American Land Data Assimilation System Research Articles

Unraveling the Relationship Between Tropical Storms and Agricultural Drought

AbstractDrought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive data set generated at the Center for Complex Hydrosystems Research at the University of Alabama, and Hurricane Database 2nd generation to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the contiguous United States from 2015 to 2019. The Cumulative Distribution Function (CDF) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS‐2), and then weekly Standardized Soil Moisture Index is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high‐resolution data set and provide potential information in terms of mitigating drought and TS for the future.

Potential caveats in land surface model evaluations using the US drought monitor: roles of base periods and drought indicators

The US drought monitor (USDM) has been widely used as an observational reference for evaluating land surface model (LSM) simulation of drought. This study investigates potential caveats in such evaluation when the USDM and LSMs use different base periods and drought indices to identify drought. The retrospective national water model (NWM) v2.0 simulation (1993–2018) was used to exemplify the evaluation, supplemented by North American land data assimilation system phase 2 (NLDAS-2). Over their common period (2000–2018), in distinct contrast with the USDM which shows high drought occurrence (>50%) in the western half of the continental US (CONUS) and the southeastern US with low occurrence (<30%) elsewhere, the NWM and NLDAS-2 based on soil moisture percentiles (SMPs) consistently show higher drought occurrence (30%–40%) in the central and southeastern US than the rest of the CONUS. Much of the differences between the LSMs and USDM, particularly the strong LSM underestimation of drought occurrence in the western and southeastern US, are not attributed to the LSM deficiencies, but rather the lack of long-term drought in the LSM simulations due to their relatively short lengths. Specifically, the USDM integrates drought indices with century-long periods of record, which enables it to capture both short-term (<6 months) drought and long-term (⩾6 months) drought, whereas the relatively short retrospective simulations of the LSMs allows them to adequately capture short-term drought but not long-term drought. In addition, the USDM integrates many drought indices whereas the NWM results are solely based on the SMP, further adding to the inconsistency. The high occurrence of long-term drought in the western and southeastern US in the USDM is further found to be driven collectively by the post-2000 long-term warm sea surface temperature (SST) trend, cold Pacific decadal oscillation and warm Atlantic multi-decadal oscillation, all of which are typical leading patterns of global SST variability that can induce drought conditions in the western, central, and southeastern US. Our findings highlight the effects of the above caveats and suggest that LSM evaluation should stay qualitative when the caveats are considerable.

Validation of North American land data assimilation system Phase 2 (NLDAS-2) air temperature forcing and downscaled data with New York State station observations

Climate and meteorological factors including temperature and associated heat exposure metrics are related to environmental health risk factors and the incidence of health effects. While meteorological station data provide a good source of point measurements, temporal and spatially consistent temperature data are needed for health studies. Reanalysis data such as the North American Land Data Assimilation System’s (NLDAS) 12 km gridded product are an effort to resolve spatio-temporal environmental data issues. With funding from the NASA Applied Sciences Program, the NLDAS 12 km air temperature product has been downscaled to 1 km using MODIS Land Surface Temperature patterns. Limited validation of the native 12 km NLDAS reanalysis data has been undertaken. Our objective is to evaluate the accuracy of both the 12 km and 1 km downscaled products using the US Historical Climatology Network station data geographically dispersed across New York State for 2006, 2009, and 2010. These years were selected by a climatological analysis over the past 10 years that found 2006 to be a reference year with near-normal temperatures, 2009 a cool year and 2010 a warm year. Statistical methods including correlation, scatterplots, time series and summary statistics were used to determine the accuracy of the remotely sensed maximum and minimum temperature products. The effects of elevation and terrain slope on remotely sensed temperature product accuracy were determined with 10 m digital elevation data. National Land Cover Land Use data was used to determine the effect of mixed pixels on the accuracy of the NLDAS products. Results show the downscaled temperature product improves accuracy over the native 12 km temperature product with average correlation improvements from 0.82 to 0.87 for minimum and 0.76 to 0.85 for maximum temperatures. Higher terrain slope was related to lower correlations between stations and remotely sensed products. Stations in pixels with higher percentages of open water tended to have higher correlations. Our results inform health studies using remotely sensed temperature products to determine health risk from excessive heat by providing a more robust assessment of the accuracy of the 12 km NLDAS product and additional accuracy gained from the 1 km downscaled product.

