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Shared groundwater resources between Mexico and the United States are facing unprecedented stressors. We reflect on how to improve water security for groundwater systems in the border region. Our reflection begins with the state of groundwater knowledge, and the challenges groundwater resources face from a physical, societal and institutional perspective. We conclude that the extent of ongoing cooperation frameworks, joint and remaining research efforts, from which alternative strategies can emerge, still need to be developed. The way forward offers a variety of cooperation models as the future offers rather complex, shared and multidisciplinary water challenges to the Mexico–US borderlands.

The cause–effect relationship and rates of change of suspended sediment dynamics at catchment scales in the tropics remain poorly understood. This study used a chrono-sequence of remotely sensed la...

Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitatively assess Costa Rica's water resources at a national scale. Data scarcity was compensated for by using adjusted global topography and remotely sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly Moderate Resolution Imaging Spectroradiometer (MODIS) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations (M1, M2, M3, M4). The calibration consisted of step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow (M2) was a better option than calibrating only with daily streamflow (M1), with similar mean Kling–Gupta efficiency (KGE ∼ 0.53) for daily streamflow time series, but with improvements to reproduce the flow duration curves, with a median root mean squared error (RMSE) of 0.42 for M2 and a median RMSE of 1.15 for M1. Additionally, including AET (M3 and M4) in the calibration statistically improved the simulated water balance and better matched hydrological signatures, with a mean KGE of 0.49 for KGE in M3–M4, in comparison to M1–M2 with mean KGE

Drought monitoring is a critical activity for drought risk management; however, the lack of ground-based observations of climatological and hydrological variables in many regions of the world hinders an adequate follow-up and investigation of this phenomenon. This paper introduces a transparent framework for monitoring the spatio-temporal distribution of drought hazard based on uni- and multivariate standardized drought indices that use reanalysis datasets of hydrological variables available freely and globally. In the case study of the 2015–2017 East-Southwest drought in Mexico, the introduced framework successfully detected the spatial and temporal patterns of drought conditions, even in regions where a benchmark drought monitoring system failed to detect deficits. In addition, the ability of the introduced framework to detect drought impacts on the annual agricultural maize production in Mexico was evaluated using data of 1980–2018, yielding scores of the false alarm ratio =0.32, the probability of detection = 0.71, and the proportion correct = 0.68 for the analysis at the national scale. Currently, the framework provides a significant extension to the capabilities for national drought monitoring, and it is being used by the Mexican water authority in the decision-making process related to drought severity assessment.

Streamflow simulation across the tropics is limited by the lack of data to calibrate and validate large-scale hydrological models. Here, we applied the process-based, conceptual HYPE (Hydrological Predictions for the Environment) model to quantitively assess Costa Rica’s water resources at a national scale. Data scarcity was compensated using adjusted global topography and remotely-sensed climate products to force, calibrate, and independently evaluate the model. We used a global temperature product and bias-corrected precipitation from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) as model forcings. Daily streamflow from 13 gauges for the period 1990–2003 and monthly MODIS (Moderate Resolution Imaging Spectroradiometer) potential evapotranspiration (PET) and actual evapotranspiration (AET) for the period 2000–2014 were used to calibrate and evaluate the model applying four different model configurations. The calibration consisted in step-wise parameter constraints preserving the best parameter sets from previous simulations in an attempt to balance the variable data availability and time periods. The model configurations were independently evaluated using hydrological signatures such as the baseflow index, runoff coefficient, and aridity index, among others. Results suggested that a two-step calibration using monthly and daily streamflow was a better option instead of calibrating only with daily streamflow. Additionally, including PET and AET in the calibration improved the simulated water balance and better matched hydrological signatures. Thus, the constrained parameter uncertainty increased the confidence in the simulation results. Such a large-scale hydrological model has the potential to be used operationally across the humid tropics informing decision making at relatively high spatial and temporal resolution.

