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For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden-Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describingMJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a directMJO influence on precipitation corresponding to propagation of the MJO's convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October-November) and southwest monsoon (SWM; May-September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method. [ABSTRACT FROM AUTHOR]

The Upper Blue Nile river basin (uBN) is vital to three countries' water, food, and energy security (Ethiopia, Sudan, and Egypt). It is also a region with substantial interannual precipitation variability and the potential for significant climate change in coming decades. The El Niño–Southern Oscillation (ENSO) affects both variability and trends in uBN precipitation, with El Niño episodes historically connected with below‐average rainfall. In this context, this study seeks to: (a) examine the representation of ENSO–uBN rainfall teleconnections in simulations from the sixth Coupled Model Intercomparison Project (CMIP6), as compared to the previous generation of models (CMIP5); (b) distinguish between teleconnections during the peak of the rainy season (July–August [JA]) and the agriculturally critical end‐of‐season rains (September–October [SO]); (c) investigate evidence for Tropical Easterly Jet (TEJ) mediation of these teleconnections in CMIP6 versus CMIP5 and for both peak rains and end of the rainy season. Using a subset of high performing models, we find that CMIP6 simulations of uBN precipitation show somewhat less bias than CMIP5 for JA total rainfall, and that correlations with ENSO have become more consistent across models, but that differences between CMIP5 and CMIP6 are modest. CMIP6 simulations, like CMIP5, overestimate SO rainfall and suggest a stronger ENSO association in SO than is indicated by observations. Model representation of the TEJ and its association with both ENSO and uBN precipitation show no systematic change between CMIP5 and CMIP6. The mediating influence of the TEJ appears to be more important in JA than SO. [ABSTRACT FROM AUTHOR]

Tropical highland environments present substantial challenges for climate projections due to sparse observations, significant local heterogeneity and inconsistent performance of global climate models (GCMs). Moreover, these areas are often densely populated, with agriculture‐based livelihoods sensitive to transient climate extremes not always included in available climate projections. In this context, we present an analysis of observed and projected trends in temperature and precipitation extremes across agroecosystems (AESs) in the northwest Ethiopian Highlands, to provide more relevant information for adaptation. Limited observational networks are supplemented with a satellite‐station hybrid product, and trends are calculated locally and summarized at the adaptation‐relevant unit of the AES. Projections are then presented from GCM realizations with divergent climate projections, and results are interpreted in the context of agricultural climate sensitivities. Trends in temperature extremes (1981–2016) are typically consistent across sites and AES, but with different implications for agricultural activities in the other AES. Trends in temperature extremes from GCM projected data also generally have the same sign as the observed trends. For precipitation extremes, there is greater site‐to‐site variability. Summarized by AES, however, there is a clear tendency towards reduced precipitation, associated with decreases in wet extremes and a tendency towards temporally clustered wet and dry days. Over the retrospective analysis period, neither of the two analysed GCMs captures these trends. Future projections from both GCMs include significant wetting and an increase in precipitation extremes across AES. However, given the lack of agreement between GCMs and observations with respect to trends in recent decades, the reliability of these projections is questionable. The present study is consistent with the "East Africa Paradox" that observations show drying in summer season rainfall while GCMs project wetting. This has an expression in summertime Ethiopian rain that has not received significant attention in previous studies. [ABSTRACT FROM AUTHOR]

Keywords: Climate services; COVID-19; Disease; Health; Societal impacts; Vulnerability EN Climate services COVID-19 Disease Health Societal impacts Vulnerability E704 E709 6 04/11/22 20220301 NES 220301 Roundtable on Compound Hazards and COVID-19 I B What: b i An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. For example, forest fires are social and environmental hazards that present multiple risks to health and well-being, and they can also force people to migrate from rural to urban centers and vice versa during a COVID-19 outbreak, potentially increasing the spread of COVID-19 ([1]). The WMO Task Team on COVID-19 Meteorological and Air Quality Factors convened the "Roundtable on Compound Hazards and COVID-19" to hear from global experts on hazard response in the context of COVID-19. [Extracted from the article]

Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term "flash drought" comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: "dry and demanding" events for which antecedent evaporative demand is high and soil moisture is low, "evaporative" events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and "stealth" flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways. [ABSTRACT FROM AUTHOR]

The drivers of precipitation variability in the Ethiopian Highlands are complex, and they have considerable impact on the local rural economy of the region. While interannual variability has been studied extensively, relatively less study has focused on intra-seasonal variability in precipitation, even though this variability can be critical for agricultural activities. In this study, we examine the impacts of the Boreal Summer Intra-Seasonal Oscillation (BSISO) on precipitation variability in the Upper Blue Nile Basin (UBNB) during the rainy season (June–September). In order to optimally capture Intra-seasonal variability, we developed a customized ISO index based on leading modes of the variability in the UBNB, and found that this index shares the general character of a standard BSISO index. Analyses performed with both the customized and standard BSISO indices show that a core convective region centered on the Indian Ocean propagates to the northwest, eventually exerting significant influence on Ethiopian rainfall. The primary mechanism of influence appears to be BSISO influence on both lower and upper level tropospheric wind fields over the western Indian Ocean that influence moisture flux, convergence, and convection in the study region. This includes BSISO modulation of the East Africa Low-Level Jet and the Tropical Easterly Jet. We also find that the BSISO influence on Ethiopia is consistent with a BSISO-associated Rossby wave signal, but the magnitude of the Rossby wave signal is small relative to other mechanisms of influence. [ABSTRACT FROM AUTHOR]

Research in meteorological prediction on subseasonal to seasonal (S2S) time scales has seen growth in recent years. Concurrent with this growth, 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, whereas 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 global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model to forecast U.S. Drought Monitor--style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts with 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 that 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, and improvements were greatest for extreme (D3) and exceptional (D4) drought categories. [ABSTRACT FROM AUTHOR]

