Large Spatial Variability Research Articles

Constrained incipient phase transformation in Ni-Mn-Ga films: A small-scale design challenge

Ni-Mn-Ga shape-memory alloys are promising candidates for large strain actuation and magnetocaloric cooling devices. In view of potential small-scale applications, we probe here nanomechanically the stress-induced austenite–martensite transition in single crystalline austenitic thin films as a function of temperature. In 0.5 µm thin films, a marked incipient phase transformation to martensite is observed during nanoindentation, leaving behind pockets of residual martensite after unloading. These nanomechanical instabilities occur irrespective of deformation rate and temperature, are Weibull distributed, and reveal large spatial variations in transformation stress. In contrast, at a larger film thickness of 2 μm fully reversible transformations occur, and mechanical loading remains entirely smooth. Ab-initio simulations demonstrate how an in-plane constraint can considerably increase the martensitic transformation stress, explaining the thickness-dependent nanomechanical behavior. These findings for a shape-memory Heusler alloy give insights into how reduced dimensions and constraints can lead to unexpectedly large transformation stresses that need to be considered in small-scale actuation design.

A Novel Densenet-324 Densely Connected Convolution Neural Network for Medical Crop Classification using Remote Sensing Hyperspectral Satellite Images

In the past few decades, importance of the medicinal Crops is extending to a large extent due to its benefits in treating life-threatening diseases. Medicinal Crop has excellent medicinal properties on its roots, stem, and leaves to prevent human and animal health. Particularly detection and identification of the Crop classes are effectively carried out using hyperspectral images as discrimination of the target feature or objects is simple and it contains rich information containing the spatial and temporal details of underlying the land cover. However, Crop classification using machine learning architectures concerning spectral characteristics obtained on the anatomical features and morphological features. Extracted features towards classification lead to several challenges such as large spatial and temporal variability and spectral signatures similarity between different objects. A further hyperspectral image poses several difficulties with changes in illumination, environment, and atmospheric aspects. To tackle those non-trivial challenges, DenseNet-324 Densely Connected convolution neural network architecture has been designed in this work to discriminate the crop and medical Crop effectively in the interested areas. Initially, the Hyperspectral image is pre-processed against a large number of noises through the employment of the noise removal technique and bad line replacement techniques. Pre-processed image is explored to image segmentation using the global thresholding method to segment it into various regions based on spatial pieces of information on grouping the neighboring similar pixels intensity or textures. Further regions of the image are processed using principle component analysis to extract spectral features of the image. That extracted feature is employed to ant colony optimization technique to obtain the optimal features. Computed optimal features are classified using Convolution Neural Network with a hyper parameter setup. The convolution Layer of the CNN architecture process spatial, temporal, and spectral feature and generates the feature map in various context, generated feature map is max pooled in the pooling layer and classified into crops and medicinal Crop in the SoftMax layer. Experimental analysis of the proposed architecture is carried out on the Indiana Pines dataset using cross-fold validation to analyze the representation ability to discriminate the features with large variance between the different classes. From the results, it is confirmed that the proposed architecture exhibits higher performance in classification accuracy of 98.43% in classifying the Crop species compared with conventional approaches.

Occurrence and accumulation characteristics of legacy and novel brominated flame retardants in surface soil and river sediments from the downstream of Chuhe River basin, East China.

Surface soil and river sediment samples were collected from the downstream of Chuhe River basin, East China, to investigate the occurrence and accumulation characteristics of legacy and novel brominated flame retardants (NBFRs). The respective concentrations of BDE-209 and nine NBFRs ranged from n.d. to 41.4ng/g dry weight (dw) and from 0.35 to 362.78ng/g dw in the collected surface soil samples and ranged from 0.29 to 19.73ng/g dw and from 0.70 to 66.83ng/g dw in the collected river sediment samples. Soil samples exhibited a higher potential to accumulate BTBPE while the relative abundance of PBT in the collected sediment samples was significantly higher than that in soils. Even so, BTBPE was the predominant NBFR in both soil and sediment samples. The concentrations and relative abundances of legacy and NBFRs exhibited large spatial variation. The calculated concentration ratios of the total of the nine NBFRs (∑9NBFRs) to BDE-209 (∑9NBFRs/BDE-209) in most of the analyzed samples far exceeded 1, implying a clear shift from legacy brominated flame retardants to NBFRs in the downstream of Chuhe River basin.