The increasing trend on water use for hydraulic fracturing (HF) in multiple plays across the U.S. has raised the need to improve the HF water management model. Such approaches require good-quality datasets, particularly in water-stressed regions. In this work, we presented a QA/QC framework for HF data using an outlier detection methodology based on five univariate techniques: two interquartile ranges at 95 and 90% (PCTL95, PCTL90), the median absolute deviation (MAD) and Z score with thresholds of two and three times the standard deviation (2STD, 3STD). The cleaning techniques were tested using multiple variables from two data sources centered on the Eagle Ford play (EFP), Texas, for the period 2011–2017. Results suggest that the PCTL95 and MAD techniques are the best choices to remove long-tailed statistical distributions of different variables, classifying the minimum number of records as outliers. Overall, outliers represent 13–23% of the total HF water volume in the EFP. In addition, outliers highly impacted minimum and maximum HF water use values (min–max range of 0–47 m3/m and 5.3–24.6 m3/m of frac length, before and after the outlier removal process, respectively), that are frequently used as a proxy to develop future water–energy scenarios in early-stage plays. The data and framework presented here can be extended to other plays to improve water footprint estimates with similar conditions.

An intensification of water use for hydraulic fracturing (HF) to extract oil and gas from deep shale formations has been observed during the last years across the USA, increasing concerns about water resources management in water-limited regions around the world. At the same time, HF has been associated to several environmental and water quality/quantity impacts in many developed plays of USA, China and Canada, nevertheless, assessing impacts on emergent plays involves several difficulties since future development of HF is generally unknown and because of the lack of local data to evaluate water resources baselines.In this work, we present a framework that combines the use of remote sensing derived data to assess the baseline of water resources and the development and application of a statistical model to project the development of HF activities. Remote sensing and global land surface model products of precipitation (CHIRPS), evapotranspiration (MODIS), recharge (WaterGAP model), infiltration and runoff (MERRA) and water storage (GRACE) were used to estimate water availability and the hydrological response of watersheds and aquifers. Scenarios of HF were generated using a statistical model that simulates HF water requirements, hydrocarbon production, flowback-produced water and economic trends, among others factors that influence the HF development.The proposed framework was applied to evaluate the impacts of HF development on the water energy-nexus at the transboundary Eagle Ford play, located across Mexico’s northeast, a water-limited region that contains substantial reserves of shale gas.Scenarios were generated following two economic projections and assuming water use trends and historical HF development from the Eagle Ford, Barnett and Haynesville plays, in Texas, which are geologically similar to the Mexican Eagle Ford play.Results suggested that the higher impacts on the water-energy nexus in Mexico resulted from reported trends in Eagle Ford, Texas, with ~14,000 wells drilled in ten years and an accumulative water use volume of ~450 millions cubic meters, representing about ~69% of the annual groundwater concessions for municipal use.The framework presented in this work can be used in other plays around the world to assess the impacts of HF on water resources and their implications in its water-energy nexus.

The emergence of high-resolution observational tools, information and communication technologies, cloud computing and big data are disrupting the water sector in an unprecedented way. In the field of hydrological sciences, research calls aimed at observing, backcasting and forecasting terrestrial water resources at finer space-time resolutions have been made over the past years by the scientific community. Here, we introduce the Hydrological Observatory of Mexico City (OH-IIUNAM), an academic initiative consisting of a dense network of 55 state-of-the-art precipitation sensors (optical disdrometers and weighing rain gauges) located within Mexico City, one of the largest urban centers of the world. The objective of OH-IIUNAM, given its open-data philosophy, is to enable scientific research within urban environments by providing a real-time hydro-meteorological observational platform at the hyper-resolution (dt=1 minute). Potential niches of opportunity ranging from atmospheric processes to hydrological modeling and design in urban areas are envisaged and discussed. Future expansion phases of OH-IIUNAM are expected to incorporate streamflow, groundwater and water quality.HTML aquí.

