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In this study, the spatiotemporal variability of drought over the entire Southeast Asia (SEA) region and its associations with the large-scale climate drivers during the period 1960–2019 are investigated for the first time. The 12-month Standardized Precipitation Evapotranspiration Index (SPEI) was computed based on the monthly Global Precipitation Climatology Centre (GPCC) precipitation and the monthly Climate Research Unit (CRU) 2-m temperature. The relationships between drought and large-scale climate drivers were examined using the principal component analysis (PCA) and maximum covariance analysis (MCA) techniques. Results showed that the spatiotemporal variability of drought characteristics over SEA is significantly different between mainland Indochina and the Maritime Continent and the difference has been increased substantially in recent decades. Moreover, the entire SEA is divided into four homogeneous drought subregions. Drought over SEA is strongly associated with oceanic and atmospheric large-scale drivers, particularly El Niño–Southern Oscillation (ENSO), following by other remote factors such as the variability of sea surface temperature (SST) over the tropical Atlantic, the Pacific decadal oscillation (PDO), and the Indian Ocean dipole mode (IOD). In addition, there exists an SST anomaly dipole over the Pacific Ocean, which modulates the atmospheric circulations and consequently precipitation over SEA, affecting drought conditions in the study region.

The Madden‐Julian Oscillation (MJO) is the leading mode of intraseasonal climate variability, having profound impacts on a wide range of weather and climate phenomena. Here, we use a wavelet‐based spectral Principal Component Analysis (wsPCA) to evaluate the skill of 20 state‐of‐the‐art CMIP6 models in capturing the magnitude and dynamics of the MJO. By construction, wsPCA has the ability to focus on desired frequencies and capture each propagative physical mode with one principal component (PC). We show that the MJO contribution to the total intraseasonal climate variability is substantially underestimated in most CMIP6 models. The joint distribution of the modulus and angular frequency of the wavelet PC series associated with MJO is used to rank models relatively to the observations through the Wasserstein distance. Using Hovmöller phase‐longitude diagrams, we also show that precipitation variability associated with MJO is underestimated in most CMIP6 models for the Amazonia, Southwest Africa, and Maritime Continent., Key Points A wavelet‐based spectral principal component analysis is used to examine CMIP6 models in reproducing the Madden‐Julian Oscillation (MJO)CMIP6 models capture the average MJO propagation speed but significantly underestimate the MJO contribution to the total intraseasonal climate variabilityPrecipitation variability related to MJO over the Amazonia, Southwest Africa, and Maritime Continent is underestimated in CMIP6 models

Saltwater intrusion has become one of the most concerning issues in the Vietnamese Mekong Delta (VMD) due to its increasing impacts on agriculture and food security of Vietnam. Reliable estimation of salinity plays a crucial role to mitigate the impacts of saltwater intrusion. This study developed a hybrid technique that merges satellite imagery with numerical simulations to improve the estimation of salinity in the VMD. The salinity derived from Landsat images and by numerical simulations was fused using the Bayesian inference technique. The results indicate that our technique significantly reduces the uncertainties and improves the accuracy of salinity estimates. The Nash-Sutcliffe coefficient is 0.73, which is much higher than that of numerical simulation (0.69) and Landsat estimation (0.67). The correlation coefficient between the merged and measured salinity is relatively high (0.75). The variance of the ensemble salinity errors (2.57 ppt) is lower than that of Landsat estimation (3.65 ppt) and numerical simulations (8.69 ppt). The proposed approach in this study shows a great potential to combine multiple data sources of a variable of interest to improve its accuracy and reliability wherever these data are available.

Author(s): Yan, Qina; Le, Phong VV; Woo, Dong K; Hou, Tingyu; Filley, Timothy; Kumar, Praveen

Expansion and intensification of managed landscapes for agriculture have resulted in severe unintended global impacts, including degradation of arable land and eutrophication of receiving water bodies. Modern agricultural practices rely on significant direct and indirect human energy inputs through farm machinery and chemical use, respectively, which have created imbalances between increased rates of biogeochemical processes related to production and background rates of natural processes. We articulate how these imbalances have cascaded through the deep inter-dependencies between carbon, soil, water, nutrient and ecological processes, resulting in a critical transition of the critical zone and creating emergent inter-dependencies and co-evolutionary trajectories. Understanding of these novel organizations and function of the critical zone is vital for developing sustainable agricultural practices and environmental stewardship.