Groundwater recharge influenced by ephemeral river flow and land use in the semiarid Limpopo Province of South Africa

Determining the sustainability of groundwater use in drylands with high climate variability is complex. Central to this determination is an understanding of groundwater recharge and associated processes and controls. Groundwater recharge in drylands can occur by diffuse and focused recharge (focused recharge being associated with intense episodic rainfall events and ephemeral river flow, predicted to increase and intensify with climate change). This study evaluated the relative significance and dominant controls on these two recharge processes. Ten groundwater hydrographs with multidecadal observations were collated from the Limpopo Province, South Africa, based on their proximity to river channels and rain gauges, representing diversity in local climate, landscape, vegetation, and hydrogeological conditions. The hydrographs showed that groundwater-level rises are sensitive to rainfall intensity during the rainy season, with generally larger increases after years with large episodic rainfall events, which disproportionately contribute to groundwater replenishment. Recharge processes and annual recharge volumes were quantified using the water-table fluctuation method and the numerical model HYDRUS-1D. This allowed for the inference of additional recharge contributions from focused recharge in proximity to ephemeral rivers, up to a factor of five relative to diffuse recharge. The analysis revealed synchronicity and linear correlation between annual river discharge and recharge close to the river, substantiating the importance of focused recharge close to the river network. The study showed that recharge in drylands is subject to large spatial and temporal variation and that consideration of focused and episodic recharge is critically important for managing groundwater resources at various scales in these regions.

Assessing environmental, economic, and social impacts of inter-basin water transfer in China

Inter-basin water transfer (IBWT) has been widely implemented to address water deficit problems in many parts of the world. IBWT projects can pose various environmental, economic, and social impacts on both water recipient and source basins. This study presents an analysis of multi-dimensional impacts of China’s IBWT at the sub-basin scale, using three newly developed indicators. The analysis is based on China’s operating IBWT projects in 2016, which had a collective capacity of transferring ∼48 billion m3/yr water across sub-basins. Results indicate that IBWT helped improve environmental flow conditions in 20 out of 26 recipient sub-basins, with 6 out of 17 source sub-basins experiencing critical environmental flow conditions. IBWT generated net economic benefits of approximately 4.2 billion US dollars and net social benefits of providing domestic water use for 170 million people. IBWT posed overall positive environmental, economic, and social impacts, with the most benefits delivered to the North China Plain (i.e., the Huai and Hai River Basins). However, a few water source basins with relatively low local water availability were faced with a high risk of low environmental flows and high socio-economic costs, especially under low water resources conditions. Large spatial variations of IBWT impacts were found across sub-basins, which cannot be known by assessment at the major basin level. This highlights the need to assess IBWT impacts at finer spatial scales, considering regional differences in local water resources and social-economic development conditions. This study for the first time reveals multi-dimensional impacts of IBWT in China at the sub-basin scale. The results can be used by policymakers to inform water transfer and water management policies.

Agricultural biomethane production in France: A spatially-explicit estimate

Biomethane production by anaerobic digestion can significantly contribute to the decarbonation and defossilization of the European energy mix. Although previous estimates have been provided about biomethane potential production in several European countries, they suffer from a coarse evaluation of substrate availability and a lack of spatial resolution. This paper provides a spatially-explicit assessment of the potential for biomethane production in France at a fine spatial resolution (NUTS 4 region) from agricultural biomass (livestock manure, crop residues and cover crops) and sewage sludge. This assessment is based on (i) a fine estimation of substrate availability, particularly for cover crop production, by combining a crop model with spatially-explicit soil and climate databases; (ii) the use of an anaerobic digestion model that predicts biomethane production from agricultural biomass; and (iii) a procedure for optimizing biomethane production while respecting the stoichiometric constraints of the substrate mixture. We found a significant potential for annual biomethane production of 64.1 TWh, mainly from cattle manure, a value that could reach 108.7 TWh through the generalization of cover crops within arable cropping systems. Our results also highlighted the importance of detailed substrate availability data to account for their large spatial variations. Accordingly, we showed that biomethane production was highly variable across the French regions and that crop residues could only partly be used for anaerobic digestion – especially in arable regions – because they lead to excessively dry substrate mixtures. This study helped to identify the most suitable regions for biomethane production and to provide accurate estimates for biomethane development in France.