Drought monitoring and forecasting allows to adopt mitigating actions in early stages of an event to reduce the vulnerability of a wide range of environmetal, economical and social sectors. In Mexico, various drought monitoring systems on national and regional scale perform a follow up of these events, such as the Drought Monitor in Mexico, and the North American Drought Monitor, but seasonal drought forecasting is still a pending task. This study aims at fill this gap applying a methodology that uses data derived from a globally available atmospheric reanalysis product and a principal component regression based model oriented to predict drought impacts in rainfed crops associated to deficits in the soil moisture, estimated by means of the standardized soil moisture index (SSI). Using the state of Guanajuato (Center-North of Mexico) as a study case, the model generated yielded RSME values of 0.74 using regional and global hydrological, climatic and atmospheric variables as predictors with a lead-time of 4 months.

Water resources development and landscape alteration exert marked impacts on water-cycle dynamics, including areas subjected to hydraulic fracturing (HF) for exploitation of unconventional oil and gas resources found in shale or tight sandstones. Here we apply a conceptual framework for linking baseflow analysis to changes in water demands from different sectors (e.g. oil/gas extraction, irrigation, and municipal consumption) and climatic variability in the semiarid Eagle Ford play in Texas, USA. We hypothesize that, in water-limited regions, baseflow ( Q b ) changes are partly due (along with climate variability) to groundwater abstraction. For a more realistic assessment, the analysis was conducted in two different sets of unregulated catchments, located outside and inside the Eagle Ford play. Three periods were considered in the analysis related to HF activities: pre-development (1980–2000), moderate (2001–2008) and intensive (2009–2015) periods. Results indicate that in the Eagle Ford play region, temporal changes in baseflow cannot be directly related to the increase in hydraulic fracturing. Instead, substantial baseflow declines during the intensive period of hydraulic fracturing represent the aggregated effects from the combination of: (1) a historical exceptional drought during 2011–2012; (2) increased groundwater-based irrigation; and (3) an intensive hydraulic fracturing activity.

Streamflow recession analysis from observed hydrographs allows to extract information about the storage-discharge relationship of a catchment and some of their groundwater hydraulic properties. The HYDRORECESSION toolbox, presented in this paper, is a graphical user interface for Matlab and it was developed to analyse streamflow recession curves with the support of different tools. The software extracts hydrograph recessions segments with three different methods (Vogel, Brutsaert and Aksoy) that are later analysed with four of the most common models to simulate recession curves (Maillet, Boussinesq, Coutagne and Wittenberg) and it includes four parameter-fitting techniques (linear regression, lower envelope, data binning and mean squared error). HYDRORECESSION offers tools to parameterize linear and nonlinear storage-outflow relationships and it is useful for regionalization purposes, catchment classification, baseflow separation, hydrological modeling and low flows prediction. HYDRORECESSION is freely available for non-commercial and academic purposes and is available at Matlab File Exchange ( http://www.mathworks.com/matlabcentral/fileexchange/51332-hydroecession ).

Among the natural hazards that threaten transportation infrastructure, flooding represents a major hazard to highways as it challenges their design, operation, efficiency and safety. In extreme cases, it may lead to massive obstruction of traffic and direct damages to the road structures themselves and indirect damages to the economic activity and development of the region. To enable the prevention of such consequences, and the proposition of adaptive measures for existing infrastructure, this paper presents an integrated framework to identify the most vulnerable points to flooding along a highway. This is done through the combination of remote sensing information (e.g. LiDAR based Digital Elevation Model, satellite imagery), a high-quality dataset, and a quasi-2D hydrodynamic model. The forcing condition is defined using a hyetograph associated to a storm with duration of 1 day and return period of 100 years. The selected highway is located in the Mexican state of Tabasco, where extreme precipitation events and floods are frequent. Results demonstrate the ability of the methodology to identify critical water levels along the road (h > 1.50 m) at those locations where flooding has been experienced, as well as points of inspection for the highway drainage. These locations were visited in the field and maintenance problems were detected that do increase its level of exposure. We show that this framework is useful for the generation of a flood management strategy to the analyzed highway, which includes an optimum location of adaptive measures to an anticipated more intense future climate.

