Large Spatial Variability Research Articles

Trends in the elements of the carbonate system of Lake Ladoga

During the observation period in 2003 – 2021, it was shown that the total concentration of dissolved inorganic carbon, represented in the water of Lake Ladoga mainly HCO3ˉ and CO2 has been increasing in recent years. The concentration of CO2 has a sufficiently large spatial and temporal variability and, in terms of carbon at the stations under consideration, can range from 0.6 to 47 % of the total concentration of dissolved inorganic carbon, depending on the change in the ratio of production and destruction processes in different seasons at different horizons. At the same time, a significant interannual trend of an increase in the total concentration of dissolved carbon dioxide and, accordingly, a decrease in the hydrogen index was revealed. Calculation of the balance of dissolved in water and atmospheric CO2 showed that, in general, CO2 concentrations in water exceed equilibrium, however, at the peak of the growing season, the surface layer of the lake in some cases, on the contrary, can absorb carbon dioxide from the air. This effect of the «biological pump» is most clearly manifested in the nutrient-rich and heated shallow part of the lake. However, in the main body of water, there is an interannual trend of increasing the calculated concentrations of dissolved carbon dioxide relative to its equilibrium concentrations with the atmosphere. Due to the shortage of nutrients, primarily phosphorus, an increase in temperature does not lead to an increase in productivity and CO2 consumption. Stoichiometric ratios of apparent oxygen and carbon deficiency, in turn, demonstrate that the quantitative change in the content of dissolved carbon in water can be fully explained by oxidation or synthesis of organic matter in only a third of the cases considered. In the remaining 2/3 of cases, the increase in the concentration of dissolved inorganic carbon may be associated with an increase in carbon runoff from the catchment area into Lake Ladoga, or with intra-reservoir processes associated with the peculiarities of accumulation and redistribution of CO2 in the water column.

Is the coupling and coordination of economic, social and environmental development crucial to the governance of relative poverty?

As the primary goal of the 17 Sustainable Development Goals (SDGs), poverty eradication is still one of the major challenges faced by countries around the world, and relative poverty is a comprehensive poverty pattern triggered by the superposition of economic, social, and environmental dimensions. Therefore, Therefore, this paper introduces the perspective of coupled coordination to consider the formation of relative poverty, constructs indicators in three major dimensions: economic, social, and environmental, proposes a fast and more accurate method of identifying relative poverty in a region by using machine learning, measures the degree of coupled coordination of China's relatively poor provinces using a coupled coordination model and analyzes the relationship with the level of relative poverty, and puts forward suggestions for poverty management on this basis using typology classification. The results of the study show that: 1) the fusion of data crawlers, remote sensing space, and other multi-source data to construct the dataset and propose a fast and efficient regional relative poverty identification method based on big data with low comprehensive cost and high identification accuracy of 0.914. 2) Currently, 70.83% of the economic-social-environmental systems of the relatively poor regions are in the dysfunctional type and are in a state of disordered development and malignant constraints. The regions showing coupling disorders are mainly clustered in the three southern prefectures of Xinjiang, Qinghai, Gansu, Yunnan, and Sichuan, and their spatial distribution is relatively concentrated. 3) The types of poverty and their coupled and coordinated development in each region show large spatial variability, requiring differentiated poverty eradication countermeasures tailored to local conditions to achieve sustainable regional economic-social-environmental development.

Plants extend root deeper rather than increase root biomass triggered by critical age and soil water depletion

