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Drought effects on tree growth and mortality are widely studied, but scant knowledge exists on its impact on stand density, size variation, or mixing proportions. Grasping drought's influence on structural and compositional diversity is crucial for stand dynamics, ecosystem services, and silvicultural adaptation. We relied on KROOF, a 5-year throughfall exclusion experiment in a mature Norway spruce (Picea abies) and European beech (Fagus sylvatica) stand, to analyze its impact on structural and compositional attributes, including Stand Density Index (SDI), Growth Dominance Coefficient (GDC), and species mixing proportion. Our study demonstrates that drought-induced growth reduction and tree loss decreased SDI by 27%, mixing proportion by 41% at Norway spruce’s expense, and homogenized stand structure. Furthermore, we reveal that stand density, mixing proportion, and structural diversity were more affected in Norway spruce, stabilizing growth at the stand level. Extended drought significantly altered growth partitioning in favor of smaller trees, with a 70% reduction in growth-size relationship slope and a 157% decrease in GDC. Species-level analysis indicated a stronger partitioning shift towards smaller trees, particularly in Norway spruce. We discuss that longer drought periods may trigger acclimation at tree and stand levels, potentially underestimated when based solely on individual drought years. Sustained stress could induce acclimation across various levels, from the stand to the species cohort, tree, and organ. Maintaining structural and compositional diversity may mitigate future drought stress effects on growth, mortality, and stand structure, as exemplified by the extended experimental drought. We suggest silvicultural approaches better attuned to natural processes amid climate change.

The West without Water documents the tumultuous climate of the American West over twenty millennia, with tales of past droughts and deluges and predictions about the impacts of future climate change on water resources. Looking at the region’s current water crisis from the perspective of its climate history, the authors ask the central question of what is “normal” climate for the West, and whether the relatively benign climate of the past century will continue into the future. The West without Water merges climate and paleoclimate research from a wide variety of sources as it introduces readers to key discoveries in cracking the secrets of the region’s climatic past. It demonstrates that extended droughts and catastrophic floods have plagued the West with regularity over the past two millennia and recounts the most disastrous flood in the history of California and the West, which occurred in 1861–62. The authors show that, while the West may have temporarily buffered itself from such harsh climatic swings by creating artificial environments and human landscapes, our modern civilization may be ill-prepared for the future climate changes that are predicted to beset the region. They warn that it is time to face the realities of the past and prepare for a future in which fresh water may be less reliable.

Lack of attention to spatial and temporal cross-scale dynamics and effects could be understood as one of the lacunas in scholarship on river basin management. Within the water-climate-food-energy nexus, an integrated and inclusive approach that recognizes traditional knowledge about and experiences of climate change and water resource management can provide crucial assistance in confronting problems in megaprojects and multipurpose river basin management projects. The Mahaweli Development Program (MDP), a megaproject and multipurpose river basin management project, is demonstrating substantial failures with regards to the spatial and temporal impacts of climate change and socioeconomic demands for water allocation and distribution for paddy cultivation in the dry zone area, which was one of the driving goals of the project at the initial stage. This interdisciplinary study explores how spatial and temporal climatic changes and uncertainty in weather conditions impact paddy cultivation in dry zonal areas with competing stakeholders’ interest in the Mahaweli River Basin. In the framework of embedded design in the mixed methods research approach, qualitative data is the primary source while quantitative analyses are used as supportive data. The key findings from the research analysis are as follows: close and in-depth consideration of spatial and temporal changes in climate systems and paddy farmers’ socioeconomic demands altered by seasonal changes are important factors. These factors should be considered in the future modification of water allocation, application of distribution technologies, and decision-making with regards to water resource management in the dry zonal paddy cultivation of Sri Lanka.

The Mediterranean long shelf-life (LSL) tomatoes are a group of landraces with a fruit remaining sound up to 6-12 months after harvest. Most have been selected under semi-arid Mediterranean summer conditions with poor irrigation or rain-fed and thus, are drought tolerant. Besides the convergence in the latter traits, local selection criteria have been very variable, leading to a wide variation in fruit morphology and quality traits. The different soil characteristics and agricultural management techniques across the Mediterranean denote also a wide range of plant adaptive traits to different conditions. Despite the notorious traits for fruit quality and environment adaptation, the LSL landraces have been poorly exploited in tomato breeding programs, which rely basically on wild tomato species. In this review, we describe most of the information currently available for Mediterranean LSL landraces in order to highlight the importance of this genetic resource. We focus on the origin and diversity, the main selective traits, and the determinants of the extended fruit shelf-life and the drought tolerance. Altogether, the Mediterranean LSL landraces are a very valuable heritage to be revalued, since constitutes an alternative source to improve fruit quality and shelf-life in tomato, and to breed for more resilient cultivars under the predicted climate change conditions.