Area-level estimates of temperature might lead to exposure misclassification in studies examining associations between heat waves and health outcomes. Our study compared the association between heat waves and preterm birth (PTB) or nonaccidental death (NAD) using exposure metrics at varying levels of spatial resolution: ZIP codes, 12.5 km, and 1 km. Using geocoded residential addresses on birth (1990–2010) and death (1997–2010) records from Alabama, we implemented a time-stratified case–crossover design to examine the association between heat waves and PTB or NAD. ZIP code and 12.5-km heat wave indexes (HIs) were derived using air temperatures from Phase 2 of the North American Land Data Assimilation System (NLDAS–2). We downscaled NLDAS–2 data, using land surface temperatures from the Moderate Resolution Imaging Spectroradiometer product, to estimate fine spatial resolution HIs (1 km). The association between heat waves and PTB or NAD was significant and positive using ZIP code, 12.5-km, and 1-km exposure metrics. Moreover, results show that these three exposure metric analyses produced similar effect estimates. Urban heat islands were evident with the 1-km metric. When analyses were stratified by rurality, we found that associations in urban areas were more positive than those in rural areas. Comparing results of models with a varying spatial resolution of the exposure metric allows for examination of potential bias associated with exposure misclassification. [ABSTRACT FROM AUTHOR]

Surface incoming shortwave (solar) radiation data are an important component of many scientific analyses, but direct measurements are not commonly available. Estimates can be obtained from gridded meteorological analysis or reanalysis systems, such as the Global Data Assimilation Systems (GDAS) and Modern Era Retrospective Reanalysis System (MERRA-2), or calculated using empirical models dependent on meteorological variables such as air temperature. The purpose of this analysis was to compare multiple methods for estimating daily shortwave radiation in a tropical highland environment in Ethiopia. Direct solar radiation outputs of GDAS and MERRA-2, topographically corrected outputs of the two analysis systems, and empirically estimated solar radiation values calculated with the systems’ air temperature data were compared to see which produced the most reliable radiation values. GDAS appeared to underestimate the seasonal variability, resulting in low correlation (R2) with in situ data and large mean bias error (MBE). In comparison, MERRA-2 did not underestimate variability, but produced larger bias than the empirical model estimates. There was an improvement in correlation and reduction in MBE when using the GDAS air temperature predictions in the empirical model, but the opposite was true for MERRA-2. The empirical model using station air temperature data (stationT) produced the highest correlation across all four stations, with best performance at the lower elevation sites. The direct shortwave radiation outputs of MERRA-2 produced comparable correlation values, with larger R2 at stations at higher elevation. Topography possibly influenced these results, as MERRA-2 performed comparably to stationT at the stations in moderate terrain, but not in steeper terrain. This work can serve as a starting point for analyses in other tropical highland regions, where continuous in situ solar radiation data are rarely available. [ABSTRACT FROM AUTHOR]

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning. [ABSTRACT FROM AUTHOR]

Background A better understanding of how heat waves affect fatal traffic crashes will be useful to promote awareness of drivers’ vulnerability during an extreme heat event. Objective and Methods We applied a time-stratified case-crossover design to examine associations between heat waves and fatal traffic crashes during May-September of 2001–2011 in the continental United States (US). Heat waves, defined as the daily mean temperature >95% threshold for ≥2 consecutive days, were derived using gridded 12.5 km 2 air temperatures from Phase 2 of the North American Land Data Assimilation System (NLDAS-2). Dates and locations of fatal traffic crash records were acquired from the National Highway Traffic Safety Administration (NHTSA). Results Results show a significant positive association between fatal traffic crashes and heat waves with a 3.4% (95% CI: 0.9, 5.9%) increase in fatal traffic crashes on heat wave days versus non-heat wave days. The association was more positive for 56–65 years old drivers [8.2% (0.3, 16.7%)] and driving on rural roadways [6.1% (2.8, 9.6%)]. Moreover, a positive association was only present when the heat wave days were characterized by no precipitation [10.9% (7.3%, 14.6%)] and medium or high solar radiation [24.6% (19.9%, 29.5%) and 19.9% (15.6%, 24.4%), respectively]. Conclusions These findings are relevant for developing targeted interventions for these driver groups and driving situations to efficiently reduce the negative effects of heat waves on fatal traffic crashes. [ABSTRACT FROM AUTHOR]

Food insecurity is a complex phenomenon with biophysical, climatic, economic, and infrastructure facets. Despite this understanding, there are few stakeholder-based modelling tools that can capture these dynamics and thereby evaluate the direct and indirect impacts that climatic change, economic change, and policy interventions can have on food security. To address this need, we have developed the Food Distributed Extendable COmplementarity (Food-DECO) model. The Food-DECO model represents individual aggregated stakeholders as decision-makers within the agricultural, transportation, and economic systems. In this paper, we demonstrate the model's capabilities by applying it to a food system based on characteristics of Ethiopia, a frequently food-insecure country. Food-DECO produces results that show the effects of seasonality and regional distribution networks on human nutrition while disaggregating those effects by age, gender, and per capita income. We explore the impacts of a regional crop failure and evaluate the possible effectiveness of several commonly proposed food security interventions. The economic integration of agriculture and transportation in Food-DECO enables us to see, counterintuitively, that improving the capacity of the existing food distribution network between regions can negatively impact the nutritional outcomes in the region experiencing crop failure; the increased ability to meet high demand elsewhere leads to an increase in regional exports – even during a food shortage. [ABSTRACT FROM AUTHOR]

The annual flood cycle of the Sudd wetland in South Sudan plays an important role in the Nile River Basin water balance. The wetland, however, is extensive and sparsely instrumented, which has inhibited credible understanding of regional flooding across space and time. Here we explore the potential to apply low resolution C-band ENVISAT Advanced SAR imagery for remote estimation of Sudd flooded area. Over a five year study period (2007–2011) the time-averaged flooded Sudd area was found to be 18,033 km 2 with an average annual high of 29,702 km 2 in late September and a low of 10,128 km 2 in early May. Annual peak flood area ranges considerably from 19,259 km 2 in 2009 to 36,649 km 2 in 2007, but we found no systematic trend over the five year study period. Derived flood frequency maps identify areas of open water and permanent flooding (12% of total area), seasonal flooding (29%), and intermittent flooding (48%). To evaluate the certainty of our results, we consider their consistency with (1) prior studies, (2) evapotranspiration estimates from the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm, (3) watershed storage anomaly estimates from GRACE, (4) supervised classification of open water area using Landsat, and (5) a rough measure of water availability (antecedent precipitation). The analyses show reasonable temporal and spatial consistency with available lines of evidence. We conclude that low resolution C-band SAR imagery shows promise for study of Sudd wetland flood dynamics. [ABSTRACT FROM AUTHOR]

