[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.

While GRACE (Gravity Recovery and Climate Experiment) satellites are increasingly being used to moni- tor total water storage (TWS) changes globally, the impact of spatial distribution of water storage within a basin is gen- erally ignored but may be substantial. In many basins, wa- ter is often stored in reservoirs or lakes, flooded areas, small aquifer systems, and other localized regions with areas typ- ically below GRACE resolution ( 200 000 km 2 ). The ob- jective of this study was to assess the impact of nonuni- form water storage distribution on GRACE estimates of TWS changes as basin-wide averages, focusing on surface water reservoirs and using a priori information on reservoir storage from radar altimetry. Analysis included numerical experiments testing effects of location and areal extent of the localized mass (reservoirs) within a basin on basin-wide average water storage changes, and application to the lower Nile (Lake Nasser) and Tigris- Euphrates basins as examples. Numerical experiments show that by assuming uniform mass distribution, GRACE esti- mates may under- or overestimate basin-wide average water storage by up to a factor of 2, depending on reservoir loca- tion and areal extent. Although reservoirs generally cover less than 1 % of the basin area, and their spatial extent may be unresolved by GRACE, reservoir storage may dominate water storage changes in some basins. For example, reservoir storage ac- counts for 95 % of seasonal water storage changes in the lower Nile and 10 % in the Tigris-Euphrates. Because reser- voirs are used to mitigate droughts and buffer against cli-

We present a quantitative approach for measuring hydrological drought occurrence and severity based on terrestrial water storage observations from NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission. GRACE measurements are applied by calculating the magnitude of the deviation of regional, monthly terrestrial water storage anomalies from the time series' monthly climatology, where negative deviations represent storage deficits. Monthly deficits explicitly quantify the volume of water required to return to normal water storage conditions. We combine storage deficits with event duration to calculate drought severity. Drought databases are referenced to identify meteorological drought events in the Amazon and Zambezi River basins and the southeastern United States and Texas regions. This storage deficit method clearly identifies hydrological drought onset, end, and duration; quantifies instantaneous severity and peak drought magnitude; and compares well with the meteorological drought databases. It also reveals information about the hydrological effects of meteorological drought on regional water storage.

Traditional in situ observation interpolation techniques that provide rainfall data from rain gauges have limitations because they are discrete point-based data records, which may not be sufficient to assess droughts from a spatiotemporal perspective. Considering this limitation, this study has developed a run-off model—a fully satellite-based method for monitoring drought in Peninsular Malaysia. The formulation of the run-off deficit uses a water balance equation based on satellite-based rainfall and evapotranspiration data extracted respectively from calibrated TRMM multi-satellites precipitation analysis data (TMPA) and moderate resolution imaging spectroradiometer data (MODIS). The run-off deficit was calculated based on per pixel spatial scale and allowed to produce the continuous and regular run-off maps. The run-off model was tested and evaluated in a one drought year (2005) within a span of three years (2003–2005) over the Kelantan (3448 km2) and Hulu Perak (3672 km2) catchments of Peninsular Malaysia. The validation results show that (1) monthly TMPA rainfall and MODIS evapotranspiration data significantly improved after calibration; (2) satellite-based run-off data is not only strongly correlated with actual steam flow, but also with spatiotemporal variation of run-off in drought-affected forest catchments. The most severely drought-affected forest catchments that experienced the run-off deficits were Hulu Perak, Ulu Gading, Gunung Stong and Relai over Kelantan. The real time run-off change analysis shows that drought started in January and reached its peak in July of 2005. It was therefore demonstrated that this fully satellite-based run-off deficit model is as good as a conventional drought-monitoring indicator, and can provide not only drought distribution information, but it also can reflect the drought-induced impacts on stream flow, forest catchment and land-use.

