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NASA NTRS2010

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

Tang, Ling↗

NASA NTRS2013

First Evaluation of the Climatological Calibration Algorithm in the Real-time TMPA Precipitation Estimates over Two Basins at High and Low Latitudes

The TRMM Multi-satellite Precipitation Analysis (TMPA) system underwent a crucial upgrade in early 2009 to include a climatological calibration algorithm (CCA) to its realtime product 3B42RT, and this algorithm will continue to be applied in the future Global Precipitation Measurement era constellation precipitation products. In this study, efforts are focused on the comparison and validation of the Version 6 3B42RT estimates before and after the climatological calibration is applied. The evaluation is accomplished using independent rain gauge networks located within the high-latitude Laohahe basin and the low-latitude Mishui basin, both in China. The analyses indicate the CCA can effectively reduce the systematic errors over the low-latitude Mishui basin but misrepresent the intensity distribution pattern of medium-high rain rates. This behavior could adversely affect TMPA's hydrological applications, especially for extreme events (e.g., floods and landslides). Results also show that the CCA tends to perform slightly worse, in particular, during summer and winter, over the high-latitude Laohahe basin. This is possibly due to the simplified calibration-processing scheme in the CCA that directly applies the climatological calibrators developed within 40 degrees latitude to the latitude belts of 40 degrees N-50 degrees N. Caution should therefore be exercised when using the calibrated 3B42RT for heavy rainfall-related flood forecasting (or landslide warning) over high-latitude regions, as the employment of the smooth-fill scheme in the CCA bias correction could homogenize the varying rainstorm characteristics. Finally, this study highlights that accurate detection and estimation of snow at high latitudes is still a challenging task for the future development of satellite precipitation retrievals.

TMPA-RT Precipitation Estimates↗

NASA NTRS2015

Deriving Scaling Factors Using a Global Hydrological Model to Restore GRACE Total Water Storage Changes for China's Yangtze River Basin

This study used a global hydrological model (GHM), PCR-GLOBWB, which simulates surface water storage changes, natural and human induced groundwater storage changes, and the interactions between surface water and subsurface water, to generate scaling factors by mimicking low-pass filtering of GRACE signals. Signal losses in GRACE data were subsequently restored by the scaling factors from PCR-GLOBWB. Results indicate greater spatial heterogeneity in scaling factor from PCR-GLOBWB and CLM4.0 than that from GLDAS-1 Noah due to comprehensive simulation of surface and subsurface water storage changes for PCR-GLOBWB and CLM4.0. Filtered GRACE total water storage (TWS) changes applied with PCR-GLOBWB scaling factors show closer agreement with water budget estimates of TWS changes than those with scaling factors from other land surface models (LSMs) in China's Yangtze River basin. Results of this study develop a further understanding of the behavior of scaling factors from different LSMs or GHMs over hydrologically complex basins, and could be valuable in providing more accurate TWS changes for hydrological applications (e.g., monitoring drought and groundwater storage depletion) over regions where human-induced interactions between surface water and subsurface water are intensive.

water↗

NASA NTRS2017

Validation of Globsnow-2 Snow Water Equivalent Over Eastern Canada

In Qubec, Eastern Canada, snowmelt runoff contributes more than 30% of the annual energy reserve for hydroelectricity production, and uncertainties in annual maximum snow water equivalent (SWE) over the region are one of the main constraints for improved hydrological forecasting. Current satellite-based methods for mapping SWE over Qubec's main hydropower basins do not meet Hydro-Qubec operational requirements for SWE accuracies with less than 15% error. This paper assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines microwave satellite data and in situ measurements, for hydrological applications in Qubec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with space- and time-matched values from a comprehensive dataset of in situ SWE measurements (a total of 38,990 observations in Eastern Canada). The root mean square error (RMSE) of the GS-2 SWE product is 94.1+/- 20.3 mm, corresponding to an overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover type. However, compared to a typical stand-alone brightness temperature channel difference algorithm, the assimilation of surface information in the GS-2 algorithm clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of trends in annual mean and maximum SWE between surface observations and GS-2 over 1980-2009 showed agreement for increasing trends over southern Qubec, but less agreement on the sign and magnitude of trends over northern Qubec. Extended at a continental scale, the GS-2 SWE trends highlight a strong regional variability.