Exploring plant root characteristics is important to understand the aboveground plant growth and ecosystem, but has rarely been conducted because of the difficulties in obtaining root information. This study aims to clarify the root distribution and rooting strategy under the combined control of vegetation types and rainfall gradients. We compiled 64 plant root and 81 soil water profiles up to 10 m deep with plant ages of up to 40 years old in China's Loess Plateau, and then fitted the shape and extinction coefficients (β) and proposed the relation of D95/D50 (ratio of depth corresponding to 95 % of total biomass to that corresponding to 50 % of total biomass) to β to characterize the rooting strategy. The cumulative root biomass increase from shallow- to deep-rooted plants, and from >550 mm to <450 mm precipitation gradients. The root system parameters have large spatial variability, dominated by vegetation type but supplemented by climate. The negative correlation between D95/D50 and β indicated a tradeoff between rooting depths and root biomass. The plants would change rooting strategy from increasing root biomass to increasing rooting depths when the plant stand age and soil water depletion degree are >25.7 ± 3.6 years and 35.7 % ± 15.1 %, respectively. These results reveal a clear plant rooting strategy that extends root deeper rather than increases root biomass triggered by critical age and soil water depletion.

High soil nitrous oxide emissions from a greenhouse vegetable production system in Shouguang, Northern China

In China, the area of greenhouse vegetable production (GVP) has exceeded 4 million ha, accounting for >80% of global GVP systems. Excessive nitrogen (N) fertilization and frequent irrigation have created GVP hotspots of nitrous oxide (N2O) emissions. There are several studies on soil N2O emissions from GVP systems, but all with low-frequency manual sampling, which may have resulted in large uncertainties regarding quantifying N2O emissions. Herein, we utilized an automatic chamber system equipped with 16 channels to measure N2O emissions under common farm practice from a GVP system in Shouguang, Northern China. We measured emissions from both furrows and ridges for 1 year from July 2020 to July 2021. Our continuous monitoring showed that the annual soil N2O emission was 102 kg N ha−1, accounting for 7.6% of the applied N (1338 kg N ha−1). The annual N2O emission from furrows (176 kg N ha−1) was significantly higher than that from ridges (28 kg N ha−1). Large spatial variations in the N2O emissions were observed, ranging from 102 to 273 kg N ha−1 yr−1 for furrows (n = 8) and from 19 to 45 kg N ha−1 yr−1 for ridges (n = 7). In addition, we found that irrigation caused N2O flux pulses from furrows, increasing by 12–396% compared with the level before irrigation. High-frequency measurements using our automatic chamber system provided detailed information on the diurnal and seasonal dynamics of N2O emissions, which could be useful for building more accurate models and developing more effective policies to reduce N2O emissions from global GVP systems.

Future climate change may pose pressures on greenhouse gas emission reduction in China’s rice production

Paddy rice cultivation is a vital food source but also a significant contributor to greenhouse gas (GHG) emissions, particularly methane. Management measures such as improved water management and fertilization are being pursued to sustain rice production while cutting down the emissions. However, climate change may pose further challenges for sustainable rice agriculture by either impacting rice production or GHG emissions. Based on a database consisting of 1216 observations across China, we established machine learning models predicting rice yield and GHG emissions, taking into account climate, soil, and management variables. We tested three machine learning methods including random forest, support vector machine and artificial neural networks. The models were trained on 70% of the dataset with the remaining 30% used to evaluate the model performance. Random forest performed best with R2 of 0.59 to 0.72 and modelling efficiency of 52% to 72%. Farmland management emerged as the most important variable to the model prediction, followed by climate and soil variables. Rice production and GHG emissions were estimated to be 245.36 Tg and 218.95 Tg CO2-eq in 2018, respectively. By the year 2050, rice yield was expected to experience a modest decrease ranging from 0.6% to 1.2% due to the combined effects of global change variables, but GHG emissions were projected to increase by 2.1% to 4.9% under different climate change scenarios (Representative Concentration Pathways) in China. A large spatial variability in the impact of climate change was observed, and climate change will have the most significant impact in the Northeast agricultural region of China due to the warming effect. Through the analysis of region-specific farmland management scenarios, this study underscores a “Code Red” situation, signifying that future climate change could pose unprecedented risks to the sustainability of rice production in China, despite ongoing management improvements.

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1–2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.