Improving seasonal forecasts in East Africa has great implications for food security and water resources planning in the region. Dynamically based seasonal forecast systems have much to contribute to this effort, as they have demonstrated ability to represent and, to some extent, predict large-scale atmospheric dynamics that drive interannual rainfall variability in East Africa. However, these global models often exhibit spatial biases in their placement of rainfall and rainfall anomalies within the region, which limits their direct applicability to forecast-based decision-making. This paper introduces a method that uses objective climate regionalization to improve the utility of dynamically based forecast-system predictions for East Africa. By breaking up the study area into regions that are homogenous in interannual precipitation variability, it is shown that models sometimes capture drivers of variability but misplace precipitation anomalies. These errors are evident in the pattern of homogenous regions in forecast systems relative to observation, indicating that forecasts can more meaningfully be applied at the scale of the analogous homogeneous climate region than as a direct forecast of the local grid cell. This regionalization approach was tested during the July-September (JAS) rain months, and results show an improvement in the predictions from version 4.5 of the Max Plank Institute for Meteorology's atmosphere-ocean general circulation model (ECHAM4.5) for applicable areas of East Africa for the two test cases presented. [ABSTRACT FROM AUTHOR]

Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL. [ABSTRACT FROM AUTHOR]

Rainfall variability in the Tigris-Euphrates headwaters is a result of interaction between topography and meteorological features at a range of spatial scales. Here, the Weather Research and Forecasting (WRF) Model, driven by the NCEP-DOE AMIP-II reanalysis (R-2), has been implemented to better understand these interactions. Simulations were performed over a domain coveringmost of the Middle East. The extended simulation period (1983-2013) enables us to study seasonality, interannual variability, spatial variability, and extreme events of rainfall. Results showed that the annual cycle of precipitation produced by WRF agrees much more closely with observations than doesR-2. This was particularly evident during the transitionmonths of April and October, which were further examined to study the underlying physical mechanisms. In both months, WRF improves representation of interannual variability relative to R-2, with a substantially larger benefit in April. This improvement results primarily from WRF's ability to resolve two low-level, terrain-induced flows in the region that are either absent or weak in R-2: one parallel to the western edge of the Zagros Mountains, and one along the east Turkish highlands. The first shows a complete reversal in its direction duringwet and dry days:when flowing southeasterly it transports moisture from the Persian Gulf to the region, and when flowing northwesterly it blocks moisture and transports it away fromthe region. The second is more directly related to synoptic-scale systems and carries moist, warm air from the Mediterranean and Red Seas toward the region. The combined contribution of these flows explains about 50% of interannual variability in both WRF and observations for April and October precipitation. [ABSTRACT FROM AUTHOR]

The Madden-Julian Oscillation (MJO) is the dominant mode of sub-seasonal climate variability in the global tropics. As such it represents an opportunity for intra-seasonal rainfall prediction and, perhaps, for explaining dynamics that underlie longer term variability and trends. This opportunity is of substantial interest for tropical Africa, where rainfall variability has significant impacts on agriculture, energy, and natural ecosystems. The objective of this review paper is to inventory and assess the state of knowledge of MJO influence on African rainfall. A number of studies have identified statistical links between MJO and sub-seasonal rainfall variability in West, East, and Southern Africa. The proposed mechanisms to explain this influence differ by region and by season, and they often involve multiscale interactions between local precipitation processes and MJO-associated atmospheric dynamics. Dynamically-based forecast systems have some skill in predicting MJO evolution to time horizons of 3–4 weeks, and some can capture teleconnections to Africa. On longer time scales, there is evidence that MJO activity both modulates and is modulated by the El Niño Southern Oscillation and the Indian Ocean Dipole. The implications of these interactions for MJO connections to Africa require further research, as does the potential for trends in MJO behavior and impacts on Africa under global climate change. [ABSTRACT FROM AUTHOR]

The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts. [ABSTRACT FROM AUTHOR]

Ranking the performance of global climate models (GCMs) is a notoriously difficult exercise. Multi-model comparison studies nearly always show that each model has strengths and weaknesses relative to others, and for many purposes the multi-model ensemble mean delivers better estimates than any individual model. Nevertheless, in regions like East Africa, where there is little consensus between models on the magnitude or sign of 21st century precipitation change, the multi-model ensemble mean approach to climate projection provides little value for adaptation planning. Here, we consider several possible frameworks for model evaluation and ranking, and assess the differences in performance of a subset of models participating in the 5th Coupled Model Intercomparison Project (CMIP5) according to each framework. Our test case is precipitation in the Nile River headwaters regions. We find that there is little consistency in the relative performance of models across frameworks based on amount and seasonality of precipitation, interannual precipitation variability, precipitation teleconnections, and continental scale climate patterns. These analyses offer some guidance on which GCMs are most likely to provide meaningful results for specific applications, but they caution that any effort to select 'best performing' GCMs for the Nile River basin must carefully consider the purposes for which GCMs are being selected. [ABSTRACT FROM AUTHOR]

Background: The escalating negative impact of climate change on our environment has the potential to result in significant morbidity of rhinologic diseases. Methods: Evidence based review of examples of rhinologic diseases including allergic and nonallergic rhinitis, chronic rhinosinusitis, and allergic fungal rhinosinusitis was performed. Results: The lower socioeconomic population, including historically oppressed groups, will be disproportionately affected. Conclusions: We need a systematic approach to improve healthcare database infrastructure and funding to promote diverse scientific collaboration to address these healthcare needs. [ABSTRACT FROM AUTHOR]

This study examines daily precipitation data during December-March over south equatorial Africa (SEA) and proposes a new zonal asymmetric pattern (ZAP) that explains the leading mode of weather-scale precipitation variability in the region. The eastern and western components of the ZAP, separated at about 30°E, appear to be a consequence of an anomalous zonal atmospheric cell triggered by enhanced low-level westerly winds. The enhanced westerlies are generated by a diagonal interhemispheric pressure gradient between the southwestern Indian and north tropical Atlantic Oceans. In eastern SEA these winds hit the East African Plateau, producing low-level convergence and convection that further intensifies the westerlies. In western SEA a subsiding branch develops in response, closing the circulation cell. The system gradually dissipates as the pressure gradient weakens. Through this mechanism, simultaneous changes in two hemispheres generate a regional zonally oriented circulation that relies on climatic communication between eastern and western equatorial Africa. [ABSTRACT FROM AUTHOR]

