1
|
Drewry KR, Jones CN, Hayes W, Beighley RE, Wang Q, Hochard J, Mize W, Fowlkes J, Goforth C, Pieper KJ. Using Inundation Extents to Predict Microbial Contamination in Private Wells after Flooding Events. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5220-5228. [PMID: 38478973 DOI: 10.1021/acs.est.3c09375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Disaster recovery poses unique challenges for residents reliant on private wells. Flooding events are drivers of microbial contamination in well water, but the relationship observed between flooding and contamination varies substantially. Here, we investigate the performance of different flood boundaries─the FEMA 100 year flood hazard boundary, height above nearest drainage-derived inundation extents, and satellite-derived extents from the Dartmouth Flood Observatory─in their ability to identify well water contamination following Hurricane Florence. Using these flood boundaries, we estimated about 2600 wells to 108,400 private wells may have been inundated─over 2 orders of magnitude difference based on boundary used. Using state-generated routine and post-Florence testing data, we observed that microbial contamination rates were 7.1-10.5 times higher within the three flood boundaries compared to routine conditions. However, the ability of the flood boundaries to identify contaminated samples varied spatially depending on the type of flooding (e.g., riverine, overbank, coastal). While participation in testing increased after Florence, rates were overall still low. With <1% of wells tested, there is a critical need for enhanced well water testing efforts. This work provides an understanding of the strengths and limitations of inundation mapping techniques, which are critical for guiding postdisaster well water response and recovery.
Collapse
Affiliation(s)
- Kyla R Drewry
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - C Nathan Jones
- Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama 35401, United States
| | - Wesley Hayes
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - R Edward Beighley
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Qi Wang
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Jacob Hochard
- Haub School of Environment and Natural Resources, University of Wyoming, Laramie, Wyoming 82072, United States
| | - Wilson Mize
- Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, North Carolina 27609, United States
| | - Jon Fowlkes
- Division of Public Health, North Carolina Department of Health and Human Services, Raleigh, North Carolina 27609, United States
| | - Chris Goforth
- State Laboratory of Public Health, North Carolina Department of Health and Human Services, Raleigh, North Carolina 27609, United States
| | - Kelsey J Pieper
- Department of Civil and Environmental Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| |
Collapse
|
2
|
Qiu K, You W, Jiang Z, Tang M. Tracking the water storage and runoff variations in the Paraná basin via GNSS measurements. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168831. [PMID: 38061646 DOI: 10.1016/j.scitotenv.2023.168831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 11/20/2023] [Accepted: 11/22/2023] [Indexed: 01/18/2024]
Abstract
The Paraná basin is the second largest river basin in South America and provides abundant water resources globally. However, current research lacks hydrological investigation of the region. The vertical crustal deformation recorded by the Global Navigation Satellite System (GNSS) can be used to accurately estimate regional-scale terrestrial water storage (TWS). Therefore, we utilized the daily vertical displacement time series data at 102 GNSS stations to recover the water storage variations in the Paraná basin from 2013 to 2020. To recognize primary spatiotemporal features of TWS changes, we applied the principal component analysis (PCA) method in the inversion strategy. Results indicate that the TWS variations inferred from GNSS generally align in spatiotemporal patterns with estimates from both the Gravity Recovery and Climate Experiment (GRACE) and the Global Land Data Assimilation System (GLDAS). However, some discrepancies are evident at local scales. The TWS changes derived from both GNSS and GRACE exhibited generally larger magnitude of oscillations than those estimated by GLDAS, while the GRACE results neglected the evident seasonal oscillation of the water mass in the southeast of the basin. Given the challenge of capturing large-scale runoff variations through in-situ observations, we innovatively applied GNSS and water budget closure method to provide a novel runoff estimate for the Paraná basin. The GNSS-inferred runoff exhibited a strong correlation (correlation coefficient of 0.72) with in-situ observations. Overall, our study fills the critical knowledge gap in geodesy-based hydrological investigation in the Paraná basin. We aim to highlight the immense potential of GNSS for hydrological parameter estimation and provide valuable reference data for regional hydrological research and for water resources management.
