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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Underlying Fundamentals of Kalman Filtering for River Network Modeling

The grand challenge of producing hydrometeorological estimates every time and everywhere has motivated the fusion of sparse observations with dense numerical models, with a particular interest on discharge in river modeling. Ensemble methods are largely preferred as they enable the estimation of error properties, but at the expense of computational load and generally with underestimations. These imperfect stochastic estimates motivate the use of correction methods, that is, error localization and inflation, although the physical justifications for their optimality are limited. The purpose of this study is to use one of the simplest forms of data assimilation when applied to river modeling and reveal the underlying mechanisms impacting its performance. Our framework based on assimilating daily averaged in situ discharge measurements to correct daily averaged runoff was tested over a 4-yr case study of two rivers in Texas. Results show that under optimal conditions of inflation and localization, discharge simulations are consistently improved such that the mean values of Nash–Sutcliffe efficiency are enhanced from211.32 to 0.55 at observed gauges and from212.24 to21.10 at validation gauges. Yet, parameters controlling the inflation and the localization have a large impact on the performance. Further investigations of these sensitivities showed that optimal inflation occurs when compensating exactly for discrepancies in the magnitude of errors while optimal localization matches the distance traveled during one assimilation window. These results may be applicable to more advanced data assimilation methods as well as for larger applications motivated by upcoming river-observing satellite missions, such as NASA’s Surface Water and Ocean Topography mission.

Streamflow↗
NASA NTRS2021

Financial feasibility of water conservation in agriculture

Global water use for food production needs to be reduced to remain within planetary boundaries, yet the financial feasibility of crucial measures to reduce water use is poorly quantified. Here, we introduce a novel method to compare the costs of water conservation measures with the added value that reallocation of water savings might generate if used for expansion of irrigation. Based on detailed water accounting through the use of a high-resolution hydrology-crop model, we modify the traditional cost curve approach with an improved estimation of demand, increasing marginal cost per water conservation measure combination and add a correction to control for impacts on downstream water availability. We apply the method to three major river basins in the Indo-Gangetic plain (Indus, Ganges and Brahmaputra), a major global food producing region but increasingly water stressed. Our analysis shows that at basin level only about 10% (Brahmaputra) to just over 20% (Indus and Ganges) of potential water savings would be realised; the equilibrium price for water is too low to make the majority of water conservation measures cost effective. The associated expansion of irrigated area is moderate, about 7% in the Indus basin, 5% in the Ganges and negligible in the Brahmaputra, but farmers' gross profit increases more substantially, by 11%. Increasing the volumetric cost of irrigation water influences supply and demand in a similar way and has little influence on water reallocation. Controlling for the impact on return flows is important and more than halves the amount of water available for reallocation.

Geography / Land Utilization↗
NASA NTRS1997

Application of Modular Modeling System to Predict Evaporation, Infiltration, Air Temperature, and Soil Moisture

Models are used for numerous application including hydrology. The Modular Modeling System (MMS) is one of the few that can simulate a hydrology process. MMS was tested and used to compare infiltration, soil moisture, daily temperature, and potential and actual evaporation for the Elinsboro sandy loam soil and the Mattapex silty loam soil in the Microwave Radiometer Experiment of Soil Moisture Sensing at Beltsville Agriculture Research Test Site in Maryland. An input file for each location was created to nut the model. Graphs were plotted, and it was observed that the model gave a good representation for evaporation for both plots. In comparing the two plots, it was noted that infiltration and soil moisture tend to peak around the same time, temperature peaks in July and August and the peak evaporation was observed on September 15 and July 4 for the Elinsboro Mattapex plot respectively. MMS can be used successfully to predict hydrological processes as long as the proper input parameters are available.