The linkage between spatial and temporal variations in epifaunal and primary producer communities in eelgrass (Zostera marina) beds

Environmental variability within estuaries and lagoons creates environmental gradients. Spatial variations in environments associated with environmental gradients shape spatial variations in primary producer and invertebrate communities. However, whether larger spatial variations are linked to greater temporal variations in the communities remained unclear. To understand the linkage between spatial and temporal variations in the communities and the factors shaping these variations, we targeted various functional groups of primary producers and invertebrates (epifauna) in eelgrass (Zostera marina) ecosystems in lagoons with and without distinct salinity gradients. We tested the following hypotheses: 1) larger spatial variation in the primary producer biomasses and epifaunal community structures (abundance, biomass, and species richness) are associated with greater temporal variation in the structures, and 2) salinity would be a more pronounced factor than water temperature and biomasses of primary producers in shaping the community structures in the lagoon with a salinity gradient. Our results showed that temporal and spatial variations in the primary producer biomasses and community structures of epifauna were larger in the lagoon with a salinity gradient. Moreover, salinity, water temperature, and the biomasses of primary producers affected the spatio-temporal variations in the community structures of epifauna, regardless of the presence of the gradients. We present from these results the connection between spatial and temporal variations of the primary producer and epifaunal community structures and the effects of abiotic and biotic factors shaping the communities.

Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018

Abstract. Seasonal snow cover of the Northern Hemisphere (NH) greatly influences surface energy balance; hydrological cycle; and many human activities, such as tourism and agriculture. Monitoring snow cover at a continental scale is only possible from satellites or using reanalysis data. This study aims to analyze the time series of snow water equivalent (SWE), snow cover extent (SCE), and surface albedo in spring in ERA5 and ERA5-Land reanalysis data and to compare the time series with several satellite-based datasets. As reference data for the SWE intercomparison, we use bias-corrected SnowCCI v1 data for non-mountainous regions and the mean of Brown, MERRA-2, and Crocus v7 datasets for the mountainous regions. For surface albedo, we use the black-sky albedo datasets CLARA-A2 SAL, based on AVHRR data, and MCD43D51, based on MODIS data. Additionally, we use Rutgers and JAXA JASMES SCE products. Our study covers land areas north of 40∘ N and the period between 1982 and 2018 (spring season from March to May). The analysis shows that both ERA5 and ERA5-Land overestimate total NH SWE by 150 % to 200 % compared to the SWE reference data. ERA5-Land shows larger overestimation, which is mostly due to very high SWE values over mountainous regions. The analysis revealed a discontinuity in ERA5 around the year 2004 since adding the Interactive Multisensor Snow and Ice Mapping System (IMS) from the year 2004 onwards considerably improves SWE estimates but makes the trends less reliable. The negative NH SWE trends in ERA5 range from −249 to −236 Gt per decade in spring, which is 2 to 3 times larger than the trends detected by the other datasets (ranging from −124 to −77 Gt per decade). SCE is accurately described in ERA5-Land, whereas ERA5 shows notably larger SCE than the satellite-based datasets. Albedo estimates are more consistent between the datasets, with a slight overestimation in ERA5 and ERA5-Land. The negative trends in SCE and albedo are strongest in May, when the albedo trend varies from −0.011 to −0.006 per decade depending on the dataset. The negative SCE trend detected by ERA5 in May (-1.22×106 km2 per decade) is about twice as large as the trends detected by all other datasets (ranging from −0.66 to -0.50×106 km2 per decade). The analysis also shows that there is a large spatial variability in the trends, which is consistent with other studies.

Ecosystems in China have become more sensitive to changes in water demand since 2001

Changes in heat and moisture significantly co-alter ecosystem functioning. However, knowledge on dynamics of ecosystem responses to climate change is limited. Here, we quantify long-term ecosystem sensitivity based on weighted ratios of vegetation productivity variability and multiple climate variables from satellite observations, greater values of which indicate more yields per hydrothermal condition change. Our results show ecosystem sensitivity exhibits large spatial variability and increases with the aridity index. A positive temporal trend of ecosystem sensitivity is found in 61.28% of the study area from 2001 to 2021, which is largely attributed to declining vapor pressure deficit and constrained by solar radiation. Moreover, carbon dioxide plays a dual role; which in moderation promotes fertilization effects, whereas in excess may suppress vegetation growth by triggering droughts. Our findings highlight moisture stress between land and atmosphere is one of the key prerequisites for ecosystem stability, offsetting part of the negative effects of heat.

