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• Rapid intensification is the main flash drought driver east of Rocky Mountains (RM). • West of the RM, rapid intensification often occurs without reaching drought. • SESR showed strong potential in identifying drought, particularly east of the RM. • SESR is able to represent meteorological and agricultural drought. Flash droughts develop rapidly (∼1 month timescale) and produce significant ecological, agricultural, and socioeconomical impacts. Recent advances in our understanding of flash droughts have resulted in methods to identify and quantify flash drought events. However, few studies have been done to isolate the individual rapid intensification and drought components of flash drought, which could further determine their causes, evolution, and predictability. This study utilized the standardized evaporative stress ratio (SESR) to quantify individual components of flash drought from 1979 – 2019, using evapotranspiration (ET) and potential evapotranspiration (PET) data from the North American Regional Reanalysis (NARR) dataset. The temporal change in SESR was utilized to quantify the rapid intensification component of flash drought. The drought component was also determined using SESR and compared to the United States Drought Monitor. The results showed that SESR was able to represent the spatial coverage of drought well for regions east of the Rocky Mountains. Furthermore, the rapid intensification component agreed well with previous flash drought studies, with the overall climatology of rapid intensification events showing similar hotspots to the flash drought climatology east of the Rocky Mountains. The rapid intensification climatology suggested areas west of the Rocky Mountains experience rapid drying more often than east of the Rocky Mountains. [ABSTRACT FROM AUTHOR]

Abstract Winter wheat (Triticum aestivum L.) and tallgrass prairie are common land cover types in the Southern Plains of the United States. During the last century, agricultural expansion into native grasslands was extensive, particularly managed pasture or winter wheat. In this study, we measured carbon dioxide (CO 2) and water vapor (H 2 O) fluxes from winter wheat and tallgrass prairie sites in Central Oklahoma using the eddy covariance in 2015 and 2016. The objective of this study was to contrast CO 2 and H 2 O fluxes between these two ecosystems to provide insights on the impacts of conversion of tallgrass prairie to winter wheat on carbon and water budgets. Daily net ecosystem CO 2 exchange (NEE) reached seasonal peaks of −9.4 and −8.8 g C m−2 in 2015 and −6.2 and −7.5 g C m−2 in 2016 at winter wheat and tall grass prairie sites, respectively. Both sites were net sink of carbon during their growing seasons. At the annual scale, the winter wheat site was a net source of carbon (56 ± 13 and 33 ± 9 g C m−2 year−1 in 2015 and 2016, respectively). In contrast, the tallgrass prairie site was a net sink of carbon (−128 ± 69 and −119 ± 53 g C m−2 year−1 in 2015 and 2016, respectively). Daily ET reached seasonal maximums of 6.0 and 5.3 mm day−1 in 2015, and 7.2 and 8.2 mm day−1 in 2016 at the winter wheat and tallgrass prairie sites, respectively. Although ecosystem water use efficiency (EWUE) was higher in winter wheat than in tallgrass prairie at the seasonal scale, summer fallow contributed higher water loss from the wheat site per unit of carbon fixed, resulting into lower EWUE at the annual scale. Results indicate that the differences in magnitudes and patterns of fluxes between the two ecosystems can influence carbon and water budgets. Graphical abstract Unlabelled Image Highlights • Carbon and water fluxes measured in two ecosystems located at 2.7 km apart are compared. • Winter wheat and tallgrass prairie are both carbon sinks during the growing season. • Summer fallow caused the wheat ecosystem to be a source of carbon at annual scale. • Management practices caused changes in the fluxes of prairie and wheat ecosystems. [ABSTRACT FROM AUTHOR]