Collapse
Affiliation(s)
- Keshan Qiu
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| | - Wei You
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China.
| | - Zhongshan Jiang
- School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China
| | - Miao Tang
- Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
| |
Collapse
|
3
|
Zhang X, Ren C, Liang Y, Liang J, Yin A, Wei Z. Research on Soil Moisture Estimation of Multiple-Track-GNSS Dual-Frequency Combination Observations Considering the Detection and Correction of Phase Outliers. SENSORS (BASEL, SWITZERLAND) 2023; 23:7944. [PMID: 37766001 PMCID: PMC10535229 DOI: 10.3390/s23187944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 09/11/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023]
Abstract
Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range.
Collapse
Affiliation(s)
- Xudong Zhang
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Chao Ren
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Yueji Liang
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Jieyu Liang
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Anchao Yin
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| | - Zhenkui Wei
- College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
| |
Collapse
|
4
|
Global Evapotranspiration Datasets Assessment Using Water Balance in South America. REMOTE SENSING 2022. [DOI: 10.3390/rs14112526] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
Evapotranspiration (ET) connects the land to the atmosphere, linking water, energy, and carbon cycles. ET is an essential climate variable with a fundamental importance, and accurate assessments of the spatiotemporal trends and variability in ET are needed from regional to continental scales. This study compared eight global actual ET datasets (ETgl) and the average actual ET ensemble (ETens) based on remote sensing, climate reanalysis, land-surface, and biophysical models to ET computed from basin-scale water balance (ETwb) in South America on monthly time scale. The 50 small-to-large basins covered major rivers and different biomes and climate types. We also examined the magnitude, seasonality, and interannual variability of ET, comparing ETgl and ETens with ETwb. Global ET datasets were evaluated between 2003 and 2014 from the following datasets: Breathing Earth System Simulator (BESS), ECMWF Reanalysis 5 (ERA5), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16, Penman–Monteith–Leuning (PML), Operational Simplified Surface Energy Balance (SSEBop) and Terra Climate. By using ETwb as a basis for comparison, correlation coefficients ranged from 0.45 (SSEBop) to 0.60 (ETens), and RMSE ranged from 35.6 (ETens) to 40.5 mm·month−1 (MOD16). Overall, ETgl estimates ranged from 0 to 150 mm·month−1 in most basins in South America, while ETwb estimates showed maximum rates up to 250 mm·month−1. ETgl varied by hydroclimatic regions: (i) basins located in humid climates with low seasonality in precipitation, including the Amazon, Uruguay, and South Atlantic basins, yielded weak correlation coefficients between monthly ETgl and ETwb, and (ii) tropical and semiarid basins (areas where precipitation demonstrates a strong seasonality, as in the São Francisco, Northeast Atlantic, Paraná/Paraguay, and Tocantins basins) yielded moderate-to-strong correlation coefficients. An assessment of the interannual variability demonstrated a disagreement between ETgl and ETwb in the humid tropics (in the Amazon), with ETgl showing a wide range of interannual variability. However, in tropical, subtropical, and semiarid climates, including the Tocantins, São Francisco, Paraná, Paraguay, Uruguay, and Atlantic basins (Northeast, East, and South), we found a stronger agreement between ETgl and ETwb for interannual variability. Assessing ET datasets enables the understanding of land–atmosphere exchanges in South America, to improvement of ET estimation and monitoring for water management.
Collapse
|
5
|
Fan J, Luo M, Han Q, Liu F, Huang W, Tan S. Evaluation of SMOS, SMAP, AMSR2 and FY-3C soil moisture products over China. PLoS One 2022; 17:e0266091. [PMID: 35390019 PMCID: PMC8989296 DOI: 10.1371/journal.pone.0266091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/14/2022] [Indexed: 11/27/2022] Open
Abstract
Microwave remote sensing can provide long-term near-surface soil moisture data on regional and global scales. Conducting standardized authenticity tests is critical to the effective use of observed data products in models, data assimilation, and various terminal scenarios. Global Land Data Assimilation System (GLDAS) soil moisture data were used as a reference for comparative analysis, and triple collocation analysis was used to validate data from four mainstream passive microwave remote sensing soil moisture products: Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Active and Passive (SMAP), Global Change Observation Mission–Water using the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument, and Fengyun-3C (FY-3C). The effects of topography, land cover, and meteorological factors on the accuracy of soil moisture observation data were determined. The results show that SMAP had the best overall performance and AMSR2 the worst. Passive microwave detection technology can accurately capture soil moisture data in areas at high altitude with uniform terrain, particularly if the underlying surface is soil, and in areas with low average temperatures and little precipitation, such as the Qinghai–Tibet Plateau. FY-3C performed in the middle of the group and was relatively optimal in northeast China but showed poor data integrity. Variation in accuracy between products, together with other factors identified in the study, provides a baseline reference for the improvement of the retrieval algorithm, and the research results provide a quantitative basis for developing better use of passive microwave soil moisture products.