Understanding the Impact of Soil Moisture on Precipitation Under Different Climate and Meteorological Conditions: A Numerical Sensitivity Study Over the CONUS

AbstractAccurate quantitative precipitation forecasts are essential for water resources management, agriculture, and other applications. Soil moisture is among the most important boundary conditions affecting the overlying atmosphere. It can affect precipitation either directly via evapotranspiration, affecting the moisture supply to the atmosphere, or indirectly, changing sensible heat fluxes, which can affect the structure of storms and their associated distributions of precipitation. In this study we investigate the impact of soil moisture uncertainty on simulated precipitation over the conterminous United States (CONUS) with the Weather Research and Forecasting model. We selected two 30‐day periods to conduct simulation scenarios with 3‐km horizontal grid spacing over the CONUS, where the different simulations were identical except for their initial soil moisture and temperature states which were derived from three different products: Global Forecast System analyses, the National Center for Atmospheric Research (NCAR) continuously cycling ensemble Kalman filter (EnKF) data assimilation system, and the North American Land Data Assimilation System (NLDAS‐2). Results indicate different characteristics of the soil moisture–precipitation relationship during the cold and warm periods and under different climate and meteorological conditions. Better representation of initial soil moisture does not consistently provide an improvement in the simulation of precipitation. Our results suggest that soil moisture affects both the magnitude and spatial distribution of precipitation. The impact of soil moisture on precipitation is more pronounced under drier soil and strong thermal and weak synoptic forcing, while in areas with complex terrain the soil moisture‐precipitation coupling becomes weaker.

Ensemble stationary-based support vector regression for drought prediction under changing climate

Non-stationarity due to climate change and/or variability can reduce the capabilities of drought prediction models. The objective of this study is to improve drought prediction by eliminating non-stationarity from temperature time series, a key factor in development and propagation of droughts in a changing climate. In order to relax the assumption of stationarity, an ensemble stationary-based support vector regression (ESSVR) method was developed and compared with the traditional support vector regression (SVR). Three types of drought indices in three time scales (monthly, seasonal, and semiannual), including multivariate, bivariate standardized drought indices, and univariate standardized drought indices were used as the target variables. In an application to the Red River of the North Basin (RRB), the North American Land Data Assimilation System (NLDAS) data from 1979 to 2016 were used for the training and testing of the prediction model. The Pearson correlation, root mean square error (RMSE), and Taylor diagram were used to evaluate the performances of the ESSVR. Remarkably, the distribution of identified change points varies by climate divisions. The results of the SVR and ESSVR in the RRB were compared, demonstrating the better performances of the ESSVR for most of the drought indices, particularly those with higher sensitivity to temperature. It was found that the extreme (high and low) values of hyperparameters mostly assigned by SVR cause a higher risk of overfitting for SVR. In contrast, ESSVR improves the drought prediction by removing the non-stationarity, thus providing more accurate drought predictions, especially for a warming climate.

Heat Waves and Road Traffic Collisions in Alabama, United States

The effects of heat waves on traffic collisions require investigation to improve traffic safety during extreme heat events. A time-stratified case-crossover design was used to examine associations between heat waves and traffic collisions in Alabama between May and September from 2009 to 2018. We derived a heat wave index, defined as the daily mean temperature greater than the 95th percentile for two or more consecutive days, by meteorological data from Phase 2 of the North American Land Data Assimilation System. We obtained traffic collision records from the Alabama Department of Transportation. A nonsignificant and negative association between traffic collisions and heat waves was noted, with a 1.4 percent decrease (95 percent confidence interval [CI] [−3.1 percent, 0.4 percent]) in traffic collisions on heat wave days compared to non–heat wave days. Similar results were found when the analysis was stratified by driver-related factors (i.e., gender, age, race, employment status, and driver residence distances), vehicle-related factors (i.e., vehicle usage), and collision-related factors (i.e., rural or urban roads, speed limits, and intersections). A significant and positive association was observed on heat wave days without precipitation, however (23.5 percent increase; 95 percent CI [7.3 percent, 42.3 percent]). In conclusion, traffic collisions were not associated with heat waves in many collision-related conditions in Alabama.