Snow water equivalent↗

NASA NTRS2017

Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Enhanced↗

NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗

NASA NTRS2023

Evaluation of SWER(Ze) Relationships by Precipitation Imaging Package (PIP) during ICE-POP 2018

Improving estimation of snow water equivalent rate (SWER) from radar reflectivity (Ze), known as a SWER(Ze) relationship, is a priority for NASA’s Global Precipitation Measurement (GPM) mission ground validation program as it is needed to comprehensively validate spaceborne precipitation retrievals. This study investigates the performance of eight operational and four research-based SWER(Ze) relationships utilizing Precipitation Imaging Probe (PIP) observations from the International Collaborative Experiment for Pyeongchang 2018 Olympic and Paralympic Winter Games (ICE-POP 2018) field campaign. During ICE-POP 2018, there were 10 snow events that are classified by synoptic conditions as either cold low or warm low, and a SWER(Ze) relationship is derived for each event. Additionally, a SWER(Ze) relationship is derived for each synoptic classification by merging all events within each class. Two new types of SWER(Ze) relationships are derived from PIP measurements of bulk density and habit classification. These two physically based SWER(Ze) relationships provided superior estimates of SWER when compared to the operational, event-specific, and synoptic SWER(Ze) relationships. For estimates of the event snow water equivalent total, the event-specific, synoptic, and best-performing operational SWER(Ze) relationships outperformed the physically based SWER(Ze) relationship, although the physically based relationships still performed well. This study recommends using the density or habit-based SWER(Ze) relationships for microphysical studies, whereas the other SWER(Ze) relationships are better suited toward hydrologic application.

Ali Tokay↗

NASA NTRS1981

Visible and infrared techniques for flash flood, hydrological, and agricultural applications

A common requirement of these agriculture, climatology and hydrology fields is the accurate and timely estimation of precipitation. Yet, it is often difficult to obtain such estimates by conventional means. The advent of satellite remote sensing however has opened the possibility of making rain estimates over time and space scale never before available. A computer automated technique that estimates a summertime convective rainfall from the thermal infrared imagery of geosynchronous satellites is reviewed.

Scofield, R. A.↗

NASA NTRS1981

The application of Heat Capacity Mapping Mission (HCMM) thermal data to snow hydrology

The application of HCMM thermal infrared data to snow hydrology and the prediction of snowmelt runoff was evaluated. Data for the Salt Verde watershed in central Arizona and the southern Sierra Nevada in California were analyzed and compared to LANDSAT and NOAA satellite data, U-2 thermal data, and other correlative data. It was determined that HCMM thermal imagery provides data as accurate for snow mapping as does visible imagery, and that in comparison with the reslution of other satellite imagery, it may be the most useful. Data from the HCMM thermal channel, with careful calibration, provides useful snow surface temperature data for hydrological purposes. An approach to an automated method of analysis is presented.

Barnes, J. C.↗

NASA NTRS2010

Terrestrial Hydrological Data from NASA's Hydrology Data and Information Services Center (HDISC): Products, Services, and Applications

Terrestrial hydrological variables are important in global hydrology, climate, and carbon cycle studies. The North American and Global Land Data Assimilation Systems (NLDAS and GLDAS, respectively) have been generating a series of land surface states (soil moisture, snow, and temperature) and fluxes (evapotranspiration, radiation, and heat flux) variables. These data, hosted at and available from NASA s Hydrology Data and Information Services Center (HDISC), include the NLDAS hourly 1/8 degree products and the GLDAS 3-hourly 0.25 and 1.0 degree products. HDISC provides easy access and visualization and analysis capabilities for these products, thus reducing the time and resources spent by scientists on data management and facilitating hydrological research. Users can perform spatial and parameter subsetting, data format transformation, and data analysis operations without needing to first download the data. HDISC is continually being developed as a data and services portal that supports weather and climate forecasts, and water and energy cycle research.

Fang, Hongliang↗

NASA NTRS2011

Integrating Data from GRACE and Other Observing Systems for Hydrological Research and Applications

The Gravity Recovery and Climate Experiment (GRACE) mission provides a unique view of water cycle dynamics, enabling the only space based observations of water on and beneath the land surface that are not limited by depth. GRACE data are immediately useful for large scale applications such as ice sheet ablation monitoring, but they are even more valuable when combined with other types of observations, either directly or within a data assimilation system. Here we describe recent results of hydrological research and applications projects enabled by GRACE. These include the following: 1) global monitoring of interannual variability of terrestrial water storage and groundwater; 2) water balance estimates of evapotranspiration over several large river basins; 3) NASA's Energy and Water Cycle Study (NEWS) state of the global water budget project; 4) drought indicator products now being incorporated into the U.S. Drought Monitor; 5) GRACE data assimilation over several regions.