Failing children with Special Educational Needs and Disabilities in England: New evidence of poor outcomes and a postcode lottery at the Local Authority level at Key Stage 1

AbstractThis paper sets out original findings from analyses of the English National Pupil Database of Key Stage 1 (KS1) attainment, to examine educational outcomes of children with Special Educational Needs and Disabilities (SEND). The schooling of these children has been entirely within the context of the current SEND system, defined by the 2014–2015 policy of the Children and Families Act and Code of Practice. With a strong focus on children's needs and outcomes, the policy intends to achieve high educational outcomes for children with SEND. Our new results show, however, that children with SEND are one of the most disadvantaged groups in education, and they are far less likely to meet expected learning standards than their peers at KS1. For instance, about 44%, 31% and 23% of children with SEND met the standards in phonics, reading and writing, respectively, compared to 88%, 83% and 78% of children with no SEND. Further, our spatial analysis shows for the first time that this disadvantage displays large spatial variability across Local Authorities: there is a postcode lottery in the education of children with SEND. The new findings provide strong evidence that the new SEND policy is failing many children with SEND, and that this performance varies markedly across space. This adds further weight and evidence to a growing recognition, even from government, that the SEND system needs to change, and that the ambitious aims of the transformation of education and care for children with SEND in 2014 and 2015 are not being realised.

Predicting Cd accumulation in rice and identifying nonlinear effects of soil nutrient elements based on machine learning methods

The spatial mismatch of Cd content in soil and rice causes difficulties in environmental management for paddy soil. To investigate the influence of soil environment on the accumulation of Cd in rice grain, we conducted a paired field sampling in the middle of the Xiangjiang River basin, examining the relationships between soil properties, soil nutrient elements, Cd content, plant uptake factor (PUFCd), and translocation factors in different rice organs (root, shoot, and grain). The total soil Cd (CdT) and available Cd (CdA) contents and PUFCd showed large spatial variability with ranges of 0.31–6.19 mg/kg, 0.03–3.07 mg/kg, and 0.02–3.51, respectively. Soil pH, CdT, CdA, and the contents of soil nutrient elements (Mg, Mn, Ca, P, Si, and B) were linearly correlated with grain Cd content (Cdg) and PUFCd. The decision tree analysis identified nonlinear effects of Si, Zn and Fe on rice Cd accumulation, which suggested that low Si and high Zn led to high Cdg, and low Si and Fe caused high PUFCd. Using the soil nutrient elements as predictor variables, random forest models successfully predicted the Cdg and PUFCd and performed better than multiple linear regressions. It suggested the impacts of soil nutrient elements on rice Cd accumulation should receive more attention.

Observations of preferential summer melt of Arctic sea-ice ridge keels from repeated multibeam sonar surveys

Abstract. Sea-ice ridges constitute a large fraction of the total Arctic sea-ice area (up to 40 %–50 %); nevertheless, they are the least studied part of the ice pack. Here we investigate sea-ice melt rates using rare, repeated underwater multibeam sonar surveys that cover a period of 1 month during the advanced stage of sea-ice melt. Bottom melt increases with ice draft for first- and second-year level ice and a first-year ice ridge, with an average of 0.46, 0.55, and 0.95 m of total snow and ice melt in the observation period, respectively. On average, the studied ridge had a 4.6 m keel bottom draft, was 42 m wide, and had 4 % macroporosity. While bottom melt rates of ridge keel were 3.8 times higher than first-year level ice, surface melt rates were almost identical but responsible for 40 % of ridge draft decrease. Average cross-sectional keel melt ranged from 0.2 to 2.6 m, with a maximum point ice loss of 6 m, showcasing its large spatial variability. We attribute 57 % of the ridge total (surface and bottom) melt variability to keel draft (36 %), slope (32 %), and width (27 %), with higher melt for ridges with a larger draft, a steeper slope, and a smaller width. The melt rate of the ridge keel flanks was proportional to the draft, with increased keel melt within 10 m of its bottom corners and the melt rates between these corners comparable to the melt rates of level ice.