The effect that climate change and variability will have on waterborne bacteria is a topic of increasing concern for coastal ecosystems, including the Chesapeake Bay. Surface water temperature trends in the Bay indicate a warming pattern of roughly 0.3–0.4°C per decade over the past 30 years. It is unclear what impact future warming will have on pathogens currently found in the Bay, including Vibrio spp. Using historical environmental data, combined with three different statistical models of Vibrio vulnificus probability, we explore the relationship between environmental change and predicted Vibrio vulnificus presence in the upper Chesapeake Bay. We find that the predicted response of V. vulnificus probability to high temperatures in the Bay differs systematically between models of differing structure. As existing publicly available datasets are inadequate to determine which model structure is most appropriate, the impact of climatic change on the probability of V. vulnificus presence in the Chesapeake Bay remains uncertain. This result points to the challenge of characterizing climate sensitivity of ecological systems in which data are sparse and only statistical models of ecological sensitivity exist. [ABSTRACT FROM AUTHOR]

Rainfall in the Sahel region of Africa is prone to large interannual variability, and it has exhibited a recent multidecadal drying trend. The well-documented social impacts of this variability have motivated numerous efforts at seasonal precipitation prediction, many of which employ statistical techniques that forecast Sahelian precipitation as a function of large-scale indices of surface air temperature (SAT) anomalies, sea surface temperature (SST), surface pressure, and other variables. These statistical models have demonstrated some skill, but nearly all have adopted conventional statistical modeling techniques-most commonly generalized linear models-to associate predictor fields with precipitation anomalies. Here, the results of an artificial neural network (ANN) machine-learning algorithm applied to predict summertime (July-September) Sahel rainfall anomalies using indices of springtime (April-June) SST and SAT anomalies for the period 1900-2011 are presented. Principal component analysis was used to remove multicollinearity between predictor variables. Predictive accuracy was assessed using repeated k-fold random holdout and leave-one-out cross-validation methods. It was found that the ANN achieved predictive accuracy superior to that of eight alternative statistical methods tested in this study, and it was also superior to that of previously published predictive models of summertime Sahel precipitation. Analysis of partial dependence plots indicates that ANN skill is derived primarily from the ability to capture nonlinear influences that multiple major modes of large-scale variability have on Sahelian precipitation. These results point to the value of ANN techniques for seasonal precipitation prediction in the Sahel. [ABSTRACT FROM AUTHOR]

• Knowledge of onset and cessation of rains is critical for crop planning in tropical regions. • Analyses of these parameters at production-relevant scale are lacking. • We quantify interannual variability and trends at agroecosystem level. • Results indicate that existing agricultural practices may need to be revised. • Extended weather and sub-seasonal forecast services should be implemented in the area. Analysis of Rainfall onset date (OD), cessation date (CD), and length of growing period (LGP) for specific sites in highly dissected topography and highly variable climate may not provide actionable information for crop production planning. In tropical highland regions information on these parameters is scant at a resolution relevant for targeted management. This study examined recent (1981-2016) OD, CD, and LGP variability and trends for the main rainy season in different agroecosystems (AESs) in the northwestern Ethiopian Highlands. Onset criteria were derived from surveys, rainfall data, and previous literature whereas cessation criteria were set from the soil water holding capacity (WHC), daily reference evapotranspiration (ETo), and daily rainfall in each site. Dry spells (DS) were analyzed for the small rainy season in higher elevation AESs where the season is relevant for potato production. All analyses were performed using site specific data grouped by agroecosystem (AES), a unit that has similar climate, soil, crop, and farm management for better agricultural decisions. Results show high inter-annual variability of OD and CD, and LGP exhibited a significant trend in some AES and greater variability in higher elevation AES. Generally, trend analysis results showed early onset and cessation of rainfall. Significant increasing trends and variabilities in DS and OD may significantly affect crop production and thus AES specific crop production calendar should be revised to minimize crop failure. The analysis also confirmed that farmers perception is consistent with meteorological analysis. The results emphasize the importance of AES-based improved seasonal weather forecasts and tailored climate information services to guide farm decisions and improve management of climate variability by smallholder farmers. It also concluded that AES level analysis can better provide actionable information for decision makers and growers than site specific and scattered studies as mosaic results are reported between sites grouped in the same agroecosystem. [ABSTRACT FROM AUTHOR]

Positive soil moisture-precipitation feedbacks can intensify heat and prolong drought under conditions of precipitation deficit. Adequate representation of these processes in regional climate models is, therefore, important for extended weather forecasts, seasonal drought analysis, and downscaled climate change projections. This paper presents the first application of the NASA Unified Weather Research and Forecasting Model (NU-WRF) to simulation of seasonal drought. Simulations of the 2006 southern Great Plains drought performed with and without soil moisture memory indicate that local soil moisture feedbacks had the potential to concentrate precipitation in wet areas relative to dry areas in summer drought months. Introduction of a simple dynamic surface albedo scheme that models albedo as a function of soil moisture intensified the simulated feedback pattern at local scale-dry, brighter areas received even less precipitation while wet, whereas darker areas received more-but did not significantly change the total amount of precipitation simulated across the drought-affected region. This soil-moisture-mediated albedo land-atmosphere coupling pathway is structurally excluded from standard versions of WRF. [ABSTRACT FROM AUTHOR]

Abstract: In coastal and estuarine environments, near-surface salinity varies significantly in space and time. As absolute salinity and salinity gradients are central to many physical and ecological processes in these environments, reliable and consistent salinity estimates are a priority for marine research and application communities. Satellite remote sensing has a great potential to meet this need, yet sensors and algorithms designed to monitor open ocean salinity are typically ill-suited for high resolution applications to coastlines and estuaries. Here we present results of multiple statistical models that predict daily, gridded surface salinity at 1km resolution across Chesapeake Bay as a function of level 2 surface reflectance estimates from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the Aqua platform. Eight statistical methods were tested and it was found that sea surface salinity can be accurately predicted via remote sensed products with an accuracy that is more than sufficient for many physical and ecological applications. For the best-performing statistical model, mean absolute error was 1.82 relative to mean Chesapeake Bay salinity of 16.5. [Copyright &y& Elsevier]