This volume provides in-depth coverage of the latest in remote sensing of hydrological extremes: both floods and droughts. The book is divided into two distinct sections floods and droughts and offers a variety of techniques for monitoring each. With rapid advances in computer modelling and observing systems, floods and droughts are studied with greater precision today than ever before. Land surface models, especially over the entire Continental United States, can map the hydrological cycle at kilometre and sub-kilometre scales. In the case of smaller areas there is even higher spatial resolution and the only limiting factor is the resolution of input data. In-situ sensors are automated and the data is directly relayed to the world wide web for many hydrological variables such as precipitation, soil moisture, surface temperature and heat fluxes. In addition, satellite remote sensing has advanced to providing twice a day repeat observations at kilometre to ten-kilometre spatial scales. We are at a critical juncture in the study of hydrological extremes, and the GPM and SMAP missions as well as the MODIS and GRACE sensors give us more tools and data than were ever available before. A global variety of chapter authors provides wide-ranging perspectives and case studies that will make this book an indispensable resource for researchers, engineers, and even emergency management and insurance professionals who study and/or manage hydrological extremes.

The mapping and forecasting of droughts and floods is an important potential field of application of global soil moisture and water storage products from satellites and models. Especially when extremes in near-surface soil moisture propagate into extremes in total water storage, agricultural production and water supply can be severely impacted. This study relates soil moisture from the WaterGAP Global Hydrology Model (WGHM) and the satellite sensors Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E) and Advanced Scatterometer (ASCAT) to total water storage variations from the satellite gravity mission GRACE. A particular focus is on destructive hydrological extreme events, as listed in the International Disaster Database EM-DAT. Data sets are analyzed via correlation, time shift, and principal component analyses. The study area is the La Plata Basin in South America. The results indicate that most of the soil moisture anomalies are linked to periods of El Nino and La Nina and associated natural disasters. For the La Plata drought of 2008/2009 and the El Nino flooding of 2009/2010, soil moisture serves as an indicator for the later deficit or surplus in total water storage. These hydrological anomalies were strongest in the southern, central, and eastern parts of the basin, but more than one hundred thousand people were also affected in the northwestern part.

The estimation of large-scale evapotranspiration (ET) is complex, and typically relies on the outputs of land surface models (LSMs) or remote sensing observations. However, over some regions of Africa, inconsistencies exist between different estimations of ET fluxes, which should be investigated. In this study, we evaluate and combine different ET estimates from moderate-resolution imaging spectroradiometer (MODIS), Global Land Data Assimilation System (GLDAS) and terrestrial water budget (TWB) approaches over the Volta Basin, West Africa. ET estimates from water balance equation are obtained as residuals from monthly terrestrial water-storage (TWS) changes derived from Gravity Recovery and Climate Experiment (GRACE), Tropical Rainfall Measurement Mission (TRMM)'s rainfall data, and in situ discharge from Akosombo Dam (Ghana). An averaged estimation of ET time series is derived from all the ET estimations under study, while taking into account their uncertainties. The resulting ensemble-averaged ET was then used to assess each of the individual ET estimates. Overall, out of the seven investigated ET estimates (two from the water balance approach of which one considers water storage using GRACE-derived TWS and the other ignoring it, four from GLDAS and one from MODIS), only MODIS (28.12 mm month–1), GLDAS–NOAH (32.74 mm month–1) and TWB (32.84 mm month–1) were found to represent the range of variability close to the computed averaged reference ET (30.25 mm month–1). ET estimations inferred from MODIS were also found to represent relatively lower magnitude of uncertainties, that is, 3.99 mm month–1 over the Volta Basin (cf. 7.06 and 18.85 mm month–1 for GLDAS-NOAH and TWB-based ET estimations, respectively).

The Haihe river basin (HRB) in the North China has been experiencing prolonged, severe droughts in recent years that are accompanied by precipitation deficits and vegetation wilting. This paper analyzed the water deficits related to spatiotemporal variability of three variables of the gravity recovery and climate experiment (GRACE) derived terrestrial water storage (TWS) data, precipitation, and EVI in the HRB from January 2003 to January 2013. The corresponding drought indices of TWS anomaly index (TWSI), precipitation anomaly index (PAI), and vegetation anomaly index (AVI) were also compared for drought analysis. Our observations showed that the GRACE-TWS was more suitable for detecting prolonged and severe droughts in the HRB because it can represent loss of deep soil water and ground water. The multiyear droughts, of which the HRB has sustained for more than 5 years, began in mid-2007. Extreme drought events were detected in four periods at the end of 2007, the end of 2009, the end of 2010, and in the middle of 2012. Spatial analysis of drought risk from the end of 2011 to the beginning of 2012 showed that human activities played an important role in the extent of drought hazards in the HRB.