Boggs, Johnny↗
NASA NTRS2022

Tropical peatland hydrology simulated with a global land surface model

Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM(Trop,Nat)) and drained (PEATCLSM(Trop,Drain)) tropical peatlands. Simulations with PEATCLSM(Trop,Nat) were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM(North,Nat)) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM(Trop,Drain) was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM(Trop,Drain) outperformed CLSM, PEATCLSM(North,Nat) and PEATCLSM(Trop,Nat) over drained sites. Despite the overall improvements of PEATCLSM(Trop,Nat) over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model.

Peatland↗
NASA NTRS1977

The utility of short wavelength /less than 1 mm/ remote sensing techniques for the monitoring and assessment of hydrologic parameters

The paper reviews advances made in remote sensing applications to the fields of hydrology and water resources management, with emphasis on sensing from spacecraft platforms. An overview is presented of a remote sensing applications program for water resources management with attention given to water resources requirements and information content research. Consideration is also given to snowcovered area mapping in the Western United States, the use of Landsat imagery in land-use mapping and in the development of hydrological watershed models employed in flood control/waterworks planning and management.

Rango, A.↗
NASA NTRS1996

Retrieval of Surface Snow Grain Size and Melt Water from AVIRIS Spectra

The Earth's energy balance and hydrology are affected by the distribution and characteristics of snow cover on the surface. Snow grain size and snow melt influence surface albedo and hydrology. A model of snow reflectance that depends on both grain size and surface melt water was developed to derive these parameters from remote spectral measurements. This reflectance model is based on a discrete ordinate radiative transfer approach that uses Mie calculations of snow optical properties, which are based on the complex refractive index of ice and water. This snow model was linked to an atmospheric radiative transfer code and a nonlinear least squares fitting algorithm. The resulting combined algorithm was applied to an AVIRIS snow data set acquired over Mammoth Mountain, California. Maps of grain size and surface snow melt were generated that are consistent with the expected ranges and distributions for conditions at the site.

Green Robert O.↗
NASA NTRS2006

Observations of Precipitation Size and Fall Speed Characteristics within Coexisting Rain and Wet Snow

Ground-based measurements of particle size and fall speed distributions using a Particle Size and Velocity (PARSIVEL) disdrometer are compa red among samples obtained in mixed precipitation (rain and wet snow) and rain in the Oregon Cascade Mountains and in dry snow in the Rock y Mountains of Colorado. Coexisting rain and snow particles are distinguished using a classification method based on their size and fall sp eed properties. The bimodal distribution of the particles' joint fall speed-size characteristics at air temperatures from 0.5 to 0 C suggests that wet-snow particles quickly make a transition to rain once mel ting has progressed sufficiently. As air temperatures increase to 1.5 C, the reduction in the number of very large aggregates with a diame ter > 10 mm coincides with the appearance of rain particles larger than 6 mm. In this setting. very large raindrops appear to be the result of aggregates melting with minimal breakup rather than formation by c oalescence. In contrast to dry snow and rain, the fall speed for wet snow has a much weaker correlation between increasing size and increasing fall speed. Wet snow has a larger standard deviation of fall spee d (120%-230% relative to dry snow) for a given particle size. The ave rage fall speed for observed wet-snow particles with a diameter great er than or equal to 2.4 mm is 2 m/s with a standard deviation of 0.8 m/s. The large standard deviation is likely related to the coexistence of particles of similar physical size with different percentages of melting. These results suggest that different particle sizes are not required for aggregation since wet-snow particles of the same size can have different fall speeds. Given the large standard deviation of fa ll speeds in wet snow, the collision efficiency for wet snow is likely larger than that of dry snow. For particle sizes between 1 and 10 mm in diameter within mixed precipitation, rain constituted I % of the particles by volume within the isothermal layer at 0 C and 4% of the particles by volume for the region just below the isothermal layer where air temperatures rise from 0" to 0.5"C. As air temperatures increa sed above 0.5 C, the relative proportions of rain versus snow particl es shift dramatically and raindrops become dominant. The value of 0.5 C for the sharp transition in volume fraction from snow to rain is sl ightly lower than the range from 1 .l to 1.7 C often used in hydrolog ical models.