• Ground-radar precip product upscaled spatiotemporally to coarser satellite resolution. • Upscaled products used to simulate discrete flood characteristics. • Satellite uncertainty shown to overpower weaker errors from upscaled products. • Upscaled products suffer from negative water balance errors after modeling. • Spatial upscaling adds uncertainty to simulations while temporal upscaling adds bias. This research focuses on the effects of spatio-temporal resolutions of global satellite precipitation on simulated flood events characteristics. The analysis is carried out by spatially, temporally, and spatiotemporally upscaling fine-scale precipitation forcings from the ground-radar-based Multi-Radar Multi-Sensor system (MRMS) to the resolution of the Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation product. These upscaled products were then run through the Ensemble Framework for Flash Flood Forecasting hydrologic modeling framework over the U.S. to assess how the different spatial and temporal resolutions impact the simulated outputs of flood event magnitude, duration, and timing. It was found that the quantitative uncertainties generated by the IMERG Early (IMERG-E) simulations overpowered those associated with resolution across all flood characteristics. When compared to native MRMS simulations, however, the upscaled precipitation estimates tended to underestimate peak discharges and event durations, associated with distinct negative water balance errors. It was also found that the upscaled precipitation simulations exhibited a scaling effect with regards to error contribution of peak discharge, with higher contributions at smaller basin sizes and decreasing contributions as basin sizes increased. Looking at how upscaling can affect the modeled output desired allows for improved understanding of which resolutions lead to the greatest changes in simulation accuracy, increasing the potential global use and utility of current or future satellite products, especially in regions where high-resolution ground radars are sparse. [ABSTRACT FROM AUTHOR]

Monitoring plants' responses to water deficit using remote sensing still faces large uncertainty, mostly due to the inaccurate characterization of plants' physiological responses. Solar induced chlorophyll fluorescence (SIF) contains information on plants' physiological processes which regulates the energy partitioning after solar radiation is absorbed by chlorophyll, providing new opportunities to monitor plant response to drought stress. However, the drought-induced physiological, biochemical, and structural changes are strongly coupled, hindering the mechanistic understanding of drought impacts on plants. Here, using tower-based observations of SIF together with high spectral resolution reflectance measurements, we derived the time series of the fraction of absorbed photosynthetically active radiation by canopy, chlorophyll content, and fluorescence efficiency using two radiative transfer model-based decomposition methods, and evaluated their responses to two consecutive dry spells at a tall-grass prairie site in the USA (34°59′05.0″ N, 97°31′20.6″ W). We observed a robust signal of afternoon depression based on the fluorescence efficiency estimates during the second dry spell, which had much lower soil moisture than the first one. The strong decline in fluorescence efficiency in the afternoon was likely caused by the high temperature and atmospheric dryness when the soil was dry. Such a direct physiological response contributed to 14.4% to 36.0% of seasonal variation of afternoon SIF, depending on the decomposition method used. Sustained water stress also caused lagged responses. Despite the subsequent rainfall after the dry spell, we observed a continued decline of SIF due to the lagged decline of chlorophyll content and green canopy coverage. Our study demonstrates the use of continuous SIF measurements to understand the development of drought effects on plants, and highlights the importance of afternoon SIF measurements for physiological stress detection. • SIF were decomposed into the structural, biochemical, and physiological components. • SIF captured the immediate drought stress through a decline in fluorescence efficiency. • SIF captured the lagged effect of a dry spell through a decrease in canopy greenness. [ABSTRACT FROM AUTHOR]

• Percentage maps of juniper forest encroachment at 1 km are generated during 2000–2015. • Climate variable changes are examined at regional scale by pairwise analysis models. • We quantified climate variable changes with each percentage increase of woody cover. • Roles of hydroclimates are assessed on climate impacts of juniper woody encroachment. Woody plant encroachment (WPE) into grasslands has been exacerbated by climate change and human activities. WPE may affect local climate by altering the exchange of mass and energy between the land surface and the atmosphere. The lack of studies on the effects of WPE on local climate hinders our understanding of the interactions between changes in regional vegetation cover and climate. Here, we analyzed the differences of land surface temperature (ΔLST), albedo (ΔAlbedo) and evapotranspiration (ΔET) between juniper-woody-encroached grasslands and adjacent pure grasslands using 16-years of remote sensing data. Our results showed that juniper woody plant encroachment (JWPE) into the semi-arid and sub-humid grasslands reduced daytime LST and albedo, but increased nighttime LST and ET from an annual scale analysis. With each one percent increase in juniper cover, annual mean daytime ΔLST decreased ~0.026 °C, nighttime ΔLST increased ~0.01 °C, daily ΔLST decreased ~0.008 °C, Δalbedo decreased ~0.053%, and ΔET increased ~1.31 mm/year. Furthermore, it is suggested that the impacts of JWPE on LST and ET were stronger in dry years than in normal and pluvial years, and that no significant variations in albedo were found among the different hydrological conditions. These results provide insights into applying satellite-based techniques to understand the feedbacks between woody vegetation dynamics and local climate change. [ABSTRACT FROM AUTHOR]