Spatial modeling via geostatistics of the bed in a compost barn system: thermal performance assessments.

For all the benefits of a compost barn system to be achieved, the management, especially with the bedding, must be done correctly. One should consider the requirements regarding the revolving of the material, use of good quality organic matter, suitable ventilation, and an ideal animal stocking rate, so that the composting occurs efficiently. Given this, this work aimed to evaluate the spatial variability of thermal characteristics of rice straw bedding, using values of temperature (surface and internal) and humidity. The temperature and moisture values of the litter were recorded at intervals during the morning shift, from 6:00 am to 7:00 am and from 8:00 am to 9:00 am, and during the afternoon shift, from 3:00 pm to 4:00 pm and from 5:00 pm to 6:00 pm, considering the management of the turn-over, that is, before and after the movement of the litter. Litter variables were collected at points distributed on a 39-point grid. There were no differences between litter surface temperatures before and after turning over in the morning shift, but in the afternoon shift, the surface temperature was lower (P < 0.05) before, when compared to after turning. The internal temperature and litter moisture were higher (P < 0.05) before revolving in both shifts. Spatial dependence was verified (IDE) for the variables studied. Based on the geostatistical analysis, it is concluded that the IDE was rated as strong for surface and internal temperatures, which demonstrates heterogeneity among the points evaluated and a large spatial variability regarding bed temperatures. In the case of litter moisture, the greatest variability occurred in the morning period. The litter surface temperature values did not exceed the ambient air temperature values.

Pastures under dry climate can be cold spots for soil nitrous oxide emissions from grazing cattle excreta

Animal excreta is an important contributor to agricultural nitrous oxide (N2O) emissions – a potent greenhouse gas. Soil N2O emissions are episodic in nature and commonly subjected to large inter-annual and spatial variation. Thus, edaphic-climatic specific studies are needed to further our knowledge on global terrestrial N2O losses and to more accurately estimate national N2O emissions. For the Canadian Prairies, multi-year studies are needed for the determination of beef cattle dung and urine derived N2O emissions under semiarid and sub-humid climate and different soil types, a knowledge gap explored by the present study. We evaluated N2O emissions in dung, urine, and unfertilized control plots following six animal excreta applications in 2009/10 and 2010/11. The experiment was conducted in two locations: (i) Swift Current, Saskatchewan (semiarid prairies/brown Chernozem); and, ii) Lacombe, Alberta (sub-humid prairies/black Chernozem). Our results indicated that beef cattle dung-N derived N2O emission was negligible for all excreta applications on both sites as cumulative N2O emissions were not significantly different from the control plots (p > 0.05). Overall, cattle urine derived soil N2O emission was greater than emissions from dung and control plots, and subjected to large inter-annual variation. For the Swift Current site, 2010 experienced record-high precipitation that stimulated N2O emissions of up to 0.47% of the applied urine-N. Except for the applications exposed to record-high precipitation, the percentage of the urine-N lost as N2O at the Swift Current site was small, ranging from 0.00% to 0.01%. Similarly, for the Lacombe site, urine-N derived N2O emissions ranged from 0.00% to 0.12%, always lower than the 0.32% default IPCC (2019) emission factor used for cattle urine-N. Our results suggest that dry climate locations under near-normal or below-normal precipitation levels are natural “cold spots” for N2O emissions and could experience negligible cattle excreta derived soil N2O emissions.