The Gravity Recovery and Climate Experiment (GRACE) satellites have been used in drought/flood monitoring by observing terrestrial water storage (TWS) change. Meteorological drought indicators or other identified disaster information were usually adopted in association with GRACE-observed changes in TWS for the determination of the occurrence and severity of droughts/floods. Inter-comparisons of dry conditions based on TWS change on a global scale, however, were very difficult because TWS anomalies are not comparable for different hydro-climatic regions. In this paper, we established a global dataset of GRACE-based dimensionless drought index, the Total Storage Deficit Index (TSDI), which is spatially comparable and capable of independently examining the characteristics of dry/wet spells globally. The globally mapped GRACE-based TSDI was examined with some reported extreme hydrologic events, which suggested that the results were fairly consistent with documented drought/flood disaster information. Moreover, comparisons of the GRACE-based TSDI with other frequently used drought indicators, such as the Standardized Precipitation Index (SPI), the Palmer Drought Severity Index (PDSI), and the Palmer Hydrological Drought Index (PHDI), suggested that the TSDI was significantly correlated with the SPI at three different time scales, the PDSI, and the PHDI over most parts of the global surface. The longer the time scale of the selected SPI, the stronger the correlation tended to be with the TSDI. Moreover, the correlation of the TSDI with the PHDI was higher than that with the PDSI over almost the whole global surface. With regard to its performance, this study suggested that the TSDI derived from GRACE-based TWS could be a useful dimensionless index for global and regional hydrological drought monitoring, especially for areas where meteo-hydrological observations are insufficient or human activities are intensive.

The Gravity Recovery and Climate Experiment (GRACE) mission ended its operation in October 2017, and the GRACE Follow‐On mission was launched only in May 2018, leading to approximately 1 year of data gap. Given that GRACE‐type observations are exclusively providing direct estimates of total water storage change (TWSC), it would be very important to bridge the gap between these two missions. Furthermore, for many climate‐related applications, it is also desirable to reconstruct TWSC prior to the GRACE period. In this study, we aim at comparing different data‐driven methods and identifying the more robust alternatives for predicting GRACE‐like gridded TWSC during the gap and reconstructing them to 1992 using climate inputs. To this end, we first develop a methodological framework to compare different methods such as the multiple linear regression (MLR), artificial neural network (ANN), and autoregressive exogenous (ARX) approaches. Second, metrics are developed to measure the robustness of the predictions. Finally, gridded TWSC within 26 regions are predicted and reconstructed using the identified methods. Test computations suggest that the correlation of predicted TWSC maps with observed ones is more than 0.3 higher than TWSC simulated by hydrological models, at the grid scale of 1° resolution. Furthermore, the reconstructed TWSC correctly reproduce the El Nino‐Southern Oscillation (ENSO) signals. In general, while MLR does not perform best in the training process, it is more robust and could thus be a viable approach both for filling the GRACE gap and for reconstructing long‐period TWSC fields globally when combined with statistical decomposition techniques.

The Global Positioning System (GPS) records monsoonal precipitable water vapor (PWV) and vertical crustal displacement (VCD) due to hydrological loading, and can thus be applied jointly to diagnose meteorological and hydrological droughts. We have analyzed the PWV and VCD observations during 2007.0–2015.0 at 26 continuous GPS stations located in Yunnan province, China. We also obtained equivalent water height (EWH) derived from the Gravity Recovery And Climate Experiment (GRACE) and precipitation at these stations with the same period. Then, we quantified the annual variations of PWV, precipitation, EWH and VCD and provided empirical relationships between them. We found that GPS-derived PWV and VCD (positive means downward movement) are in phase with precipitation and GRACE-derived EWH, respectively. The annual signals of VCD and PWV show linearly correlated amplitudes and a two-month phase lag. Furthermore, the results indicate that PWV and VCD anomalies can also be used to explore drought, such as the heavy drought during winter/spring 2010. Our analysis results verify the capability of GPS to monitor monsoon variations and drought in Yunnan and show that a more comprehensive understanding of the characteristics of regional monsoon and drought can be achieved by integrating GPS-derived PWV and VCD with precipitation and GRACE-derived EWH.