Predictability of hydrological response in dryland environments: a comparative study across North America project; University of Arizona; CONACYT

To characterize some of the world’s ocean physical processes such as its wave height, wind speed and sea surface elevation is a major need for coastal and marine infrastructure planning and design, tourism activities, wave power and storm surge risk assessment, among others. Over the last decades, satellite remote sensing tools have provided quasi-global measurements of ocean altimetry by merging data from different satellite missions. While there is a widely use of altimeter data for model validation, practical tools for model validation remain scarce. Our purpose is to fill this gap by introducing ALTWAVE, a MATLAB user-oriented toolbox for oceanographers and coastal engineers developed to validate wave model results based on visual features and statistical estimates against satellite derived altimetry. Our toolbox uses altimetry information from the GlobWave initiative, and provides a sample application to validate a one year wave hindcast for the Gulf of Mexico. ALTWAVE also offers an effective toolbox to validate wave model results using altimeter data, as well as a guidance for non-experienced satellite data users. This article is intended for wave modelers with no experience using altimeter data to validate their results.

An important driver of the terrestrial hydrological cycle is atmospheric evaporative demand. Recent studies using measurements of pan evaporation have found evidence that the atmospheric evaporative demand has been declining over the second half of the 20th century. This work analyses long-term time series of pan evaporation obtained from approximately 150 ± 30 weather stations located in Mexico with aridity indexes ranging from 0.3 to 10 for 1961–2010. The results show a consistent decline in annual pan evaporation for 1960–1990 (−3.8 mm year−2) and for 1990–2010 (−2.6 mm year−2) periods whereas the average change during the complete period corresponds to −3.3 mm year−2. Statistically significant negative changes using the non-parametric Mann–Kendall test were found in 43% of the stations for the early and 27% for the recent periods, respectively. The temperature, relative humidity, radiative and aerodynamic controls attributed to the observed changes are analysed with the Noah model output from the Global Land Data Assimilation System Version 2 (GLDAS-2). Among the climatological variables extracted from GLDAS-2, it was the annual wind speed and net radiation that gave the highest statistical correlations. This work agrees with previous studies that pan evaporation rates have been in a declining trend during the second half of the 20th century though milder decline rates have been observed over the last 20 years. Finally, we show that the magnitude of change in regions dominated by wind and in those dominated by radiative processes can strongly differ.

We present a flood risk mapping framework created in the context of the update of the Mexican flood risk atlas. This framework is based on a nation-wide GIS database of map time-series. Those maps are used as forcing for a deterministic, raster-based numerical model. For each catchment of interest, the model retrieves the data from the GIS and perform the computation on the specified area. The results are written directly in the GIS database, which facilitate their post-processing. This methodology allows 1) the generation of flood risk maps in cities located across the national territory, without too much effort in the pre and post-processing of information and 2) a very efficient process to create new flood maps for urban areas that have not been included in the original batch.

Road infrastructure networks are exposed to multiple damages caused by the occurrence of floods, generally, associated to hydrometeorological extreme phenomena. This can be attributed to the lack of drainage efficiency in the current designed hydraulic infrastructure. In order to renew the current methodologies for designing and improving the performance of road drainage, we proposed an approach that allows to revise the road drainage under current and future climate scenarios. For this, digital elevation information combined with a simplified shallow water equations model were used to reproduce the transversal flows to a highway located in the state of Oaxaca, Mexico. The drainage performance for both scenarios was tested along a set of identified critical points of the highway, and in those locations where hydrological design showed to be insufficient, adaptation measures were provided. This approach proved to be scalable and useful for identifying points where road drainage redesign or adaptation were needed and, to prioritize actions that minimize direct and indirect damages on roads.