Snow cover over land has a significant impact on the surface radiation budget, turbulent energy fluxes to the atmosphere, and local hydrological fluxes. For this reason, inaccuracies in the representation of snow-covered area (SCA) within a land surface model (LSM) can lead to substantial errors in both offline and coupled simulations. Data assimilation algorithms have the potential to address this problem. However, the assimilation of SCA observations is complicated by an information deficit in the observation—SCA indicates only the presence or absence of snow, not snow water equivalent—and by the fact that assimilated SCA observations can introduce inconsistencies with atmospheric forcing data, leading to nonphysical artifacts in the local water balance. In this paper, a novel assimilation algorithm is presented that introduces Moderate Resolution Imaging Spectroradiometer (MODIS) SCA observations to the Noah LSM in global, uncoupled simulations. The algorithm uses observations from up to 72 h ahead of the model simulation to correct against emerging errors in the simulation of snow cover while preserving the local hydrologic balance. This is accomplished by using future snow observations to adjust air temperature and, when necessary, precipitation within the LSM. In global, offline integrations, this new assimilation algorithm provided improved simulation of SCA and snow water equivalent relative to open loop integrations and integrations that used an earlier SCA assimilation algorithm. These improvements, in turn, influenced the simulation of surface water and energy fluxes during the snow season and, in some regions, on into the following spring. [ABSTRACT FROM AUTHOR]

Assimilation of data from the Gravity Recovery and Climate Experiment (GRACE) system of satellites yielded improved simulation of water storage and fluxes in the Mississippi River basin, as evaluated against independent measurements. The authors assimilated GRACE-derived monthly terrestrial water storage (TWS) anomalies for each of the four major subbasins of the Mississippi into the Catchment Land Surface Model (CLSM) using an ensemble Kalman smoother from January 2003 to May 2006. Compared with the open-loop CLSM simulation, assimilation estimates of groundwater variability exhibited enhanced skill with respect to measured groundwater in all four subbasins. Assimilation also significantly increased the correlation between simulated TWS and gauged river flow for all four subbasins and for the Mississippi River itself. In addition, model performance was evaluated for eight smaller watersheds within the Mississippi basin, all of which are smaller than the scale of GRACE observations. In seven of eight cases, GRACE assimilation led to increased correlation between TWS estimates and gauged river flow, indicating that data assimilation has considerable potential to downscale GRACE data for hydrological applications. [ABSTRACT FROM AUTHOR]

The authors propose that a heat-driven circulation from the Zagros Plateau has a significant impact on the climate of the Middle East Plain (MEP), especially summertime winds, air temperature, and aridity. This proposal is examined in numerical experiments with a regional climate model. Simulations in which the Zagros Plateau was assigned a highly reflective, “snowlike” albedo neutralized the heat-driven circulation and produced an extra summertime warming of 1°–2°C in the MEP, measured relative to a control simulation and to the records of the NCEP–NCAR reanalysis project. This effect was largest in midsummer, when heating on the plateau was greatest. Additionally, simulations with high albedo on the Zagros showed reduced subsidence and enhanced precipitation in the MEP. These sensitivities are interesting because the Zagros Plateau lies downwind of the MEP. Analysis of model results indicates that the sensitivity of the upwind subsidence region to Zagros albedo can be understood as a linear atmospheric response to plateau heating, communicated upwind by a steady heat-driven circulation that influences the thermodynamic balance of the atmosphere. This regional phenomenon adds to the large-scale subsidence patterns established by the Hadley circulation and the Asian monsoon. Observed patterns of vertical motion in the Middle East, then, are a combined product of Zagros-induced subsidence and hemispheric-scale circulations. [ABSTRACT FROM AUTHOR]

The Euphrates Plain (EP) experiences large interannual variability in vegetation cover, especially in areas of marginal rain-fed agriculture. Vegetation in this region is primarily limited by available soil moisture, as determined by winter precipitation, spring precipitation, and air temperature. Satellite analyses indicate that the springtime normalized difference vegetation index (NDVI) is negatively correlated with surface albedo, and that interannual variability in albedo in the EP produces an estimated forcing on the radiation balance that peaks at 16.0 W m-2 in May. Simulations with a regional climate model indicate that surface energy fluxes during a drought year (1999) differed substantially from those during a year with normal precipitation (2003). These differences were geographically specific, with the EP exhibiting increased albedo and decreased sensible heat flux while the neighboring Zagros Plateau region showed no albedo effect, a large increase in sensible heat flux, and an offsetting reduction in latent heat flux. In both the EP and the Zagros there was a potential for positive feedbacks on temperature and drought in late spring, though the most likely feedback mechanisms differed between the two regions: in the EP surface brightening leads to cooling and reduced turbulent heat flux, while in the Zagros region reduced latent heat flux leads to warming and a deepening of the planetary boundary layer. [ABSTRACT FROM AUTHOR]

A combination of satellite imagery, meteorological station data, and the NCEP/NCAR reanalysis has been used to explore the spatial and temporal evolution of the 2003 heat wave in France, with focus on understanding the impacts and feedbacks at the land surface. Vegetation was severely affected across the study area, especially in a swath across central France that corresponds to the Western European Broadleaf (WEB) Forests ecological zone. The remotely sensed surface temperature anomaly was also greatest in this zone, peaking at +15.4 °C in August. On a finer spatial scale, both the vegetation and surface temperature anomalies were greater for crops and pastures than for forested lands. The heat wave was also associated with an anomalous surface forcing of air temperature. Relative to other years in record, satellite-derived estimates of surface-sensible heat flux indicate an enhancement of 48-61% (24.0-30.5 W m−2) in WEB during the August heat wave maximum. Longwave radiative heating of the planetary boundary layer (PBL) was enhanced by 10.5 W m−2 in WEB for the same period. The magnitude and spatial structure of this local heating is consistent with models of the late twenty-first century climate in France, which predict a transitional climate zone that will become increasingly affected by summertime drought. Models of future climate also suggest that a soil-moisture feedback on the surface energy balance might exacerbate summertime drought, and these proposed feedback mechanisms were tested using satellite-derived heat budgets. Copyright © 2006 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

In arid and semiarid parts of the world, evaporation from irrigated fields may significantly influence humidity, near-surface winds, and precipitation. Using Moderate Resolution Imaging Spectroradiometer (MODIS) Terra imagery from summer and autumn 2000 the authors attempt to improve the realism of a regional climate model (the fifth-generation Pennsylvania State University–NCAR Mesoscale Model) with respect to irrigated agriculture. MODIS data were used to estimate spatially distributed vegetation fraction and to identify areas of irrigated land use. Additionally, a novel surface flux routine designed to simulate traditional flood irrigation was implemented. Together these modifications significantly improved model predictions of water flux and the surface energy balance when judged against independent weather station data and known crop requirements. Model estimates of watershed-level water consumption were more than doubled relative to simulations that did not incorporate MODIS data, and there were small but systematic differences in predicted temperature and humidity near the surface. The modified version of the mesoscale model also predicts the existence of heat-driven circulations around large irrigated features, and these circulations are similar in structure and magnitude to those predicted by linear theory. Based on these results, it was found that accurate representation of irrigated agriculture is a prerequisite to any study of the impact of land-use change on climate or on water resources. [ABSTRACT FROM AUTHOR]

Demonstrates models for the representation of uncertainty and spatially unbiased evaluation of a geographical information system (GIS)-based landslide model. Natural variability of soil properties accounted for in a set of model simulations based on parameter distributions; Landslide model uncertainty and scale; Mechanical and hydrologic parameters used in the slope stability model.