Ensemble Skill Gains Obtained From the Multi‐Physics Versus Multi‐Model Approaches for Continental‐Scale Hydrological Simulations

AbstractMulti‐physics ensembles have emerged as a promising approach to hydrological simulations. As multi‐physics ensembles are constructed by perturbing the model physics, the ensemble members share a substantial portion of the same physics and hence are not independent of each other. It is unknown whether and to what extent this nonindependence affects the skill gain of the ensemble method, especially compared with the multi‐model ensemble approach. This study compares a multi‐physics ensemble configured from the Noah land surface model with multi‐parameterization options (Noah‐MP) with the North American Land Data Assimilation System (NLDAS) multi‐model ensemble. The two ensembles are evaluated in terms of the annual cycle and interannual anomaly at 12 River Forecast Centers over the conterminous United States. The ensemble skill gain is measured by the difference between the performance of the ensemble mean and the average of the ensemble members' performance, and the inter‐member independence is measured by error correlations. Results show that, due to the improved model physics, the Noah‐MP configurations outperform, on average, the NLDAS models, especially in the snow‐dominated areas. The Noah‐MP ensemble almost always obtains an outstanding member that performs the best among the two ensembles, reflecting its dense sampling of the feasible model physics space. However, these two performance superiorities do not lead to a superiority of the ensemble mean. The Noah‐MP ensemble has a lower ensemble skill gain, which corresponds to the lower inter‐member independence. These results highlight the importance of inter‐member independence, particularly when most hydrological ensemble methods have overlooked it.

Evaluation of soil state representation in the NCAR ensemble analysis system

Accurate characterization of soil state is imperative for weather and climate research, hydrological modeling, agriculture, water resources management, and drought monitoring. Limited-area data assimilation (DA) systems can provide high-resolution real-time soil state analyses. However, soil state representations from regional continuously cycling DA systems have not been examined. This study evaluates soil moisture and temperature estimates and spread characteristics from a real-time, limited-area, continuously-cycling ensemble Kalman filter (EnKF) data assimilation (DA) system run at the National Center for Atmospheric Research (NCAR) for 2.7 years over the conterminous United States (CONUS). Soil moisture and temperature data from both the EnKF and operational Global Forecast System (GFS) analysis are compared against the North American Land Data Assimilation System (NLDAS-2) soil state estimates. Results demonstrate the continuously-cycling EnKF was generally able to represent the spatial and temporal characteristics of soil moisture and temperature across the CONUS, demonstrating the feasibility of using limited-area continuously cycling EnKFs as a method to improve simulations of soil moisture and temperature. Soil state estimates from the NCAR ensemble exhibited lower Bias and RMSE compared to GFS analyses over west and southeast CONUS. Additionally, the ensemble spread characteristics suggest that future limited-area continuously cycling EnKF systems could successfully assimilate both in situ and remotely-sensed soil moisture and temperature observations to further improve soil state analyses.

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

AbstractResearch in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling

Abstract. A deep learning rainfall–runoff model can take multiple meteorological forcing products as input and learn to combine them in spatially and temporally dynamic ways. This is demonstrated with Long Short-Term Memory networks (LSTMs) trained over basins in the continental US, using the Catchment Attributes and Meteorological data set for Large Sample Studies (CAMELS). Using meteorological input from different data products (North American Land Data Assimilation System, NLDAS, Maurer, and Daymet) in a single LSTM significantly improved simulation accuracy relative to using only individual meteorological products. A sensitivity analysis showed that the LSTM combines precipitation products in different ways, depending on location, and also in different ways for the simulation of different parts of the hydrograph.