Collapse
Affiliation(s)
- Jiazhi Fan
- China Meteorological Administration Training Centre Hunan Branch, Hunan Meteorological Bureau, Changsha, China
- Key Laboratory of Hunan Province for Meteorological Disaster Prevention and Mitigation, Hunan Meteorological Bureau, Changsha, China
- International Center for Ecology, Meteorology and Environment, Jiangsu Key Laboratory of Agricultural Meteorology, Nanjing University of Information Science and Technology, Nanjing, China
| | - Man Luo
- China Meteorological Administration Training Centre Hunan Branch, Hunan Meteorological Bureau, Changsha, China
| | - Qinzhe Han
- Key Laboratory of Hunan Province for Meteorological Disaster Prevention and Mitigation, Hunan Meteorological Bureau, Changsha, China
- Hunan Research Institute of Meteorological Sciences, Hunan Meteorological Bureau, Changsha, China
| | - Fulai Liu
- Key Laboratory of Hunan Province for Meteorological Disaster Prevention and Mitigation, Hunan Meteorological Bureau, Changsha, China
| | - Wanhua Huang
- Key Laboratory of Hunan Province for Meteorological Disaster Prevention and Mitigation, Hunan Meteorological Bureau, Changsha, China
| | - Shiqi Tan
- Hunan Meteorological Service Center, Hunan Meteorological Bureau, Changsha, China
- * E-mail:
| |
Collapse
|
6
|
GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications. REMOTE SENSING 2020. [DOI: 10.3390/rs12040614] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed.
Collapse
|
7
|
Flash Flood Risk Analysis Based on Machine Learning Techniques in the Yunnan Province, China. REMOTE SENSING 2019. [DOI: 10.3390/rs11020170] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Flash flood, one of the most devastating weather-related hazards in the world, has become more and more frequent in past decades. For the purpose of flood mitigation, it is necessary to understand the distribution of flash flood risk. In this study, artificial intelligence (Least squares support vector machine: LSSVM) and classical canonical method (Logistic regression: LR) are used to assess the flash flood risk in the Yunnan Province based on historical flash flood records and 13 meteorological, topographical, hydrological and anthropological factors. Results indicate that: (1) the LSSVM with Radial basis function (RBF) Kernel works the best (Accuracy = 0.79) and the LR is the worst (Accuracy = 0.75) in testing; (2) flash flood risk distribution identified by the LSSVM in Yunnan province is near normal distribution; (3) the high-risk areas are mainly concentrated in the central and southeastern regions, where with a large curve number; and (4) the impact factors contributing the flash flood risk map from higher to low are: Curve number > Digital elevation > Slope > River density > Flash Flood preventions > Topographic Wetness Index > annual maximum 24 h precipitation > annual maximum 3 h precipitation.
Collapse
|
8
|
Khaki M, Awange J. The application of multi-mission satellite data assimilation for studying water storage changes over South America. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 647:1557-1572. [PMID: 30180360 DOI: 10.1016/j.scitotenv.2018.08.079] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/03/2018] [Accepted: 08/05/2018] [Indexed: 06/08/2023]
Abstract
Constant monitoring of total water storage (TWS; surface, groundwater, and soil moisture) is essential for water management and policy decisions, especially due to the impacts of climate change and anthropogenic factors. Moreover, for most countries in Africa, Asia, and South America that depend on soil moisture and groundwater for agricultural productivity, monitoring of climate change and anthropogenic impacts on TWS becomes crucial. Hydrological models are widely being used to monitor water storage changes in various regions around the world. Such models, however, comes with uncertainties mainly due to data limitations that warrant enhancement from remotely sensed satellite products. In this study over South America, remotely sensed TWS from the Gravity Recovery And Climate Experiment (GRACE) satellite mission is used to constrain the World-Wide Water Resources Assessment (W3RA) model estimates in order to improve their reliabilities. To this end, GRACE-derived TWS and soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and Soil Moisture and Ocean Salinity (SMOS) are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) in order to separately analyze groundwater and soil moisture changes for the period 2002-2013. Following the assimilation analysis, Tropical Rainfall Measuring Mission (TRMM)'s rainfall data over 15 major basins of South America and El Niño/Southern Oscillation (ENSO) data are employed to demonstrate the advantages gained by the model from the assimilation of GRACE TWS and satellite soil moisture products in studying climatically induced TWS changes. From the results, it can be seen that assimilating these observations improves the performance of W3RA hydrological model. Significant improvements are also achieved as seen from increased correlations between TWS products and both precipitation and ENSO over a majority of basins. The improved knowledge of sub-surface water storages, especially groundwater and soil moisture variations, can be largely helpful for agricultural productivity over South America.