Investigates the development of Zizania aquatica male flowers in North America. Analysis on the histology and morphology of the specie; Details on the evolution of Zizania unisexual flowers; Exploration on the pattern of cell death and ontogenetic timing of abortion; Comparison between the male flower development of Zizania and the subfamily Panicoideae.

Over the last decade, an increase of gastrointestinal illness due to Vibrio parahaemolyticus in the consumption of raw shellfish has been reported in multiple regions around the United States. Studies mainly attribute this increase to rising sea surface temperatures and prolonged warm seasons in the mid-latitudes. Historically, temperature has been the main environmental determinant used to predict V. parahaemolyticus concentrations in shellfish and surface water. However, studies using in situ sampling campaigns have shown that additional water quality parameters can be useful in predicting the bacterium. While the time and cost of obtaining in situ samples throughout the Chesapeake Bay at regular time intervals can exceed available resources, satellite remote sensing has the potential to provide predictions at higher temporal and spatial resolutions. This study uses satellite ocean color remote sensing and sea surface temperature (SST) from the Moderate Resolution Imaging Spectroradiometer (MODIS) to investigate the utility of remotely sensed information for Vibrio parahaemolyticus predictions in the Chesapeake Bay and whether additional remotely sensed information can improve predictions over conventional SST-based models. We find that the addition of remotely sensed salinity, total suspended solids, and chlorophyll-a generally improves presence and abundance predictions compared to SST-only models. Models using remote sensing reflectances and SST also show potential for V. parahaemolyticus predictions, which could bypass the intermediary step of deriving water quality products from reflectances. Remotely sensed ocean color products and SST from one week prior to in situ V. parahaemolyticus measurements are evaluated and shown to be useful in bacterium predictions, which could provide lead-time for management decisions. The forecast models using ocean color products in addition to SST showed improvement over SST-only forecast models. The results of this study suggest that remote sensing can be a valuable tool to aid in higher resolution V. parahaemolyticus predictions and forecasts in the Chesapeake Bay, particularly when multiple environmental predictors are employed. However, the complexities of using remotely sensed data for ecological modeling applications and evaluating model performance also highlight the need for more research in this area. • Addition of SSS, TSS, CHL-A inputs improved Vp predictions over SST-only models. • Remote sensing reflectances can be used as model inputs with SST to predict Vp. • Remote sensing-based models performed similarly to in situ -based models. • Remotely sensed model inputs can be used to forecast the Vp one week in advance. [ABSTRACT FROM AUTHOR]

Background: Diarrhea remains a leading cause of childhood illness throughout the world that is increasing due to climate change and is caused by various species of ecologically sensitive pathogens. The emerging Planetary Health movement emphasizes the interdependence of human health with natural systems, and much of its focus has been on infectious diseases and their interactions with environmental and human processes. Meanwhile, the era of big data has engendered a public appetite for interactive web-based dashboards for infectious diseases. However, enteric infectious diseases have been largely overlooked by these developments. Methods: The Planetary Child Health & Enterics Observatory (Plan-EO) is a new initiative that builds on existing partnerships between epidemiologists, climatologists, bioinformaticians, and hydrologists as well as investigators in numerous low- and middle-income countries. Its objective is to provide the research and stakeholder community with an evidence base for the geographical targeting of enteropathogen-specific child health interventions such as novel vaccines. The initiative will produce, curate, and disseminate spatial data products relating to the distribution of enteric pathogens and their environmental and sociodemographic determinants. Discussion: As climate change accelerates there is an urgent need for etiology-specific estimates of diarrheal disease burden at high spatiotemporal resolution. Plan-EO aims to address key challenges and knowledge gaps by making and disseminating rigorously obtained, generalizable disease burden estimates. Pre-processed environmental and EO-derived spatial data products will be housed, continually updated, and made publicly available for download to the research and stakeholder communities. These can then be used as inputs to identify and target priority populations living in transmission hotspots and for decision-making, scenario-planning, and disease burden projection. Study registration: PROSPERO protocol #CRD42023384709. [ABSTRACT FROM AUTHOR]

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate. [ABSTRACT FROM AUTHOR]

Headwater streams in the highland regions of Ethiopia are exposed to severe human disturbances because of rapid population growth. Protection and management of headwater streams require the development of informative indices that can capture disturbance and assess streams water quality in highland regions. Therefore, we developed a multi-metric index based on macroinvertebrate communities, to assess the ecological condition of the headwater streams in the Ethiopian highlands. We reviewed 38 potential metrics, representing various aspects of the macroinvertebrate assemblages, and selected four of them [total family richness, Simpson index, percentage of shredder, and family richness of EOT (Ephemeroptera, Odonata, and Trichoptera)] as final core metrics. A trisection inter-quartile range system was applied to derive scores for each core metric at impaired and reference sites. The macroinvertebrate multi-metric index (MMI) distinguished well between impaired and reference stream sites and was negatively correlated with the degree of disturbance. The results suggest that the MMI is robust measure of disturbance and can be used as a sensitive tool for evaluating the ecological condition of headwater streams in Ethiopian highlands. [ABSTRACT FROM AUTHOR]

The article offers information on a workshop on arthropodborne viruses (arboviruses) dynamics that involve environmental and ecological systems in a manner that often leads to significant climate sensitivity, held in Manaus, Brazil, from November 30 to December 3, 2015. It sought to engage virologists, epidemiologists, entomologists, social scientists, geographers and climate scientists in focused discussion of arbovirus risks and responses.