Summary Retrieval of the terrestrial moisture storage dataset from the Gravity Recovery and Climate Experiment (GRACE) satellite remote sensing system is possible when the catchment of interest is of large spatial scale. These dataset are of paramount importance for the estimation of the total storage deficit index (TSDI), which enables the characterization of a particular drought event from the perspective of the terrestrial moisture storage over that catchment. Incidentally, the GRACE gravity signal over the 13,000 km 2 Upper Assiniboine River Basin on the drought-prone Canadian Prairie is so poor therefore making the computation of the total storage deficit index for this basin infeasible. Consequently, the estimation of the terrestrial moisture storage from other reliable sources becomes imperative in order to enable the computation of the TSDI over this basin. This study explores the utilization of the Variable Infiltration Capacity (VIC) model, a physically based, spatially distributed hydrologic model to simulate the total moisture storage over the Upper Assiniboine River Basin which was then employed in the estimation of the TSDI over this basin for subsequent characterization of the recent Prairie-wide drought. Interestingly, the temporal patterns in the computed TSDI from the VIC model reveal a strong resemblance with the same drought characterization undertaken over the larger adjacent Saskatchewan River Basin, which was accomplished utilizing terrestrial moisture storage from the GRACE-based approach. Additionally, these independent techniques employed in the characterization of the last Prairie drought over the two adjacently situated basins resulted in similar drought severity classification from the standpoint of the total moisture storage deficits over these basins. This study has therefore shown that in the computation of the total storage deficit index over small-scale catchments during anomalous climatic conditions that propagate extreme dryness through the terrestrial hydrologic systems, simulations of the total water storage from a structurally sound model such as the VIC model could be resourceful for the computation of the monthly total storage deficit index if no constraint is placed on the availability of accurate meteorological forcing.

Summary Lake Chad has recently been perceived to be completely desiccated and almost extinct due to insufficient published ground observations. Given the high spatial variability of rainfall in the region, and the fact that extreme climatic conditions (for example, droughts) could be intensifying in the Lake Chad basin (LCB) due to human activities, a spatio-temporal approach to drought analysis becomes essential. This study employed independent component analysis (ICA), a fourth-order cumulant statistics, to decompose standardised precipitation index (SPI), standardised soil moisture index (SSI), and terrestrial water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) into spatial and temporal patterns over the LCB. In addition, this study uses satellite altimetry data to estimate variations in the Lake Chad water levels, and further employs relevant climate teleconnection indices (El-Nino Southern Oscillation-ENSO, Atlantic Multi-decadal Oscillation-AMO, and Atlantic Meridional Mode-AMM) to examine their links to the observed drought temporal patterns over the basin. From the spatio-temporal drought analysis, temporal evolutions of SPI at 12 month aggregation show relatively wet conditions in the last two decades (although with marked alterations) with the 2012–2014 period being the wettest. In addition to the improved rainfall conditions during this period, there was a statistically significant increase of 0.04 m/yr in altimetry water levels observed over Lake Chad between 2008 and 2014, which confirms a shift in the hydrological conditions of the basin. Observed trend in TWS changes during the 2002–2014 period shows a statistically insignificant increase of 3.0 mm/yr at the centre of the basin, coinciding with soil moisture deficit indicated by the temporal evolutions of SSI at all monthly accumulations during the 2002–2003 and 2009–2012 periods. Further, SPI at 3 and 6 month scales indicated fluctuating drought conditions at the extreme south of the basin, coinciding with a statistically insignificant decline in TWS of about 4.5 mm/yr at the southern catchment of the basin. Finally, correlation analyses indicate that ENSO, AMO, and AMM are associated with extreme rainfall conditions in the basin, with AMO showing the strongest association (statistically significant correlation of 0.55) with SPI 12 month aggregation. Therefore, this study provides a framework that will support drought monitoring in the LCB.