Vegetation acclimation resulting from elevated atmospheric CO2 concentration, along with response to increased temperature and altered rainfall pattern, is expected to result in emergent behavior in ecologic and hydrologic functions. We hypothesize that microtopographic variability, which are landscape features typically of the length scale of the order of meters, such as topographic depressions, will play an important role in determining this dynamics by altering the persistence and variability of moisture. To investigates these emergent ecohydrologic dynamics, we develop a modeling framework, Dhara, which explicitly incorporates the control of microtopographic variability on vegetation, moisture, and energy dynamics. The intensive computational demand from such a modeling framework that allows coupling of multi-layer modeling of the soil-vegetation continuum with 3-D surface-subsurface flow processes is addressed using hybrid CPU-GPU parallel computing framework. The study is performed for different climate change scenarios for an intensively managed agricultural landscape in central Illinois, U.S.A., which is dominated by row crop agriculture, primarily soybean (Glycine max) and maize (Zea mays). We show that rising CO2 concentration will decrease evapotranspiration, thus increasing soil moisture and surface water ponding in topographic depressions. However, increased atmospheric demand from higher air temperature overcomes this conservative behavior resulting in a net increase of evapotranspiration, leading to reduction in both soil moisture storage and persistence of ponding. These results shed light on the linkage between vegetation acclimation under climate change and microtopography variability controls on ecohydrologic processes.

Green roofs and cool roofs are alternative roofing strategies that mitigate urban heat island effects and improve building energy performance. Green roofs consist of soil and vegetation layers that provide runoff reduction, thermal insulation, and potential natural habitat, but can require regular maintenance. Cool roofs involve a reflective layer that reflects more sunlight than traditional roofing materials, but require additional insulation during winter months. This study evaluates several roofing strategies in terms of energy performance, urban heat island mitigation, water consumption, and economic cost. We use MLCan, a multi-layer canopy model, to simulate irrigated and non-irrigated green roof cases with shallow and deep soil depths during the spring and early summer of 2012, a drought period in central Illinois. Due to the dry conditions studied, periodic irrigation is implemented in the model to evaluate its effect on evapotranspiration. Traditional and cool roof scenarios are also simulated by altering surface albedo and omitting vegetation and soil layers. We find that both green roofs and cool roofs significantly reduce surface temperature compared to the traditional roof simulation. Cool roof temperatures always remain below air temperature and, similar to traditional roofs, require low maintenance. Green roofs remain close to air temperature and also provide thermal insulation, runoff reduction, and carbon uptake, but might require irrigation during dry periods. Due to the longer lifetime of a green roof compared to cool and traditional roofs, we find that green roofs realize the highest long term cost savings under simulated conditions. However, using longer-life traditional roof materials (which have a higher upfront cost) can help decrease this price differential, making cool roofs the most affordable option due to the higher maintenance costs associated with green roofs.

Commonly, the topographic influence on soil hydrology is calculated as a Topographic Wetness Index (TWI), which often correlates with surface soil properties, such as carbon and nitrogen, across broad spatial scales. However, traditional TWI methods can be ineffective at capturing finer scale variations when depression filling approaches are used and they do not incorporate localized soil texture controls on infiltration. We developed a new Dynamic Wetness Index (DWI) that attempts to account for the persistence of soil moisture over time at the microtopographic scale (~1 m2) by including inputs of measured soil texture, and information from the Dhara modeling framework that incorporates canopy process and surface-subsurface hydrologic models. DWI and TWI values were correlated with measured soil geochemical properties across six study sites (four agricultural sites, one restored prairie, and one forest site) within the Upper Sangamon River Basin, in central Illinois, USA. Relative to TWI, DWI improved correlations with certain measured soil surface geochemistry (pH R = −0.53), δ13C R = 0.13, δ15N R = 0.44) and certain lignin phenols (vanillyl, cinnamyl/vanillyl, syringyl-vanillyl-cinnamyl/substituted fatty acids). DWI positively correlated with indicators of lignin oxidation, indicating that wetter soils have higher potential for lignin decomposition. In this small dataset, relative to TWI the data show DWI increased significance and decreased the range of correlations with soil moisture and certain surface soil geochemistry parameters driving plant chemistry decay and nitrogen cycling.