The objective of this study was to model a new drought index called the Fusion-based Hydrological Meteorological Drought Index (FHMDI) to simultaneously monitor hydrological and meteorological drought. Aiming to estimate drought more accurately, local measurements were classified into various clusters using the AGNES clustering algorithm. Four single artificial intelligence (SAI) models—namely, Gaussian Process Regression (GPR), Ensemble, Feedforward Neural Networks (FNN), and Support Vector Regression (SVR)—were developed for each cluster. To promote the results of single of products and models, four fusion-based approaches, namely, Wavelet-Based (WB), Weighted Majority Voting (WMV), Extended Kalman Filter (EKF), and Entropy Weight (EW) methods, were used to estimate FHMDI in different time scales, precipitation, and runoff. The performance of single and combined products and models was assessed through statistical error metrics, such as Kling–Gupta efficiency (KGE), Mean Bias Error (MBE), and Normalized Root Mean Square Error (NRMSE). The performance of the proposed methodology was tested over 24 main river basins in Iran. The validation results of the FHMDI (the compliance of the index with the pre-existing drought index) revealed that it accurately identified drought conditions. The results indicated that individual products performed well in some river basins, while fusion-based models improved dataset accuracy more compared to local measurements. The WMV with the highest accuracy (lowest NRMSE) had a good performance in 60% of the cases compared to all other products and fusion-based models. WMV also showed higher efficiency in 100% of the cases than all other fusion-based and SAI models for simultaneous hydrological and meteorological drought estimation. In light of these findings, we recommend the use of fusion-based approach to improve drought modeling.

Along with population growth, global climate change represents a critical threat to agricultural production, compromising the goal of achieving food and nutrition security for all. It is urgent to create sustainable and resilient agri-food systems capable of feeding the world without debilitating the planet. The Food and Agriculture Organization of the United Nations (FAO) refers to pulses as a superfood, as one of the most nutritious crops with high health benefits. Considered to be low-cost, many can be produced in arid lands and have an extended shelf-life. Their cultivation helps reduce greenhouse gases and increases carbon sequestration, also improving soil fertility. Cowpea, Vigna unguiculata (L.) Walp. is particularly drought tolerant, with a wide diversity of landraces adapted to different environments. Considering the importance of knowing and valuing the genetic variability of this species in Portugal, this study assessed the impact of drought on four landraces of cowpea (L1 to L4) from different regions of the country and a national commercial variety (CV) as a reference. The development and evaluation of morphological characteristics were monitored in response to terminal drought (imposed during the reproductive phase), and its effects were evaluated on the yield and quality of the produced grain, namely the weight of 100 grains, color, protein content, and soluble sugars. Under drought conditions, the landraces L1 and L2 showed early maturation as a strategy to avoid water deficit. Morphological alteration of the aerial part of the plants was evident in all genotypes, with a rapid reduction in the number of leaves and a reduction in the number of flowers and pods by between 44 and 72%. The parameters of grain quality, the weight of 100 grains, color, protein, and soluble sugars did not vary significantly, except for sugars of the raffinose family that is associated with the adaptive mechanisms of plants to drought. The performance and maintenance of the evaluated characteristics reflect the adaptation acquired in the past by exposure to the Mediterranean climate, highlighting the potential agronomic and genetic value, still little exploited, that could contribute to production stability, preserved nutritional value, and food safety under water stress.

Soil moisture plays a crucial role in drought monitoring, flood forecasting, and water resource management. Data assimilation methods can integrate the strengths of land surface models (LSM) and remote sensing data to generate high-precision and spatio-temporally continuous soil moisture products. However, one of the challenges of the land data assimilation system (LDAS) is how to accurately estimate model and observation errors. To address this, we had previously proposed a dual-cycle assimilation algorithm that can simultaneously estimate the model and observation errors, LSM parameters, and observation operator parameters. However, this algorithm requires a large ensemble size to guarantee stable parameter estimates, resulting in low efficiency and limiting its large-scale applications. To address this limitation, the authors employed the following approaches: (1) using automatic differentiation to compute the Jacobian matrix of LSM instead of constructing a tangent linear model of LSM; and (2) replacing the ensemble Kalman filter framework with the extended Kalman filter (EKF) framework to improve the efficiency of parameter optimization for the dual-cycle algorithm. The EKF-based dual-cycle algorithm accelerated the parameter estimation efficiency near 60 times during a 90-day time period with a model integration time step of 1 h. To evaluate the dual-cycle LDAS at the regional-scale, it was applied to assimilate the SMAP soil moisture over the Tibetan Plateau, and soil moisture estimates were validated using in situ observations from four different climatic areas. The results showed that the EKF-based dual-cycle LDAS corrected biases in both the model and observations and produced more accurate estimates of soil moisture, land surface temperature, and evapotranspiration than did the open loop with default parameters. Furthermore, the spatial distribution of soil parameters (sand content, clay content, and porosity) obtained from the LDAS was more reasonable than those of default values. The EKF-based dual-cycle algorithm developed in this study is expected to improve the assimilation skills of land surface, ecological, and hydrological studies.

