Inter-comparison of high-resolution satellite precipitation products over Central Asia

This paper examines the spatial error structures of eight precipitation estimates derived from four different satellite retrieval algorithms including TRMM Multi-satellite Precipitation Analysis (TMPA), Climate Prediction Center morphing technique (CMORPH), Global Satellite Mapping of Precipitation (GSMaP) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). All the original satellite and bias-corrected products of each algorithm (3B42RTV7 and 3B42V7, CMORPH_RAW and CMORPH_CRT, GSMaP_MVK and GSMaP_Gauge, PERSIANN_RAW and PERSIANN_CDR) are evaluated against ground-based Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over Central Asia for the period of 2004 to 2006. The analyses show that all products except PERSIANN exhibit overestimation over Aral Sea and its surrounding areas. The bias-correction improves the quality of the original satellite TMPA products and GSMaP significantly but slightly in CMORPH and PERSIANN over Central Asia. 3B42RTV7 overestimates precipitation significantly with large Relative Bias (RB) (128.17%) while GSMaP_Gauge shows consistent high correlation coefficient (CC) (>0.8) but RB fluctuates between -57.95% and 112.63%. The PERSIANN_CDR outperforms other products in winter with the highest CC (0.67). Both the satellite-only and gauge adjusted products have particularly poor performance in detecting rainfall events in terms of lower POD (less than 65%), CSI (less than 45%) and relatively high FAR (more than 35%)

Land use, urban, environmental, and cartographic applications, chapter 2, part D

Microwave data and its use in effective state, regional, and national land use planning are dealt with. Special attention was given to monitoring land use change, especially dynamic components, and the interaction between land use and dynamic features of the environment. Disaster and environmental monitoring are also discussed

Development of watershed-based modeling approach to pollution source identification

Identification of unknown pollution sources is essential to environmental protection and emergency response. A review of recent publications in source identification revealed that there are very limited numbers of research in modeling methods for rivers. What’s more, the majority of these attempts were to find the source strength and release time, while only a few of them discussed how to identify source locations. Comparisons of these works indicated that a combination of biological, mathematical and geographical method could effectively identify unknown source area(s), which was a more practical trial in a watershed. This thesis presents a watershed-based modeling approach to identification of critical source area. The new approach involves (1) identification of pollution source in rivers using a moment-based method and (2) identification of critical source area in a watershed using a hydrograph-based method and high-resolution radar rainfall data. In terms of the moment-based method, the first two moment equations are derived through the Laplace transform of the Variable Residence Time (VART) model. The first moment is used to determine the source location, while the second moment can be employed to estimate the total mass of released pollutant. The two moment equations are tested using conservative tracer injection data collected from 23 reaches of five rivers in Louisiana, USA, ranging from about 3km to 300 km. Results showed that the first moment equation is able to predict the pollution source location with a percent error of less than 18% in general. The predicted total mass has a larger percent error, but a correction could be added to reduce the error significantly. Additionally, the moment-based method can be applied to identify the source location of reactive pollutants, provided that the special and temporal concentrations are recorded in downstream stations. In terms of the hydrograph-based method, observed hydrographs corresponding to pollution events can be utilized to identify the critical source area in a watershed. The time of concentration could provide a unique fingerprint for each subbasin in the watershed. The observation of abnormally high bacterial levels along with high resolution radar rainfall data can be used to match the most possible storm events and thus the critical source area

3D Characterization of a Coastal Freshwater Aquifer in SE Malta (Mediterranean Sea) by Time-Domain Electromagnetics

Electromagnetic (EM) geophysical methods are well equipped to distinguish electrical resistivity contrasts between freshwater-saturated and seawater-saturated formations. Beneath the semi-arid, rapidly urbanizing island of Malta, offshore groundwater is an important potential resource but it is not known whether the regional mean sea-level aquifer (MSLA) extends offshore. To address this uncertainty, land-based alongshore and across-shore time-domain electromagnetic (TDEM) responses were acquired with the G-TEM instrument (Geonics Ltd., Mississauga, ON, Canada) and used to map the onshore structure of the aquifer. 1-D inversion results suggest that the onshore freshwater aquifer resides at 4–24 m depth, underlain by seawater-saturated formations. The freshwater aquifer thickens with distance from the coastline. We present 2D and 3D electromagnetic forward modeling based on finite-element (FE) analysis to further constrain the subsurface geometry of the onshore freshwater body. We interpret the high resistivity zones that as brackish water-saturated bodies are associated with the mean sea-level aquifer. Generally, time-domain electromagnetic (TDEM) results provide valuable onshore hydrogeological information, which can be augmented with marine and coastal transition-zone measurements to assess potential hydraulic continuity of terrestrial aquifers extending offshore

A study to define meteorological uses and performance requirements for the Synchronous Earth Observatory Satellite

The potential meteorological uses of the Synchronous Earth Observatory Satellite (SEOS) were studied for detecting and predicting hazards to life, property, or the quality of the environment. Mesoscale meteorological phenonmena, and the observations requirements for SEOS are discussed along with the sensor parameters