Blending Noah, SMOS and In-Situ Soil Moisture Using Multiple Weighting and Sampling Schemes

AbstractSoil moisture can be obtained from in-situ measurements, satellite observations, and model simulations. This study evaluates the importance of in-situ observations in soil moisture blending, and compares different weighting and sampling methods for combining model, satellite, and in-situ soil moisture data to generate an accurate and spatially-continuous soil moisture product at 4-km resolution. Four different datasets are used: Antecedent Precipitation Index (API), KAPI, which incorporates in-situ soil moisture observations with the API using regression kriging, SMOS L3 soil moisture, and model-simulated soil moisture from the Noah model as part of the North American Land Data Assimilation System (NLDAS). Triple collocation, least square weighting, and equal weighting are used to generate blended soil moisture products. An enumerated weighting scheme is designed to investigate the impact of different weighting schemes. The sensitivity of the blended soil moisture products to sampling schemes, station density and data formats (absolute, anomalies and percentiles) are also investigated. The results reveal KAPI outperforms API. This indicates that incorporating in-situ soil moisture improves the accuracy of the blended soil moisture products. There are no statistically significant (p&gt;0.05) differences between blended soil moisture using triple collocation and equal weighting approaches, and both methods provide sub-optimal weighting. Optimal weighting is achieved by assigning larger weights to KAPI and smaller weights to SMOS. Using multiple sources of soil moisture is helpful for reducing uncertainty and improving accuracy, especially when the sampling density is low, or the sampling stations are less representative. These results are consistent regardless of how soil moisture is represented (absolute, anomalies or percentiles).

Seasonal hydrological loading in the Great Lakes region detected by GNSS: a comparison with hydrological models

SUMMARY Crustal deformation caused by hydrological processes has long been detected using space geodetic techniques, yet questions remain about the relative contributions of surface water and groundwater to the geodetic signals in different regions. Here, we investigate forward models of elastic loading deformation caused by a variety of water-storage changes within the Great Lakes region, including fluctuations in lake-water volume, soil moisture and snow load. We use lake-level data from the Great Lakes Environmental Research Laboratory, soil-moisture content from the North American Land Data Assimilation System (NLDAS), snow load from the Snow Data Assimilation System (SNODAS) and background hydrological load at the global scale from Gravity Recovery and Climate Experiment (GRACE). We compare the modelled surface deformation with estimates of hydrological loading deformation inferred from Global Navigation Satellite System (GNSS) measurements. We find that seasonal deformation measured by GNSS is dominated by regional-scale hydrological loading based on strong correlations with the modelled loading displacements. The mean correlation coefficient for the study network is 0.56. The correlation coefficients vary spatially within the study region and exceed 0.9 at some stations near to the Great Lakes. We assess the relative contribution of each individual hydrological component to the total integrated hydrological load. We find that soil moisture consistently explains the largest percentage (27–69 per cent) of the total vertical loading deformation for 87 per cent of GNSS stations in the Great Lakes region. Snow loading and soil moisture contribute relatively equally in the northern reaches of the study area (e.g. Canadian shield, northern Superior basin). Lake loading accounts for about 10–25 per cent of the total loading signal in the immediate vicinity of the lakes. We also investigate the sensitivities of the surface loading displacements to three different Earth models, including two with lateral variations in structure. The structural variations considered here have limited impact (&amp;lt;0.2 mm) on the predicted hydrological loading displacements and could be neglected at the current level of observational precision.

Data-driven approaches for runoff prediction using distributed data

Accurate rainfall-runoff modelling is particularly challenging due to complex nonlinear relationships between various factors such as rainfall characteristics, soil properties, land use, and temporal lags. Recently, with improvements to computation systems and resources, data-driven models have shown good performances for runoff forecasting. However, the relative performance of common data-driven models using small temporal resolutions is still unclear. This study presents an application of data-driven models using artificial neural network, support vector regression and long-short term memory approaches and distributed forcing data for runoff predictions between 2010 and 2019 in the Russian River basin, California, USA. These models were used to predict hourly runoff with 1–6 h of lead time using precipitation, soil moisture, baseflow and land surface temperature datasets provided from the North American Land Data Assimilation System. The predicted results were evaluated in terms of seasonal and event-based performance using various statistical metrics. The results showed that the long-short term memory and support vector regression models outperforms artificial neural network model for hourly runoff forecasting, and the predictive performance of the models was greater during the wet seasons compared to the dry seasons. In addition, a comparison of the data-driven model results with the National Water Model, a fully distributed physical-based hydrologic model, showed that the long-short term memory and support vector regression models provide comparable performance. The results demonstrate that data-driven models for hourly runoff forecasting are sufficiently predictive and useful in areas where observation systems are not available.