Irrigation water use (IWU) or withdrawal is a key component for the water management of a region since it tends to exceed the crops consumptive water use, especially in water-stressed regions where groundwater is the main source of water. Nevertheless, temporal IWU information is missing in many irrigation areas. Remote sensing (RS) data is commonly used for crop water requirements estimations in areas with lack of data, however, IWU is more complex to approach since it also depends on water use efficiency, irrigation system type, irrigation scheduling, and water availability, among others. This work explores the use of remote sensing data (TRMM, MODIS) and land surface hydrological products (GLDAS 2 and MERRA 2) to obtain insights about the space-time annual IWU patterns across croplands located within Mexico’s northeast region. Reported IWU in three irrigation districts (Don Martín, Región Lagunera and Bajo Río Bravo) was used to obtain a functional model using satellite data derived. Results suggest strong relationship between reported IWU with soil moisture content from GLDAS and the maximum annual EVI from MODIS, where a potential regression shown statistical correlations of 0.83 and 0.77, respectively.

Precipitation undergoes a two-step partitioning when it falls on the land surface. At the land surface and in the shallow subsurface, rainfall or snowmelt can either runoff as infiltration/saturation excess or quick subsurface flow. The rest will be stored temporarily in the root zone. From the root zone, water can leave the catchment as evapotranspiration or percolate further and recharge deep storage. It was recently shown that an index of vegetation water use efficiency, the Horton index (HI), could predict deep storage dynamics. Here we test this finding using 247 MOPEX catchments across the conterminous US. Our results show that the observed HI is indeed a reliable predictor of deep storage dynamics. We also find that the HI can reliably predict the long-term average recharge rate. Our results compare favorably with estimates of average recharge rates from the US Geological Survey. Previous research has shown that HI can be estimated based on aridity index, mean slope and mean elevation of a catchment (Voepel et al., 2011). We recalibrated Voepel’s model and used it to predict the HI for our catchments. We then used these predicted values of the HI to estimate average recharge rates for our catchments, and compared them with those estimated from observed HI. We find that the accuracies of our predictions based on observed and predicted HI are similar. This provides a novel estimation method of catchment-scale long-term average recharge rates based on simple catchment characteristics, such as climate and topography, and free of discharge measurements.

This investigation aims to study the propagation of meteorological uncertainty within a cascade modelling approach to flood prediction. The methodology was comprised of a numerical weather prediction (NWP) model, a distributed rainfall–runoff model and a 2-D hydrodynamic model. The uncertainty evaluation was carried out at the meteorological and hydrological levels of the model chain, which enabled the investigation of how errors that originated in the rainfall prediction interact at a catchment level and propagate to an estimated inundation area and depth. For this, a hindcast scenario is utilised removing non-behavioural ensemble members at each stage, based on the fit with observed data. At the hydrodynamic level, an uncertainty assessment was not incorporated; instead, the model was setup following guidelines for the best possible representation of the case study. The selected extreme event corresponds to a flood that took place in the southeast of Mexico during November 2009, for which field data (e.g. rain gauges; discharge) and satellite imagery were available. Uncertainty in the meteorological model was estimated by means of a multi-physics ensemble technique, which is designed to represent errors from our limited knowledge of the processes generating precipitation. In the hydrological model, a multi-response validation was implemented through the definition of six sets of plausible parameters from past flood events. Precipitation fields from the meteorological model were employed as input in a distributed hydrological model, and resulting flood hydrographs were used as forcing conditions in the 2-D hydrodynamic model. The evolution of skill within the model cascade shows a complex aggregation of errors between models, suggesting that in valley-filling events hydro-meteorological uncertainty has a larger effect on inundation depths than that observed in estimated flood inundation extents.