Satellite Radar Altimetry for Inland Hydrologic Studies

This research is conducted under the supervision of Dr. C.K. Shum, Professor of Geodetic Science, School of Earth Sciences, The Ohio State University. The research results documented in this report resulted in a PhD Dissertation. NASA and CNES provided the TOPEX/POSEIDON (Geophysical Data Record and Sensor Data Record, GDR and SDR) data products; LEGOS, USDA/NASA/GSFC provided high-level radar altimetry data products; ANA Brazil, and Environment Canada provided the stage gauge data used for this research. This research is supported by grants from NSF’s Hydrology Program (EAR-0440007) and NGA’s NURI Program (HM1582-07-1-2024), and the study was conducted with the objective to contribute to the Climate, Water, and Carbon Program at The Ohio State University.Satellite radar altimetry, which is originally designed to measure global ocean surface height, has been applied to inland surface water hydrologic studies. We have developed a water-detection algorithm based on statistical analysis of decadal TOPEX/POSEIDON height measurement time series, used the backscatter coefficient to classify the inland surface properties, and the 10-Hz (corresponding to an along track spatial resolution of 700m) radar waveform-retracked TOPEX data, to be able to observe small (<300Km2) inland bodies of water for hydrologic studies. We applied the algorithm to the selected study regions in Manitoba and northwestern (SW) Ontario, Canada, Amazon River Basin, and southwestern Taiwan. Finally we studied the application of TOPEX altimetry to the 1997 Red River flood monitoring. For the study regions in western Manitoba, the correlation coefficient between stage and TOPEX altimetry data in the large Lakes reaches 0.98 using the 10-Hz retracked data, thus verifying the validity and accuracy of the satellite measurement. The importance of the waveform retracking for the inland water applications is validated by the improvement of the correlation coefficients from 0.34 to 0.87 before and after retracking. We detected the bodies of water, which are otherwise missed by using the original 1-Hz data from the Geophysical Data Records, and illustrated that a higher spatial resolution could be achieved using the individual 10-Hz retracked data. In the Amazon River Basin, the capability of the water-detection algorithm is compared with the use of a high water level mask generated by SAR and other data with a spatial resolution of 100m. It is shown that the algorithm could detect the bodies of water, which are missed by the mask primarily because that the frequency of water fluctuation is more than twice a year at some locations. The bodies of water detected only by the algorithm are confirmed using the detailed local hydrological maps in 3 tested regions. The retrieved water height over the small (<300Km2) body of water was compared with the nearby stage measurement and showed good seasonal agreement. In the southwest Taiwan, the monthly variation of 10-Hz AGC from 1992 to 2002 were examined, it is found that the high AGC values could be used to indicate inundated area. We detected the annual and semi-annual variations from the 10-Hz AGC and 10-Hz retracked water height time series, which are attributable to two rainy seasons per year in the study region. For the study of the 1997 Red River flood, we compared the geographic distribution of 0 σ0 before, during and after the 1997 flood and found the high 0 σ0 values (>35dB) indicate the inundated regions. In addition, the comparison of the geographically distributed 0 σ0 during Winter, Spring, Summer and Autumn of 1997 showed that the low 0 σ values (<10dB) indicate snow coverage. The retrieved water height measurements in the flooded regions are compared with the nearby USGS stage measurements and showed good agreements. The comparison of 10-Hz individual retracked measurements with the 1-Hz nonretracked height measurements confirmed the importance of the retracked data (with higher spatial variations) in the flood monitoring. Using 0 σ0 and the retrieved water height measurements, we detected the 1997 flooded regions include the Red River Basin of the North in North Dakota and in western Minnesota, the upper Mississippi River Basin in Minnesota, the Missouri River Basin in southern North Dakota and in South Dakota. The observed flood extents from TOPEX agree well with and complement the USGS stage gauge records