Empirical insights on the use of sun-induced chlorophyll fluorescence to estimate short-term changes in crop transpiration under controlled water limitation

Knowledge of actual crop transpiration (T) is important for advanced crop management but challenging to obtain due to the large spatial and temporal variation of T. Remote sensing offers various possibilities to assess T dynamics, while particularly sun-induced chlorophyll fluorescence (SIF) has been demonstrated as a sensitive empirical proxy for T. Despite this success, the advancement of the mechanistic understanding of how SIF relates to T dynamics is key for the future development and implementation of robust and reliable SIF-based T products. This study aims to contribute insights by experimentally assessing the sensitivity of several SIF-based T estimation strategies for evolving soil water limitation. We investigated extensive in situ and airborne data acquired during a water limitation experiment in a maize canopy in northern Italy. We evaluated five empirical strategies to integrate SIF in a T modelling framework based on the Penman-Monteith (PM) and the Ball-Berry-Leuning (BBL) concepts. Our results indicate that replacing model parameters sensitive to canopy conductance with SIF results in the best agreement between modelled and measured T under evolving water limitation. Our study contributes expanding existing knowledge with empirical insights on the sensitivity of SIF based T approaches under increasing soil water limitation at short time scales.

Changes in Water-Use Efficiency of Eucalyptus Plantations and Its Driving Factors in a Small County in South China

Ecosystem water-use efficiency (WUE) has been central in revealing the variability in terrestrial carbon and water cycles. Short-rotation plantations such as Eucalyptus plantations can simultaneously impact net primary production (NPP) and actual evapotranspiration (ETa), components of WUE, resulting in changes in terrestrial carbon and water cycles. However, there are few detailed studies on the changes in the WUE of Eucalyptus plantations at the catchment scale with high spatial remote sensing imagery. Here, we present the changes in the WUE of Eucalyptus plantations and its driving factors (i.e., NPP and ETa) using satellite-based models combined with 5-m spatial resolution RapidEye imagery in a small county in South China. The increases in ETa of Eucalyptus plantations are primarily the result of climate warming and result in low WUE of Eucalyptus plantations. The management practice used (short rotation in this study) can enhance the effect of climate warming on WUE by varying the NPP of Eucalyptus plantations. A high value of NPP leads to a high WUE of Eucalyptus plantations at the end of a short rotation, while a low value of NPP results in a low WUE at the beginning of another short rotation. Changes in the WUE of Eucalyptus plantations indicated large spatial and temporal variability, associated with climate warming and short-rotation practices.

Satellite-based land surface temperature and soil moisture observations accurately predict soil respiration in temperate deciduous and coniferous forests

Soil respiration, defined as the total flux of carbon dioxide (CO2) from the soil to the atmosphere, is a key ecosystem process that affects the regional and global carbon (C) cycles and is highly sensitive to temperature and soil moisture. It is challenging to quantify soil respiration at the ecosystem level from commonly used in-situ soil chamber measurements because of large spatial variability. Methods that provide temporally and spatially continuous estimates of soil respiration at various scales are vital to understand the impact of climate change on soil C stock. In this study, we evaluate three commonly used empirical models and a Random Forest machine learning algorithm applied to satellite derived estimates of land surface temperature (LST) and soil moisture to estimate soil respiration in temperate deciduous and coniferous forests in Canada. The models were calibrated using in-situ soil temperature and moisture and validated against in-situ measurements of soil CO2 fluxes (gCm−2day−1) from automatic soil chambers. We separately evaluate the performance of nighttime and daytime satellite-based LST and soil moisture observations in modeling soil respiration. The soil respiration models were also evaluated at daily and monthly time scales against in-situ measurements. Results indicate that models based on satellite LST, and soil moisture can explain more than 70% of the variability in observed soil respiration. Nighttime LST at a monthly time scale resulted in consistently higher accuracy than daytime LST in estimating soil respiration. Satellite observations resulted in comparable accuracy in estimating soil respiration as in-situ measurements. Satellite LST and soil moisture observations are indispensable data sources to estimate soil respiration at ecosystem level and its upscaling to regional and global scales.

Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents

AbstractAimTheoretically, woody biomass turnover time () quantified using outflux (i.e. tree mortality) predicts biomass dynamics better than using influx (i.e. productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody in mature forests. We further compared woody estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes—CARDAMOM.LocationContinents.Time PeriodHistoric from 1951 to 2018.Major Taxa StudiedTrees and forests.MethodsWe compared the approaches of using outflux versus influx for estimating woody and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody and generated a spatially explicit map of woody at a 0.25‐degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody .ResultsWoody quantified by the outflux approach better (with R2 0.4–0.5) predicted the biomass accumulation rates than the influx approach (with R2 0.1–0.4) across continents. We found large spatial variations of woody for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (73.9 ± 3.6 y) and tropical forests. The map of woody extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody across continents. The highest woody in temperate forests was not captured by either DGVMs or CARDAMOM.Main ConclusionsOur study empirically demonstrated the preference of using outflux over influx to estimate woody for predicting biomass accumulation rates. The spatially explicit map of woody and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs.

On the Significance of Divje babe I Cave for the Stratigraphy, Sedimentology, and Chronology of Palaeolithic Cave Sites in Slovenia

&#x0D; &#x0D; &#x0D; The article reviews the development and interpretation of procedures used in the study of the sedimentary sequence of the Divje babe I Palaeolithic cave site. The data were obtained from publications, archives, and personal information provided by Ivan Turk, who directed the excavations and directed the laboratory investigations. The profile of Divje babe I is currently the best researched and chronologically defined Late Pleistocene cave profile in Slovenia. For the first time, various sedimentological and other data from the standard profile were upgraded with the same data obtained on standardized ground plan surfaces and depths. A large spatial (lateral) variability was discovered, associated with generally contemporaneous but different cave microenvironments, which were crucially influenced by different amounts of water in all aggregate states. Without taking into account temporal and spatial dimensions, it would not be possible to combine all statistical sets of collected data on sediments, their contents, and climatic parameters into a unified system.&#x0D; &#x0D; &#x0D;

Physics-based modeling of climate change impact on hurricane-induced coastal erosion hazards

Coastal erosion is an adverse impact of extreme water levels during major hurricanes. A warmer climate is expected to increase storm surge and wave hazards due to hurricane climatology change (HCC) and sea level rise (SLR). We conduct physics-based morphodynamic modeling to quantify the regional impacts of HCC and SLR on erosion hazards to sandy beaches and dunes along the barrier islands of New Jersey in the United States. Under the RCP8.5 scenario, we find a substantial increase in erosion hazards from the late-20th-century to late-21st-century. The regionally averaged 100-year eroded volume of beach-dune systems would increase by 58 and 84%, respectively, under the HCC and HCC + SLR scenarios. Our projections show a large spatial variability in future changes to erosion hazards, suggesting that, in addition to HCC and SLR, the morphological characteristics of beach-dune systems play an important role in the impacts of climate change on coastal erosion.

Evaluation of remotely sensed imagery to monitor temporal changes in soil organic carbon at a long-term grazed pasture trial

Temporal variation of soil organic carbon (SOC) is driven by land use/management practice, ecosystem conditions and climatic variation. Robust quantification of changes in SOC that is cost-effective and provides a statistical assessment of uncertainty is challenging, particularly in the face of large spatial variability and slow soil SOC changes. Remote-sensing indicators of above-ground vegetation provide some indication of the amount of fresh organic material being supplied to the soil. Although, because of the time taken for this organic material to decay and become incorporated into the soil, there will be a lag between the changes in the indicator of vegetation growth and the resulting changes in SOC. In this work, we investigate how a remotely sensed indicator of vegetation cover can be used with a lag period to predict or indicate changes in SOC for grazed pasture sites at a long-term monitoring study, which has been monitoring soil under different land uses for over forty years. We assessed how well this worked for indicating the SOC changes for different depths in the soil profile. Results suggested that a lagged remotely sensed vegetation cover—the average cover of the two preceding years—provides some indication of SOC changes for the 0–10 cm soil depth, but changes for deeper soil depths were not well predicted. Further, we investigated the potential of using soil data from a point-in-time spatial dataset (e.g. data from a baseline sampling round) to calibrate a relationship between the remotely sensed cover and SOC, which can then be applied to predict or indicate the temporal variation of SOC. Results showed this approach gave large prediction errors, likely because the temporal variation (at a fixed point in space) and spatial variation (for a fixed point in time) of SOC that is predictable by cover differences are not interchangeable.