The El Niño Southern Oscillation (ENSO) and other climate patterns can have profound impacts on the occurrence of infectious diseases ranging from dengue to cholera. In Africa, El Niño conditions are associated with increased rainfall in East Africa and decreased rainfall in southern Africa, West Africa, and parts of the Sahel. Because of the key role of water supplies in cholera transmission, a relationship between El Niño events and cholera incidence is highly plausible, and previous research has shown a link between ENSO patterns and cholera in Bangladesh. However, there is little systematic evidence for this link in Africa. Using high-resolution mapping techniques, we find that the annual geographic distribution of cholera in Africa from 2000 to 2014 changes dramatically, with the burden shifting to continental East Africa—and away from Madagascar and portions of southern, Central, and West Africa—where almost 50,000 additional cases occur during El Niño years. Cholera incidence during El Niño years was higher in regions of East Africa with increased rainfall, but incidence was also higher in some areas with decreased rainfall, suggesting a complex relationship between rainfall and cholera incidence. Here, we show clear evidence for a shift in the distribution of cholera incidence throughout Africa in El Niño years, likely mediated by El Niño’s impact on local climatic factors. Knowledge of this relationship between cholera and climate patterns coupled with ENSO forecasting could be used to notify countries in Africa when they are likely to see a major shift in their cholera risk. [ABSTRACT FROM AUTHOR]

Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 1981-2014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios. [ABSTRACT FROM AUTHOR]

Ethiopia is a largely agrarian country with nearly 85% of its employment coming from agriculture. Nevertheless, it is not known how much land is under cultivation. Mapping land cover at finer resolution and global scales has been particularly difficult in Ethiopia. The study area falls in a region of high mapping complexity with environmental challenges which require higher quality maps. Here, remote sensing is used to classify a large area of the central and northwestern highlands into eight broad land cover classes that comprise agriculture, grassland, woodland/shrub, forest, bare ground, urban/impervious surfaces, water, and seasonal water/marsh areas. We use data from Landsat spectral bands from 2000 to 2011, the Normalized Difference Vegetation Index (NDVI) and its temporal mean and variance, together with a digital elevation model, all at 30-m spatial resolution, as inputs to a supervised classifier. A Support Vector Machines algorithm (SVM) was chosen to deal with the size, variability and non-parametric nature of these data stacks. In post-processing, an image segmentation algorithm with a minimum mapping unit of about 0.5 hectares was used to convert per pixel classification results into an object based final map. Although the reliability of the map is modest, its overall accuracy is 55%-encouraging results for the accuracy of agricultural uses at 85% suggest that these methods do offer great utility. Confusion among grassland, woodland and barren categories reflects the difficulty of classifying savannah landscapes, especially in east central Africa with monsoonal-driven rainfall patterns where the ground is obstructed by clouds for significant periods of time. Our analysis also points out the need for high quality reference data. Further, topographic analysis of the agriculture class suggests there is a significant amount of sloping land under cultivation. These results are important for future research and environmental monitoring in agricultural land use, soil erosion, and crop modeling of the Abay basin. [ABSTRACT FROM AUTHOR]

Representation of irrigation in Earth System Models has advanced over the past decade, yet large uncertainties persist in the effective simulation of irrigation practices, particularly over locations where the on‐ground practices and climate impacts are less reliably known. Here we investigate the utility of assimilating remotely sensed vegetation data for improving irrigation water use and associated fluxes within a land surface model. We show that assimilating optical sensor‐based leaf area index estimates significantly improves the simulation of irrigation water use when compared to the USGS ground reports. For heavily irrigated areas, assimilation improves the evaporative fluxes and gross primary production (GPP) simulations, with the median correlation increasing by 0.1–1.1 and 0.3–0.6, respectively, as compared to the reference datasets. Further, bias improvements in the range of 14–35 mm mo−1 and 10–82 g m−2 mo−1 are obtained in evaporative fluxes and GPP as a result of incorporating vegetation constraints, respectively. These results demonstrate that the use of remotely sensed vegetation data is an effective, observation‐informed, globally applicable approach for simulating irrigation and characterizing its impacts on water and carbon states. Plain Language Summary: Agricultural irrigation accounts for more than 70% of freshwater use over the globe and can impact local and regional water resources, crop productivities, and climate and weather systems. Investigating impact of irrigation heavily relies on models, which vary in terms of modeling structure, input data sources, assumptions, etc. Given these variations, models are often subject to large uncertainties in estimating irrigation water use. The goal of this study is to explore the potential to integrate satellite observations of vegetation conditions with models to improve estimates of irrigation and its impact across the Contiguous United States. We find that integrating satellite observations is helpful in correcting simulation of vegetation growth, leading to a better estimation of irrigation water use and its impact on surface soil moisture, evapotranspiration, and agricultural productivities. These results underscore the effectiveness of using satellite vegetation observations to improve irrigation modeling. Key Points: Moderate Resolution Imaging Spectroradiometer leaf area index (LAI) data are assimilated into the Noah‐MP land surface model to inform simulation of irrigation schedules and volumesIrrigation simulations without LAI assimilation overestimate irrigation amount and increase the BIAS for evapotranspiration (ET) and GPPAssimilating LAI to constrain irrigation shows the overall best performance for surface soil moisture, ET, and GPP [ABSTRACT FROM AUTHOR]

An estimated 1 billion people suffer from hunger worldwide, and climate change, urbanization, and globalization have the potential to exacerbate this situation. Improved models for predicting food security are needed to understand these impacts and design interventions. However, food insecurity is the result of complex interactions between physical and socio-economic factors that can overwhelm linear regression models. More sophisticated data-mining approaches could provide an effective way to model these relationships and accurately predict food insecure situations. In this paper, we compare multiple regression and data-mining methods in their ability to predict the percent of a country's population that suffers from undernourishment using widely available predictor variables related to socio-economic settings, agricultural production and trade, and climate conditions. Averaging predictions from multiple models results in the lowest predictive error and provides an accurate method to predict undernourishment levels. Partial dependence plots are used to evaluate covariate influence and demonstrate the relationship between food insecurity and climatic and socio-economic variables. By providing insights into these relationships and a mechanism for predicting undernourishment using readily available data, statistical models like those developed here could be a useful tool for those tasked with understanding and addressing food insecurity. [ABSTRACT FROM AUTHOR]