Investigating the contribution of tropical cyclones to the terrestrial water cycle can help quantify the benefits and hazards caused by the rainfall generated from this type of hydro-meteorological event. Rainfall induced by tropical cyclones can enhance both flood risk and groundwater recharge, and it is therefore important to characterise its minimum, mean and maximum contributions to a region or country’s water balance. This work evaluates the rainfall contribution of tropical depressions, storms and hurricanes across Mexico from 1998 to 2013 using the satellite-derived precipitation dataset TMPA 3B42. Additionally, the sensitivity of rainfall to other datasets was assessed: the national rain gauge observation network, real-time satellite rainfall and a merged product that combines rain gauges with non-calibrated space-borne rainfall measurements. The lower Baja California peninsula had the highest contribution from cyclonic rainfall in relative terms (∼40% of its total annual rainfall), whereas the contributions in the rest of the country showed a low-to-medium dependence on tropical cyclones, with mean values ranging from 0% to 20%. In quantitative terms, southern regions of Mexico can receive more than 2400 mm of cyclonic rainfall during years with significant TC activity. Moreover, (a) the number of tropical cyclones impacting Mexico has been significantly increasing since 1998, but cyclonic contributions in relative and quantitative terms have not been increasing, and (b) wind speed and rainfall intensity during cyclones are not highly correlated. Future work should evaluate the impacts of such contributions on surface and groundwater hydrological processes and connect the knowledge gaps between the magnitude of tropical cyclones, flood hazards, and economic losses.

This commentary emphasizes the role of remote sensing tools for detecting extreme values in precipitation. Here, we provide a synthesis of recent data from the Tropical Rainfall Measurement Mission satellite to detect the rainfall maxima depth versus duration across the terrestrial surface since 1998. Observations with rain gauges and satellite merged products suggest similarities in the power scaling law between maximum rainfall depth and duration. Satellites have shown the potential to identify regions of extreme precipitation and this is the first study where intensity-duration curves from rainfall events measured by satellites are compared with the same curves obtained with traditional rain gauges. Previous compilations of the world's greatest rainfall depths for different durations are based on point mea- surements sparsely located on the global terrestrial sur- face with significant gaps in remote continental regions and oceans. Such observations have suggested an intrin- sic scaling relationship, also known as the Jennings law (Jennings, 1950) that states that the maximum possible rainfall (R) for a given cumulative duration (D) ranging from hours to years is limited by local thermodynamics and large-scale physical factors. As noted by Field and Shutts (2009), several studies have suggested that the distribution of rainfall responds to the local concentra- tion of water and energy. Such phenomenon seems to follow a fractal-type attenuation process as the spatial scale decreases and, as shown by Zhang et al. (2013), the Jennings law is not the exception. This precipitation-duration relationship follows a power law R ≈ aD b where the precipitation intensity R is in mm and the duration D in minutes. The b expo- nent usually ranges between 0.4 for historical records observed by single rain gauges and it gets close to 0.5 for a worldwide compilation of the maximum observed rainfall over a given time scale (Zhang et al., 2013). Recent studies have provided probabilistic (Galmarini et al. 2004) and stochastic (Zhang et al., 2013) expla- nations of such scaling law at the rain gauge point scale. It is however unknown how that relationship behaves for larger spatial scales in comparison to the conventional ∼ 1m 2 footprint of a typical rain gauge. Given the sparse geographical distribution of weather radars over the world, rainfall estimates from satellite observations offer the possibility of monitoring rain- fall extremes over tropical and temperate regions across the world and hence to explore the Jennings law at a mesoscale. Also, for the first time, satellite-based precipitation products allow to describe Jennings law over oceanic regions. This work provides a compari- son of Jennings law at the microscale (rain gauge) as observed during the 20th century (WMO, 1994) and at the mesoscale (0.25 ∘ × 0.25 ∘ ) derived from satellite