• First study to evaluate the impacts of MODIS vs. LS2 on GPP VPM and T VTM estimates. • Combined Landsat/Sentinel-2 (LS2) tracked phenology of prairie, alfalfa, and wheat. • LS2-enhanced vegetation index accurately identified site level optimal temperature. • LS2 data produced better gross primary production (GPP) estimates than MODIS. • The vegetation transpiration model is a simpler approach to estimating transpiration (T). Although agroecosystems have a significant potential to offset carbon dioxide (CO 2), the amount of CO 2 captured can vary significantly depending on management practices. Accurate estimation of gross primary production (GPP) and transpiration (T) of agroecosystems at the field scale are essential for the study of food security and water resource management. To date, the carbon and water fluxes data products for commercial agroecosystems are limited, mostly at the moderate spatial resolution (MSR, hundreds of meters), which cannot be used to assess the temporal dynamics of GPP and T at the field scale. This study used the vegetation photosynthesis model (VPM) and vegetation transpiration model (VTM) to estimate field-level daily GPP (GPP VPM) and T (T VTM), respectively, in native prairie, alfalfa (Medicago sativa L.), and winter wheat (Triticum aestivum L.) in central Oklahoma, USA. We evaluated the reliability and advantages of vegetation indices (enhanced vegetation index, EVI and land surface water index, LSWI) in monitoring the land surface phenology using moderate spatial resolution data from Moderate Resolution Imaging Spectroradiometer (MODIS) and high spatial resolution (HSR, tens of meters) data from Landsat and Sentinel-2. The accuracy of GPP VPM and T VTM estimates at different spatial scales was evaluated using GPP (GPP EC) and evapotranspiration (ET EC) from the eddy flux tower sites, respectively. Results demonstrate the capacity of VPM and VTM to estimate the field-level carbon and water flux dynamics and their responses to weather conditions. The use of HSR vegetation indices helped to address certain challenges faced by MSR indices, especially in capturing the crop phenology in smaller areas with conservation measures or disturbances. The findings highlight the importance of using HSR GPP estimates to reduce uncertainty in quantifying CO 2 fluxes for croplands and grasslands. The findings also demonstrate the ability of the models to track field-level vegetation phenology, carbon uptake, and water use in agroecosystems under different management practices. [ABSTRACT FROM AUTHOR]

• IMERG satellite and MRMS radar precipitation applied for integrated hydrologic validation. • Discrete flood characteristics assessed with respect to USGS stream gauges. • Hydrologic model impacts on flood discharge, duration, and timing are investigated. • IMERG-E discharge errors are higher than MRMS, but IMERG-E duration errors are lower. • Flood timings are better simulated at smaller basin sizes with low systematic error. This study investigates the hydrologic utility of satellite precipitation estimates from the Global Precipitation Measurement mission by comparing flood signals produced across the Continental United States by a ten-year span of in-situ, ground-based radar and satellite-based precipitation data. The flood characteristics generated with radar and satellite precipitation through a distributed hydrologic model are contrasted against reference stream gauge data as a method of integrated validation to assess and quantify error budgets between precipitation products by highlighting precipitation products' accuracy, hydrologic scaling effects, and the impact of the hydrologic model. It is found that systematic and random errors associated with flood characteristics behave similarly to trends previously seen in precipitation rate errors between precipitation products, establishing a clear link through propagation of errors into the water cycle. Additionally, behaviors associated with both water balance and routing schemes within the hydrologic model were shown to affect outputs. Errors generated by water balance tend to cause overestimation of peak discharge values, while errors associated with routing tend to cause underestimation of flood durations and push flood timings earlier than the stream gauge reference. [ABSTRACT FROM AUTHOR]