Satellite Radar Altimetry for Inland Hydrologic Studies

This research is conducted under the supervision of Dr. C.K. Shum, Professor of Geodetic Science, School of Earth Sciences, The Ohio State University. The research results documented in this report resulted in a PhD Dissertation. NASA and CNES provided the TOPEX/POSEIDON (Geophysical Data Record and Sensor Data Record, GDR and SDR) data products; LEGOS, USDA/NASA/GSFC provided high-level radar altimetry data products; ANA Brazil, and Environment Canada provided the stage gauge data used for this research. This research is supported by grants from NSF’s Hydrology Program (EAR-0440007) and NGA’s NURI Program (HM1582-07-1-2024), and the study was conducted with the objective to contribute to the Climate, Water, and Carbon Program at The Ohio State University.Satellite radar altimetry, which is originally designed to measure global ocean surface height, has been applied to inland surface water hydrologic studies. We have developed a water-detection algorithm based on statistical analysis of decadal TOPEX/POSEIDON height measurement time series, used the backscatter coefficient to classify the inland surface properties, and the 10-Hz (corresponding to an along track spatial resolution of 700m) radar waveform-retracked TOPEX data, to be able to observe small (<300Km2) inland bodies of water for hydrologic studies. We applied the algorithm to the selected study regions in Manitoba and northwestern (SW) Ontario, Canada, Amazon River Basin, and southwestern Taiwan. Finally we studied the application of TOPEX altimetry to the 1997 Red River flood monitoring. For the study regions in western Manitoba, the correlation coefficient between stage and TOPEX altimetry data in the large Lakes reaches 0.98 using the 10-Hz retracked data, thus verifying the validity and accuracy of the satellite measurement. The importance of the waveform retracking for the inland water applications is validated by the improvement of the correlation coefficients from 0.34 to 0.87 before and after retracking. We detected the bodies of water, which are otherwise missed by using the original 1-Hz data from the Geophysical Data Records, and illustrated that a higher spatial resolution could be achieved using the individual 10-Hz retracked data. In the Amazon River Basin, the capability of the water-detection algorithm is compared with the use of a high water level mask generated by SAR and other data with a spatial resolution of 100m. It is shown that the algorithm could detect the bodies of water, which are missed by the mask primarily because that the frequency of water fluctuation is more than twice a year at some locations. The bodies of water detected only by the algorithm are confirmed using the detailed local hydrological maps in 3 tested regions. The retrieved water height over the small (<300Km2) body of water was compared with the nearby stage measurement and showed good seasonal agreement. In the southwest Taiwan, the monthly variation of 10-Hz AGC from 1992 to 2002 were examined, it is found that the high AGC values could be used to indicate inundated area. We detected the annual and semi-annual variations from the 10-Hz AGC and 10-Hz retracked water height time series, which are attributable to two rainy seasons per year in the study region. For the study of the 1997 Red River flood, we compared the geographic distribution of 0 σ0 before, during and after the 1997 flood and found the high 0 σ0 values (>35dB) indicate the inundated regions. In addition, the comparison of the geographically distributed 0 σ0 during Winter, Spring, Summer and Autumn of 1997 showed that the low 0 σ values (<10dB) indicate snow coverage. The retrieved water height measurements in the flooded regions are compared with the nearby USGS stage measurements and showed good agreements. The comparison of 10-Hz individual retracked measurements with the 1-Hz nonretracked height measurements confirmed the importance of the retracked data (with higher spatial variations) in the flood monitoring. Using 0 σ0 and the retrieved water height measurements, we detected the 1997 flooded regions include the Red River Basin of the North in North Dakota and in western Minnesota, the upper Mississippi River Basin in Minnesota, the Missouri River Basin in southern North Dakota and in South Dakota. The observed flood extents from TOPEX agree well with and complement the USGS stage gauge records

HIRIS (High-Resolution Imaging Spectrometer: Science opportunities for the 1990s. Earth observing system. Volume 2C: Instrument panel report

The high-resolution imaging spectrometer (HIRIS) is an Earth Observing System (EOS) sensor developed for high spatial and spectral resolution. It can acquire more information in the 0.4 to 2.5 micrometer spectral region than any other sensor yet envisioned. Its capability for critical sampling at high spatial resolution makes it an ideal complement to the MODIS (moderate-resolution imaging spectrometer) and HMMR (high-resolution multifrequency microwave radiometer), lower resolution sensors designed for repetitive coverage. With HIRIS it is possible to observe transient processes in a multistage remote sensing strategy for Earth observations on a global scale. The objectives, science requirements, and current sensor design of the HIRIS are discussed along with the synergism of the sensor with other EOS instruments and data handling and processing requirements

Groundwater level assessment and prediction in the Nebraska Sand Hills using LIDAR-derived lake water level

The spatial variability of groundwater levels is often inferred from sparsely located hydraulic head observations in wells. The spatial correlation structure derived from sparse observations is associated with uncertainties that spread to estimates at unsampled locations. In areas where surface water represents the nearby groundwater level, remote sensing techniques can estimate and increase the number of hydraulic head measurements. This research uses light detection and ranging (LIDAR) to estimate lake surface water level to characterize the groundwater level in the Nebraska Sand Hills (NSH), an area with few observation wells. The LIDAR derived lake groundwater level accuracy was within 40 cm mean square error (MSE) of the nearest observation wells. The lake groundwater level estimates were used to predict the groundwater level at unsampled locations using universal kriging (UK) and kriging with an external drift (KED). The results indicate unbiased estimates of groundwater level in the NSH. UK showed the influence of regional trends in groundwater level while KED revealed the local variation present in the groundwater level. A 10-fold cross-validation demonstrated KED with better mean squared error (ME) [–0.003, 0.007], root mean square error (RMSE) [2.39, 4.46], residual prediction deviation (RPD) [1.32, 0.71] and mean squared deviation ratio (MSDR) [1.01, 1.49] than UK. The research highlights that the lake groundwater level provides an accurate and cost-effective approach to measure and monitor the subtle changes in groundwater level in the NSH. This methodology can be applied to other locations where surface water bodies represent the water level of the unconfined aquifer and the results can aid in groundwater management and modeling