The impacts of climate extremes and water use on groundwater storage across large aquifers can be quantified using Gravity Recovery and Climate Experiment (GRACE) satellite monitoring. We present new methods to improve estimates of changes in groundwater storage by incorporating irrigation soil moisture corrections to common data assimilation products. These methods are demonstrated using data from the High Plains Aquifer (HPA) for 2003 to 2013. Accounting for the impacts of observed and inferred irrigation on soil moisture significantly improves estimates of groundwater storage changes as verified by interpolated measurements from ~10,000 HPA wells. The resulting estimates show persistent declines in groundwater storage across the HPA, more severe in the southern and central HPA than in the north. Groundwater levels declined by an average of approximately 276 ± 23 mm from 2003 to 2013, resulting in a storage loss of 125 ± 4.3 km3, based on the most accurate of the three methods developed here.

This paper describes the most important results of a theoretical, experimental and in situ investigation developed in connection with a water supply pumping pipeline failure. This incident occurred after power failure of the pumping system that caused the burst of a prestressed concrete cylinder pipe (PCCP). Subsequently, numerous hydraulic transient simulations for different scenarios and various air pockets combinations were carried out in order to fully validate the diagnostic. As a result, it was determined that small air pocket volumes located along the pipeline profile were recognized as the direct cause of the PCCP rupture. Further, a detail survey of the pipeline was performed using a combination of non-destructive technologies in order to determine if immediate intervention was required to replace PCC pipes. In addition, a hydraulic model was employed to analyze the behavior of air pockets located at high points of the pipeline.

Drought has been a recurrent phenomenon in Mexico. For its assessment and monitoring, several studies have monitored meteorological droughts using standardized indices of precipitation deficits. Such conventional studies have mostly relied on rain gauge-based measurements, with the main limitation being the scarcity of rain gauge spatial coverage. This issue does not allow a robust spatial characterization of drought. A recent alternative for monitoring purposes can be found in satellite-based remote sensing of meteorological variables. The main objective of this study is to evaluate the standardized precipitation index (SPI) in Mexico during the period 1998 to 2013, using the Tropical Rainfall Measuring Mission (TRMM) satellite product 3B42. Results suggest that Mexico experienced the driest conditions during the great drought between 2011 and 2012; however, temporal variability in the SPI was found across different climatic regions. Nevertheless, a comparison of the SPI derived by TRMM against the rain gauge-based SPI computed by the official Mexican Drought Monitor showed low to medium correlation of the time series though both SPIs managed to capture the most relevant droughts at the national scale. We conclude that the TRMM product can properly monitor meteorological droughts despite its relative short dataset length (~15 years). Finally, we recommend an assimilation of rain gauge and satellite-based precipitation data to provide more robust estimates of meteorological drought severity.

The emergence and development of improved tools for monitoring the occurrence, timing and duration of droughts and heatwaves are very important for water security purposes, with further implications for food security, water quality and hydropower production, among others. Unlike conventional studies that use climate and ecosystem data to assess the impact of droughts and heatwaves on land surface processes occurring at the terrestrial surface (precipitation, temperature and vegetation indexes) or in the shallow layers of the subsurface (soil water content), recent studies have characterized the severity of droughts over large regions (>100 000 km2) based on satellite observations of terrestrial water storage variations (Houbourg et al. 2012, Thomas et al. 2014). Such indicators can be used as a proxy to estimate the duration and magnitude of water deficit events. This work proposes a new methodology aimed at describing the variability of water storage deficits over space and time in three regions that suffered prominent dry spells over the last decade: Eastern Russia, the Middle East and the Western Sahel (Fig. 1). To accomplish this objective, we investigate the impact of the 2010 Northern Hemisphere summer heatwave on cumulative terrestrial water storage anomalies detected by the Gravity Recovery and Climate Experiment (GRACE) followed by a space–time aggregation. The concept of water storage deficit as defined by Thomas et al. (2014) is estimated and a deficit–duration–area curve is built for each region.