• Managed pasture showed higher sensitivity to drought than native pasture. • Spring temperature and fall rainfall were critical for managed pasture. • Management activities modulated the effects of environmental variables on productivity. Future weather and climates, especially rainfall, are expected to have larger variability in the Southern Plains of the United States. However, the degree and timing of environmental variability that affect productivity of pastures managed differently have not been well studied. We examined the impacts of environmental variability on grassland productivity using 17 years of gross primary productivity (GPP) data for co-located native and managed prairie pastures in Oklahoma. We also considered the interactive effects of management factors and environmental variability into the regression models and identified the critical temporal windows of environmental variables (CWE) that influence annual variability in GPP. Managed pasture (MP) showed greater variability of GPP than did native pasture (NP), particularly with reduced GPP in drought years. The resilience of native prairies under unfavorable climate extremes was evident by lower GPP anomalies in NP than MP during the 2011–2012 drought. Although both pastures experienced the same degree of environmental variability, the CWE affecting GPP was significantly different between NP and MP due to the modulating impact of management practices on the responses of GPP. Not only the range but also the timings of the CWE were different between NP and MP as MP was more responsive to the spring temperature and fall rainfall. Our findings warrant the incorporation of MP as a different commodity from NP when accounting for the ecosystem responses to environmental variability in global climate models. [ABSTRACT FROM AUTHOR]

Severe droughts in the Southern Great Plains (SGP: Kansas, Oklahoma, and Texas) in recent years have reduced the productivity of tallgrass prairie and resulted in substantial economic losses to the beef cattle industry in this region. Understanding spatial and temporal patterns of agricultural drought in the SGP can help ranchers to develop and implement drought mitigation strategies. In this study, the Land Surface Water Index (LSWI), calculated from the Moderate Resolution Imaging Spectroradiometer (MODIS) near infrared and shortwave infrared bands, was used to assess agricultural drought in the tallgrass prairie region of the SGP during 2000–2013. The number of consecutive days with LSWI < 0 (DNLSWI) during the growing season was defined as the drought duration, which, was then used to identify and analyze frequency of summer drought and whole growing season drought (WGSD). The spatial pattern of DNLSWI was consistent with the east-to-west decreasing precipitation gradient across the SGP region. Summer drought duration as depicted by the DNLSWI in the western portion of the study area was around one and a half month. The occurrence of WGSD increased from one year in the east to up to six years in the west, demonstrating the susceptibility of the tallgrass prairie region to drought. In addition to the total amount of precipitation, its intra-annual distribution also played an important role in drought development. A comparison with other widely used national drought products, namely the Evaporative Stress Index (ESI), the Vegetation Drought Response Index (VegDRI), and the United States Drought Monitor (USDM), shows that LSWI-based drought has good agreement with ESI and USDM. Quantitative analyses indicate that LSWI-based drought agreed better with ESI in severe drought conditions than in moderate or pre-drought conditions. Severe drought periods characterized by the USDM also had low LSWI values. The areas affected by drought derived from the LSWI-based drought index were significantly correlated with hay production. As an indicator of vegetation water stress at moderate spatial resolution (∼500 m), the LSWI has the potential to show drought conditions for an individual ranch and offer guidance for drought mitigation activities and livestock production. [ABSTRACT FROM AUTHOR]

• A novel framework is developed for integrated hydrologic validation of satellites. • Assessment focuses on flood characteristics in favor of bulk metrics. • The effects of IMERG resolution are seen on flood peak discharge. • Basin size is also found to play a role in over or underestimation of peak discharge. • IMERG simulated floods events start earlier and end later than the MRMS benchmark. In recent years, a great amount of research has been done towards evaluating precipitation data generated by satellites, but less has focused on how these estimates and their uncertainties manifest further into the water cycle. In this study, ten years of satellite-based and ground-based radar data are used as forcings for a distributed hydrologic model across the Continental United States. They are compared using a methodology designed to assess the flood signals and characteristics generated by the model. By focusing on how well the model reproduces flood characteristics rather than fits traditional bulk statistics, this research provides robust insights into satellite precipitation deficiencies. It is found that satellite data has greater success at resolving lower magnitude flood events while tending to generate floods of longer durations. Additionally, flood managers should note that satellites tend to generate floods that characteristically both begin earlier and end later than the ground radar reference. Subsequent research is recommended into other satellite data products in order to better understand these discrepancies and mitigate or plan for them in the future. In recent years, satellites have been increasingly used to provide valuable insights on rainfall across the globe, especially in locations where radars are unable to be installed on the ground. By nature, however, the rainfall data provided by satellites has uncertainties. Large amounts of research has gone into the difference in accuracy between satellite radar and ground references, but less has focused on how these differences impact flood simulations, respectively. In this study it was found that the rainfall data from satellites tends to predict floods that are more severe (have higher magnitudes), last longer (have longer durations), and have different timings (start earlier and end later) than the reference. It is also found that satellites struggle with predictions involving extreme rainfall values. Further research is suggested to better understand these issues, as well as compare against other forms of satellite rainfall data. [ABSTRACT FROM AUTHOR]

Burning, grazing, and baling (hay harvesting) are common management practices in grassland. To develop and adopt sustainable management practices, it is essential to better understand and quantify the impacts of management practices on plant phenology and carbon fluxes. In this study, we combined multiple data sources, including in-situ PhenoCam digital images, eddy covariance data, and satellite data (Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS)) to examine the impacts of burning, baling, and grazing on canopy dynamics, plant phenology, and carbon fluxes in a pasture in El Reno, Oklahoma in 2014. Landsat images were used to assess the baling area and the trajectory of vegetation recovery. MODIS vegetation indices (VIs) were used in the Vegetation Photosynthesis Model (VPM) to estimate gross primary production (GPP VPM ) at a MODIS pixel for the flux tower (baled) site. For comparison between baled and unbaled conditions, we used MODIS VIs for a neighbor MODIS pixel (unbaled) and ran VPM. Daily PhenoCam images and green chromatic coordinate (GCC) tracked canopy dynamics and plant phenology well. The grassland greened up immediately after burning in April. GCC values showed two peaks with the similar magnitude because of quick recovery of grassland after baling. Satellite-derived VIs and GPP VPM showed that the pasture recovered in one month after baling. The GPP VPM matched well (R 2 = 0.89) with the eddy covariance-derived GPP (GPP EC ). Grazing in the late growing season did not influence plant phenology (VIs and GCC) and carbon uptake (GPP) as plants were in the late growing stage. Neither did it affect GPP differently in those two conditions because of even grazing intensity. The reduction in GPP after baling was compensated by higher GPP after large rain events in late July and early September, causing little seasonal differences in GPP (-0.002 g C m −2 day −1 ) between the baled and unbaled conditions. Interactions of different management practices with climate make it complicated to understand the impacts of different management practices on carbon dynamics and plant phenology. Thus, it is necessary to further investigate the responses of pastures to different management practices under different climate regimes at multiple temporal and spatial scales. [ABSTRACT FROM AUTHOR]

Understanding the factors controlling evapotranspiration (ET) of spatially distributed tallgrass prairie is crucial to accurately upscale ET and to predict the response of tallgrass prairie ecosystems to current and future climate. The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) and ground-based climate variables were integrated with eddy covariance tower-based ET (ET EC ) at six AmeriFlux tallgrass prairie sites in the central United States to determine major climatic factors that control ET over multiple timescales and to develop a simple and robust statistical model for predicting ET. Variability in ET was nearly identical across sites over a range of timescales, and it was most strongly driven by photosynthetically active radiation (PAR) at hourly-to-weekly timescales, by vapor pressure deficit (VPD) at weekly-to-monthly timescales, and by temperature at seasonal-to-interannual timescales at all sites. Thus, the climatic drivers of ET change over multiple timescales. The EVI tracked the seasonal variation of ET EC well at both individual sites (R 2 > 0.70) and across six sites (R 2 = 0.76). The inclusion of PAR further improved the ET-EVI relationship (R 2 = 0.86). Based on this result, we used ET EC , EVI, and PAR (MJ m −2 d −1 ) data from four sites (15 site-years) to develop a statistical model (ET = 0.11 PAR + 5.49 EVI − 1.43, adj. R 2 = 0.86, P < 0.0001) for predicting daily ET at 8-day intervals. This predictive model was evaluated against additional two years of ET EC data from one of the four model development sites and two independent sites. The predicted ET (ET EVI+PAR ) captured the seasonal patterns and magnitudes of ET EC , and correlated well with ET EC , with R 2 of 0.87-0.96 and RMSE of 0.35-0.49 mm d −1 , and it was significantly improved compared to the standard MODIS ET product. This study demonstrated that tallgrass prairie ET can be accurately predicted using a multiple regression model that uses EVI and PAR which can be readily derived from remote sensing data. [ABSTRACT FROM AUTHOR]

