which flexibly discretizes river segments was adopted in order to replace the traditional grid-based map which is based on a Cartesian grid coordinate system. The computational efficiency of the proposed flow routing and river network map was tested by executing hydrodynamic simulations with the CaMa-Flood global river model. The simulation results suggest that the computational efficiency can be improved by more than 300% by applying the local inertial equation. It can be improved by a further 60% by implementing the vector-based river network map instead of a grid-based map. It is found that the vector-based map with evenly distributed flow distances between calculation units allows longer time steps compared to the grid-based map because the latter has very short flow distances between calculation units at high latitudes which critically limit time step length. Considering the improvement in simulation speed, the local inertial equation and a vector-based river network map are preferable in global hydrodynamic simulations with high computational demands such as ensemble or long-term experiments.

Flood risk is expected to increase as the climate warms. This study, for the first time, uses several climate models to estimate the global risk of flooding at the end of the century. Projections show a large increase in flood frequency in some areas, whereas other regions can expect a decrease. Vulnerability is dependent on the degree of warming and the interannual variability in precipitation.

Elevated arsenic (As) concentrations in groundwater-fed drinking water supplies in Bangladesh are a major public health problem but the hydrogeological conditions that give rise to the mobilisation and regional-scale distribution of As in shallow groundwater remain unknown. Published hypotheses developed from highly localised case studies are, to date, untested regionally and contradictory. My doctoral thesis makes a novel and substantial contribution to knowledge of the relationship between groundwater dynamics and As mobilisation in the Bengal Basin by (1) characterising national-scale groundwater storage dynamics and recharge processes in the shallow aquifer of Bangladesh and (2) relating statistically static and dynamic hydrogeological factors to the observed variation of As concentrations in groundwater. After constructing a national database of shallow groundwater levels from a network of 1267 monitoring stations, robust statistical techniques are applied to characterise long-term (1985 to 2005) trends and seasonality in groundwater levels, net recharge, and groundwater storage; the latter is supported by analysis of remotely sensed data derived from GRACE (Gravity Recovery and Climate Experiment). These characterisations highlight the critical influence of groundwater abstraction on net recharge to the shallow aquifer. Net annual recharge in Bangladesh has increased in response to intensive abstraction challenging conventional definitions of "safe yield". To examine the national-scale variability in groundwater As concentrations generalised regression models were constructed using geology and hydrological factors. Crucially, these models reveal that areas of increasing groundwater-fed irrigation and net recharge are associated with lower As concentrations. These findings are inconsistent with current hypotheses that contend irrigation-induced recharge mobilises groundwater As in shallow aquifers. Inverse associations between As concentrations and both mean annual recharge and trends in groundwater-fed irrigation suggest that As has been actively flushed from the shallow aquifer as a result of recently increased net recharge induced by intensive irrigation in Bangladesh.

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