Topographic depressions, areas of no lateral surface flow, are ubiquitous characteristics of the land surface that control many ecosystem and biogeochemical processes. High density of depressions increases the surface storage capacity, whereas lower depression density increases runoff, thus influencing soil moisture states, hydrologic connectivity, and the climate-soil-vegetation interactions. With the widespread availability of high-resolution lidar-based digital elevation model (lDEM) data, it is now possible to identify and characterize the structure of the spatial distribution of topographic depressions for incorporation in ecohydrologic and biogeochemical studies. Here we use lDEM data to document the prevalence and patterns of topographic depressions across five different landscapes in the United States and quantitatively characterize the probability distribution of attributes, such as surface area, storage volume, and the distance to the nearest neighbor. Through the use of a depression identification algorithm, we show that these probability distributions of attributes follow scaling laws indicative of a structure in which a large fraction of land surface areas can consist of high number of topographic depressions of all sizes and can account for 4 to 21 mm of depression storage. This implies that the impacts of small-scale topographic depressions in the landscapes on the redistribution of material fluxes, evaporation, and hydrologic connectivity are quite significant.

Background The transmission of malaria is highly variable and depends on a range of climatic and anthropogenic factors. This study investigates the combined, i.e. direct and indirect, impacts of climate change on the dynamics of malaria through modifications in: (i) the sporogonic cycle of Plasmodium induced by air temperature increase, and (ii) the life cycle of Anopheles vector triggered by changes in natural breeding habitat arising from the altered moisture dynamics resulting from acclimation responses of vegetation under climate change. The study is performed for a rural region in Kilifi county, Kenya. Methods and findings We use a stochastic lattice-based malaria (SLIM) model to make predictions of changes in Anopheles vector abundance, the life cycle of Plasmodium parasites, and thus malaria transmission under projected climate change in the study region. SLIM incorporates a nonlinear temperature-dependence of malaria parasite development to estimate the extrinsic incubation period of Plasmodium. It is also linked with a spatially distributed eco-hydrologic modeling framework to capture the impacts of climate change on soil moisture dynamics, which served as a key determinant for the formation and persistence of mosquito larval habitats on the land surface. Malaria incidence data collected from 2008 to 2013 is used for SLIM model validation. Projections of climate change and human population for the region are used to run the models for prediction scenarios. Under elevated atmospheric CO2 concentration ([CO2]) only, modeled results reveal wetter soil moisture in the root zone due to the suppression of transpiration from vegetation acclimation, which increases the abundance of Anopheles vectors and the risk of malaria. When air temperature increases are also considered along with elevated [CO2], the life cycle of Anopheles vector and the extrinsic incubation period of Plasmodium parasites are shortened nonlinearly. However, the reduction of soil moisture resulting from higher evapotranspiration due to air temperature increase also reduces the larval habitats of the vector. Our findings show the complicated role of vegetation acclimation under elevated [CO2] on malaria dynamics and indicate an indirect but ignored impact of air temperature increase on malaria transmission through reduction in larval habitats and vector density. Conclusions Vegetation acclimation triggered by elevated [CO2] under climate change increases the risk of malaria. In addition, air temperature increase under climate change has opposing effects on mosquito larval habitats and the life cycles of both Anopheles vectors and Plasmodium parasites. The indirect impacts of temperature change on soil moisture dynamics are significant and should be weighed together with the direct effects of temperature change on the life cycles of mosquitoes and parasites for future malaria prediction and control.