Evaluating Wind Fields for Use in Basin‐Scale Distributed Snow Models

AbstractMountain winds are the driving force behind snow accumulation patterns in mountainous catchments, making accurate wind fields a prerequisite to accurate simulations of snow depth for ecological or water resource applications. In this study, we examine the effect that wind fields derived from different coarse data sets and downscaling schemes has on simulations of modeled snow depth at resolutions suitable for basin‐scale modeling (&gt;50 m). Simulations are run over the Tuolumne River Basin, CA for the accumulation season of Water Year 2017 using the distributed snow model, SnowModel. We derived wind fields using observations from either sensor networks, the North American Land Data Assimilation System (12.5 km resolution), or High‐Resolution Rapid Refresh (HRRR, 3 km resolution) data, and downscaled using terrain‐based multipliers (MicroMet), a mass‐conserving flow model (WindNinja), or bilinear interpolation. Two wind fields derived from 3 km HRRR data and downscaled with respect to terrain produced snow depth maps that best matched observations of snow depth from airborne LiDAR. We find that modeling these wind fields at 100 and 50 m resolutions do not produce improvements in simulated snow depth when compared to wind fields modeled at a 150 m resolution due to their inability to represent wind dynamics at these scales. For input to distributed snow models at the basin‐scale, we recommend deriving wind fields from high resolution numerical weather prediction model output and downscaling with respect to terrain. Future studies should compare suspension schemes used in blowing snow models and investigate wind downscaling schemes of complexity between statistical and fluid dynamic models.

Modification of the Cardiovascular Effects of Temperature by Neighborhood Socio-Economic Deprivation

Background: Ambient daily temperature, both elevated and low, has been associated with daily cardiovascular mortality; studies have consistently observed larger associations for neighborhoods of higher socio-economic deprivation (NSED). However, no study has assessed whether NSED modifies the timing of this effect. We investigated how NSED modifies the association between daily temperature and risk of myocardial infarction (MI) in New York City (NYC).Methods: We used 2000-2015 MI hospitalization and residence data from the Statewide Planning and Research Cooperative System (SPARCS) dataset of the New York Department of Health. Temperature and relative humidity came from the North American Land Data Assimilation System (NLDAS) at 11x14 km2 resolution. We aggregated data to 42 neighborhoods defined by NYC Department of Health. We estimated NSED with a Neighborhood Deprivation Index (NDI), from principal components analysis of US Census variables. We employed a time series design with a distributed lag nonlinear model for 14 days lag of temperature. We controlled for seasonality, long-term trends, relative humidity, NDI, and random effects for neighborhood. We then stratified by NDI quartile.Results: We observed 329,457 MI hospitalizations over the study period. Same-day high temperature was nonlinearly associated with MI; an increase from mean temperature (12.3oC) to the 95th percentile (28oC) was associated with a 7.0% increase (95%CI: 3.7, 10.4%) in MI hospitalization rate. The second quartile of NDI showed the largest association (8.6%; 95%CI: 5.2, 12.0%); the least deprived quartile had the smallest association (6.0%; 95%CI: 2.6, 9.4%). For all subpopulations, only same-day temperature was statistically significant.Conclusion: Our findings suggest that same-day temperature is associated with daily MI rates, across levels of NDI, and this was strongest in the second-most-deprived neighborhoods. The timing of the association was consistent across quartiles of NDI. Further research is needed to elucidate factors that provide resilience to heat exposure.