Yuter, Sandra E.↗
NASA NTRS2021

GEWEX (Global Energy and Water Exchanges Project): Surface Radiation Budget (SRB) Release 4 Integrated Product (IP4) - Algorithm Theoretical Basis Document and Evaluation

The World Climate Research Programme’s (WCRP) Global Energy and Water Exchanges (GEWEX) program is an integrated program of research, observations, and science activities with the mission to “Observe, understand and model the hydrological cycle and energy fluxes in the Earth’s atmosphere and at the surface.” Since the public release in 2010 of SRB Release 3, GEWEX reorganized to formulate the Data and Assessments Panel (GDAP) aimed at performing community assessments of the long-term global data products with focus on integrating various data products to address issues in the closing of the global energy and water cycles. SRB Release 4 Intergrated Product (Rel4-IP) integrates data products from the cloud, aerosol, atmosphere, ocean surface, and land surface projects within GDAP to produce a long-term time series of Top-of-Atmosphere (TOA) and surface radiative estimates. This Algorithm Theoretical Basis Document (ATBD) describes the various inputs, algorithms and resulting top-of-atmosphere and surface flux products that span from July 1983 through June 2017. Comparisons of the TOA flux products are made to overlapping CERES data products when possible. The surface radiative flux products are assessed relative to the globally distribution Baseline Surface Radiation Network measurements sites. Variability of the various products are compared to ERBE and CERES during overlapping time periods. The variability of the product show excellent correspondent to CERES during overlap years and improved statistical agreement with surface measurements. This document also discusses known issues with the data products to help the user understand the uncertainty and known issues.

Surface radiation budget↗
NASA NTRS2021

The sensitivity of runoff generation to spatial snowpack uniformity in an alpine watershed: Green Lakes Valley, Niwot Ridge Long-Term Ecological Research station

Seasonal water storage in high-elevation alpine catchments are critical sources of water for mountainous regions like the western U.S. The spatial distribution of snow in these topographically complex catchments is primarily governed by orography, solar radiation, and wind redistribution. While the effect of solar shading is relatively consistent from year-to-year, the redistribution of snow due to wind is more variable–capable of producing snowpacks that have varying degrees of uniformity across these hydrologically-important catchments. A reasonable hypothesis is that a warmer climate will cause snowfall to become more dense (i.e. wetter and heavier), possibly leading to less wind redistribution and thus produce a more uniformly distributed snowpack across the landscape. In this study, we investigate the role of increasingly uniform spatial snowpack distributions on streamflow generation in the Green Lakes Valley Niwot Ridge Long Term Ecological Research station, within the headwaters of the Boulder Creek watershed in Colorado. A set of idealized hydrologic simulation experiments driven by reconstructed snowpacks spanning 2001–2014 show that more a more uniform spatial snowpack distribution leads to an earlier melt-out of31 days on average and tends to produce less total streamflow, with maximum decreases as large as 7.5%. Isolating the role of snowpack heterogeneity from melt-season precipitation, we find that snowpack uniformity reduces total streamflow by as much as 13.2%. Reductions in streamflow are largely explained by greater expo-sure to solar radiation in the uniformly distributed case relative to a more heterogeneous snowpack, with this exposure driving shifts towards earlier snowmelt and changes in soil water storage. Overall, we find that the runoff efficiency from shallower snowpacks is more sensitive to the effects of uniformity than deeper snowpacks, which has potential implications for a warming climate where shallower snowpacks and enhanced sensitivities may be present.