Pluvial Flood Risk Assessment in Urban Areas: A Case Study for the Archaeological Site of the Roman Agora, Athens

Ancient monuments located in urbanized areas are subject to numerous short- and long-term environmental hazards with flooding being among the most critical ones. Flood hazards in the complex urban environment are subject to large spatial and temporal variability and, thus, require location-specific risk assessment and mitigation. We devise a methodological scheme for assessing flood hazard in urban areas, at the monument’s scale, by directly routing rainfall events over a fine-resolution digital terrain model at the region of interest. This is achieved using an open-source 2D hydraulic modelling software under unsteady flow conditions, employing a scheme known as ‘direct rainfall modelling’ or ‘rain-on-grid’. The method allows for the realistic representation of buildings and, thus, is appropriate for detailed storm-induced (pluvial) flood modelling in urbanized regions, within which a major stream is usually not present and conventional hydrological methodologies do not apply. As a case study, we perform a pilot assessment of the flood hazard in the Roman Agora, a major archaeological site of Greece located in the center of Athens. The scheme is incorporated within an intelligent decision-support system for the protection of monumental structures (ARCHYTAS), allowing for a fast and informative assessment of the flood risk within the monument’s region for different scenarios that account for rainfall’s return period and duration as well as uncertainty in antecedent wetness conditions.

Shallow sources of upper mantle seismic anisotropy in East Africa

The East African rift overlies one or more mantle upwellings and it traverses heterogeneous Archaean-Paleozoic lithosphere rifted in Mesozoic and Cenozoic time. We re-analyze XKS shear wave splitting at publicly available stations to evaluate models for rifting above mantle plumes. We use consistent criteria to compare and contrast both splitting direction and strength, infilling critical gaps with new data from the Turkana Depression and North Tanzania Divergence sectors of the East African rift system. Our results show large spatial variations in the amount of splitting (0.1–2.5 s), with fast axes predominantly sub-parallel to the orientation of Cenozoic rifts underlain by thinned lithosphere with and without surface magmatism. The amount of splitting increases with lithospheric thinning and magmatic modification. Nowhere are fast axes perpendicular to the rift, arguing against the development of extensional strain fabrics. Thick cratons are characterized by small amounts of splitting (≤0.5 s) with a variety of orientations that may characterize mantle plume flow. Splitting rotates to rift parallel and increases in strength over short distances into rift zones, implying a shallow depth range for the anisotropy in some places. The shallow source and correlation between splitting direction and the shape of upper mantle thin zones suggests that the combination of channel flow and oriented melt pockets contribute > 1 s to the observed splitting delays. Enhanced flow, metasomatism, and melt intrusion at the lithosphere-asthenosphere boundary suggest that fluid infiltration to the base of the lithosphere may facilitate rifting of cratonic lithosphere.

Attribution of Lake Surface Water Temperature Change in Large Lakes Across China Over Past Four Decades

AbstractLake surface water temperature (LSWT) is a key parameter in lake energy budget and is highly vulnerable to climate change. However, the long‐term trends in LSWT across China and their driving factors remain uncertain. Here, we used a calibrated lake model to simulate LSWT over 1979–2018 for 91 large lakes (&gt;100 km2) across China. Simulations reveal an overall LSWT warming trend (0.040°C yr−1, p &lt; 0.05), but with large spatial variations. The majority of these lakes show significant warming trends (84%, 0.053°C yr−1), while a significant cooling trend is found in the seven lakes in the northwestern Tibetan Plateau (−0.064°C yr−1). LSWT of approximately 42% of the lakes increases more rapidly than the corresponding ambient air temperature. Regionally, the warming trend is highest for lakes in the Eastern Plain (0.049°C yr−1) and the lowest in the Yunnan‐Guizhou Plateau (0.016°C yr−1). The increases in simulated LSWT also vary across seasons, with a higher rate in winter and spring than in summer and autumn. Changes in air temperature, downward longwave radiation, and wind speed are the most important climatic drivers for LSWT changes. Lake surface warming could be more rapid under future global warming, necessitating greater attention to lake‐atmosphere interactions.