Quantitative Mechanisms of the Responses of Abrupt Seasonal Temperature Changes and Warming Hiatuses in China to Their Influencing Factors

Abrupt temperature changes and warming hiatuses have a great impact on socioeconomic systems; however, their mechanisms remain unclear. In this study, the quantitative mechanisms of the responses of abrupt seasonal temperature changes and warming hiatuses in China to their influencing factors were analysed using the monthly mean temperature (Tav), mean minimum temperature (Tnav), and mean maximum temperature (Txav) from 622 meteorological stations in China covering 1951–2018, the CMIP6 model data, and data at large spatial scales, including Atlantic multidecadal oscillation (AMO) data. The results showed that the contributions of the influencing factors to the abrupt changes in Tav, Tnav, and Txav showed large spatial variability and peaked in the spring and summer and bottomed out in the autumn. The Pacific decadal oscillation (PDO) greatly impacted the abrupt temperature changes in Northeast China and North China at a contribution rate of approximately 12%, strongly influenced the abrupt temperature changes south of the Yangtze River, and markedly influenced the abrupt temperature changes in Northwest China. The AMO had a large impact on temperature in most regions of China in all seasons except for the summer. The MEI mainly affected the abrupt seasonal temperature changes in the region between 25° N and 35° N. The Arctic oscillation (AO) substantially impacted the warming hiatuses in Northeast China in the winter at a contribution rate of approximately 12%. These influencing factors contributed less to warming hiatuses than to abrupt temperature changes. Among the regional influencing factors, AP and WS greatly impacted warming hiatuses, more so than abrupt temperature changes, while relative humidity (RH) and solar radiation (SR) contributed little to warming hiatuses.

Dissolution rate variability at carbonate surfaces: 4D X-ray micro-tomography and stochastic modeling investigations

We provide a detailed 3D characterization of the geometry evolution and dissolution rate mapping at the surface of four carbonate samples, namely a calcite spar crystal, two limestone rock fragments, and an aragonite ooid, using time-lapse X-ray micro-tomography during dissolution experiments at pH 4.0. Evaluation of the retreat and mapping of the reaction rates at the whole surface of the samples reveals a large spatial variability in the dissolution rates, reflecting the composition and the specific contributions of the different regions of the samples. While crystal edges and convex topographies record the highest dissolution rates, the retreat is slower for flat surfaces and in topographic lows (i.e., concave areas), suggesting surface-energy related and/or diffusion-limited reactions. Microcrystalline aragonite has the highest rate of dissolution compared to calcite. Surprisingly, rough microcrystalline calcite surface dissolves globally more slowly than the {101̄4} faces of the calcite spar crystal. The presence of mineral impurities in rocks, through the development of a rough interface that may affect the transport of species across the surface, may explain the slight decrease in reactivity with time. Finally, a macroscopic stochastic model using the set of detachment probabilities at corner, edge, and face (terrace) sites obtained from kinetic Monte Carlo simulations is applied at the spar crystal scale to account for the effect of site coordination onto reactivity. Application of the model to the three other carbonate samples is discussed regarding their geometry and composition. The results suggest that the global dissolution process of carbonate rocks does not reflect only the individual behavior of their forming minerals, but also the geometry of the crystals and the shape of the fluid-mineral interface.