Alpine catchment↗
NASA NTRS2023

InVEST Urban Development: Incorporating Earth Observation Data into the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation Model Python API

Urban flooding poses as one of the biggest issues for cities today as its impacts are amplified by both climate change and urbanization. The Natural Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation (UFRM) model, which benefits from its simplicity and robustness, is commonly used in NASA DEVELOP projects for disaster mitigation, urban planning, and environmental justice issues. While the InVEST UFRM model was able to produce the surface water runoff and retention map sufficient for the scopes of past projects, the model’s accuracy and spatial variability need improvement. Since the current InVEST UFRM model employs constant rainfall depth for all pixels in the area of interest (AOI), the model suffers from inaccurately estimating rainfall depth, runoff volume, and flood depth. Therefore, we adapted the model so that satellite-based precipitation raster datasets (i.e., Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [GPM IMERG]) can be used instead of a single constant value. We simulated the flood events on August 21st and August 22nd, 2017, in Wyandotte County, Kansas using both our modified and the original InVEST UFRM model and then compared the results after incorporating rainfall raster into the model. Areas with developed land on the land use map predicted moderate to high flood volume in the original volume regardless of the actual amount of precipitation. The modified model considered the rainfall depth’s spatial variation achieving less overestimation of flood runoff and volume at low-to-moderate rainfall area.

Urban flooding↗
NASA NTRS2010

Utilization of Hydrologic Remote Sensing Data in Land Surface Modeling and Data Assimilation: Current Status and Challenges

Recent advances in remote sensing technologies have enabled the monitoring and measurement of the Earth's land surface at an unprecedented scale and frequency. The myriad of these land surface observations must be integrated with the state-of-the-art land surface model forecasts using data assimilation to generate spatially and temporally coherent estimates of environmental conditions. These analyses are of critical importance to real-world applications such as agricultural production, water resources management and flood, drought, weather and climate prediction. This need motivated the development of NASA Land Information System (LIS), which is an expert system encapsulating a suite of modeling, computational and data assimilation tools required to address challenging hydrological problems. LIS integrates the use of several community land surface models, use of ground and satellite based observations, data assimilation and uncertainty estimation techniques and high performance computing and data management tools to enable the assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. This presentation will focus on describing the results, challenges and lessons learned from the use of remote sensing data for improving land surface modeling, within LIS. More specifically, studies related to the improved estimation of soil moisture, snow and land surface temperature conditions through data assimilation will be discussed. The presentation will also address the characterization of uncertainty in the modeling process through Bayesian remote sensing and computational methods.

Kumar, Sujay V.↗
NASA NTRS2021

Evaluating the Large-Scale Hydrological Cycle Response within the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) Ensemble

The mid-Pliocene (∼3 Ma) is one of the most recent warm periods with high CO2 concentrations in the atmosphere and resulting high temperatures, and it is often cited as an analog for near-term future climate change. Here, we apply a moisture budget analysis to investigate the response of the large-scale hydrological cycle at low latitudes within a 13-model ensemble from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The results show that increased atmospheric moisture content within the mid-Pliocene ensemble (due to the thermodynamic effect) results in wetter conditions over the deep tropics, i.e., the Pacific intertropical convergence zone (ITCZ) and the Maritime Continent, and drier conditions over the subtropics. Note that the dynamic effect plays a more important role than the thermodynamic effect in regional precipitation minus evaporation (PmE) changes (i.e., northward ITCZ shift and wetter northern Indian Ocean). The thermodynamic effect is offset to some extent by a dynamic effect involving a northward shift of the Hadley circulation that dries the deep tropics and moistens the subtropics in the Northern Hemisphere (i.e., the subtropical Pacific). From the perspective of Earth's energy budget, the enhanced southward cross-equatorial atmospheric transport (0.22 PW), induced by the hemispheric asymmetries of the atmospheric energy, favors an approximately 1∘ northward shift of the ITCZ. The shift of the ITCZ reorganizes atmospheric circulation, favoring a northward shift of the Hadley circulation. In addition, the Walker circulation consistently shifts westward within PlioMIP2 models, leading to wetter conditions over the northern Indian Ocean. The PlioMIP2 ensemble highlights that an imbalance of interhemispheric atmospheric energy during the mid-Pliocene could have led to changes in the dynamic effect, offsetting the thermodynamic effect and, hence, altering mid-Pliocene hydroclimate.

mid-Pliocene↗
NASA NTRS2023

Probabilistic Hydrological Estimation of LandSlides (PHELS): Global Ensemble Landslide Hazard Modelling

In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7-day antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.