Spatio-Temporal Variability of Rainfall Over Kathmandu Valley of Nepal

The present study used and analyzed rainfall data from 18 meteorological stations from 1971 to 2013 to examine the spatial and temporal variability of seasonal and annual rainfall based on rain gauge measurements. The monthly to annual rainfall analysis was carried out for each site of Kathmandu Valley. Rainfall amounts in Kathmandu Valley vary considerably in space and time. The minimum mean monthly rainfall is observed in November which is 6.5 mm and maximums of 447.8 mm in July. Monsoon is the main contributor of the rainfall which is 80% followed by pre-monsoon with 13.6%, post-monsoon with 3.6% and winter with 2.8%. Spatial interpolation was used to explore spatial variability of seasonal and annual rainfall over Kathmandu Valley. There is large spatial variability of monsoon rainfall; generally, the upper parts of the Kathmandu Valley received the heavy monsoon rainfall than the lower parts of the Valley floor. The rainfall of Kathmandu has marked decreasing in recent a couple of decades. Annual rainfall has decreased by 0.96 mm/year observed in Kathmandu Valley.

Impacts of large-scale climatic circulation on floods through precipitation and temperature in the Lancang-Mekong River Basin

The importance of large-scale climatic circulation impacts on precipitation and floods in the Lancang-Mekong River Basin (LMRB) has been widely acknowledged. However, the mechanisms related to the impacts of circulation on floods are not yet fully understood. To address this issue, circulations were characterized by using the climate indices, and floods were represented by the flood volume and simulated using an improved hydrological-hydrodynamic model. The linear regression model was used to assess the impacts of circulation on precipitation and temperature, and also to quantify the impacts of precipitation, temperature, and circulation on floods. The results show that circulation affects precipitation with large spatial variability, while affects temperature with small spatial variability. On average, a unit change in different climate indices can cause a 2.5 %–6.1 % change in precipitation and a 0.3 %–1.2 % (0.09 °C–0.16 °C) change in temperature. Further, precipitation has a positive impact on floods, while temperature mainly has a negative impact. On average, a unit increase in precipitation and temperature can cause a 22.6 % increase and a 9.3 % decrease in floods, respectively. Consequently, circulation affects floods mainly through its impact on precipitation, while its impact on temperature also plays an important role. On average, a unit change in different climate indices can cause a 3.1 %–8.7 % change in floods. In addition, a multi-linear regression model was used to discuss the explained variance and relative contribution of climate. The results show that circulation can explain 12 %–23 % of the precipitation, temperature, and flood variances, while temperature and precipitation can explain 69 % of the flood variance. Among all circulations, the monsoon systems, North Atlantic Oscillation, and El Niño–Southern Oscillation dominate precipitation, temperature, and floods in the LMRB with the largest contributions and the most affected regions.

Similarity and Change Detection of Relief in a Proglacial River Valley (Scott River, SW Svalbard)