Collapse
Affiliation(s)
- M Khaki
- School of Earth and Planetary Sciences, Spatial Sciences, Curtin University, Perth, Australia; School of Engineering, University of Newcastle, Callaghan, New South Wales, Australia.
| | - J Awange
- School of Earth and Planetary Sciences, Spatial Sciences, Curtin University, Perth, Australia
| |
Collapse
|
9
|
Prediction of Drought on Pentad Scale Using Remote Sensing Data and MJO Index through Random Forest over East Asia. REMOTE SENSING 2018. [DOI: 10.3390/rs10111811] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Rapidly developing droughts, including flash droughts, have frequently occurred throughout East Asia in recent years, causing significant damage to agricultural ecosystems. Although many drought monitoring and warning systems have been developed in recent decades, the short-term prediction of droughts (within 10 days) is still challenging. This study has developed drought prediction models for a short-period of time (one pentad) using remote-sensing data and climate variability indices over East Asia (20°–50°N, 90°–150°E) through random forest machine learning. Satellite-based drought indices were calculated using the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture, Tropical Rainfall Measuring Mission (TRMM) precipitation, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST), and normalized difference vegetation index (NDVI). The real-time multivariate (RMM) Madden–Julian oscillation (MJO) indices were used because the MJO is a short timescale climate variability and has important implications for droughts in East Asia. The validation results show that those drought prediction models with the MJO variables (r ~ 0.7 on average) outperformed the original models without the MJO variables (r ~ 0.4 on average). The predicted drought index maps showed similar spatial distribution to actual drought index maps. In particular, the MJO-based models captured sudden changes in drought conditions well, from normal/wet to dry or dry to normal/wet. Since the developed models can produce drought prediction maps at high resolution (5 km) for a very short timescale (one pentad), they are expected to provide decision makers with more accurate information on rapidly changing drought conditions.
Collapse
|
10
|
Humphrey V, Zscheischler J, Ciais P, Gudmundsson L, Sitch S, Seneviratne SI. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 2018; 560:628-631. [DOI: 10.1038/s41586-018-0424-4] [Citation(s) in RCA: 198] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 06/14/2018] [Indexed: 11/09/2022]
|
11
|
Drought and Flood Monitoring of the Liao River Basin in Northeast China Using Extended GRACE Data. REMOTE SENSING 2018. [DOI: 10.3390/rs10081168] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
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.