Real-Time Prediction of Areas Susceptible to Flash Drought Development

Flash drought is a type of drought that develops quickly (usually within 2–4 weeks) in contrast to conventional, slowly evolving drought. Due to its sudden onset, flash drought is more difficult to predict and can cause major agricultural losses if it is not forecasted in a timely manner. To improve our ability to predict flash drought, we develop a subseasonal tool to predict areas susceptible to flash drought development using the Phase 2 of the North American Land Data Assimilation System (NLDAS-2) data. The tool calculates the rapid change index (RCI) using 7-day mean evapotranspiration anomalies. RCI is the accumulated magnitude of moisture stress changes (standardized differences) occurring over multiple weeks, and drought is likely to develop when RCI is negative. Since RCI changes with time, like all drought variables, it is difficult to capture drought development signals by monitoring RCI maps. In order to create an intuitive drought prediction map that directly depicts drought tendency, we use a threshold method to identify grid points with large decreases of 7-day mean evapotranspiration anomaly (i.e., RCI less than −0.5) in the last 30 days and under the condition that 3-month standardized precipitation index is less than −0.4. The real-time tool started running on 1 April 2018 at the NOAA Climate Prediction Center (CPC) and has been used to support CPC’s Monthly Drought Outlook efforts. The performance of the tool is evaluated using both retrospective and real-time predictions. The assessment shows promising results in predicting potential flash drought development, and the interplay between precipitation and high temperatures appears to be a challenge for flash drought prediction.

The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation, Columbia River basin, Canada

AbstractGlaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best.

Impact of Wildfire on the Surface Energy Balance in Six California Case Studies

We investigate the impact of wildfires on surface energy exchange through the assessment of six California wildfires that occurred in the last 20 years. A burned–unburned binary mask was generated from the MODIS approximate date of burn product and implemented into the Simplified Simple Biosphere model for a series of simulations. Model performance was evaluated against the North American Land Data Assimilation System and is found to simulate surface temperature and net radiation accurately. Simulations show a decrease in latent heat flux in every case study except the Rush fire, which occurred in Lassen County in August 2012. Post-fire changes in net radiation and sensible heat followed similar trends, decreasing in each of the domains except for the Rush and Cedar fires. The greatest changes in temporally-averaged net radiation occurred in the Rim (− 41.7 W m−2), Basin Complex (− 31.6 W m−2), and Rush (26.5 W m−2) fires. Initial increases in sensible heat flux, caused by the decrease in albedo from ash deposition, are balanced by decreases in latent heat flux in the Zaca, Rim, and Basin Complex case studies. Results also indicate a relationship between decreases in average sensible heat flux and leaf-area-index change. Wildfires that burn in sparsely vegetated areas are associated with increases in sensible heat flux, an effect that is magnified by long ash-deposition periods (i.e., Rush fire), while wildfires that burn in densely vegetated areas are associated with large decreases in sensible and latent heat flux (i.e., Rim, Basin Complex fires).

Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system.

Assessment of Remotely Sensed and Modelled Soil Moisture Data Products in the U.S. Southern Great Plains

Soil moisture (SM) plays a crucial role in the water and energy flux exchange between the atmosphere and the land surface. Remote sensing and modeling are two main approaches to obtain SM over a large-scale area. However, there is a big difference between them due to algorithm, spatial-temporal resolution, observation depth and measurement uncertainties. In this study, an assessment of the comparison of two state-of-the-art remotely sensed SM products, Soil Moisture Active Passive (SMAP) and European Space Agency Climate Change Initiative (ESACCI), and one land surface modeled dataset from the North American Land Data Assimilation System project phase 2 (NLDAS-2), were conducted using 17 permanent SM observation sites located in the Southern Great Plains (SGP) in the U.S. We first compared the daily mean SM of three products with in-situ measurements; then, we decompose the raw time series into a short-term seasonal part and anomaly by using a moving smooth window (35 days). In addition, we calculate the daily spatial difference between three products based on in-situ data and assess their temporal evolution. The results demonstrate that (1) in terms of temporal correlation R, the SMAP (R = 0.78) outperforms ESACCI (R = 0.62) and NLDAS-2 (R = 0.72) overall; (2) for the seasonal component, the correlation R of SMAP still outperforms the other two products, and the correlation R of ESACCI and NLDAS-2 have not improved like the SMAP; as for anomaly, there is no difference between the remotely sensed and modeling data, which implies the potential for the satellite products to capture the variations of short-term rainfall events; (3) the distribution pattern of spatial bias is different between the three products. For NLDAS-2, it is strongly dependent on precipitation; meanwhile, the spatial distribution of bias represents less correlation with the precipitation for two remotely sensed products, especially for the SMAP. Overall, the SMAP was superior to the other two products, especially when the SM was of low value. The difference between the remotely sensed and modeling products with respect to the vegetation type might be an important reason for the errors.