Water availability and seasonality shape elemental stoichiometry across space and time.

The interaction of climate change and increasing anthropogenic water withdrawals is anticipated to alter surface water availability and the transport of carbon (C), nitrogen (N), and phosphorus (P) in river networks. But how changes to river flow will alter the balance, or stoichiometry, of these fluxes is unknown. The Lower Flint River Basin (LFRB) is part of an interstate watershed relied upon by several million people for diverse ecosystem services, including seasonal crop irrigation, municipal drinking water access, and public recreation. Recently, increased water demand compounded with intensified droughts have caused historically perennial streams in the LFRB to cease flowing, increasing ecosystem vulnerability. Our objectives were to quantify how riverine dissolved C:N:P varies spatially and seasonally and determine how monthly stoichiometric fluxes varied with overall water availability in a major tributary of LFRB. We used a long-term record (21-29 years) of solute water chemistry (dissolved organic carbon, nitrate/nitrite, ammonia, and soluble reactive phosphorus) paired with long-term stream discharge data across six sites within a single LFRB watershed. We found spatial and seasonal differences in soluble nutrient concentrations and stoichiometry attributable to groundwater connections, the presence of a major floodplain wetland, and flow conditions. Further, we showed that water availability, as indicated by the Palmer Drought Severity Index (PDSI), strongly predicted stoichiometry with generally lower C:N and C:P and higher N:P fluxes during periods of low water availability (PDSI < -4). These patterns suggest there may be long-term and significant changes to stream ecosystem function as water availability is being dramatically altered by human demand with consequential impacts on solute transport, in-stream processing, and stoichiometric ratios.

A Near Four-Decade Time Series Shows the Hawaiian Islands Have Been Browning Since the 1980s.

The Hawaiian Islands have been identified as a global biodiversity hotspot. We examine the Normalized Difference Vegetation Index (NDVI) using Climate Data Records products (0.05 × 0.05°) to identify significant differences in NDVI between neutral El Niño-Southern Oscillation years (1984, 2019) and significant long-term changes over the entire time series (1982-2019) for the Hawaiian Islands and six land cover classes. Overall, there has been a significant decline in NDVI (i.e., browning) across the Hawaiian Islands from 1982 to 2019 with the islands of Lana'i and Hawai'i experiencing the greatest decreases in NDVI (≥44%). All land cover classes significantly decreased in NDVI for most months, especially during the wet season month of March. Native vegetation cover across all islands also experienced significant declines in NDVI, with the leeward, southwestern side of the island of Hawai'i experiencing the greatest declines. The long-term trends in the annual total precipitation and annual mean Palmer Drought Severity Index (PDSI) for 1982-2019 on the Hawaiian Islands show significant concurrent declines. Primarily positive correlations between the native ecosystem NDVI and precipitation imply that significant decreases in precipitation may exacerbate the decrease in NDVI of native ecosystems. NDVI-PDSI correlations were primarily negative on the windward side of the islands and positive on the leeward sides, suggesting a higher sensitivity to drought for leeward native ecosystems. Multi-decadal time series and spatially explicit data for native landscapes provide natural resource managers with long-term trends and monthly changes associated with vegetation health and stability.

Drought identification based on Palmer drought severity index and return period analysis of drought characteristics in Huaibei Plain China.

The threshold level method for drought identification is challenging due to the problems of selection of drought index reflecting the drought process associated with water supply and demand as well as the underlying physical meaning of drought thresholds. The frequently used hydrological drought indices (e.g., runoff) are susceptible to being affected by human activities, and drought characteristics are incapable of revealing spatial and temporal comparability. Furthermore, the drought process with the same severity but a longer duration is more likely to be evaluated as a more severe event, which contradicts the actual drought situation. In this study, the Palmer drought severity index (PDSI) method, in which the meteorological factors less influenced by human activities were taken as the input, was adopted to determine the dry/wet states and the PDSI value at each period firstly. The dry/wet states were defined with dry period, wet period, transition period, transition period in dry spell, and transition period in a wet spell. Following that, drought identification criteria were established through the dry/wet states and PDSI value according to the consistency of the identified results and the actual drought situations. Particularly, drought severity and peak intensity were taken as drought characteristics in this paper, and the joint return periods of the characteristics were estimated based on the Gumbel-Hougaard copula function. And eventually, a case study was conducted in Huaibei Plain, China. The results showed that the most severe droughts identified by PDSI had a good consistency with the actual drought situations, drought severity and peak intensity were applicable to reflect the drought impacts. It is worth noting that the implications of the joint return period and the relationships among different types of them. The occurrence probability of a multi-characteristic drought event should be calculated by the integration of joint probability density function over the region corresponding to the event of interest, and the joint frequency of drought characteristics should not be used as the occurrence probability of the drought disaster losses greater (or less) than that caused by the drought with the same characteristics. In addition, drought processes identified by PDSI and standardized precipitation indices (SPI) from monthly and seasonal scales were compared, indicating the drought identified results through PDSI are almost consistent with the actual situations.