In the United States, irrigation represents the largest consumptive use of freshwater and accounts for approximately one-third of total water usage. Irrigation impacts soil moisture and can ultimately influence clouds and precipitation through land-planetary boundary layer (PBL) coupling processes. This study utilizes NASA's Land Information System (LIS) and the NASA Unified Weather Research and Forecasting Model (NU-WRF) framework to investigate the effects of drip, flood, and sprinkler irrigation methods on land-atmosphere interactions, including land-PBL coupling and feedbacks at the local scale. To initialize 2-day, 1-km WRF forecasts over the central Great Plains in a drier-than-normal (2006) and a wetter-than-normal year (2008), 5-yr irrigated LIS spinups were used. The offline and coupled simulation results show that regional irrigation impacts are sensitive to time, space, and method and that irrigation cools and moistens the surface over and downwind of irrigated areas, ultimately resulting in both positive and negative feedbacks on the PBL depending on the time of day and background climate conditions. Furthermore, the results portray the importance of both irrigation method physics and correct representation of several key components of land surface models, including accurate and timely land-cover and crop-type classification, phenology (greenness), and soil moisture anomalies (through a land surface model spinup) in coupled prediction models. [ABSTRACT FROM AUTHOR]

Background: Satellite observations following flooding coupled with electronic health data collected through syndromic surveillance systems (SyS) may be useful in efficiently characterizing and responding to health risks associated with flooding.Results: There was a 10% (95% Confidence Interval (CI): 1%-19%) increase in asthma related ED visits and 22% (95% CI: 5%-41%) increase in insect bite related ED visits in the flooded ZCTAs compared to non-flooded ZCTAs during the flood period. One month following the floods, diarrhea related ED visits were increased by 15% (95% CI: 4%-27%) for flooded ZCTAs and children and adolescents from flooded ZCTAs had elevated risk for dehydration related ED visits. During the protracted period (2-3 months after the flood period), the risk for asthma, insect bite, and diarrhea related ED visits were elevated among the flooded ZCTAs. Effect modification by reported age, ethnicity and race was observed.Conclusion: Combining satellite observations with SyS data can be helpful in characterizing the location and timing of environmentally mediated adverse health outcomes, which may be useful for refining disaster resilience measures to mitigate health outcomes following flooding. [ABSTRACT FROM AUTHOR]

Background: A deeper understanding of how heat wave definition affects the relationship between heat exposure and health, especially as a function of rurality, will be useful in developing effective heat wave warning systems. Objective: We compared the relationships between different heat wave index (HI) definitions and preterm birth (PTB) and nonaccidental death (NAD) across urban and rural areas. Methods: We used a time-stratified case-crossover design to estimate associations of PTB and NAD with heat wave days (defined using 15 HIs) relative to non-heat wave control days in Alabama, USA (1990-2010). ZIP code-level HIs were derived using data from the North American Land Data Assimilation System. Associations with heat wave days defined using different HIs were compared by bootstrapping. We also examined interactions with rurality. Results: Associations varied depending on the HI used to define heat wave days. Heat waves defined as having at least 2 consecutive days with mean daily temperatures above the 98th percentile were associated with 32.4% (95% CI: 3.7, 69.1%) higher PTB, and heat waves defined as at least 2 consecutive days with mean daily temperatures above the 90th percentile were associated with 3.7% (95% CI: 1.1, 6.3%) higher NAD. Results suggest that significant positive associations were more common when relative-compared with absolute-HIs were used to define exposure. Both positive and negative associations were found in each rurality stratum. However, all stratum-specific significant associations were positive, and NAD associations with heat waves were consistently positive in urban strata but not in middle or rural strata. Conclusions: Based on our findings, we conclude that a relative mean-temperature-only heat wave definition may be the most effective metric for heat wave warning systems in Alabama. [ABSTRACT FROM AUTHOR]

A simulation experiment was conducted to evaluate the effects of planting time, nitrogen rate, and crop variety choice on potato productivity and to assess the adaptation role of these practices in a changing climate in the northwestern tropical highlands of Ethiopia. The study used agroecosystem (AES) as the lens for spatial analysis. Thirty years of simulations were performed for the baseline (1981–2010), near-term (2011–2040), and mid-century (2041–2070) climate periods using a calibrated and validated SUBSTOR-Potato (DSSAT) model. The results showed that shifting planting time forward and changing the nitrogen application rate had greater productivity benefits than switching varieties in all climate periods and AESs. Late planting and higher nitrogen rates increased tuber yield during the near-term climate period. However, in the mid-century climate period, early planting of both medium and long maturity varieties with higher nitrogen rates showed potential adaptation benefits despite its negative impact on yield in the current climate. This result highlights the distinction between optimal management under current conditions and practices that are truly adaptive to climate change. [Display omitted] • Potato growing tropical highlands are experiencing the effects of climate change. • The adaptive potential of agronomic practices for potato production was assessed. • Combinations of agronomic practices can contribute to adaptation. • Current optimal practices may not be adaptive under future climate conditions. [ABSTRACT FROM AUTHOR]

Summary: A land surface model’s ability to simulate states (e.g., soil moisture) and fluxes (e.g., runoff) is limited by uncertainties in meteorological forcing and parameter inputs as well as inadequacies in model physics. In this study, anomalies of terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission were assimilated into the NASA Catchment land surface model in western and central Europe for a 7-year period, using a previously developed ensemble Kalman smoother. GRACE data assimilation led to improved runoff estimates (in temporal correlation and root mean square error) in 17 out of 18 hydrological basins, even in basins smaller than the effective resolution of GRACE. Improvements in root zone soil moisture were less conclusive, partly due to the shortness of the in situ data record. GRACE data assimilation also had significant impacts in groundwater estimates including trend and seasonality. In addition to improving temporal correlations, GRACE data assimilation also reduced increasing trends in simulated monthly TWS and runoff associated with increasing rates of precipitation. The assimilation downscaled (in space and time) and disaggregated GRACE data into finer scale components of TWS which exhibited significant changes in their dryness rankings relative to those without data assimilation, suggesting that GRACE data assimilation could have a substantial impact on drought monitoring. Signals of drought in GRACE TWS correlated well with MODIS Normalized Difference Vegetation Index (NDVI) data in most areas. Although they detected the same droughts during warm seasons, drought signatures in GRACE derived TWS exhibited greater persistence than those in NDVI throughout all seasons, in part due to limitations associated with the seasonality of vegetation. Mass imbalances associated with GRACE data assimilation and challenges of using GRACE data for drought monitoring are discussed. [ABSTRACT FROM AUTHOR]

There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth's land–atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013–2018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29–2.65 K), root mean square error (2.92–3.25 K), and coefficients of determination (0.82–0.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition. [ABSTRACT FROM AUTHOR]