Collapse
|
12
|
An Improved Approach for Evapotranspiration Estimation Using Water Balance Equation: Case Study of Yangtze River Basin. WATER 2018. [DOI: 10.3390/w10060812] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
13
|
Using GRACE Satellite Gravimetry for Assessing Large-Scale Hydrologic Extremes. REMOTE SENSING 2017. [DOI: 10.3390/rs9121287] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
14
|
Hu K, Awange JL, Forootan E, Goncalves RM, Fleming K. Hydrogeological characterisation of groundwater over Brazil using remotely sensed and model products. THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 599-600:372-386. [PMID: 28482297 DOI: 10.1016/j.scitotenv.2017.04.188] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 04/12/2017] [Accepted: 04/13/2017] [Indexed: 06/07/2023]
Abstract
For Brazil, a country frequented by droughts and whose rural inhabitants largely depend on groundwater, reliance on isotope for its monitoring, though accurate, is expensive and limited in spatial coverage. We exploit total water storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellites to analyse spatial-temporal groundwater changes in relation to geological characteristics. Large-scale groundwater changes are estimated using GRACE-derived TWS and altimetry observations in addition to GLDAS and WGHM model outputs. Additionally, TRMM precipitation data are used to infer impacts of climate variability on groundwater fluctuations. The results indicate that climate variability mainly controls groundwater change trends while geological properties control change rates, spatial distribution, and storage capacity. Granular rocks in the Amazon and Guarani aquifers are found to influence larger storage capability, higher permeability (>10-4 m/s) and faster response to rainfall (1 to 3months' lag) compared to fractured rocks (permeability <10-7 m/s and lags > 3months) found only in Bambui aquifer. Groundwater in the Amazon region is found to rely not only on precipitation but also on inflow from other regions. Areas beyond the northern and southern Amazon basin depict a 'dam-like' pattern, with high inflow and slow outflow rates (recharge slope > 0.75, discharge slope < 0.45). This is due to two impermeable rock layer-like 'walls' (permeability <10-8 m/s) along the northern and southern Alter do Chão aquifer that help retain groundwater. The largest groundwater storage capacity in Brazil is the Amazon aquifer (with annual amplitudes of > 30cm). Amazon's groundwater declined between 2002 and 2008 due to below normal precipitation (wet seasons lasted for about 36 to 47% of the time). The Guarani aquifer and adjacent coastline areas rank second in terms of storage capacity, while the northeast and southeast coastal regions indicate the smallest storage capacity due to lack of rainfall (annual average is rainfall <10cm).
Collapse
Affiliation(s)
- Kexiang Hu
- Department of Spatial Sciences, Curtin University, Perth, Australia
| | - Joseph L Awange
- Department of Spatial Sciences, Curtin University, Perth, Australia
| | - Ehsan Forootan
- School of Earth and Ocean Sciences, Cardiff University, Cardiff, UK
| | - Rodrigo Mikosz Goncalves
- Department of Cartographic Engineering, Geodetic Science and Technology of Geoinformation Post Graduation Program, Federal University of Pernambuco (UFPE), Recife, PE, Brazil
| | - Kevin Fleming
- Centre for Early Warning Systems, GFZ German Research Centre for Geosciences, Potsdam, Germany
| |
Collapse
|
15
|
Analysis of Current and Future SPEI Droughts in the La Plata Basin Based on Results from the Regional Eta Climate Model. WATER 2017. [DOI: 10.3390/w9110857] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
16
|
Humphrey V, Gudmundsson L, Seneviratne SI. Assessing Global Water Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes. SURVEYS IN GEOPHYSICS 2016; 37:357-395. [PMID: 27471333 PMCID: PMC4944666 DOI: 10.1007/s10712-016-9367-1] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 01/29/2016] [Indexed: 05/05/2023]
Abstract
Throughout the past decade, the Gravity Recovery and Climate Experiment (GRACE) has given an unprecedented view on global variations in terrestrial water storage. While an increasing number of case studies have provided a rich overview on regional analyses, a global assessment on the dominant features of GRACE variability is still lacking. To address this, we survey key features of temporal variability in the GRACE record by decomposing gridded time series of monthly equivalent water height into linear trends, inter-annual, seasonal, and subseasonal (intra-annual) components. We provide an overview of the relative importance and spatial distribution of these components globally. A correlation analysis with precipitation and temperature reveals that both the inter-annual and subseasonal anomalies are tightly related to fluctuations in the atmospheric forcing. As a novelty, we show that for large regions of the world high-frequency anomalies in the monthly GRACE signal, which have been partly interpreted as noise, can be statistically reconstructed from daily precipitation once an adequate averaging filter is applied. This filter integrates the temporally decaying contribution of precipitation to the storage changes in any given month, including earlier precipitation. Finally, we also survey extreme dry anomalies in the GRACE record and relate them to documented drought events. This global assessment sets regional studies in a broader context and reveals phenomena that had not been documented so far.
Collapse
Affiliation(s)
- Vincent Humphrey
- Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
| | - Lukas Gudmundsson
- Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
| | - Sonia I. Seneviratne
- Institute for Atmospheric and Climate Science, ETH Zurich, Universitaetstrasse 16, 8092 Zurich, Switzerland
| |
Collapse
|