Assessing the effect of spatial-temporal droughts on dominant crop yield changes in Central Malawi.

Central Malawi has intensely been subjected to different climate-related shocks such as floods, dry spells, and droughts, resulting in decreases in crop yields. Due to their recurrence arising from the effects of climate change, drought characterization, monitoring, and prediction are crucial in guiding agriculture-water users and planners to prepare drought risk management plans and early warning systems. This research analyzed droughts, using multiple drought indices and their impacts on dominant crops over Central Malawi. Forty years of hydro-meteorological data (1977-2017) from nine rain-gauging stations and crop yield data from 1983 to 2017 from four districts were analyzed. The study discovered that drought events in the Agricultural Development Division (ADD) are highly a function of rainfall deficit and high temperatures. The results highlighted that the rainfall patterns in the area are not dependable, calling for the utilization of climate-smart irrigation systems such as drip irrigation and rainwater harvesting technologies. Furthermore, we achieved that crops such as cassava and groundnuts must be promoted to withstand the long water stress duration. These crops also have a multiplier effect; hence, they can enhance food security in the region. This study recommends that using more robust variables in drought analysis studies is necessary for effective drought monitoring and early warning systems. In corroboration with disaster management NGOs, it is recommended that the government should be proactive in developing integrated drought management policies and planning strategies for drought adaptation and mitigation.

Estimation and easy calculation of the Palmer Drought Severity Index from the meteorological data by using the advanced machine learning algorithms.

Drought, which has become one of the most severe environmental problems worldwide, has serious impacts on ecological, economic, and socially sustainable development. The drought monitoring process is essential in the management of drought risks, and drought index calculation is critical in the tracking of drought. The Palmer Drought Severity Index is one of the most widely used methods in drought calculation. The drought calculation according to Palmer is a time-consuming process. Such a troublesome can be made easier using advanced machine learning algorithms. Therefore, in this study, the advanced machine learning algorithms (LR, ANN, SVM, and DT) were employed to calculate and estimate the Palmer drought Z-index values from the meteorological data. Palmer Z-index values, which will be used as training data in the classification process, were obtained through a special-purpose software adopting the classical procedure. This special-purpose software was developed within the scope of the study. According to the classification results, the best R-value (0.98) was obtained in the ANN method. The correlation coefficient was 0.98, Mean Squared Error was 0.40, and Root Mean Squared Error was 0.56 in this success. Consequently, the findings showed that drought calculation and prediction according to the Palmer Index could be successfully carried out with advanced machine learning algorithms. Graphical Abstract.

Climate and disturbance influence self-sustaining stand dynamics of aspen (Populus tremuloides) near its range margin.

Species that are primarily seral may form stable (self-sustaining) communities under certain disturbance regimes or environmental conditions, yet such populations may also be particularly vulnerable to ecological change. Aspen (Populus spp.) are generally considered seral throughout the Northern Hemisphere, including P. tremuloides, the most widely distributed tree species in North America. Recent declines in aspen populations have occurred, especially along drought-sensitive margins of its range and where fire exclusion and herbivory have promoted community transition. However, aspen also forms stable stands, and examination of the mechanisms that influence persistence can offer conservation insights, especially where populations are vulnerable to changing climate or altered disturbance dynamics. We sampled tree age and stand characteristics of isolated aspen forests in the arid Great Basin (USA) to determine if (1) aspen communities are more fire-dependent and seral or fire-independent and stable; (2) ungulate browsing inhibits aspen stability; and (3) temporal patterns of vegetative reproduction (i.e., ramet establishment or "suckering") are correlated with climate. Aspen size and age class densities strongly fit negative exponential distributions, whether grouped geographically or by functional type, suggesting landscape-scale persistence. Continuous age distributions and high proportions of recruitment-sized to overstory trees suggest stability at stand scales, with exceptions including stands with higher browsing pressure. Few stands had evidence of fire, and relationships between dead tree size and variability in live tree size suggest a lack of fire dependency. Several 5-yr averaged climate variables and one sea surface temperature index were correlated with aspen ramet establishment densities over time, with strongest relationships occurring ~5 yr prior to establishment year, often followed by inverse relationships ~1 yr after. Indeed, aspen establishment density for a recent 41-yr period was reliably reconstructed using antecedent climate conditions derived from a single drought index. Temporally synchronized aspen ramet establishment across the study region may be due to climate-driven storage of nonstructural carbohydrate reserves in clonal root systems later used for regeneration. Complex regeneration dynamics of these self-sustaining aspen stands, especially sensitivity to climate variability, suggest they may serve as harbingers of ecological change in the arid Great Basin and in other aspen populations near their range margin.