This study focuses on contemporary geomorphic changes in the proglacial valley floor of the Scott River catchment (northwest of Wedel Jarlsberg Land, southwestern Spitsbergen). The similarity and variability of landforms along the entire 3.3 km length of the unglaciated valley floor was assessed using precision terrestrial laser scanning (TLS) measurements made in July/August 2010–2013. Digital terrain models (DTMs) were generated from the high-resolution TLS survey data, followed by a geomorphon map, which was then used for a similarity and changes of morphology analysis performed with GeoPAT2 software. The study revealed a large spatial variation of contemporary processes shaping the valley floor and changes in its morphology. Their spatial distribution relates to the geologically determined split of the valley floor into three morphological zones separated by gorges. The upper gorge cuts the terminal moraine rampart, which limits the uppermost section of the valley floor, which is up to 700 m wide and is occupied by the outwash plain. The study showed that this is the area characterised by the greatest dynamics of contemporary geomorphic processes and relief changes. The similarity index value here is characterised by a large spatial variation that in some places reaches values close to 0. In the middle section stretching between the upper gorge (cutting the terminal moraine) and the lower gorge (cutting the elevated marine terraces), a much smaller variability of processes and landforms is observed, and the found changes of the valley floor relief mainly include the area of braided channel activity. Similarity index values in this zone do not fall below 0.65. The lowest section, the mouth of the alluvial fan, on the other hand, is characterised by considerable spatial differentiation. The southern part of the fan is stable, while the northern part is intensively re-shaped and has a similarity index that locally falls below 0.5. The most dynamic changes are found within the active channel system along the entire length of the unglaciated section of the Scott River. The peripheral areas, located in the outer zones of the valley floor, show great stability.

Construction and evaluation of pedotransfer functions for saturated hydraulic conductivity in the granite red soil regions of southern China

Study regionSix typical regions of granite red soil in southern China. Study focusRegional large-scale Ks analysis of granite red soil in South China is still limited to a large number of repetitive and expensive workloads. It is necessary to construct Ks PTFs suitable for the granite red soil region of South China. In this study, six different regions granite red soil in South China were used to validate the prediction of classical PTFs for Ks, the best combination of predictors was screened and PTFs developed based on stepwise regression methods and machine learning algorithms. New hydrologic insights for the region(1) The coefficient of variation for Ks is 69.87% in the granite red soil region of South China, with a large spatial variability. (2) The six typical PTFs of Ks were poor predictors of Ks for soils in southern China, R2 < 0.4. (3) "Clay+Silt+Sand+BD+SOM+NCP" is the best combination of predictors of soil Ks in the granite red soil region of South China, which can be used in different machine learning algorithms. (4) The constructed MLR model “Ks= 1.501 + 0.043 ×NCP-0.995 ×BD+ 0.034 ×SOM-0.013 ×Clay” with KNN, ANN and BRT machine algorithm models have a prediction accuracy R2 greater than 0.8. This study provides different prediction models for soil Ks in the granitic regions of southern China and lays a theoretical basis for hydrological research in the these regions.

Quantification of soil N2O and CH4 fluxes using the flux gradient method on a drainage water managed farm on the eastern shore of Maryland

Drainage water management (DWM) is an effective best management practice (BMP) to reduce hydrological nitrate export from croplands to surface and ground water by controlling the timing and the amount of ditch discharge and retaining water within ditches and adjacent fields using drainage control structures (DCS). While the intended consequences of maintaining higher water table levels are to increase denitrification and thereby decrease nitrate leaching, an unintended consequence is possible increased production of nitrous oxide (N2O) from denitrification and methane (CH4) from methanogenesis, both potent greenhouse gasses (GHGs). Hence, application of DWM may lead to a "pollution swapping" concern – i.e., does DWM trade reduction of nitrate concentrations in ditch water for increases in soil emissions of N2O and CH4 to the atmosphere?Here we employed the micrometeorological flux gradient (FG) technique to a corn-soybean rotation on an operational farm with DCS in eastern Maryland on the Delmarva Peninsula to answer this question. Soil N2O and CH4 fluxes were quantified under DWM and non-DWM management of a soybean crop without N fertilization (2018) and a corn crop with synthetic N fertilization (2019). To our knowledge, this is the first study employing a micrometeorological method to evaluate the effects of agricultural DWM on GHG emissions. No consistent or statistically significant differences in N2O and CH4 fluxes were observed between DWM and non-DWM fields, suggesting that DWM did not trade reduced nitrate leaching for increased soil GHG emissions. The FG measurements were also compared with chamber measurements, which revealed similar patterns, but chamber measurements had large spatial variability and overall higher daily mean flux estimates, while the tower measurements revealed more frequent, short-lived N2O pulses following fertilization and precipitation events. This research adds to the existing understanding of benefits and risks of DWM as a BMP.