Stream water temperatures influence water quality, with effects on aquatic biodiversity, drinking-water provision, electricity production, agriculture, and recreation. Therefore, stakeholders would benefit from an operational forecasting service that would support timely action. Deep-learning models are well-suited to providing probabilistic forecasts at individual stations of a monitoring network. Here, we train and evaluate several state-of-the-art models using 10 years of data from 54 stations across Switzerland. Static catchment features, time of the year, meteorological observations from the past 64 d, and their ensemble forecasts for the following 32 d are included as predictors in the models to estimate daily maximum water temperature over the next 32 d. Results show that the temporal fusion transformer (TFT) model performs best, with a continuous rank probability score (CRPS) of 0.70 °C averaged over all lead times, stations, and 90 forecasts distributed over 1 year. The TFT is followed by the recurrent neural network encoder–decoder, with a CRPS of 0.74 °C, and the neural hierarchical interpolation for time series, with a CRPS of 0.75 °C. These deep-learning models outperform other simpler models trained at each station: random forest (CRPS = 0.80 °C), multi-layer perceptron neural network (CRPS = 0.81 °C), and autoregressive linear model (CRPS = 0.96 °C). The average CRPS of the TFT degrades from 0.38 °C at lead a time of 1 d to 0.90 °C at a lead time of 32 d, largely driven by the uncertainty of the meteorological ensemble forecasts. In addition, TFT water temperature predictions at new and ungauged stations outperform those from the other models. When analyzing the importance of model inputs, we find a dominant role of observed water temperature and future air temperature, while including precipitation and time of the year further improves predictive skill. Operational probabilistic forecasts of daily maximum water temperature are generated twice per week with our TFT model and are publicly available at https://www.drought.ch/de/impakt-vorhersagen-malefix/wassertemperatur-prognosen/ (last access: 20 March 2025). Overall, this study provides insights into the extended-range predictability of stream water temperature and into the applicability of deep-learning models in hydrology.

Seed inoculation with microbial cells is one of the potential invigouration techniques for enhancing the emergence and growth of plants. Herein, we approached a new localized delivery of beneficial microbial cells (Methylobacterium) by invigorating seeds with electrospun Polyvinyl alcohol (PVA) nanofibre containing microbial cells. Methylobacterium is a growth promoting bacteria that has recently drawn attention in agriculture, particularly for drought management. PVA was used in this research because of its electrospinnability and biodegradability. Encapsulation study shows effective immobilization of bacteria cells (Methylorubrum aminovorans) in PVA nanofibre. SEM and TEM characterization further confirmed the entrapment of microbial cells. The microbial plating enumeration reveals 6.6 × 105 CFU g−1 of nanofibre to the initial loading population of 1 × 108 CFU. Viability of nanofibre encapsulated bacterial cells under ambient environment found 1.85 × 105 CFU g−1, 2.2 × 104 CFU g−1 and 1.2 × 104 CFU g−1 on 10, 20 and 30 days after storage, respectively. In vitro bio-efficacy study exhibits that the seeds coated by PVA nanofibres containing M. aminovorans recorded higher germination, root & shoot length, seedling vigor, drymatter production, plant biomass, plant root volume, nodule numbers and fresh weight of nodules. The study concludes that microbial cells could be immobilized in electrospun nanofibre for extended shelf-life of microbial cells and as an effective seed coating for localized delivery.

Background Accelerated vegetation changes are predicted for Southwestern forests due to changing disturbance regimes and climate. The 2001 Leroux Fire burned across a landscape with pre-existing permanent plots during one of the most extreme drought periods over the last few decades, providing a rare opportunity to assess wildfire−drought interactions. The wildfire burned with variable severity across a mountainous transition zone. We took advantage of this opportunity to re-measure plots originally established in 2000, and extend the temporal scale of response data to 15 years post-fire. Results Although fire severity classification adequately described initial effects, tree mortality was indistinguishable among the burned plots between 2002 and 2016, ranging from 38 to 41%. Our results indicated extensive secondary tree mortality that nearly equaled initial mortality in three of four fire severity classes. Mortality from 2002 to 2016 varied by species, with quaking aspen ( Populus tremuloides Michx.) experiencing disproportionately higher mortality (46 to 62% tree loss) than other species across all fire severities. Ponderosa pine ( Pinus ponderosa Engelm.) mortality (34% tree loss) between 2002 and 2016 on unburned plots, along with similar mortality on moderate- and high-severity plots, show that even the most drought-tolerant species on our site was affected by exceptionally warm and dry conditions. Regeneration immediately post fire was dominated by aspen sprouts across all fire severities. By 2016, however, aspen densities were lower than pre-fire observations in unburned, moderate- and high-severity plots, and conifer seedlings primarily established in unburned plots. Ponderosa pine seedlings established between 2011 and 2016 and were observed in only one unburned plot in 2016. The lack of pine regeneration and the relatively small size of high-severity patches in the Leroux Fire suggest that factors other than seed dispersal limited ponderosa pine regeneration. Conclusions Substantial mortality (68% tree loss) and 39% basal area reduction over the 15-year study, plus variable aspen regeneration and sparse conifer regeneration, led to non-forested conditions and isolated aspen stands. Although small in size, the Leroux Fire provided an example of continuing post-fire forest community changes driven by temperature increases and drought. Thus, wildfire and climate change interactions may accelerate shifts in structure and composition in Southwest forest ecosystems.