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations of the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including groundwater. The U.S. and North American Drought Monitors rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors by filling this observational gap. GRACE TWS data were assimilating into the Catchment Land Surface Model using an ensemble Kalman smoother enabling spatial and temporal downscaling and vertical decomposition into soil moisture and groundwater components. The Drought Monitors combine several short- and long-term drought indicators expressed in percentiles as a reference to their historical frequency of occurrence. To be consistent, we generated a climatology of estimated soil moisture and ground water based on a 60-year Catchment model simulation, which was used to convert seven years of GRACE assimilated fields into drought indicator percentiles. At this stage we provide a preliminary evaluation of the GRACE assimilated moisture and indicator fields.

Life on Earth depends on water. Yet water resources are severely stressed by the rapid growth of the human population and activities. In arid environments the exploration and monitoring of water resources is a prerequisite for water accessibility and rational use and management. To survey large arid areas for water, conventional land-based techniques must be complemented by using satellite and airborne remote sensors. Surface water systems can be mapped using multispectral and radar sensors; soil moisture in the unsaturated zone can be remotely sensed with microwave radiometers using indirect indicators, such as microwave emissivity; freshwater wetlands can be mapped using multispectral cameras; and freshwater springs can be detected using thermal infrared radiometers. Satellite remote sensors and satellite gravitational surveys can be used in combination with ancillary data analysis to infer groundwater behavior from surface expressions and to estimate groundwater aquifer storage. This chapter provides an overview of satellite and airborne remote sensing techniques for managing water resources and monitoring drought in arid and semiarid regions.

Large-scale hydrology is often insufficiently represented by ground monitoring networks and land surface models. The launch of the Gravity Recovery and Climate Experiment (GRACE) satellite mission provides an independent source to quantify water cycle dynamics over large basins. The main objective of this PhD thesis is to improve our understanding of water resources in water-limited environments through the use of spatially-improved GRACE observations. Firstly, the benefits and challenges from readily available GRACE global solutions at a 4° resolution were examined. GRACE global solutions from CSR and GRGS were applied to investigate the recent decadal groundwater depletion trend over the Hai River Basin in China. Results show an agreement between GRACE and in-situ groundwater observations: the Hai River Basin, especially the North China Plain, exhibited a constant declining trend in groundwater storage between 2003 and 2012. Continuous groundwater depletion is mainly attributed to anthropogenic over-extraction rather than climatic variability. This study highlights the value of using GRACE observations to provide rapid and independent estimates of regional groundwater storage change, which can supplement the scarcity of ground measurements. However, the coarse spatial resolution associated with GRACE global solutions prevents the analysis of spatial variability in groundwater depletions within the basin. Secondly, the benefits of applying newly developed the regional GRACE solutions at a 2° resolution for the estimation of large-scale evapotranspiration (ET) in two semi-arid and arid Australian basins were assessed. The GRACE ET estimates from these regional solutions were compared with three continental ET products; one derived from a land surface model and two from energy-based satellite models. The results demonstrate that the satellite energy balance ET models poorly represent water availability at different temporal and spatial scales; ET was overestimated during water stressed conditions and underestimated when it was wet. Integrated with optical and thermal satellite retrievals, however, these energy-based models are capable of detecting ET heterogeneities over land surfaces. In comparison, water balance constrained GRACE (P-△S) estimates are able to capture dynamic ET variations over large spatial domains. As a result of these findings, a wavelet fusion approach was proposed to further refine GRACE ET estimates, as well as to better constrain energy-based ET estimates. The improvements were achieved by combining GRACE (P-△S) and an ET product forced by optical and thermal remote sensing retrievals. A wavelet-based multi-scale fusion approach embodied with a flexible coefficient weighting scheme highlights the large-scale ET patterns from GRACE but also the small-scale ET features from the energy balance constrained model. Compared with original ET sources, our fused ET results display a significant improvement in spatial accuracy and a better sensibility to rainfall variability over temporal scales. Overall, this research confirms GRACE's capability and potential contributions in understanding the hydrology of arid and semi-arid environments. In these regions, water fluxes are limited by water availability rather than energy, hence the water balance framework used in GRACE studies is ideal. A prospective analysis highlights that the GRACE fusion method developed in this thesis has potential applications for other components of the water cycle for the purpose of enhancing estimation accuracy in time and/or space.