The interaction of drought and habitat explain space-time patterns of establishment in saguaro (Carnegiea gigantea).

The long-lived columnar saguaro cactus (Carnegiea gigantea) is among the most studied plants in the world. Long-term studies have shown saguaro establishment to be generally episodic and strongly influenced by precipitation and temperature. Water limitation through lower-than-average seasonal rainfall and elevated temperatures increasing evaporative loss can reduce survivorship of recent germinates. Thus, multi-year, extended drought could cause populations to decline as older saguaros die without replacement. Previous studies have related establishment to temporal variation in rainfall, but most studies have been on non-randomized plots in ideal habitat and thus might not have captured the full variability within the local area. We studied how saguaro establishment varied in space and which habitat features may buffer responses to drought on 36 4-ha plots located randomly across an elevation gradient, including substantial replication in landscape position (bajada, foothills, and slopes) in the two disjunct districts of Saguaro National Park in southern Arizona, USA. Recent, severe drought coincided with drastic declines in saguaro establishment across this ~25,000-ha area. Establishment patterns derived from the park-wide data set was strongly correlated with drought, but the Park's two districts and diversity of plots demonstrated substantially different population outcomes. Saguaro establishment was best explained by the interaction of drought and habitat type; establishment in bajada and foothill plots dropped to near-zero under the most severe periods of water limitation but remained higher in slope plots during the same time span. Combined with saguaro density estimates, these data suggest that the most suitable habitat type for saguaro establishment shifted to higher elevations during the time span of the recent drought. These results place into context the extent to which historical patterns of demography provide insight into future population dynamics in a changing climate and reveal the importance of understanding dynamics across the distribution of possible local habitat types with response to variation in weather.

Optimal population prediction of sandhill crane recruitment based on climate-mediated habitat limitations.

1. Prediction is fundamental to scientific enquiry and application; however, ecologists tend to favour explanatory modelling. We discuss a predictive modelling framework to evaluate ecological hypotheses and to explore novel/unobserved environmental scenarios to assist conservation and management decision-makers. We apply this framework to develop an optimal predictive model for juvenile (<1 year old) sandhill crane Grus canadensis recruitment of the Rocky Mountain Population (RMP). We consider spatial climate predictors motivated by hypotheses of how drought across multiple time-scales and spring/summer weather affects recruitment. 2. Our predictive modelling framework focuses on developing a single model that includes all relevant predictor variables, regardless of collinearity. This model is then optimized for prediction by controlling model complexity using a data-driven approach that marginalizes or removes irrelevant predictors from the model. Specifically, we highlight two approaches of statistical regularization, Bayesian least absolute shrinkage and selection operator (LASSO) and ridge regression. 3. Our optimal predictive Bayesian LASSO and ridge regression models were similar and on average 37% superior in predictive accuracy to an explanatory modelling approach. Our predictive models confirmed a priori hypotheses that drought and cold summers negatively affect juvenile recruitment in the RMP. The effects of long-term drought can be alleviated by short-term wet spring-summer months; however, the alleviation of long-term drought has a much greater positive effect on juvenile recruitment. The number of freezing days and snowpack during the summer months can also negatively affect recruitment, while spring snowpack has a positive effect. 4. Breeding habitat, mediated through climate, is a limiting factor on population growth of sandhill cranes in the RMP, which could become more limiting with a changing climate (i.e. increased drought). These effects are likely not unique to cranes. The alteration of hydrological patterns and water levels by drought may impact many migratory, wetland nesting birds in the Rocky Mountains and beyond. 5. Generalizable predictive models (trained by out-of-sample fit and based on ecological hypotheses) are needed by conservation and management decision-makers. Statistical regularization improves predictions and provides a general framework for fitting models with a large number of predictors, even those with collinearity, to simultaneously identify an optimal predictive model while conducting rigorous Bayesian model selection. Our framework is important for understanding population dynamics under a changing climate and has direct applications for making harvest and habitat management decisions.

A Place-Based Approach to Drought Forecasting in South-Central Oklahoma.

To assist water managers in south-central Oklahoma prepare for future drought, reliable place-based drought forecasts are produced. Past-, present-, and future-forecasted climate indices (Multivariate ENSO Index, Pacific Decadal Oscillation index, and Atlantic Multidecadal Oscillation index) and past and present Palmer Drought Severity Index (PDSI) are employed as predictor variables to forecast PDSI using a multivariate regression technique. PDSI is forecasted 18 months in advance with sufficient skill to provide water managers early warning of drought. Using a training data set obtained from the period January 1901 to November 2021, a second-order model equation that contains, without any restriction, all the predictors and their interaction terms is built to predict drought intensity. Significant predictors are selected through stepwise regression, with cross-validation producing the simplest restricted model that describes the data well. PDSI values are predicted using 1000 fitted restricted models produced from bootstrapping, then averaged monthly. The technique found the best-fitting model and estimated the model coefficients that minimized the sum of squared deviations between the fitted model and the predictor variables. The adjusted R-squared value of the restricted model is large enough to explain an adequately accurate model, and relatively low values of error measures point to good predictive ability of the model. Although the model slightly overestimates the PDSI forecast maxima and minima, it necessarily captures the timing of the periods of severe to exceptional drought.

Comparative Assessment of Grassland Dynamic and Its Response to Drought Based on Multi-Index in the Mongolian Plateau.

This study applied grassland related multi-index and assessed the effects of climate change by investigating grassland responses to drought. This process was performed to study grassland vegetation dynamic accurately and evaluate the effect of drought in the Mongolian Plateau (MP). The spatial-temporal characteristics of grassland dynamic in terms of coverage (Fv), surface bareness (Fb), and net primary production (NPP) from 2000 to 2013 were explored. We implemented the maximum Pearson correlation to analyze the grassland vegetation in response to drought by using self-calibrating Palmer Drought Severity Index (scPDSI). Results show that Fv and NPP present an increasing trend (0.18 vs. 0.43). Fb showed a decreasing trend with a value of -0.16. The grassland Fv and NPP positively correlated with scPDSI, with a value of 0.12 and 0.85, respectively, and Fb was -0.08. The positive correlation between Fv and NPP accounted for 84.08%, and the positive correlation between Fv and scPDSI accounted for 93.88%. On the contrary, the area with a negative correlation between Fb and scPDSI was 57.43%. The grassland in the MP showed a recovery tendency. The increase in grassland caused by positive reaction was mainly distributed in the middle of Mongolia (MG), whereas that caused by counter response was mainly distributed in the east and west MG and northeast Inner Mongolia autonomous region of China (IM). The relevant results may provide useful information for policymakers about mitigation strategies against the inverse effects of drought on grassland and help to ease the losses caused by drought.

Evidence for increasingly variable Palmer Drought Severity Index in the United States since 1895.

Annual and summertime trends towards increasingly variable values of the Palmer Drought Severity Index (PDSI) over a sub-decadal period (five years) were investigated within the contiguous United States between 1895 and the present. For the contiguous United States as a whole, there is a significant increasing trend in the five-year running minimum-maximum ranges for the annual PDSI (aPDSI5 yr(min|max, range)). During this time frame, the average aPDSI5 yr(min|max, range) has increased by about one full unit, indicating a substantial increase in drought variability over short time scales across the United States. The end members of the running aPDSI5 yr(min|max, range) highlight even more rapid changes in the drought index variability within the past 120 years. This increasing variability in the aPDSI5 yr(min|max, range) is driven primarily by changes taking place in the Pacific and Atlantic Ocean coastal climate regions, climate regions which collectively comprise one-third the area of the contiguous United States. Similar trends were found for the annual and summertime Palmer Hydrological Drought Index (PHDI), the Palmer Modified Drought Index (PMDI), and the Palmer Z Index (PZI). Overall, interannual drought patterns in the contiguous United States are becoming more extreme and difficult to predict, posing a challenge to agricultural and other water-resource related planning efforts.