Data Assimilation of Terrestrial Water Storage to Adjust Precipitation Fluxes

The Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. TWS is defined as the sum of groundwater, soil moisture, snow, surface water, ice and biomass water. Data assimilation of GRACE TWS observations has been shown to improve simulation of groundwater, streamflow, and snow water equivalent, and has also proven useful for drought monitoring and identifying human impacts on the water cycle. From a modeling perspective, the TWS components are defined as "prognostic hydrological states". Existing GRACE data assimilation schemes update these prognostic states directly. In this work, we propose an alternate approach in which precipitation fluxes are adjusted in order to achieve the desired change in the hydrological prognostic states. Limitations of such an approach include the assumption that all errors in TWS originate from errors in precipitation. Nonetheless, benefits comprise (1) the water balance is maintained, as opposed to having to add increments to the water budget components, (2) the model automatically determines how to distribute the updates among the TWS prognostic states, and (3) it is not necessary to know the exact time of the observation TWS, because the TWS change timing is determined by the precipitation forcing

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes

Modeling Errors in Daily Precipitation Measurements: Additive or Multiplicative?

The definition and quantification of uncertainty depend on the error model used. For uncertainties in precipitation measurements, two types of error models have been widely adopted: the additive error model and the multiplicative error model. This leads to incompatible specifications of uncertainties and impedes intercomparison and application.In this letter, we assess the suitability of both models for satellite-based daily precipitation measurements in an effort to clarify the uncertainty representation. Three criteria were employed to evaluate the applicability of either model: (1) better separation of the systematic and random errors; (2) applicability to the large range of variability in daily precipitation; and (3) better predictive skills. It is found that the multiplicative error model is a much better choice under all three criteria. It extracted the systematic errors more cleanly, was more consistent with the large variability of precipitation measurements, and produced superior predictions of the error characteristics. The additive error model had several weaknesses, such as non constant variance resulting from systematic errors leaking into random errors, and the lack of prediction capability. Therefore, the multiplicative error model is a better choice

The Effect of Rainfall Error Characterization on the Efficiency of a Land Data Assimilation System for Soil Moisture Prediction

This study assesses the impact of satellite-rainfall error structure on soil moisture simulations with the NASA Catchment Land Surface Model (CLSM) and the efficiency of assimilating near-surface soil moisture observations in the NASA Land Data Assimilation System (NASA-LDAS). A multi-dimensional satellite rainfall error model (SREM2D) is compared to the standard error model (CTRL) used to generate rainfall ensembles as part of the NASA-LDAS. The error analysis is assessed in terms of rainfall ensembles and corresponding soil moisture predictions. Comparisons of rainfall ensembles generated by SREM2D and CTRL against reference radar rainfall show that both rainfall error models preserve the rainfall error characteristics across a range of spatial scales. However, SREM2D generates rainfall replicates with higher variability that better envelope the reference rainfall than those generated by CTRL. On the other hand, uncertainty in soil moisture is shown to be less sensitive to the complexity of the precipitation error model. This is attributed to the fact that soil moisture processes dampen the variability in the precipitation forcing. A sensitivity analysis is then conducted to investigate the contribution of rainfall-forcing uncertainty relative to model uncertainty in predicting soil moisture. Specifically, rainfall-forcing uncertainty is introduced by SREM2D, whereas errors in CLSM are modeled with two approaches: either by perturbing model parameters or by adding randomly generated noise to model prognostic variables. A reasonable spread in soil moisture is achieved with relatively few parameter perturbations, while the same ensemble width requires stronger perturbations with the prognostics perturbation method. The probability of encapsulating the reference soil moisture simulation increases when rainfall-forcing uncertainty and model uncertainty approaches are combined (compared to rainfall uncertainty alone). This improvement is more significant when perturbing parameters as opposed to perturbing prognostics. When assimilating near-surface soil moisture data through LDAS, soil moisture estimates exhibit improved performance metrics (higher anomaly correlation coefficients and lower root mean square errors). However, no significant dependence on the rainfall error model complexity is shown when assimilating actual satellite soil moisture observations, suggesting that the simple rainfall error model may be adequate in many applications.

Assessment of Level-3 Gridded Global Precipitation Mission (GPM) Products Over Oceans

The performance of Level-3 gridded Global Precipitation Mission (GPM)-based precipitation products (IMERG, Integrated Multi-satellite Retrievals for GPM) is assessed against two references over oceans: the OceanRAIN dataset, derived from oceanic shipboard disdrometers, and a satellite-based radar product (the Level-3 Dual-frequency Precipitation Radar, 3DPRD). Daily IMERG products (early, late, final) and microwave-only (MW) and Infrared-only (IR) precipitation components are evaluated at four different spatial resolutions (0.5°, 1°, 2°, and 3°) during a 3-year study period (March 2014⁻February 2017). Their performance is assessed based on both categorical and continuous performance metrics, including correlation coefficient, probability of detection, success ratio, bias, and root mean square error (RMSE). A triple collocation analysis (TCA) is also presented to further investigate the performance of these satellite-based products. Overall, the IMERG products show an underestimation with respect to OceanRAIN. Rain events in OceanRAIN are correctly detected by all IMERG products ~80% of the times. IR estimates show relatively large errors and low correlations with OceanRAIN compared to the other products. On the other hand, the MW component performs better than other products in terms of both categorical and continuous statistics. TCA reveals that 3DPRD performs consistently better than OceanRAIN in terms of RMSE and coefficient of determination at all spatial resolutions. This work is part of a larger effort to validate GPM products over nontraditional regions such as oceans

Application of Rough Set Theory to Water Quality Analysis: A Case Study

This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015⁻December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature

Investigating Water Quality Data Using Principal Component Analysis and Granger Causality

This work investigates the inter-relationships among stream water quality indicators, hydroclimatic variables (e.g., precipitation, river discharge), and land characteristics (e.g., soil type, land use), which is crucial to developing effective methods for water quality protection. The potential of using statistical tools, such as Principal Component (PC) and Granger causality analyses, for this purpose is assessed across 10 watersheds in the Eastern United States. The PC analysis shows consistency across the ten locations, with most of the variation explained by the first two PCs, except for the least developed watershed that presents three PCs. Results show that stronger Granger causality relationships and correlation coefficients are identified when considering a lag of one day, compared to longer lags. This is mainly due to the watersheds’ limited size and, thus, their fast hydrological response. The strongest Granger causalities are observed when water temperature and dissolved oxygen concentration are considered as the effect of the other variables, which corroborates the importance of these two water properties. This work also demonstrates how watershed size and land use can impact causalities between hydrometeorological variables and water quality, thus, highlighting how complex these relationships are even in a region characterized by overall similar climatology

Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM) satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Goddard Profiling Algorithm—Version 2014 (GPROF2014) algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM), is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm