nasa-gesdisc/es-publications-researchareas huggingface dataset:
Drought impact in the Bolivian Altiplano agriculture associated with the El Niño–Southern Oscillation using satellite imagery data (2021)

Abstract. Drought is a major natural hazard in the Bolivian Altiplano that causes large agricultural losses. However, the drought effect on agriculture varies largely on a local scale due to diverse factors such as climatological and hydrological conditions, sensitivity of crop yield to water stress, and crop phenological stage among others. To improve the knowledge of drought impact on agriculture, this study aims to classify drought severity using vegetation and land surface temperature data, analyse the relationship between drought and climate anomalies, and examine the spatio-temporal variability of drought using vegetation and climate data. Empirical data for drought assessment purposes in this area are scarce and spatially unevenly distributed. Due to these limitations we used vegetation, land surface temperature (LST), precipitation derived from satellite imagery, and gridded air temperature data products. Initially, we tested the performance of satellite precipitation and gridded air temperature data on a local level. Then, the normalized difference vegetation index (NDVI) and LST were used to classify drought events associated with past El NinoSouthern Oscillation (ENSO) phases. It was found that the most severe drought events generally occur during a positive ENSO phase (El Nino years). In addition, we found that a decrease in vegetation is mainly driven by low precipitation and high temperature, and we identified areas where agricultural losses will be most pronounced under such conditions. The results show that droughts can be monitored using satellite imagery data when ground data are scarce or of poor data quality. The results can be especially beneficial for emergency response operations and for enabling a proactive approach to disaster risk management against droughts.

Research areas: Droughts
Drought constraints on transpiration and canopy conductance in mature aspen and jack pine stands (2006)

Half-hourly mean values of transpiration measured by eddy covariance over the course of six growing seasons in two boreal forest sites were used to develop stand-level relationships between transpiration and soil water content. The two sites were an aspen site on fine-textured soil and over five growing seasons for a jack pine site on coarse-textured soil in Saskatchewan, Canada. About half of the data record covered a multi-year drought that was more severe at the aspen site than the jack pine site. Measurements of transpiration and environmental variables were used to adjust a transpiration model to each site, with environmental variables retained in the model based on their capacity to improve the model adjustment. The model was also used to produce estimates of maximum canopy conductance (g <SUB>cMAX</SUB>). The fit of the model to the aspen half-hourly transpiration is better than to the jack pine data (r <SUP>2</SUP> of 0.86 versus 0.60). Relative soil water content explains more of the variability in half-hourly transpiration at the aspen site (46%) than at the jack pine site (10%). The relationships between transpiration and environmental variables are stable throughout the drought suggesting an absence of acclimation. Published soil water modifier curves for loamy clay soils compare well with the modifier function we obtained for a similar soil at the aspen site, but the agreement between the published curve and our curve is poor for the sandy soil of the jack pine site. Values of g <SUB>cMAX</SUB> computed at the half-hourly scale are greater at the aspen site (14.3 mm s <SUP>-1</SUP>) than at the jack pine site (10.2 mm s <SUP>-1</SUP>), but we hypothesize that the coarse soil and perennially lower water content of the jack pine site may cause this difference. Finally, we also present values of g <SUB>cMAX</SUB> computed at the daily and monthly scales for use in models that operate at these time steps.

Research areas: Droughts
Post-drought decline of the Amazon carbon sink (2018)

Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3  0.2 (95% CI) PgC yr1 after the 2005 mega-drought, which continued persistently over the next 3 years (20052008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35  1.80 MgC ha1 a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.

Research areas: Droughts
Development of earth observational diagnostic drought prediction model for regional error calibration: A case study on agricultural drought in Kyrgyzstan (2022)

Drought is a natural disaster that occurs globally and is a main trigger of secondary environmental and socio-economic damages, such as food insecurity, land degradation, and sand-dust storms. As climate change is being accelerated by human activities and environmental changes, both the severity and uncertainties of drought are increasing. In this study, a diagnostic drought prediction model (DDPM) was developed to reduce the uncertainties caused by environmental diversity at the regional level in Kyrgyzstan, by predicting drought with meteorological forecasts and satellite image diagnosis. The DDPM starts with applying a prognostic drought prediction model (PDPM) to 1) estimate future agricultural drought by explaining its relationship with the standardized precipitation index (SPI), an accumulated precipitation anomaly, and 2) compensate for regional variances, which were not reflected sufficiently in the PDPM, by taking advantage of preciseness in the time-series vegetation condition index (VCI), a satellite-based index representing land surface conditions. Comparing the prediction results with the monitored VCI from June to August, it was found that the DDPM outperformed the PDPM, which exploits only meteorological data, in both spatiotemporal and spatial accuracy. In particular, for June to August, respectively, the results of the DDPM (coefficient of determination [R2] = 0.27, 0.36, and 0.4; root mean squared error [RMSE] = 0.16, 0.13, and 0.13) were more effective in explaining the spatial details of drought severity on a regional scale than those of the PDPM (R2 = 0.09, 0.10, and 0.11; RMSE = 0.17, 0.15, and 0.16). The DDPM revealed the possibility of advanced drought assessment by integrating the earth observation big data comprising meteorological and satellite data. In particular, the advantage of data fusion is expected to be maximized in areas with high land surface heterogeneity or sparse weather stations by providing observational feedback to the PDPM. This research is anticipated to support policymakers and technical officials in establishing effective policies, action plans, and disaster early warning systems to reduce disaster risk and prevent environmental and socio-economic damage.

Research areas: Droughts
How predictable is short-term drought in the northeastern United States? (2022)

Abstract We investigated the predictability (forecast skill) of short-term droughts using the Palmer Drought Severity Index (PDSI). We incorporated a sophisticated data training (of decadal range) to evaluate the improvement of forecast skill of short-term droughts (3-month). We investigated whether the data training of the synthetic North American Multi-Model Ensemble (NMME) climate has some influence on enhancing short-term drought predictability. The central elements are the merged information among PDSI and NMME with two post-processing techniques. (1) The bias correction  spatial disaggregation (BC-SD) method improves spatial resolution by using a refined soil information introduced in the available water capacity of the PDSI calculation to assess water deficit that better estimates drought variability. (2) The ensemble model output statistic (EMOS) approach includes systematically trained decadal information of the multi model-ensemble simulations. Skill of drought forecasting improves when using EMOS, but BC-SD does not increase the forecast skill when compared with an analysis using BC (low spatial resolution). This study suggests that predictability forecast of drought (PDSI) can be extended without any change in the core dynamics of the model but instead by using the sophisticated EMOS post-processing technique. We pointed out that using NMME without any postprocessing is of limited use in the suite of model variations of the NMME, at least for the US Northeast. From our analysis, 1-month is the most extended range we should expect, which is below the range of the seasonal scale presented with EMOS (2-month). Thus, we propose a new design of drought forecasts that explicitly includes the multi-model ensemble signal.

Research areas: Droughts
Groundwater recharge indicator as tool for decision makers to increase socio-hydrological resilience to seasonal drought (2018)

Communities in regions with seasonal rainfall face annual dry seasons, during which water shortages and conflicts between different water use sectors may erupt. These difficulties increase following wet seasons with low rainfall, such as in relation to El Nino events in the wet-dry tropics of Central America. Hydrologic data are typically scarce in this region, making the development of drought adaptation strategies challenging. Many communities in the region depend on groundwater as their primary water source. For instance in the Province of Guanacaste in Costa Rica, groundwater supplied 78% of the total domestic water demand in 2015 and groundwater recharge from the wet season provides the primary water supply for the subsequent dry season. In this research we present a groundwater recharge indicator that can support water managers in preparing for seasonal droughts. We developed this tool for an aquifer in northwestern Costa Rica where we conducted hydrological monitoring of streams and groundwater levels for 2.5 years, obtained further hydrological data (groundwater levels from 2005 to 2016), and worked with communities to assemble water use data. We combined these datasets in a hydrologic model (the Water Evaluation And Planning system, WEAP) and modelled groundwater recharge from 2005 to 2016, permitting a characterization of the relationship between rainfall and groundwater recharge. The groundwater recharge indicator is based on this relationship, and allows estimating total groundwater recharge for a wet season from cumulative rainfall measured to date. The indicator permits water managers to assess if the current year will likely fall into a low recharge category prior to the end of the wet season. This information can then be used to trigger short-term adaptation strategies with the goal to bank groundwater while surface water sources are still available in the wet season. This indicator-based tool was refined through feedback provided in local stakeholder workshops. We also assessed the overall accuracy of predicting end-of-wet-season groundwater recharge with cumulative rainfall to date, and found that after the first 23 months of the wet season, prediction accuracies are high, leaving 56 months of wet season to respond adaptively to the prediction. The indicator can help water managers to plan ahead, and communicate the need for water conservation (demand management) and consideration of other water sources such as rain and surface water (supply management). This idea is transferable to other communities in regions with seasonal rainfall, and can support decision makers in increasing the socio-hydrological resilience of communities to seasonal droughts.

Research areas: Droughts, Land Surface/Agriculture Indicators
Drought monitoring over the Northeast China Using GRACE satellite data from 2002 to 2016 (2018)

Drought is one of the most common natural disasters in the world. The frequent occurrence and long-term persistence of drought will result in huge losses in industrial and agricultural production. Understanding and quantification of the time and spatial dynamic patterns of drought, including the occurrence, duration and intensity, are the primary goals of the agriculture drought monitoring and yield forecasting in the agricultural region in Northeast China. Fundamentally, drought monitors rely on satellite-gauge precipitation data and temperature, yet is also depends on underground water storage, evapotranspiration and runoff. Since 2002, NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission provide a revolutionary approach to view the water stored in soil, track the movement of water and retrieve the total water storage (TWS) via weight. And the TWS change can provide the regional information of precipitation, evapotranspiration, and surface and subsurface runoff. In this study, the GRACE TWS anomaly will be revealed to Northeast China to characterize more objective identification of drought for 157 months climatology (from April 2002 to December 2016). Comparing with the drought incidents from 2002 to 2016 in the Northeast China, the result shows that the trends of GRACE TWS with months can depict the temporal and spatial characteristic of drought in the study area. According to the negative GRACE terrestrial water storage anomalies (TWSA), which can represent storage deficits, we find that severe drought happened in western part of the study area in 2003, and southern part of the study area in 2007 and 2011. The April-May average GRACE TWS noted the spring drought occurred in most part of study area in 2003, 2009, 2011 and 2012. The significant decreases of TWS from May 2007 to June 2008 reveled by GRACE TWS lead to a serious drought conditions. The five-month averages of GRACE TWS from June to October were computed in each year, and deficit during 2014 lead to an agricultural drought and crop production loss in Liaoning and Jilin provinces. However, GRACE TWS changes show no one-to-one relationship with precipitation, as the precipitation is not the primary source of TWS changes. The present result demonstrates that GRACE TWS as referenced data is capable of identify and mapping drought in the Northeast China. It will help better understand the cause of drought, and observe the occurrence, trends and spatiotemporal characteristics of drought. It would be valuable for regional water management.

Research areas: Droughts
Impacts of experimentally imposed drought on leaf respiration and morphology in an Amazon rain forest (2010)

Summary 1. The Amazon region may experience increasing moisture limitation over this century. Leaf dark respiration (R) is a key component of the Amazon rain forest carbon (C) cycle, but relatively little is known about its sensitivity to drought. 2. Here, we present measurements of R standardized to 25 °C and leaf morphology from different canopy heights over 5 years at a rain forest subject to a large‑scale through‑fall reduction (TFR) experiment, and nearby, unmodified Control forest, at the Caxiuanã reserve in the eastern Amazon. 3. In all five post‑treatment measurement campaigns, mean R at 25 °C was elevated in the TFR forest compared to the Control forest experiencing normal rainfall. After 5 years of the TFR treatment, R per unit leaf area and mass had increased by 65% and 42%, respectively, relative to pre‑treatment means. In contrast, leaf area index (L) in the TFR forest was consistently lower than the Control, falling by 23% compared to the pre‑treatment mean, largely because of a decline in specific leaf area (S). 4. The consistent and significant effects of the TFR treatment on R, L and S suggest that severe drought events in the Amazon, of the kind that may occur more frequently in future, could cause a substantial increase in canopy carbon dioxide emissions from this ecosystem to the atmosphere.

Research areas: Droughts
Human influence on the increasing drought risk over Southeast Asian monsoon region (2021)

Southeast Asian monsoon region is regularly stricken by drought, but less attention is paid due to its slow-onset and less visual impact. This study investigated the observed drought changes over Southeast Asian monsoon region and impacts of anthropogenic forcing using the Coupled Model Intercomparison Project Phase 6 models. We revealed an increasing drought risk for 1951-2018 due to more frequent and wide-spread droughts. The influence of anthropogenic forcing is successfully detected, which has increased the likelihood of the extreme droughts in historical simulation by reducing precipitation and enhancing evapotranspiration. The time of emergence of anthropogenic forcing in extreme drought occurrence and affected area occurs around the 1960s. The future projected severe and extreme drought risks are still beyond natural only forced changes under all scenarios. Our findings demonstrate a robust impact of anthropogenic forcing on drought risk over Southeast Asia, and highlight the importance of future pathway choice.

Research areas: Droughts
Analysis of the Drought Mitigated Mechanism in Terraced Paddy Fields (2019)

Food security is often threatened by droughts during rice production. Although most of the rice is produced in lowland or irrigated wet rice fields, terraced paddy fields are important in the rice production system in island or mountainous countries. With the intensifying frequency of El Nino periods in recent decades, there has been a risk of droughts in terraced paddy areas. To mitigate drought, remote sensing data analysis could be an efficient and reliable tool for obtaining scarce ground monitoring data. In this study, crop water stress index (CWSI) and temperature vegetation dryness index (TVDI) were applied to evaluate the drought intensity, and hydrological monitoring data was provided as a support for the evaluation. The results indicated that droughts normally occurred during the dry season, and intensified during El Nino periods. CWSI and TVDI were visible to predict drought occurrences in the watershed area. TVDI overestimated the drought inside Keduang watershed compared to CWSI because of the complex condition of the terraced paddy area, including the hydrology in this area. The complex topography, high groundwater table, and continuous plot-to-plot irrigation helped to maintain the water availability and mitigated the drought impact for rice production in the studied terraced paddy field.

Research areas: Droughts
Climate change reduces extent of temperate drylands and intensifies drought in deep soils (2017)

Drylands cover 40% of the global terrestrial surface and provide important ecosystem services. While drylands as a whole are expected to increase in extent and aridity in coming decades, temperature and precipitation forecasts vary by latitude and geographic region suggesting different trajectories for tropical, subtropical, and temperate drylands. Uncertainty in the future of tropical and subtropical drylands is well constrained, whereas soil moisture and ecological droughts, which drive vegetation productivity and composition, remain poorly understood in temperate drylands. Here we show that, over the twenty first century, temperate drylands may contract by a third, primarily converting to subtropical drylands, and that deep soil layers could be increasingly dry during the growing season. These changes imply major shifts in vegetation and ecosystem service delivery. Our results illustrate the importance of appropriate drought measures and, as a global study that focuses on temperate drylands, highlight a distinct fate for these highly populated areas.

Research areas: Droughts
A comprehensive assessment of water storage dynamics and hydroclimatic extremes in the Chao Phraya River Basin during 2002–2020 (2021)

A holistic assessment of the hydroclimatic extremes, which have caused tremendous environmental, societal, and economic losses globally, is imperative for the highly vulnerable Chao Phraya River Basin. In this study, the water storage dynamics and extremes in the basin during 20022020 were quantified, for the first time, using GRACE (Follow-On) based terrestrial water storage anomaly (TWSA) with the help of a novel artificial neural network-based model for the data gap filling. TWSA showed a linear trend of 1.12  0.05 cm yr1 (equivalent to a volumetric trend of 1.79  0.08 km3 yr1) in the basin, and segregation of the constituent components of TWS revealed that the groundwater storage is a significant contributor (45%) to TWS with a linear trend of 0.51  0.06 cm yr1 (-0.82  0.10 km3 yr1) followed by surface water storage (i,e., cumulative of the water storage in the reservoirs, flood inundation, and rivers) (36%) and soil moisture storage (19%). The hydroclimatic extremes detected in TWSA are primarily triggered by the variations in precipitation during the monsoon season (May to October) and further amplified by the subsequent water storage and abstraction. TWSA attained a maximum of 42.86 cm in October 2011 during severe floods of 2011 (~95% increase in net precipitation during 2010 and 2011) and a minimum of 31.81 cm during the drought of May 2020 (~82% decrease in net precipitation during 2019 and 2020). All other flood and drought events in some years, e.g., 2006, 2010, 2015, 2016, are also well recorded in TWSA, albeit with a lag time of up to a maximum of two months from precipitation. Further, the basins increasing potential of severe drought, as assessed by the effective water-storage-based novel drought potential index (DPI), underscored the need for multifaceted water management essentially focused on the demand side rather than the supply side in the basin. The proposed framework can be utilized for policymaking for adequate and equitable water allocation, developing the early warning systems for the droughts and floods, and employing the optimal adaptation measures in the Chao Phraya River Basin and other data-scarce river basins globally.

Research areas: Land Surface/Agriculture Indicators, Droughts
Ambiguous agricultural drought: Characterising soil moisture and vegetation droughts in europe from earth observation (2021)

Long-lasting precipitation deficits or heat waves can induce agricultural droughts, which are generally defined as soil moisture deficits that are severe enough to negatively impact vegetation. However, during short soil moisture drought events, the vegetation is not always negatively affected and sometimes even thrives. Due to this duality in agricultural drought impacts, the term agricultural drought is ambiguous. Using the ESAs remotely sensed CCI surface soil moisture estimates and MODIS NDVI vegetation greenness data, we show that, in major European droughts over the past two decades, asynchronies and discrepancies occurred between the surface soil moisture and vegetation droughts. A clear delay is visible between the onset of soil moisture drought and vegetation drought, with correlations generally peaking at the end of the growing season. At lower latitudes, correlations peaked earlier in the season, likely due to an earlier onset of water limited conditions. In certain cases, the vegetation showed a positive anomaly, even during soil moisture drought events. As a result, using the term agricultural drought instead of soil moisture or vegetation drought, could lead to the misclassification of drought events and false drought alarms. We argue that soil moisture and vegetation drought should be considered separately.

Research areas: Droughts