Rodell, M.↗

NASA NTRS1981

Agricultural requirements for precipitation measurements

The requirements for precipitation measurements from space for hydrologic forecasting applications are discussed. The structure of the hydrologic forecasting service of the National Weather Service (NWS) is described, and an attempt is made to estimate the sampling and accuracy requirements for a range of spatial and temporal averaging scales corresponding to various NWS hydrologic applications. Finally, the data base requirements are addressed. The critical point is made that for the data source to be most useful operationally at the NWS River Forecast Centers, the data must be available on-line in a format compatible with computer processing. Several data base systems are illustrated in a scenario for a multi-sensor rainfall analysis system (MSRANS). Actually, MSRANS is the software existing within the various computer environments required to preprocess, process, and analyze rainfall information from multiple sources.

Strommen, N. D.↗

NASA NTRS2009

Global Land Data Assimilation System (GLDAS) Products, Services and Application from NASA Hydrology Data and Information Services Center (HDISC)

The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.

Fang, Hongliang↗

NASA NTRS1983

Application of remote sensing to hydrological problems and floods

The main applications of remote sensors to hydrology are identified as well as the principal spectral bands and their advantages and disadvantages. Some examples of LANDSAT data applications to flooding-risk evaluation are cited. Because hydrology studies the amount of moisture and water involved in each phase of hydrological cycle, remote sensing must be emphasized as a technique for hydrological data acquisition.

Parada, N. D. J.↗

NASA NTRSDate not supplied

Skillful Forecasts of Basic Hydrological Quantities Through the Application of SMAP-Based Soil Moisture Retrievals

The top five centimeters of soil lie at the interface between the atmosphere and land; hydrological variations in the atmosphere communicate themselves to the land largely through this layer, and vice-versa. The estimates of near-surface soil moisture provided by the SMAP mission are thus central to studies of hydrological variability. In fact, recent analyses show that the hydrological variability captured in the SMAP soil moisture retrievals can be parlayed into useful hydrological predictions at various leads (weekly out to seasonal). Specifically, at a given location, using the antecedent time series of SMAP Level 2 soil moisture retrievals up to the start of a forecast, skillful predictions can be made of surface soil moisture anomalies at a 1-week lead, of evapotranspiration stress anomalies at a ~1 month lead, and of streamflow anomalies at a multi-month lead. The skill is derived in part from soil moisture memory (along with the interpretation of deeper soil moisture from the surface measurements) and from joint analyses, outside the forecast period, of the SMAP retrievals with existing observational hydrological datasets.

soil moisture retrievals↗

NASA NTRS2011

Evaluating the Application of Multi-Satellite Observations in Hydrologic Modeling

When monitoring local or regional hydrosphere dynamics for applications such as agricultural productivity or drought and flooding events, it is necessary to have accurate, high-resolution estimates of terrestrial water and energy storages. Though in-situ observations provide reliable estimates of hydrologic states and fluxes, they are only capable of accurately capturing the dynamics at relatively discrete points in space and time, which makes them inadequate for characterizing the variability of the water budget across scales. In contrast, satellite-based remote sensing is ideal for providing observations of hydrological states and fluxes because it provides spatially-distributed observations at spatial and temporal scales required for regional land surface process modeling. Due to the continued progress in algorithm development and emerging satellite technology, we now have near-real time monitoring of several components of the water cycle including precipitation, soil moisture, lake and river height, terrestrial water storage, snow cover, and evapotranspiration. As these data become more readily available, their application to hydrologic modeling is becoming more common, however there remains little consensus on the most appropriate method for optimal integration and evaluation in regard to hydrological applications. Here we present two case studies operationally applying several remotely sensed products from AMSR-E, GRACE, and MODIS and discuss assimilation strategies, ease of integration and interpretation, and methods for quantifying the success of the application methodology.

Bolten, John↗

NASA NTRS2012

Advancing Data Assimilation in Operational Hydrologic Forecasting: Progresses, Challenges, and Emerging Opportunities

Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.

forecasting↗

NASA NTRS1971

Remote sensing in hydrology: A survey of applications with selected bibliography and abstracts

Remote infrared sensing as a water exploration technique is demonstrated. Various applications are described, demonstrating that infrared sensors can locate aquifers, geothermal water, water trapped by faults, springs and water in desert regions. The potentiality of airborne IR sensors as a water prospecting tool is considered. Also included is a selected bibliography with abstracts concentrating on those publications which will better acquaint the hydrologist with investigations using thermal remote sensors as applied to water exploration.

Sers, S. W.↗