In this study, we use observations from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to evaluate freshwater storage trends in the north-central Middle East, including portions of the Tigris and Euphrates River Basins and western Iran, from January 2003 to December 2009. GRACE data show an alarming rate of decrease in total water storage of approximately −27.2±0.6 mm yr−1 equivalent water height, equal to a volume of 143.6 km3 during the course of the study period. Additional remote-sensing information and output from land surface models were used to identify that groundwater losses are the major source of this trend. The approach used in this study provides an example of “best current capabilities” in regions like the Middle East, where data access can be severely limited. Results indicate that the region lost 17.3±2.1 mm yr−1 equivalent water height of groundwater during the study period, or 91.3±10.9 km3 in volume. Furthermore, results raise important issues regarding water use in transboundary river basins and aquifers, including the necessity of international water use treaties and resolving discrepancies in international water law, while amplifying the need for increased monitoring for core components of the water budget.

In recent years, alternating periods of floods and droughts, possibly related to climate change and/or human activity, have occurred in the Liao River Basin of China. To monitor and gain a deep understanding of the frequency and severity of the hydro-meteorological extreme events in the Liao River Basin in the past 30 years, the total storage deficit index (TSDI) is established by the Gravity Recovery and Climate Experiment (GRACE)-based terrestrial water storage anomalies (TWSAs) and the general regression neural network (GRNN)-predicted TWSA. Results indicate that the GRNN model trained with GRACE-based TWSA, model-simulated soil moisture, and precipitation observations was optimal, and the correlation coefficient and the root mean square error (RMSE) of the predicted TWSA and GRACE TWSA for the testing period equal 0.90 and 18 mm, respectively. The drought and flood conditions monitored by the TSDI were consistent with those of previous studies and records. The extreme climate events could indirectly reflect the status of the regional hydrological cycle. By monitoring the extreme climate events in the study area with TSDI, which was based on the TWSA of GRACE and GRNN, the decision of water resource management in the Liao River Basin could be made reasonably.

Global assessment of the spatiotemporal variability in terrestrial total water storage anomalies (TWSA) in response to hydrologic extremes is critical for water resources management. Using TWSA derived from the gravity recovery and climate experiment (GRACE) satellites, this study systematically assessed the skill of the TWSA-climatology (TC) approach and breakpoint (BP) detection method for identifying large-scale hydrologic extremes. The TC approach calculates standardized anomalies by using the mean and standard deviation of the GRACE TWSA corresponding to each month. In the BP detection method, the empirical mode decomposition (EMD) is first applied to identify the mean return period of TWSA extremes, and then a statistical procedure is used to identify the actual occurrence times of abrupt changes (i.e., BPs) in TWSA. Both detection methods were demonstrated on basin-averaged TWSA time series for the world’s 35 largest river basins. A nonlinear event coincidence analysis measure was applied to cross-examine abrupt changes detected by these methods with those detected by the Standardized Precipitation Index (SPI). Results show that our EMD-assisted BP procedure is a promising tool for identifying hydrologic extremes using GRACE TWSA data. Abrupt changes detected by the BP method coincide well with those of the SPI anomalies and with documented hydrologic extreme events. Event timings obtained by the TC method were ambiguous for a number of river basins studied, probably because the GRACE data length is too short to derive long-term climatology at this time. The BP approach demonstrates a robust wet-dry anomaly detection capability, which will be important for applications with the upcoming GRACE Follow-On mission.

Fil: Aragon, Myriam Roxana. Universidad Nacional de Tucuman. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Laboratorio de Investigaciones Ecologicas de las Yungas; Argentina. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tucuman; Argentina