Anne Felsberg↗
NASA NTRS2020

Supporting Information for ”The 2019-2020 Australian Drought and Bushfires Altered the Partitioning of Hydrological Fluxes

The model configuration employs the modified International Geosphere Biosphere Programme (IGBP) MODIS 20 category landcover data, soil parameters derived from the International Soil Reference and Information Centre, and the Shuttle Radar Topography Mission based elevation, slope, and aspect data. Statistical downscaling approaches are used to transform the coarse resolution MERRA2 meteorological inputs to 1km. The input meteorological fields of air temperature, humidity, surface pressure, wind, downward shortwave radiation, and downward longwave radiation are downscaled to 1km by adjusting for terrain differences in elevation, slope, and aspect. The high resolution monthly precipitation climatology from WorldClim is used to spatially disaggregate input MERRA2 precipitation to 1km. The initial conditions for the model simulations are generated from a long spinup of NoahMP starting from year 2000. All model integrations and evaluations are conducted using the NASA Land Information System and the Land surface Verification Toolkit.

Sujay V Kumar↗
NASA NTRS1990

A strategy to study regional hydrology and terrestrial ecosystem processes using satellite remote sensing, ground-based data and computer modeling

A strategy is presented for integrating scanning multichannel microwave radiometer data from the Nimbus-7 satellite with meteorological station records and computer simulations of land surface hydrology, terrestrial nutrient cycling, and trace gas emission. Analysis of the observations together with radiative transfer analysis shows that in the tropics the temporal and spatial variations of the polarization difference are determined primarily by the structure and phenology of vegetation and seasonal inundations of major rivers and wetlands. It is concluded that the proposed surface hydrology model, along with climatological records, and, potentially, 37-GHz data for phenology, will provide inputs to a terrestrial ecosystem model that predicts regional net primary production and CO2 gas exchange.

Vorosmarty, C.↗
NASA NTRSDate not supplied

Improved Peatland Hydrology and L-Band Microwave Radiative Transfer Modeling in Version 7 of the SMAP Level-4 Soil Moisture Data Assimilation Product

The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface (0-5 cm) and root-zone (0-100 cm) soil moisture from April 2015 to present with a mean latency of 2.5 days from the time of observation. The L4_SM estimates are derived from the assimilation of SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model. The recently released Version 7 of L4_SM comprises two key advances. First, the Catchment model now includes the recently developed PEATCLSM hydrology module for peatlands and uses an updated global map of peatlands. Second, revised parameters are used in the L-band radiative transfer model that converts the simulated soil moisture and temperature estimates into Tb predictions for use in the radiance-based L4_SM analysis. Specifically, the L-band scattering albedo, soil roughness, and vegetation opacity climatology are obtained from the SMAP Level-2 dual-channel soil moisture retrieval product. In this presentation, we examine the improvements gained with the model and parameter changes. In peatlands, the dynamics of water table depth, surface soil moisture and evapotranspiration are considerably improved in L4_SM Version 7 over Version 6 when evaluated against in situ measurements. These improvements are also manifested in smaller Tb observation-minus-forecast residuals.

SMAP↗
NASA NTRS1987

Determination of vegetated fraction of surface from satellite measurements

One input to the ground hydrology component of general circulation models is the fraction of gridbox covered (i.e., shaded) by vegetation. The FV is needed in order to specify the partitioning of evaporation between vegetated and nonvegetated surfaces. Satellite data could provide global and seasonally varying specification of FV. In this work, FV is derived from Landsat data for a site in western Kenya; the accuracy of the estimate is evaluated and then compared to the accuracy requirements of a ground hydrology model. Results show that the accuracy of Landsat estimation of FV is + or - 5 percent and that transpiration, evaporation from bare soil and the seasonality of evapotranspiration are strongly dependent on FV.

Cary, Ernestine↗