Geospatial Perspective for Innovative Planning and Management of Watershed

Innovative watershed management is intense to unravelling watershed related problems on a sustainable manner. Planning and managing developmental practices in the watershed on a sustainable basis usually integrate water or land and both in the present in such a way to attain its goal in the future also. The water-related problems in rain-fed agriculture are often related to high intensity and short duration rainfall, with large spatial and temporal variability, rather than to the low cumulative amount of rainfall. For the innovative planning and Management in the watershed, analysing land degradation through the prioritisation watersheds, assessing the surface runoff, evaluating the Spatio-temporal distribution of the hydrological constraints, groundwater potential zone delineations and the suitable sites for the Rainwater Harvesting (RWH) are considered by spatial multi-criteria evaluation from the several thematic layers that are determined from the remote sensing and GIS technologies. These innovative spatial technologies contribute to reducing vulnerability to climate change and help minimise or even reverse land degradation. The sustainable RWH will improve the surface and ground water level, this in turn improve the economy of the watershed. Keywords: Geospatial, Innovations, Watershed, Rain­fed agriculture, Prioritisation, Groundwater

Unevenness of temporal distribution of precipitation over the Yangtze River basin and its main influencing factors

AbstractUnderstanding the unevenness of precipitation distribution within a year is important for water resources management and agricultural production. In this study, spatiotemporal variations of precipitation unevenness in the Yangtze River basin (YRB) during 1961–2020 and its main influencing factors are investigated by the relative entropy (RE) and rotated empirical orthogonal function. The timings of different percentiles of annual total precipitation show that the temporal distribution of precipitation in the YRB is uneven. The unevenness of precipitation distribution exhibits a large spatial variability with a high‐value centre in the northeast and a low value centre in the northwest of the YRB, and an upward trend in almost the entire YRB. Precipitation becomes more uneven with the decrease of annual number of wet days and low‐intensity precipitation amount and the increase of high‐intensity precipitation amount during the study period. Precipitation unevenness of different regions is modulated by the different combinations of different seasonal climate indices with specific time lags. For example, in the south‐central YRB, precipitation unevenness generally tends to be enhanced by the positive Tropical Northern Atlantic Index in current summer and sea surface temperature anomalies over South China Sea (SCS) in previous summer, and negative North Atlantic Oscillation in current winter, while in the northeast YRB, be weakened by negative Quasi‐Biennial Oscillation in current spring, Atlantic Meridional Mode in current summer and positive SCS in current autumn. Besides, the identified significant climate indices can partly explain the temporal changes of precipitation unevenness, and their explanatory ability is more powerful in the south‐central, southeast and northwest YRB than in the north‐central and northeast YRB, which reveals a strong negative correlation with the degree of precipitation unevenness.

Isotopic and chemical signatures of high mountain rivers in catchments with contrasting glacier and rock glacier cover

Glaciers and rock glaciers are key elements of mountain hydrological systems, but their relative influence on the chemical and isotopic conditions of streams within the river continuum is still overlooked. During three consecutive years (2019–2021), we studied 24 stream sections in two catchments (Plima and Schnals, Eastern Italian Alps) with varying cover of glaciers and rock glaciers. End-member mixing models based on δ2H and d-excess revealed a large spatial and temporal variability in the contribution of different water sources to stream runoff. Overall, snowmelt (77 ± 17 %) and rainwater (5 ± 5 %) were the largest and the smallest runoff components, respectively. The ice melt contribution was high in streams fed by glaciers (23 ± 15 %) and rock glaciers (16 ± 16 %). In the highly-glacierised Plima basin, the tracer-based estimation of annual ice melt fraction matched reasonably well (90–167%) the mean annual glacial ice loss estimated by geodetic mass balance. In contrast, we found a large overestimation of the ice melt component derived from mixing models in the poorly glacierised (but rock glacier-rich) Schnals catchment. In streams influenced by rock glaciers, at both catchments the particular temporal patterns of electrical conductivity resulted in unreliable estimates of the meltwater/groundwater fractions of runoff. Depending on the local lithology, concentrations of trace elements (Sr, Ni, Ba, Mn, Zn, Al) were high in streams fed by rock glaciers and glaciers, and close/below the limits of quantification in non-glacial streams. In alpine areas, the abundance of rock glaciers can confound the isotopic and chemical signature imparted by glaciers, thus hindering the use of tracer-based methods for hydrograph separation. Under the combined influence from glaciers and rock glaciers, concentrations of trace elements can surpass the limits for drinking water quality even in downstream areas, as we observed at the Schnals catchment for nickel.