Understanding of the underlying causes of spatial variation in exchange of carbon and water vapor fluxes between grasslands and the atmosphere is crucial for accurate estimates of regional and global carbon and water budgets, and for predicting the impact of climate change on biosphere–atmosphere feedbacks of grasslands. We used ground-based eddy flux and meteorological data, and the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) from 12 grasslands across the United States to examine the spatial variability in carbon and water vapor fluxes and to evaluate the biophysical controls on the spatial patterns of fluxes. Precipitation was strongly associated with spatial and temporal variability in carbon and water vapor fluxes and vegetation productivity. Grasslands with annual average precipitation <600 mm generally had neutral annual carbon balance or emitted small amount of carbon to the atmosphere. Despite strong coupling between gross primary production (GPP) and evapotranspiration (ET) across study sites, GPP showed larger spatial variation than ET, and EVI had a greater effect on GPP than on ET. Consequently, large spatial variation in ecosystem water use efficiency (EWUE = annual GPP/ET; varying from 0.67 ± 0.55 to 2.52 ± 0.52 g C mm −1 ET) was observed. Greater reduction in GPP than ET at high air temperature and vapor pressure deficit caused a reduction in EWUE in dry years, indicating a response which is opposite than what has been reported for forests. Our results show that spatial and temporal variations in ecosystem carbon uptake, ET, and water use efficiency of grasslands were strongly associated with canopy greenness and coverage, as indicated by EVI. [ABSTRACT FROM AUTHOR]

Vegetation growth is one of the important indicators of drought events. Greenness-related vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) are often used for the assessment of agricultural drought. There is a need to evaluate the sensitivity of water-related vegetation indices such as Land Surface Water Index (LSWI) to assess drought and associated impacts. Moderate-Resolution Imaging Spectroradiometer (MODIS) derived time series NDVI, EVI and LSWI data during 2000–2013 were compared for their sensitivity to drought at two tallgrass prairie sites in the Oklahoma Mesonet (Marena and El Reno). Each site has continuous soil moisture measurements at three different depths (5, 25 and 60 cm) and precipitation data for the study period (2000–2013) at 5-min intervals. As expected, averaged values of vegetation indices consistently lower under drought conditions than normal conditions. LSWI decreased the most in drought years (2006, 2011 and 2012) when compared to its magnitudes in pluvial years (2007, 2013), followed by EVI and NDVI, respectively. Because green vegetation has positive LSWI values (>0) and dry vegetation has negative LSWI values (<0), much longer durations of LSWI < 0 were found in the summer periods of drought years rather than in pluvial years. A LSWI-based drought severity scheme (LSWI > 0.1; 0 < LSWI ⩽ 0.1; −0.1 < LSWI ⩽ 0; LSWI ⩽ −0.1) corresponded well with the drought severity categories (0; D0; D1: D2; D3 and D4) defined by the United States Drought Monitor (USDM) at these two study sites. Our results indicate that the number of days with LSWI < 0 during the summer and LSWI-based drought severity scheme can be simple, effective and complementary indicator for assessing drought in tallgrass prairie grasslands at a 500-m spatial resolution. [ABSTRACT FROM AUTHOR]